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Research and Ranking Review: Charges, Services, Trial, Complaints

Last updated on December 27, 2023

Established in the year 2016, Research & Ranking is a SEBI registered equity advisory which aims to aid equity investors in wealth building through long-term investing. The firm offers a robust technology-enabled platform backed by detailed research that helps in wealth creation by generating personalized portfolios as per investors financial goals. We dived into the firm’s service offerings and here is Research and Ranking Review.

Research & Ranking is a part of Equentis Group, established in the year 2009. Over the years, the advisory built a team of  professionals across Mumbai, Thane, Delhi and Bengaluru, while the company’s registered office is based in Mumbai.

Over the next 2-3 years, Research & Ranking plans to get listed as well as go overseas and serve international investors by mirroring their technology-based investment advisory model’s success in developed markets including the UK, the USA and Middle-east.

Research and Ranking (Equentis Wealth Advisory Services Private Limited) Details

Research and ranking review: services.

Research and Ranking services primarily focus on long-term investing and has built its investment schemes accordingly.

5 in 5 Wealth Creation Strategy: It is designed to help investors to invest in a personalized and competently diversified portfolio of 20-25 stocks with the potential to generate 4-5 times returns in 5-6 years.

MisPriced Opportunities (MPO): This service helps investors to profit from market fluctuations by investing in stocks trading below their underlying value, thereby having the potential to generate 25-50% returns in 6-12 months.

Research & Ranking service named Dhanwaan and EWA Exclusive are their two premium offering for HNI and Ultra-HNI clients who wish to create massive wealth by investing in high growth businesses with the potential to grow at a rapid rate with India’s growth trajectory, thus compounding investor wealth at a CAGR rate of 25-35%.

Dhanwaan – A Premium Non-Discretionary Portfolio Management Service: Invest in a highly personalized and concentrated portfolio of 20-25 stocks with the strong past track record of 4-5 times returns in 5-6 years.

Informed InvestoRR: It helps investors to by giving regular relevant information which thus impact your investment as an investor. Research and Ranking is collecting all the quality information on stocks and market and is providing it on a single platform.

Research and Ranking Charges

Research and Ranking charges for its services on yearly basis, with the plans for one and five years and the paying cycle being in every 6 months. Please note that GST charges of 18% are imposed separately in addition to the prices shown below:

As seen above, Research and Rankings charges goes from moderate to high. Customers can choose subscription plans according to their needs and service required, as the plans seem to cater to small as well as large & HNI investors.

Read also: Upcoming IPOs in 2024

Research and Ranking Complaints and Customer Service

Research and Ranking reviews have been quite positive according to their customers for the services they got, and its registered complaints on the site portal has been also very low. Most of the business websites have rated it with good ratings with moderate customer complaints. Research and Ranking customer service seem to be quite nice according to its long-term users.

Research and Ranking Review: Pros and Cons

• Good customer service
• Variety of services
• Long-term investments only
• No free trial

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Disclaimer  – The objective behind Research and Ranking   review is to offer an unbiased view of the company’s service offerings and cost of subscription services. This review of Research and Ranking service offerings is only for information purpose and is not a recommendation for investors

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Home > Ecommerce and Online Business > Business and Finance Websites > Research And Ranking > Dont believe to research and ranking team

Research And Ranking

2 sep 2021 i subscribe there MPO plan they asked me they will give me atleast 25 to 35 percent gain but as they are cheater i am in big loss after investing of 8 companies they suggested 6 are in loss loss loss and it is painful to me so guys dont go for research and ranking they charge me for subscription and still i am facing loss of money. so please keep away from research and ranking

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Caution Advised

I feel compelled to share my experience with Zacks Investment Research, which, regrettably, fell short of my expectations. While the company frequently promotes its proprietary analytics, named after their brand, the content of their articles tends to be overly abstract with little actionable information. This creates an impression that by subscribing, one would gain exclusive insights into optimal investment timings and strategies. Unfortunately, my experience as a subscriber did not reflect this promise. The investment recommendations provided during my subscription consistently underperformed, which was disappointing. Moreover, the customer service experience was far from professional. When I reached out to cancel my subscription, the interactions were discourteous and unhelpful. It's important for potential subscribers to be aware of these practices. It seems that Zacks might be more focused on attracting novice investors rather than providing genuinely valuable investment insights. Approach with caution and consider a diverse array of sources and opinions before making investment decisions.

Date of experience : May 13, 2024

My e-mail address find on darkweb

Following a scan done on my android tablet Zacks has put, directly or indirectly, my e-mail address on the darkweb on June 16 2023. This is probably the reason why I receive daily messages that my tablet is no longer safe

Date of experience : June 16, 2023

Don't be lazy, do your own research

I have to second some other negative reviews. Most decisions I've made through Zack's have turned out into major losses. Fortunately others I've made without considering Zack's ranking have more than made up for those losses, so that was a big learning lesson for me, i.e. don't be lazy and blindly trust a scoring system to cut your research time short.

Date of experience : February 10, 2024

Scam Stay Away. May as well throw darts at a board.

Well clearly Zack’s has absolutely no idea what they are doing. I purchased over 30 stocks in a year and 23 of them lost money. Zacks has a “Money back guarantee” - DONT BELIVE IT. They use the loopholes to get out of it. This is what Kurt Fraser who works for Zacks - gave as a response ( see below) Even though I had 23 stocks lose money because I didn’t sell exactly within 24 hrs after they said to they won’t refund my $6000! They say even though the stock lost 10% when they sold, because I didn’t sell within 24hrs they say it doesn’t qualify lol. I’m not understanding, if they say buy at $10 and they say sell at $9.00 it’s not a loss unless I sold within the exact time they say to sell? I have 23 of these Losing recommendations. I have no doubt they have never given a refund. Complete scam STAY AWAY! From Kurt Fraser: David, Despite what you say, it literally cannot get more clear that stating ‘trades must be executed within 24 hours of newsletter notifications.’ And, the wording of the performance guarantee has not changed in the 10 years that I’ve been here. It was the same when you started and the same today. Again, everyone plays by the same rules. The performance guarantee is something customers must qualify for; it’s not an obligation to refund any customer that loses $. Many customers make money using our trades, and some lose money. However, if a customer isn’t following the exact timing of our trades, then they are not following our advice. Performance refunds are reserved for customers that diligently follow our trades and timing, including 20 trades all within 1 day of the notifications. They are not reserved for customers that do not follow our advice or ‘partially’ follow our advice.

Date of experience : April 18, 2024

People commenting here just rely on #1…

People commenting here just rely on #1 rank and see that the stocks tank. Offcourse nobody should rely on just 1 metric. I did that mistake for an year but after that I educated myself. I am a subscriber of zacks and an active trader. I have made profits with stocks under $10 and daily I trade using a screen that I have custom created using zacks premium screen in addition i also look at the news for the stock from my broker after which i pick it. I have bet dow, S&P, NASDAQ, Russell for last 6 months and still continuing.

Date of experience : November 16, 2023

"I value Zacks Home Run Investor #1 Ranks

"I value Home Run Investor, #1 Ranks and Stocks Under $10 portfolios as part of Zacks Investor Collection. A high majority of the Zacks Rank #1 (Strong Buy) stocks went up in value within 2-6 weeks and I sold them off for +4-6% profits, which is better than a CD for a year." In this bear market I can't now find the few stocks moving against the tide. The life time collect paied for itself in a matter of months.

Date of experience : March 21, 2023

Crap earnings report advice

Crap earnings report advice. I invested in 3 stocks they said would beat earnings, none of them did, I lost money, The same week that Disney had their earnings report due, zacks rated them a strong sell, long story short, Disney actually beat earnings and went up. What a joke.

Date of experience : November 10, 2023

GLIMBSE OF LIGHT AT THE TUNNEL END

I posted two days ago on my experience dating back over 90days. The effect of that post is yielding some result The company that refused reaching out to me has started responding after Dycelp reached out on my behalf. Must it be that hard? They may not be bad as thought... only needs a strong force. There my just be light at the end of the tunnel! The Aberdeen experience is coming into play!

Date of experience : October 03, 2023

Worst Customer Service in America!

This company has worse then zero customer service. They refuse to help, don't respond to emails and when you ask for a supervisor, it is ignored. Finally after demanding to take action and leave reviews and file a BBB complaint if a supervisor didn't contact me, I got a call from a man claiming to be a supervisor. He yelled at me, told me they weren't doing business with me, refused to give me his name and hung up on me. These guys are TERRIBLE! STAY AWAY!!!!!

Date of experience : September 14, 2023

There are a couple of times when Zack…

There are times when Zack view #3 hold, which it should be buy for TFI international ( TFI), After did my own research, I loaded up my positions. Made a great profits especially last quarter. You should always do your own homework, remember trends are your friend ( based on 3-5 years+) along with the fundamentals of the companies.

Date of experience : April 20, 2023

No logic or system and won't respond to questions.

When I was tired of managing my own account I had Zack's take over. I lost 20% of my portfolio in a couple of months before I fired them. I analyzed their ranking system and found there was no system or logic. Momentum scores of "A" are given in both up and down trends. ???? They will not respond to critical questions. They will auto-renew with no warning and try to get your money back!

Date of experience : November 24, 2023

The website needs serious…

The website needs serious update/upgrade. You can't sort the stocks in your portfolio if it contains over 200-300 entries. It will show the Rank #5 at the top. You will have sort to show 100 entires per page, and then click last page to reach your Rank #1 and #2. Ridiculous! It's also not possible to edit your portfolio. Say you want to select multiple stocks from one portfolio and move those to another portfolio - that's not a possibility. With that said, the website is outdated. Management needs to seriously consider innovating and fixing the overall experience.

Date of experience : April 17, 2022

I find Zacks to be completely useless

I find Zacks to be completely useless. I have tried to give them the benefit of the doubt over several years. However, their "reports" are simply copy and paste with a new ticker and some different data. There is absolutely no meaningful insight to be found, anywhere in their "content". I really don't understand how material this shallow can occupy so much space on Yahoo Finance. I have found outfits like Seeking Alpha and Motley Fool to be far superior in every way. I highly recommend NOT wasting your time ingesting the refuse spewed out by Zacks.

Date of experience : April 13, 2023

I subscribed for Zack's 30 day trial

I subscribed for Zack's 30 day trial - bought 2 stocks in their stocks under $10 recommendations. NEXA and BRY - down 29% and 14% respectively in a month. Fortunately only lost $450 to date - will be closing these positions out and investing in a good mutual fund. They are happy to charge you to recommend stocks that have an equal chance of losing and / or gaining. Better off to get a financial page and your dart set out.

Date of experience : November 29, 2021

Signup for Spam!

When will they learn? It’s Millie Mouse to continue to bombard PAID customers with sell-up emails??? Plus, it denigrates their main service to near worthless. It gives the appearance of going out of business! I did not pay for the opportunity to receive solicitations for their add-on ‘packages!

Date of experience : September 02, 2023

Terrible advice & 'surprise' £239 subscription fee

Terrible advice, and after the one month $1 trial expires they try to take like £239 from your bank account. (Luckily I had used an inactive account that bounced. They tried a couple more times then gave up. If it happens to you, try a chargeback with your bank.) There might be some redemption if their recommendations were any good, but they're not. I'm a beginner, and the two stocks I picked myself early in the year, with just a basic bit of research, are performing at 31% and 12%, respectively. I took a punt on three strongly recommended buys from Zacks back in late spring, and they are performing at -17%, -18%, and -12%, respectively. (Coty, Paypal, Uipath). I wonder who is benefitting from talking up the rating of these poor performers. Don't imagine it's a coincidence that they've all tanked since. Luckily I only invested fairly small amounts and am currently down 'only' about £90. Utter crap.

Date of experience : May 31, 2023

Don't subscribe to the Zacks Investment…

Don't subscribe to the Zacks Investment Research emails they publish on the dark web. If you go to your Google account, then security and select a run scan with Google one. Then it will show you who has published your personal info and which Zacks is one of them.

Date of experience : September 19, 2023

I followed Zacks recommendations…

I followed Zacks recommendations several times and was making excuses for my losses until finally I figured it out: their recommendations are nonsense. ZIM was a strong buy for awhile, then within a few days moved to strong sell! If people buy every time they say ‘buy’, then sell as soon as they see ‘sell’, they will see nothing but massive losses. Zacks is B.S!!!!!

Date of experience : December 01, 2022

Bought lifetime membership for research…

Bought lifetime membership for research wizard 5k. I was green and swallowed the promises… it was the best experiential purchase of my life… In summary, Zacks is always right ( hindsight) they are consistently a day late and a dollar short. YET they’re always willing to upsell you a product that will give you another edge over the edge they previous sold you? Embarrassed to have bit the hook. Work hard, read and learn there are no shortcuts. Education can be expensive…

Date of experience : July 09, 2022

I rely on Zacks for all my buys &…

I rely on Zacks for all my buys & sells, not only for my accounts, but also for my clients accounts. We all did well so far. Thank you for the great advice. The only confusion I have, when I see a sell on one of your different venues, but it's a buy on a different area of you Ultimate. Furthermore, I get a sell notification, but that holding is still in your portfolio. I need to understand that approach. Thanks. Rod Skaf

Date of experience : August 02, 2022

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Research & Ranking’s unique strategy creates phenomenal wealth for investors

Creating wealth from the stock market is both a science and an art which requires detailed research and a little patience..

With whopping 480% Gains in 6 Years, India’s leading equity advisory Research & Ranking has proved this right and helped over 15000 investors and 1500 lifetime investors fulfil their financial goals effortlessly. The advisory strongly believes in educating and empowering investors, and their registered users include over 3,50,000 investors who are only growing by the day.

Research & Ranking's main product offering, the 5 in 5 Wealth Creation Strategy is designed to help investors to invest in a personalized and adequately diversified portfolio of 20-25 stocks with the potential to generate 4-5 times returns in 5-6 years. The company's second offering, known as the Mispriced Opportunities strategy helps investors to encash on the market fluctuations by investing in stocks trading below their intrinsic value, thereby having the potential to generate 25-50% returns in 6-12 months.

Research & Ranking also offers two premium advisory services named Dhanwaan and EWA Exclusive for HNI and Ultra-HNI clients which aims to create massive wealth by investing in high growth businesses with the potential to grow at a rapid rate with India's growth trajectory, thus compounding investor wealth at a CAGR rate of 25-35%.

With a dedicated team of over 200 professionals across Mumbai, Thane, Delhi and Bengaluru, Research & Ranking considers customer satisfaction one of its top priorities. Having achieved many milestones in a short span of few years, Research & Ranking has set its sight on new achievements for 2021. This includes becoming a True Fintech company which delivers best returns to their customers.

In line with their firm belief that ‘Knowledge is the key to success in investing' the team at Research & Ranking plans to launch an Ed-Tech platform soon to facilitate easy and quick learning for investors.

In the current calendar year, the company is also planning to launch a new product offering named Multiplierr.

“Our revolutionary new product offering is targeted at those investors who wish to invest a small amount in the stock market. We’re excited about this upcoming product launch as it is designed keeping in mind the limitations of a retail investor. At the same time, this new product demonstrates how real wealth can be made through smart investing. We are sure that this strategic decision will pave the way towards enhancing our reach towards investors across the country,” stated Research & Ranking’s Founder-Director, Manish Goel.

Over the next 2-3 years, Research & Ranking intends get listed as well as go global and cater to international investors by replicating their technology-based investment advisory model's success in developed markets including the USA, UK and the Middle-east.

Established in the year 2016, Research & Ranking is a SEBI registered equity advisory which aims to help equity investors create wealth through long-term investing. The advisory offers a robust technology-enabled platform backed by detailed research that guides investors in their wealth creation journey by creating personalized portfolios as per their financial goals.

Research & Ranking is a part of Equentis Group, incorporated in the year 2009 and offers complete support to investors in their journey of wealth creation right from onboarding.

To learn more about the different types of wealth creation strategies offered by Research & Ranking, visit www.researchandranking.com.

Disclaimer: This content is distributed by Digpu News Network. No HT journalist is involved in creation of this content.

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Now look at the top 10 stock advisory firms in India that are currently trending on top.

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List of best stock advisory in india, capitalvia global research limited, research and ranking.

• Bajaj Capital limited
•  Kotak Private Equity Group

Unit Trust of India

• BTS Asset management
• ANM Investment
•  Asset Villa Financial Advisors

Mister Market.in

Streetgains.

It is undoubtedly the best stock advisory company in India. Established in 2006, CapitalVia Global Research Limited is headquartered in Mumbai and a top players in the stock advisory landscape. Till date, the best SEBI registered investment advisor has served more than 75000 clients with a proven success record.

Being the best stock advisor in India, CapitalVia has carved a niche in the segment by offering insightful, value-oriented stock advisory services, best stock advice reliably and professionally. The best stock advisory firm is driven by a mission to deliver unparalleled value to their customers by ensuring their success. This is achieved by adhering to the highest standards of business conduct for a trustworthy, effective, and accountable delivery of financial research services to the clients and help them create wealth out of their savings.

Salient Features of CapitalVia Global Research Limited

• Provides a specific mobile application to provide a smooth customer experience
• Team of academics with NISM certification providing stock advising services
• Gives analysis on Malaysian stocks as well
• A team of dedicated and experienced stock investment advisors
• More than 15 years of business legacy
• Dedicated client support
• Market Pro, Market Neuron, TechniQ, QuantIQ are the top financial products by the stock advisor

Stated its operations in 2016, Research and Ranking is considered among the best financial advisors in India. The best stock advisory company has a clientele of over 12K across India. it is a part of the prestigious Equentis Group which is one of the pioneers in the investment domain, focusing majorly on Indian equity market since 2009.

Research and Ranking is India’s first online non-discretionary equity advisory company which also offers personalized market research reports. They are governed by the SEBI and well supported by a team of dedicated professionals having considerable expertise in financial markets, technology, and equity research.

Manish Goel is the founder and director of Research and Ranking, the best sock advisor in India.

The stock advisory team is made up of more than 150 professionals from various sections of India. The team has delivered 25-30%.

Salient Features:

• Over 12000 clients served
• Headquartered in Mumbai
• A venture of Equentis Capital
• Dhanwaan for HNIs, UNHNI, and family offices
• Informed InvestoRR to impart stock market education to people
• 5 in 5 strategy to build a well-diversified portfolio of multibagger stocks that will assist investors in achieving their investment objectives
• Mispriced opportunities to invest in 12 high-growth stocks every month

Bajaj Capital Limited

Bajaj Capital is a renowned and reliable name in the Indian financial sector and offers a host of financial products and services to both retail and commercial clients. Its major offerings include general insurance, health insurance, motor insurance, mutual fund apps , stock investments, stock advisory, etc.

Counted among the best stock advisory service companies in India, Bajaj Capital is known for its client-centric approach which helps its clients witness amazing growth on their investments. In addition, the insightful stock-related information imparted by the firm enable them to make sagacious stock investment decisions in the long term.

Bajaj Capital was set up in 1964 which is truly the longest tenure at present. The company was the first to come up with the Companies Fixed Deposit in 1965.

• 6 decades of legacy
• A wide range of products & services
• 45 years of experience as Investment Advisors and Financial Planners
• Unbiased, research-based and goal-based advice
• Customized wealth management support
• Quick, chivalrous service
• 24 x 7 online accessibility
• Countrywide network of over 200 branches
• Strong team of qualified and experienced professionals including CAs, MBAs, MBEs, CFPs, CSs, Insurance Experts, Legal Experts and others
• SEBI-Approved Advisor

Kotak Private Equity Group

Being a part of India’s renowned Kotak Group, Kotak Private Equity Group is considered as best stock advisor in India which helps people fulfil their investment goals successfully. Since 1997, the Kotak Mahindra Group has been involved in private equity transactions. Kotak Investment Advisors Limited was established in 2005 as the Group’s Alternate Assets practise with its first structured third party private equity and real estate funds in order to bring about a stronger focus to this sector.

The stock advising company, founded by industry pioneers in the Indian private equity sector, is a disciplined investor with a long-term value creation and growth emphasis. They collaborate with excellent leadership teams and promoters to build growth across sectors.

The Kotak Private Equity team has frequently been among the first to invest in India’s burgeoning industries, including Internet-driven companies, Quick Service Restaurants, Media & Entertainment, Retail, and the Life Sciences & Healthcare sector.

• Leaders in Indian private equity sector
• IPR driven stock advisory with emphasis on technology
• A tech-driven business model
• A team of experienced and passionate individuals
• Unmatched growth opportunities for investors

Setup in 1964, Unit Trust of India (UTI) is one of the top financial service organizations in India. Whenever there is a need of funds, Unit Trust of India (UTI) offers the investor a secure return on their investment by providing best stock advisory. The Unit Trust of India publishes a daily pricing record and makes newspaper advertisements for it. As a result, two prices are consistently offered each day. The two costs represent the cost of buying and selling the units.

The UTI’s main goal is to provide both small and big investors with the means of purchasing shares in the properties as a consequence of the nation’s sustained industrial expansion.

• A team of stock investment experts at your disposal
• More than 50 years of industrial legacy
• Investors can choose to diversify their investments
• Promotes high level of liquidity as investors can sell the units back to the trust
• Avail the benefit of greater economies of scale

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BTS Asset Management

Considered the best stock advisor in India, BTS Asset Management was established in 1979 and manages conventional assets in an unconventional way. BTS has a history of managing equities and tactical fixed income over a number of years. BTS has expertise leading teams through several market cycles, downturns, and upturns in interest rates, as well as recessions and recoveries.

A majority of Indian enterprises receive assured financial development from the corporation. Their ability to manage conventional assets in an unconventional way is one of their distinctive qualities. They adapt to the unstable market conditions and handle them with experience in risk management, opportunity detection, and even a comprehension of downtime to manage losses effectively.

• Tactical Asset Allocation
• Focus on Risk Management
• Proprietary Research and Models
• Diverse Product Offerings
• Long Track Record

ANM Investments

Set up in 2019, ANM Investments is a reliable share market advisor and offers full-fledged best stock advisory support to people looking to invest in stocks.

The best stock advisor in India is counted among top stock advisory companies in India and serve all sections of the society with its value-centric stock advisory services at reasonable rates. Though the firm has been launched not much ago, it is still managed to carve a niche in the field of stock advisory with its advanced stock investment services to investors.

• Diverse Portfolio Options
• Advanced Research and Analytics
• Personalized Financial Planning
• Experienced and Knowledgeable Advisors
• Strong Customer Support and Communication

Asset Villa Financial Advisors

Asset Villa Financial Advisors is one of the best financial advisors in India with offices in Mumbai, India. The business offers both individuals and families all-inclusive financial planning services. They provide a wide range of financial services, such as loans, insurance, mutual funds, financial consulting, and other financial costs. All of their products and best stock advice services are focused on the needs of the client. They also want to give their affiliated clients the greatest consulting and growth trajectory possible. The firm has over 2500 happy and satisfied clients.

• A passionate team of skilled and experienced professionals
• User-access control and opportunities to keep a track of holdings and risk profiles
• Easy to use financial plans for different types of customers
• Provides dynamic and in-depth research
• Customized stock advisory services

Mister Market is renowned for their excellence in investing education and has distinguished themselves by providing their clients with superior, thoroughly researched financial knowledge. The stock advisory company is deeply involved into informing and educating their customers on multiple market fronts, asset classes, trading strategies, complex taxation rules, investment hedging, and diverse market instruments while attending to their clients’ personal financial requirements. With the support of their knowledgeable finance staff, the best stock advisor in India wants to support their clients in their endeavor to advancing their financial development.

• Insightful stock market education for clients
• A highly safer option for new investors
• Support can also be availed on WhatsApp

Streetgains, the best SEBI registered investment advisor, specializes in commodities and stocks sector. Their offerings can be customized to the specific requirements of the clients. All of its clients receive beneficial research recommendations from the business. They are a reputable consulting and research business, as Indian retail traders firmly support and attest. Their staff of skilled and knowledgeable specialists offers their clients a wealth of assistance. Additionally, they want to use technology to expand their services and provide the greatest trading advice. It will improve their clients’ investing experiences and let them freely trade.

• It is well known for its long-term investment plan advice services
• provides thorough advice to clients
• centered on generating riches

Best Stock Advisory for Options Trading

Stock market has several stock advisory company in India that provide expert guidance and recommendations for options trading. These stock advisories also help traders to choose the best options strategies and best option timing according to their goals and preferences. Here are top 5 stock advisors for Options trading –

1. CapitalVia Global Research Limited 2. Multibagger Securities Research & Advisory 3. AssetVilla Financial Advisors 4. Streetgains 5. Mister Market.In

Charges for Options trading advisory services –

We have listed best stock advisors and their charges for option trading advisory in India. They also offer live market support, accurate information, and option trading tips in India.

Top 5 Stock Brokers with best advisory services:

The conclusion – best stock advisor in india.

Whether you are looking for free stock tips or top stock advisory support, the aforementioned top 10 stock advisory firms in India emerge as the best choice for both new and seasoned investors. Their services are top-notch, reliable, affordable and most importantly value-oriented. So, whatever investment you wish to make in stocks, find the best share market advisor or the best stock advisor in India from these best financial advisors in India.

FAQs about Best Stock Advisory

Where can i find free stock market tips in india.

There are lots of stock advisors in India that provide free stock market tips and Bajaj Capital is the best among all of them. It provides free stock tips, stock tips for today and tomorrow which tends to help investors in making an informed decision related to their investments.

What are the top 5 stock advisors in India?

The top 5 share market advisor firms in India are Bajaj Capital Limited, CapitalVia Global Research, Research and Ranking, Unit Trust of India, and Streegains.

What role does a stock advisor play in the stock market?

The main responsibility of an equity adviser is to help investors locate profitable investment opportunities in the stock market. stock investors are educated, skilled individuals who know how to manage stock investments.

Who is known as the stock king of India?

Widely called the “Warren Buffett of India,” Rakesh Jhunjhunwala is undoubtedly the best stock market investor in India till date. Born on July 5, 1960, Rakesh Jhunjhunwala was a promising player in the stock market for several years.

Is it good to bet on advisory shares?

When making long-term investments, investors should take advisory shares into account as a key investment strategy. Investors can benefit from a company’s success without physically owning any of its assets.

Disclaimer: This blog is written for educational purpose only. Data, Securities, Advisory and Quotes mentioned here are for guidance only. Doing research by investors itself is highly recommended.

• Tata Steel share price
• 167.25 0.81%
• Power Grid Corporation Of India share price
• 313.35 0.26%
• State Bank Of India share price
• 817.85 0.73%
• NTPC share price
• 365.40 1.11%
• HDFC Bank share price
• 1,464.90 0.33%

Research & Ranking's Model Portfolio clocks 79% gains

The portfolio stands among the top five multicap pms performers basis one year returns for the period ending september 2021..

Mumbai, November 29, 2021: Research and Ranking announced the portfolio clocking 79% gains in the one year ending 30th September 2021. This milestone is a major achievement for Equentis Wealth Advisory on its mission to make wealth through long-term investment. The returns not only beat the Nifty returns of 57% hands down but also stood amongst the top five multi-cap PMS in the country.

“Our performance is a result of focus on outperforming sectors such as consumer discretionary, building materials, and chemicals amongst others," says Jaspreet Singh Arora, Chief Investment Officer, at Research & Ranking. With a 60-40 mix between Large Cap and Mid/Small cap and beta of less than 1, the concentrated 20 stock portfolio has been designed to withstand volatility, provide moderate risk, and high reward for our retail and HNI investors.

The outperformance v/s Nifty and superior standing amongst the best multi-cap PMS in the country continues quarter after quarter. "Research & Ranking continues to improve its services and also add more offerings to diversify and expand the business and offer enhanced solutions to investors across the country," said Manish Goel, Founder-Director at Research & Ranking.

This news comes in the wake of the recent initiatives of the company. The company launched Informed InvestoRR, a product that will help investors discern facts and data from the noise around. To learn more about Informed InvestoRR, click here.

Most portfolio management services focus on ‘Wealth Management, while Research & Ranking focuses solely on long-term wealth creation. What makes Research & Ranking’s approach novel is the company’s philosophy of building a fortune through deploying patient long-term capital on predominantly high-growth stocks. The team at Research & Ranking strongly believes creating wealth overnight is not possible. So, they undertake thorough due diligence to create a mini-universe of attractive opportunities followed by the creation of a personalized portfolio tailored to the customer’s risk appetite and financial goals.

Stock markets are complex, where wealth creation from equity is not easy. However, with some patience, disciplined investing, and detailed research, anybody can create wealth. While persistence and restraint are traits investors must develop themselves, investors can have an edge if they avail the services of financial advisory services like Research & Ranking.

About Research & Ranking

Research & Ranking is a leading technology-enabled equity advisory service in India. With a team strength of 200+ professionals spread across Mumbai, Thane, Noida, Bangalore, and Chennai in India, Research & Ranking focuses on making stock market investing hassle-free, rewarding, and easy process for investors pan-India. Since its inception in 2014, Research & Ranking has helped over 23000 investors to fulfill their financial goals.

Disclaimer: This content is distributed by Digpu News Network. No HT journalist is involved in the creation of this content.

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More From Forbes

The role of tracking reviews in the age of healthcare consumerism.

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Lauren Parr is the co-founder and Product Director at RepuGen , one of the leading healthcare reputation management SaaS platforms.

The way patients choose healthcare providers has fundamentally changed. Today, online reviews hold immense power, influencing decisions almost as much as a doctor's qualifications.

My company recently surveyed 1,426 patients across the U.S. from different U.S. regions and socioeconomic backgrounds to understand how they use online reviews when selecting healthcare providers. The survey found that 73% of patients consider online reviews when selecting a healthcare provider.

These findings coincide with other recent surveys that show this shift in healthcare consumerism. BrightLocal, for example, found that 50% of patients now trust reviews as much as personal recommendations, and they are more empowered to search and choose healthcare providers based on their own needs and preferences. In fact, they now rely on digital sources 3.1 times more than provider referrals when looking for a new primary care provider.

Additionally, a study of 1,000 consumers by PressGaney found that 1 in 3 patients says that not having enough reviews would discourage them from seeing any particular provider, further solidifying the importance of review tracking and management.

Google Chrome Gets Third Emergency Update In A Week As Attacks Continue

Japanese fans are puzzled that yasuke is in assassin s creed shadows, forbes releases 2024 30 under 30 asia list, the benefits of tracking reviews.

Understanding patient reviews is crucial for healthcare organizations in this age of consumerism. By proactively tracking online patient reviews, healthcare practices can unveil valuable insights and use the data to improve patient experiences, acquire new patients and ultimately build a strong online reputation.

1. Track And Respond To Reviews: Building Trust And Gaining Insights

Responding to reviews, both positive and negative demonstrates that you value their feedback and are committed to providing exceptional care.

Responding promptly and professionally shows potential patients you're engaged and dedicated to continuous improvement. According to a BrightLocal survey, 88% of consumers would use a business that replies to all of its reviews. However, it's important to keep HIPAA regulations in mind when creating your review responses.

For positive reviews:

1. Express gratitude and avoid mentioning specifics about the patient's experience. For example, instead of saying "We're glad you had a great visit!," a simple "Thank you for your kind words!" is enough.

2. Showcase these positive reviews as testimonials on your website and social media. Reviews from happy patients can build trust and credibility, making you a leading choice for potential patients seeking care.

For negative reviews:

1. Acknowledge the patient's concerns without disclosing any personal health information. Phrases like "We're sorry to hear about your experience" or "Thank you for bringing this to our attention" are fine. You can then offer to take the conversation offline to address their concerns privately.

2. Apologize for any shortcomings in your care without admitting fault and explain any general steps you've taken or will take to improve the patient experience.

2. Uncovering Valuable Insights: Understanding Your Patients

Nearly half of all patients, based on the RepuGen survey cited above, base their healthcare decisions on the sentiment expressed in online reviews.

By actively listening to patient feedback and taking action, you demonstrate your commitment to continuous improvement. This not only translates into happier patients but also positions your practice as a leader in patient-centered care.

4. Boosting Your Practice: The Ripple Effect Of Positive Reviews

Positive reviews not only showcase patient satisfaction but also influence future healthcare decisions. Our survey also found that 83.23% of patients require a minimum rating of four stars even to consider an online review. Positive reviews essentially act as a confirmation bias, making it more likely patients will choose your practice.

Furthermore, high-star ratings can significantly impact your search engine ranking on platforms like Google.

Beyond search ranking, analyzing reviews can help you identify the features and benefits of your practice that resonate most with patients.

Effective Review Tracking Strategies: Tools And Techniques

There are two primary strategies for effectively tracking online reviews:

1. Building A Manual Review Tracking System

For smaller practices or those with limited budgets, a manual review tracking system can be an effective option. Here are some key steps:

• Identify key review platforms. Research which platforms your target audience uses most frequently to leave reviews. Popular options include Google My Business, Yelp and Facebook. You can use tools like Google Alerts to track mentions of your practice name on these platforms.

• Schedule review monitoring. Dedicate regular time (daily or weekly depending on your review volume) to monitor new reviews. Assigning this task to a specific team member can help ensure consistent monitoring.

2. Online Reputation Management (ORM) Tools

ORM tools are designed to help businesses monitor and manage their online reputation across various platforms. These tools typically offer functionalities like:

• Setting up alerts for new reviews across various platforms (e.g., Google Business Profile, Yelp, Facebook).

• Utilizing sentiment analysis features to understand the tone and overall message of reviews.

• Managing responses to reviews directly from the ORM platform.

Proactive Healthcare Review Management

Healthcare providers and marketing professionals, in particular, face additional regulations and guidelines when managing and responding to online reviews. Here are some specific considerations for the industry:

• HIPAA compliance: Always prioritize patient privacy. Avoid disclosing any personal health information in your review responses, even positive ones.

• Escalation: Develop protocols for escalating serious reviews (e.g., patient care or staff behavior) through designated channels.

• Transparency builds trust: Address negative reviews directly. Acknowledge shortcomings and craft steps to resolve the shortcomings. This will showcase your commitment to patient satisfaction.

The Future Of Healthcare Reviews

I believe the healthcare review landscape is constantly evolving, and healthcare practices should keep an eye out for emerging trends for better healthcare outcomes.

New AI-powered tools can streamline review sentiment analysis, allowing providers to prioritize responses and resource allocation. As telehealth becomes more common, healthcare organizations must adopt tools and technologies to capture virtual care experiences.

However, the core focus will remain on patient experience. Healthcare providers and practices prioritizing patient satisfaction and addressing feedback will continue to see a positive impact on their online reputation.

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IMF to Launch New Ukraine Mission to Review Loan, Latest Economic Developments

A destroyed tank is seen on a road, amid Russia's attack on Ukraine, in Kharkiv Region, Ukraine May 16, 2024. REUTERS/Valentyn Ogirenko

By David Lawder

WASHINGTON (Reuters) - The International Monetary Fund will start a new Ukraine mission in coming weeks to assess the war-torn country's $15.6 billion loan program and latest economic developments there amid stepped up Russian military pressure, IMF spokesperson Julie Kozack said on Thursday.

The loan review mission also will revise the IMF's analysis of Ukraine's debt sustainability, Kozack said. That assessment will influence the country's efforts to restructure $20 billion of debt with bondholders before a payment freeze expires at the end of August.

In March, the IMF executive board approved the third review of Ukraine's Extended Fund Facility loan program, resulting in an $880 million disbursement.

Kozack said the IMF hoped that an agreement on the fourth review could be brought up for board approval by the end of June. The IMF typically bases disbursement amounts on program reviews and performance towards reform goals.

"The Ukrainian economy has shown remarkable resilience. Although the Outlook does remain subject to exceptionally high war-related uncertainty," she said. "The authorities have made good progress for their EFF commitments under very challenging conditions."

But since the last review, Russia has stepped up its missile attacks on Ukrainian power infrastructure, forcing it to import record amounts of electricity. Russian forces have also opened up a new front in the northeast close to Ukraine's second largest city, Kharkiv.

The U.S. Congress has also approved a long-delayed $61 billion military and economic aid package for Ukraine. The IMF has previously estimated that Ukraine will need about $42 billion in external budgetary support this year.

The Latest Photos From Ukraine

Asked about deliberations by Western industrial democracies to harness the income from frozen sovereign Russian assets to aid Ukraine, Kozack said the IMF's position has not changed: that this was a matter for relevant jurisdictions and courts to decide.

"What is important for the Fund -- for us -- is that any action that is taken has sufficient legal underpinnings and does not undermine the functioning of the international monetary system," Kozack added.

(Reporting by David Lawder; Editing by Franklin Paul and Frances Kerry)

Copyright 2024 Thomson Reuters .

Photos You Should See - May 2024

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Tags: Ukraine , United Nations , Russia , loans , Europe

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McKinsey Global Private Markets Review 2024: Private markets in a slower era

At a glance, macroeconomic challenges continued.

McKinsey Global Private Markets Review 2024: Private markets: A slower era

If 2022 was a tale of two halves, with robust fundraising and deal activity in the first six months followed by a slowdown in the second half, then 2023 might be considered a tale of one whole. Macroeconomic headwinds persisted throughout the year, with rising financing costs, and an uncertain growth outlook taking a toll on private markets. Full-year fundraising continued to decline from 2021’s lofty peak, weighed down by the “denominator effect” that persisted in part due to a less active deal market. Managers largely held onto assets to avoid selling in a lower-multiple environment, fueling an activity-dampening cycle in which distribution-starved limited partners (LPs) reined in new commitments.

About the authors

This article is a summary of a larger report, available as a PDF, that is a collaborative effort by Fredrik Dahlqvist , Alastair Green , Paul Maia, Alexandra Nee , David Quigley , Aditya Sanghvi , Connor Mangan, John Spivey, Rahel Schneider, and Brian Vickery , representing views from McKinsey’s Private Equity & Principal Investors Practice.

Performance in most private asset classes remained below historical averages for a second consecutive year. Decade-long tailwinds from low and falling interest rates and consistently expanding multiples seem to be things of the past. As private market managers look to boost performance in this new era of investing, a deeper focus on revenue growth and margin expansion will be needed now more than ever.

Perspectives on a slower era in private markets

Global fundraising contracted.

Fundraising fell 22 percent across private market asset classes globally to just over $1 trillion, as of year-end reported data—the lowest total since 2017. Fundraising in North America, a rare bright spot in 2022, declined in line with global totals, while in Europe, fundraising proved most resilient, falling just 3 percent. In Asia, fundraising fell precipitously and now sits 72 percent below the region’s 2018 peak.

Despite difficult fundraising conditions, headwinds did not affect all strategies or managers equally. Private equity (PE) buyout strategies posted their best fundraising year ever, and larger managers and vehicles also fared well, continuing the prior year’s trend toward greater fundraising concentration.

The numerator effect persisted

Despite a marked recovery in the denominator—the 1,000 largest US retirement funds grew 7 percent in the year ending September 2023, after falling 14 percent the prior year, for example 1 “U.S. retirement plans recover half of 2022 losses amid no-show recession,” Pensions and Investments , February 12, 2024. —many LPs remain overexposed to private markets relative to their target allocations. LPs started 2023 overweight: according to analysis from CEM Benchmarking, average allocations across PE, infrastructure, and real estate were at or above target allocations as of the beginning of the year. And the numerator grew throughout the year, as a lack of exits and rebounding valuations drove net asset values (NAVs) higher. While not all LPs strictly follow asset allocation targets, our analysis in partnership with global private markets firm StepStone Group suggests that an overallocation of just one percentage point can reduce planned commitments by as much as 10 to 12 percent per year for five years or more.

Despite these headwinds, recent surveys indicate that LPs remain broadly committed to private markets. In fact, the majority plan to maintain or increase allocations over the medium to long term.

Investors fled to known names and larger funds

Fundraising concentration reached its highest level in over a decade, as investors continued to shift new commitments in favor of the largest fund managers. The 25 most successful fundraisers collected 41 percent of aggregate commitments to closed-end funds (with the top five managers accounting for nearly half that total). Closed-end fundraising totals may understate the extent of concentration in the industry overall, as the largest managers also tend to be more successful in raising non-institutional capital.

While the largest funds grew even larger—the largest vehicles on record were raised in buyout, real estate, infrastructure, and private debt in 2023—smaller and newer funds struggled. Fewer than 1,700 funds of less than $1 billion were closed during the year, half as many as closed in 2022 and the fewest of any year since 2012. New manager formation also fell to the lowest level since 2012, with just 651 new firms launched in 2023.

Whether recent fundraising concentration and a spate of M&A activity signals the beginning of oft-rumored consolidation in the private markets remains uncertain, as a similar pattern developed in each of the last two fundraising downturns before giving way to renewed entrepreneurialism among general partners (GPs) and commitment diversification among LPs. Compared with how things played out in the last two downturns, perhaps this movie really is different, or perhaps we’re watching a trilogy reusing a familiar plotline.

Dry powder inventory spiked (again)

Private markets assets under management totaled $13.1 trillion as of June 30, 2023, and have grown nearly 20 percent per annum since 2018. Dry powder reserves—the amount of capital committed but not yet deployed—increased to $3.7 trillion, marking the ninth consecutive year of growth. Dry powder inventory—the amount of capital available to GPs expressed as a multiple of annual deployment—increased for the second consecutive year in PE, as new commitments continued to outpace deal activity. Inventory sat at 1.6 years in 2023, up markedly from the 0.9 years recorded at the end of 2021 but still within the historical range. NAV grew as well, largely driven by the reluctance of managers to exit positions and crystallize returns in a depressed multiple environment.

Private equity strategies diverged

Buyout and venture capital, the two largest PE sub-asset classes, charted wildly different courses over the past 18 months. Buyout notched its highest fundraising year ever in 2023, and its performance improved, with funds posting a (still paltry) 5 percent net internal rate of return through September 30. And although buyout deal volumes declined by 19 percent, 2023 was still the third-most-active year on record. In contrast, venture capital (VC) fundraising declined by nearly 60 percent, equaling its lowest total since 2015, and deal volume fell by 36 percent to the lowest level since 2019. VC funds returned –3 percent through September, posting negative returns for seven consecutive quarters. VC was the fastest-growing—as well as the highest-performing—PE strategy by a significant margin from 2010 to 2022, but investors appear to be reevaluating their approach in the current environment.

Private equity entry multiples contracted

PE buyout entry multiples declined by roughly one turn from 11.9 to 11.0 times EBITDA, slightly outpacing the decline in public market multiples (down from 12.1 to 11.3 times EBITDA), through the first nine months of 2023. For nearly a decade leading up to 2022, managers consistently sold assets into a higher-multiple environment than that in which they had bought those assets, providing a substantial performance tailwind for the industry. Nowhere has this been truer than in technology. After experiencing more than eight turns of multiple expansion from 2009 to 2021 (the most of any sector), technology multiples have declined by nearly three turns in the past two years, 50 percent more than in any other sector. Overall, roughly two-thirds of the total return for buyout deals that were entered in 2010 or later and exited in 2021 or before can be attributed to market multiple expansion and leverage. Now, with falling multiples and higher financing costs, revenue growth and margin expansion are taking center stage for GPs.

Real estate receded

Demand uncertainty, slowing rent growth, and elevated financing costs drove cap rates higher and made price discovery challenging, all of which weighed on deal volume, fundraising, and investment performance. Global closed-end fundraising declined 34 percent year over year, and funds returned −4 percent in the first nine months of the year, losing money for the first time since the 2007–08 global financial crisis. Capital shifted away from core and core-plus strategies as investors sought liquidity via redemptions in open-end vehicles, from which net outflows reached their highest level in at least two decades. Opportunistic strategies benefited from this shift, with investors focusing on capital appreciation over income generation in a market where alternative sources of yield have grown more attractive. Rising interest rates widened bid–ask spreads and impaired deal volume across food groups, including in what were formerly hot sectors: multifamily and industrial.

Private debt pays dividends

Debt again proved to be the most resilient private asset class against a turbulent market backdrop. Fundraising declined just 13 percent, largely driven by lower commitments to direct lending strategies, for which a slower PE deal environment has made capital deployment challenging. The asset class also posted the highest returns among all private asset classes through September 30. Many private debt securities are tied to floating rates, which enhance returns in a rising-rate environment. Thus far, managers appear to have successfully navigated the rising incidence of default and distress exhibited across the broader leveraged-lending market. Although direct lending deal volume declined from 2022, private lenders financed an all-time high 59 percent of leveraged buyout transactions last year and are now expanding into additional strategies to drive the next era of growth.

Infrastructure took a detour

After several years of robust growth and strong performance, infrastructure and natural resources fundraising declined by 53 percent to the lowest total since 2013. Supply-side timing is partially to blame: five of the seven largest infrastructure managers closed a flagship vehicle in 2021 or 2022, and none of those five held a final close last year. As in real estate, investors shied away from core and core-plus investments in a higher-yield environment. Yet there are reasons to believe infrastructure’s growth will bounce back. Limited partners (LPs) surveyed by McKinsey remain bullish on their deployment to the asset class, and at least a dozen vehicles targeting more than $10 billion were actively fundraising as of the end of 2023. Multiple recent acquisitions of large infrastructure GPs by global multi-asset-class managers also indicate marketwide conviction in the asset class’s potential.

Private markets still have work to do on diversity

Private markets firms are slowly improving their representation of females (up two percentage points over the prior year) and ethnic and racial minorities (up one percentage point). On some diversity metrics, including entry-level representation of women, private markets now compare favorably with corporate America. Yet broad-based parity remains elusive and too slow in the making. Ethnic, racial, and gender imbalances are particularly stark across more influential investing roles and senior positions. In fact, McKinsey’s research  reveals that at the current pace, it would take several decades for private markets firms to reach gender parity at senior levels. Increasing representation across all levels will require managers to take fresh approaches to hiring, retention, and promotion.

Artificial intelligence generating excitement

The transformative potential of generative AI was perhaps 2023’s hottest topic (beyond Taylor Swift). Private markets players are excited about the potential for the technology to optimize their approach to thesis generation, deal sourcing, investment due diligence, and portfolio performance, among other areas. While the technology is still nascent and few GPs can boast scaled implementations, pilot programs are already in flight across the industry, particularly within portfolio companies. Adoption seems nearly certain to accelerate throughout 2024.

Private markets in a slower era

If private markets investors entered 2023 hoping for a return to the heady days of 2021, they likely left the year disappointed. Many of the headwinds that emerged in the latter half of 2022 persisted throughout the year, pressuring fundraising, dealmaking, and performance. Inflation moderated somewhat over the course of the year but remained stubbornly elevated by recent historical standards. Interest rates started high and rose higher, increasing the cost of financing. A reinvigorated public equity market recovered most of 2022’s losses but did little to resolve the valuation uncertainty private market investors have faced for the past 18 months.

Within private markets, the denominator effect remained in play, despite the public market recovery, as the numerator continued to expand. An activity-dampening cycle emerged: higher cost of capital and lower multiples limited the ability or willingness of general partners (GPs) to exit positions; fewer exits, coupled with continuing capital calls, pushed LP allocations higher, thereby limiting their ability or willingness to make new commitments. These conditions weighed on managers’ ability to fundraise. Based on data reported as of year-end 2023, private markets fundraising fell 22 percent from the prior year to just over $1 trillion, the largest such drop since 2009 (Exhibit 1).

The impact of the fundraising environment was not felt equally among GPs. Continuing a trend that emerged in 2022, and consistent with prior downturns in fundraising, LPs favored larger vehicles and the scaled GPs that typically manage them. Smaller and newer managers struggled, and the number of sub–$1 billion vehicles and new firm launches each declined to its lowest level in more than a decade.

Despite the decline in fundraising, private markets assets under management (AUM) continued to grow, increasing 12 percent to $13.1 trillion as of June 30, 2023. 2023 fundraising was still the sixth-highest annual haul on record, pushing dry powder higher, while the slowdown in deal making limited distributions.

Investment performance across private market asset classes fell short of historical averages. Private equity (PE) got back in the black but generated the lowest annual performance in the past 15 years, excluding 2022. Closed-end real estate produced negative returns for the first time since 2009, as capitalization (cap) rates expanded across sectors and rent growth dissipated in formerly hot sectors, including multifamily and industrial. The performance of infrastructure funds was less than half of its long-term average and even further below the double-digit returns generated in 2021 and 2022. Private debt was the standout performer (if there was one), outperforming all other private asset classes and illustrating the asset class’s countercyclical appeal.

Private equity down but not out

Higher financing costs, lower multiples, and an uncertain macroeconomic environment created a challenging backdrop for private equity managers in 2023. Fundraising declined for the second year in a row, falling 15 percent to $649 billion, as LPs grappled with the denominator effect and a slowdown in distributions. Managers were on the fundraising trail longer to raise this capital: funds that closed in 2023 were open for a record-high average of 20.1 months, notably longer than 18.7 months in 2022 and 14.1 months in 2018. VC and growth equity strategies led the decline, dropping to their lowest level of cumulative capital raised since 2015. Fundraising in Asia fell for the fourth year of the last five, with the greatest decline in China.

Despite the difficult fundraising context, a subset of strategies and managers prevailed. Buyout managers collectively had their best fundraising year on record, raising more than $400 billion. Fundraising in Europe surged by more than 50 percent, resulting in the region’s biggest haul ever. The largest managers raised an outsized share of the total for a second consecutive year, making 2023 the most concentrated fundraising year of the last decade (Exhibit 2).

Despite the drop in aggregate fundraising, PE assets under management increased 8 percent to $8.2 trillion. Only a small part of this growth was performance driven: PE funds produced a net IRR of just 2.5 percent through September 30, 2023. Buyouts and growth equity generated positive returns, while VC lost money. PE performance, dating back to the beginning of 2022, remains negative, highlighting the difficulty of generating attractive investment returns in a higher interest rate and lower multiple environment. As PE managers devise value creation strategies to improve performance, their focus includes ensuring operating efficiency and profitability of their portfolio companies.

Deal activity volume and count fell sharply, by 21 percent and 24 percent, respectively, which continued the slower pace set in the second half of 2022. Sponsors largely opted to hold assets longer rather than lock in underwhelming returns. While higher financing costs and valuation mismatches weighed on overall deal activity, certain types of M&A gained share. Add-on deals, for example, accounted for a record 46 percent of total buyout deal volume last year.

Real estate recedes

For real estate, 2023 was a year of transition, characterized by a litany of new and familiar challenges. Pandemic-driven demand issues continued, while elevated financing costs, expanding cap rates, and valuation uncertainty weighed on commercial real estate deal volumes, fundraising, and investment performance.

Managers faced one of the toughest fundraising environments in many years. Global closed-end fundraising declined 34 percent to $125 billion. While fundraising challenges were widespread, they were not ubiquitous across strategies. Dollars continued to shift to large, multi-asset class platforms, with the top five managers accounting for 37 percent of aggregate closed-end real estate fundraising. In April, the largest real estate fund ever raised closed on a record $30 billion.

Capital shifted away from core and core-plus strategies as investors sought liquidity through redemptions in open-end vehicles and reduced gross contributions to the lowest level since 2009. Opportunistic strategies benefited from this shift, as investors turned their attention toward capital appreciation over income generation in a market where alternative sources of yield have grown more attractive.

In the United States, for instance, open-end funds, as represented by the National Council of Real Estate Investment Fiduciaries Fund Index—Open-End Equity (NFI-OE), recorded $13 billion in net outflows in 2023, reversing the trend of positive net inflows throughout the 2010s. The negative flows mainly reflected $9 billion in core outflows, with core-plus funds accounting for the remaining outflows, which reversed a 20-year run of net inflows.

As a result, the NAV in US open-end funds fell roughly 16 percent year over year. Meanwhile, global assets under management in closed-end funds reached a new peak of $1.7 trillion as of June 2023, growing 14 percent between June 2022 and June 2023.

Real estate underperformed historical averages in 2023, as previously high-performing multifamily and industrial sectors joined office in producing negative returns caused by slowing demand growth and cap rate expansion. Closed-end funds generated a pooled net IRR of −3.5 percent in the first nine months of 2023, losing money for the first time since the global financial crisis. The lone bright spot among major sectors was hospitality, which—thanks to a rush of postpandemic travel—returned 10.3 percent in 2023. 2 Based on NCREIFs NPI index. Hotels represent 1 percent of total properties in the index. As a whole, the average pooled lifetime net IRRs for closed-end real estate funds from 2011–20 vintages remained around historical levels (9.8 percent).

Global deal volume declined 47 percent in 2023 to reach a ten-year low of $650 billion, driven by widening bid–ask spreads amid valuation uncertainty and higher costs of financing (Exhibit 3). 3 CBRE, Real Capital Analytics Deal flow in the office sector remained depressed, partly as a result of continued uncertainty in the demand for space in a hybrid working world.

During a turbulent year for private markets, private debt was a relative bright spot, topping private markets asset classes in terms of fundraising growth, AUM growth, and performance.

Fundraising for private debt declined just 13 percent year over year, nearly ten percentage points less than the private markets overall. Despite the decline in fundraising, AUM surged 27 percent to $1.7 trillion. And private debt posted the highest investment returns of any private asset class through the first three quarters of 2023.

Private debt’s risk/return characteristics are well suited to the current environment. With interest rates at their highest in more than a decade, current yields in the asset class have grown more attractive on both an absolute and relative basis, particularly if higher rates sustain and put downward pressure on equity returns (Exhibit 4). The built-in security derived from debt’s privileged position in the capital structure, moreover, appeals to investors that are wary of market volatility and valuation uncertainty.

Direct lending continued to be the largest strategy in 2023, with fundraising for the mostly-senior-debt strategy accounting for almost half of the asset class’s total haul (despite declining from the previous year). Separately, mezzanine debt fundraising hit a new high, thanks to the closings of three of the largest funds ever raised in the strategy.

Over the longer term, growth in private debt has largely been driven by institutional investors rotating out of traditional fixed income in favor of private alternatives. Despite this growth in commitments, LPs remain underweight in this asset class relative to their targets. In fact, the allocation gap has only grown wider in recent years, a sharp contrast to other private asset classes, for which LPs’ current allocations exceed their targets on average. According to data from CEM Benchmarking, the private debt allocation gap now stands at 1.4 percent, which means that, in aggregate, investors must commit hundreds of billions in net new capital to the asset class just to reach current targets.

Private debt was not completely immune to the macroeconomic conditions last year, however. Fundraising declined for the second consecutive year and now sits 23 percent below 2021’s peak. Furthermore, though private lenders took share in 2023 from other capital sources, overall deal volumes also declined for the second year in a row. The drop was largely driven by a less active PE deal environment: private debt is predominantly used to finance PE-backed companies, though managers are increasingly diversifying their origination capabilities to include a broad new range of companies and asset types.

Infrastructure and natural resources take a detour

For infrastructure and natural resources fundraising, 2023 was an exceptionally challenging year. Aggregate capital raised declined 53 percent year over year to $82 billion, the lowest annual total since 2013. The size of the drop is particularly surprising in light of infrastructure’s recent momentum. The asset class had set fundraising records in four of the previous five years, and infrastructure is often considered an attractive investment in uncertain markets.

While there is little doubt that the broader fundraising headwinds discussed elsewhere in this report affected infrastructure and natural resources fundraising last year, dynamics specific to the asset class were at play as well. One issue was supply-side timing: nine of the ten largest infrastructure GPs did not close a flagship fund in 2023. Second was the migration of investor dollars away from core and core-plus investments, which have historically accounted for the bulk of infrastructure fundraising, in a higher rate environment.

The asset class had some notable bright spots last year. Fundraising for higher-returning opportunistic strategies more than doubled the prior year’s total (Exhibit 5). AUM grew 18 percent, reaching a new high of $1.5 trillion. Infrastructure funds returned a net IRR of 3.4 percent in 2023; this was below historical averages but still the second-best return among private asset classes. And as was the case in other asset classes, investors concentrated commitments in larger funds and managers in 2023, including in the largest infrastructure fund ever raised.

The outlook for the asset class, moreover, remains positive. Funds targeting a record amount of capital were in the market at year-end, providing a robust foundation for fundraising in 2024 and 2025. A recent spate of infrastructure GP acquisitions signal multi-asset managers’ long-term conviction in the asset class, despite short-term headwinds. Global megatrends like decarbonization and digitization, as well as revolutions in energy and mobility, have spurred new infrastructure investment opportunities around the world, particularly for value-oriented investors that are willing to take on more risk.

Private markets make measured progress in DEI

Diversity, equity, and inclusion (DEI) has become an important part of the fundraising, talent, and investing landscape for private market participants. Encouragingly, incremental progress has been made in recent years, including more diverse talent being brought to entry-level positions, investing roles, and investment committees. The scope of DEI metrics provided to institutional investors during fundraising has also increased in recent years: more than half of PE firms now provide data across investing teams, portfolio company boards, and portfolio company management (versus investment team data only). 4 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023.

In 2023, McKinsey surveyed 66 global private markets firms that collectively employ more than 60,000 people for the second annual State of diversity in global private markets report. 5 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023. The research offers insight into the representation of women and ethnic and racial minorities in private investing as of year-end 2022. In this chapter, we discuss where the numbers stand and how firms can bring a more diverse set of perspectives to the table.

The statistics indicate signs of modest advancement. Overall representation of women in private markets increased two percentage points to 35 percent, and ethnic and racial minorities increased one percentage point to 30 percent (Exhibit 6). Entry-level positions have nearly reached gender parity, with female representation at 48 percent. The share of women holding C-suite roles globally increased 3 percentage points, while the share of people from ethnic and racial minorities in investment committees increased 9 percentage points. There is growing evidence that external hiring is gradually helping close the diversity gap, especially at senior levels. For example, 33 percent of external hires at the managing director level were ethnic or racial minorities, higher than their existing representation level (19 percent).

Yet, the scope of the challenge remains substantial. Women and minorities continue to be underrepresented in senior positions and investing roles. They also experience uneven rates of progress due to lower promotion and higher attrition rates, particularly at smaller firms. Firms are also navigating an increasingly polarized workplace today, with additional scrutiny and a growing number of lawsuits against corporate diversity and inclusion programs, particularly in the US, which threatens to impact the industry’s pace of progress.

Fredrik Dahlqvist is a senior partner in McKinsey’s Stockholm office; Alastair Green  is a senior partner in the Washington, DC, office, where Paul Maia and Alexandra Nee  are partners; David Quigley  is a senior partner in the New York office, where Connor Mangan is an associate partner and Aditya Sanghvi  is a senior partner; Rahel Schneider is an associate partner in the Bay Area office; John Spivey is a partner in the Charlotte office; and Brian Vickery  is a partner in the Boston office.

The authors wish to thank Jonathan Christy, Louis Dufau, Vaibhav Gujral, Graham Healy-Day, Laura Johnson, Ryan Luby, Tripp Norton, Alastair Rami, Henri Torbey, and Alex Wolkomir for their contributions

The authors would also like to thank CEM Benchmarking and the StepStone Group for their partnership in this year's report.

This article was edited by Arshiya Khullar, an editor in the Gurugram office.

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Exploring the impact of SEO-based ranking factors for voice queries through machine learning

• Open access
• Published: 16 May 2024
• Volume 57 , article number  144 , ( 2024 )

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• Zafar Saeed 1 ,
• Fozia Aslam 2 ,
• Adnan Ghafoor 2 ,
• Muhammad Umair 2 &
• Imran Razzak 3  

The use of voice search is proliferating and expected to grow into the foreseeable future; this is why websites increasingly optimize their content associated with voice-based search to improve their ranking. In this era of rapid growth in voice search technology, it is a topical matter that needs research. Moreover, many predictions about its future excite the subject and require systematic investigation. This research aims to analyze important features that contribute to the SEO of webpages. Therefore, there is a need to examine various ranking factors that improve the ranking of the webpages for voice search queries on the Search Engine Results Page (SERP). This study consists of two phases. The first phase comprises systematic data acquisition and identifying important SEO-based ranking factors. The second phase includes a longitudinal case study to evaluate the impact and significance of identified factors. To achieve this goal, we conduct experiments on methodical combinations of features through machine learning algorithms such as Support Vector Machine, Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbors, Decision Trees and Random Forest. Comparing results for multiple feature designs evaluates the contributing nature of specific features in SEO-based optimization for ranking. Results suggest the importance of the newly identified feature set (FF) outperforms baselines (EF and EFN) by a significant margin. A longitudinal case study on a blog over four months confirms that optimizing these features improves page ranking; therefore, webmasters must optimize these features while preparing the webpage.

Avoid common mistakes on your manuscript.

1 Introduction

Search engines help their users find the appropriate and most relevant information they are looking for by retrieving a ranked list of results that matches the query. Once results are displayed in SERP, users decide whether to click on the results presented on the first page or need to explore more results on later pages. There are many search engines, but among all search engines, Google Footnote 1 is the top search engine with two main competitors Yahoo Footnote 2 and Microsoft’s Bing. Footnote 3 Search engines work in three phases: crawling, indexing, and ranking. Crawlers are robots, also called bots or spiders. They continuously search for new or updated content on the internet. The new content can be a document, webpage, image, or video. Once the Google crawler discovers new webpages, it analyzes what each page is about (e.g., content, images, or videos). The latest content is processed and indexed using Google Caffeine, Footnote 4 to retrieve it later when a user generates a query. This process is called indexing. The last phase of the information retrieval process is ranking, which happens after the information is found using  the Google index against a user’s query. The search algorithm considers numerous ranking factors to determine the order of search results. Ranking organizes the results based on relevancy, webpage quality, and authority as well. However, higher rankings depend on Google’s algorithm functionality. Footnote 5 Search Engine Optimization (SEO) is a process to improve website performance to achieve high visibility in search engines. High visibility or high ranking leads to more visitors’ attention, improving the quantity of website traffic. Unlike Google’s paid ads, you cannot pay Google for a higher rank to get organic search traffic. SEO is further divided into two categories: on-page SEO and off-page SEO. On-page SEO includes all practices that ensure webpage optimization for users and search engines. Common on-page SEO factors include meta-tag optimization, content uniqueness, internal linking, keywords and image optimization, URL optimization, mobile-friendliness, and code minification (Patil and Patil 2018b ). Off-page SEO plays a vital role in a successful ranking in the Google SERP and higher traffic. It involves a holistic approach to building your website’s reputation, authority, and online presence through inbound links. For instance, if a reputable industry blog links to your site as a reference, it boosts your site’s credibility and increases the chances of web users landing on your webpage. Standard practices for off-page SEO include acquiring backlinks from authoritative websites to boost page credibility via blog posts, social media engagement, online reviews, content marketing, brand mentioning, making classified ads, forum posts, sharing images, infographics, and videos related to the page.

1.1 Motivation and research Gap

The Google ranking algorithm serves as a pivotal mechanism for the assessment and ranking of websites. In pursuit of maintaining the integrity of its search results, Google consistently improves the ranking algorithm, Footnote 6 notable among them being ’Penguin,’ ’Panda,’ and ’Hummingbird’. These updates are designed to identify and penalize websites that fail to align with Google’s prescribed search optimization guidelines. Google’s algorithm uses over 200 factors to rank webpages. These factors have been intentionally kept confidential; however, content quality, keyword density, and engagement on social media platforms are an open secret (Khan and Mahmood 2018 ). Due to the undisclosed ranking algorithm, the existing research studies propose SEO-related important factors through reverse engineering; hence, they quickly become outdated due to the continuous change in Google’s ranking algorithm. Formulating an adaptable framework is necessary to identify significant factors and comprehend their influence on webpage ranking. After reviewing the available literature on search engine optimization trends, SEO factors, and analysis, it has been observed that the previous studies have several limitations. First, they did not introduce the crucial factors related to speed-related factors (mobile and desktop speed), h1 length, content word count, domain authority (DA), page authority (PA), image links, and website categories. Thus, the need arises to collect data about these factors and investigate the impact of the existing and newly collected factors through different classification methods of machine learning, identifying the contributing factors that can help webmasters achieve higher rankings. Second, the existing literature concerns the significant ranking factors of the search engine, which collects the dataset of webpages through different text queries. Advancements in technology have enabled users to search and browse online content through their hand-held and voice-activated devices such as mobile phones. It is easier for such users to generate voice queries; hence, the SEO factors contributing to the voice-generated queries are an important area that requires investigation. So, there is a need to collect the webpages through voice search queries and analyze the factors that influence Google to select the top-ranked pages. Therefore, there is a need to explore the impact of voice search on SEO by collecting the voice search-related factors such as snippet type, question words in page title/voice search-related terms, structured data: markup size, schema markup usage, and question words in URL/voice search-related term.

1.1.1 Research questions

The existing research gap under the motivational scenarios leads to the following Research Questions addressed in this research study:

Can a framework be designed to express an adaptive process to acquire voice query-based data and evaluate the impact of SEO-related factors?

What are the top factor(s) involved in the SEO process?

Can optimizing significant factors identified through machine learning improve webpage ranking?

1.1.2 Research contributions

This research aims to analyze the factors that influence the ranking of webpages over voice search queries. The study results would help webmasters understand the factors they must consider to get a higher search rank. The Key contributions in the underlying studies are as follows:

We devised a systematic design to formulate voice-based search queries.

We acquire data and develop a benchmark based on voice-based search queries for evaluation.

We identify on-page SEO-related factors that have not been investigated for voice search queries.

We perform feature engineering and identify significant factors contributing to the page ranking.

We conduct a longitudinal case study on a blog to evaluate the ranking impact when optimizing SEO-related factors identified in this research.

The rest of this work is organized as follows: Sect.  2 describes the background knowledge, then categorically discusses the recent trends and use of machine learning in SEO. Section  3 discusses the data acquisition and feature characterization in detail. Section  4 discusses the framework and methodology. Section  5 discusses the experimental setup, evaluation measures, and feature designs. Section  6 discusses the results and significance of the proposed features. Section  7 discusses a longitudinal study of a Blog and verifies the impact of identified SEO factors. The study concludes with Sect.  8 and describes possible future directions in Sect.  9 .

2 Related work

The following section briefly describes the background of SEO, its trends, usage of machine learning, ranking algorithms, and discusses the previous research in the preceding literature to analyze the major ranking factors of the Google search engine.

2.1 Search engine optimization (SEO)

In order to get a better rank in search engines, a set of techniques called search engine optimization (SEO) is applied (Sharma and Verma 2020 ). According to Lemos and Joshi ( 2017 ), the overall SEO process comprises six different phases that webmasters should carry out for effective optimization of the webpages (see Fig.  1 ). These phases include keyword research, goal setting, content building, webpage optimization, link building, and creating progress reports. In (Patil and Patil 2018b , a ), the authors researched the techniques to provide users with reliable and ranked results. Generally, if a webpage’s rank is higher, it will be visited by a large number of users. The primary purpose of SEO is to improve the quality and quantity of website traffic and ensure that the webpages appeal to search engines. Patel and Atkotiya ( 2020 ) divided SEO ranking factors into two categories: on-page and off-page optimization. On-page factors include Meta tags, Heading tags, Sitemaps, and Robot files. Off-page optimization comprises link building, social sharing, comments, business listings, and blogging. Roslina and Shahirah ( 2019 ) performed testing by creating a website using cPanel and WordPress after implementing SEO. Their results show an improvement in the keyword ranking on Google’s first page. Vyas ( 2019 ) evaluated tourism websites using search engine optimization tools. They use seven SEO tools to analyze the six tourism websites: traffic estimate, Twitter search, Google trends, Alexa, similar websites, SEMrush, SEO analyzer, and Moz. They use all these tools for different purposes, such as searcher preference, judging keywords, global rank, organic search traffic, domain authority, page authority, and how a website ranks in SERP. Mittal and Sridaran ( 2019 ) shows multiple factors that need to be considered to improve the site performance, e.g. page size, page requests, page speed, browser caching, page redirects, compression, render blocking, responsive design, website viewport, page title, meta description, headings, sitemap and SSL certification.

SEO Process

2.2 Voice queries Vs text queries

Generally, voice queries differ from how people make regular queries, i.e., searching through text. Regarding text queries, users choose keywords to translate their information needs and browse the results to find relevant information. However, when a user performs a voice search, they expect a direct answer from the search engine that best describes their information needs (Strzelecki and Rutecka 2020a ). The language of voice queries is closer to natural language than text queries. The research by (Guy 2016 ) found that the average query length for voice search is significantly longer. Voice queries mainly include question words and natural language such as what is, in the, show me, I am looking for, etc. For instance “Looking for a restaurant that serves oysters in San Francisco”. Meanwhile, the text queries, such as “oyster’s restaurant sf”, are very specific. Moreover, they concluded that only 13.1% of the voice queries were identical to the text samples.

2.3 Trends in SEO

Moreover, SEO is an ongoing process, and Google engineers continuously update their algorithms for the best user experience. The major and core updates can be seen on Google’s official forum. Footnote 7 On the other hand, the growth of voice-enabled devices and voice search is also undeniable. The number of devices with virtual assistants is also on the rise. Voice search is simply a matter of speaking the search query. Voice search is a concept that started with mobile phones but quickly spread to smart speaker devices, in the car, and televisions (Strzelecki and Rutecka 2020b ). Regarding voice search on mobile, user experience is considered the most essential ranking factor. And Google prefers mobile-friendly websites that must meet the requirements of mobile users. In 2016, for the first time, it was analyzed that mobile traffic surpassed desktop traffic, accounting for 51.3%. In 2019, mobile devices claimed the top position among devices browsing networks, comprising 51.6% of the total. It shows a prominent increase in mobile phone users, leading to a huge number of voice search queries. Therefore, there is a need for information retrieval researchers to understand this new medium of search and its differences from traditional text searches. With this, there is also a need to make a web structure mobile-friendly and optimized for voice search.

The proliferation of the use of voice search has quickly gained attention over the last decades and is poised to continue through 2021. We can’t ignore its rapid growth and adoption during the COVID-19 pandemic. In July 2019, Adobe Footnote 8 released a survey that shows that 48% of users use voice search for general web queries. Along with voice search, there are also other trends to consider that will affect SEO. These trends include artificial intelligence in future SEO, the effect of voice search on search queries, quality content, featured snippets, image optimization, videos, and local listings. Footnote 9 The usage of featured snippets cannot be ignored. Featured snippets represent the most recent and popular type of snippet. Search engines extract pieces of information from webpages to display them in the form of a box alongside organic results. A featured snippet is also known as the direct answer or Google answer box (Strzelecki and Rutecka 2020a ). The studies (Strzelecki and Rutecka 2019 , 2020b ) show that snippets appear in several different formats, e.g. paragraph, list, table, ordered and unordered list. Snippets taxonomy shows that most of the snippets in Google SERP are the results of long-tail keywords. These are often 3-5 words long and are likely to be used during searches. Long-tail keywords are mostly informational keywords that are used to find specific information, e.g., How far is California from San Jose? Long Tail keywords are combinations of words that represent a very long and specific search query (Jerkovic and Warrior 2009 ; Anuradha et al. 2021 ).

2.4 Machine learning in SEO

The machine learning approach helps search engines understand the page ranking criteria. Nowadays, most search engines like Google, Yahoo, Bing, Ask, and many more use machine learning techniques for webpage classification and webpage ranking. To sort the search results, Google uses the “RankBrain” algorithm. RankBrain is a machine learning algorithm that helps Google understand and process search queries. It is considered the third most essential algorithm that understands what users are asking and allows them to provide results by bringing up similar results in response to the search query. The first primary task of RankBrain is to understand the search queries, then measure the user experience and anticipate how users interact with the web results (Sunny 2020 ). Google’s current figure confirms approximately 4.2 billion active webpages (Hingoro and Nawaz 2021 ). PageRank (PR) plays a crucial role in ranking the most relevant results in response to search queries. PR is a ranking algorithm that evolves a set of rules. The PR algorithm’s working depends on the link structure of the webpage. At the same time, the rank score of the webpage given by PR is based on the backlinks of the webpage. A webpage linked by many high authority pages receives a high PR score (Selvan et al. 2012 ), and the range of PR is from (0 to 10) (Jadav and Shrivastava 2021 ). However, Google claims to use more than 200 parameters in its ranking algorithm (Khan and Mahmood 2018 ; Su et al. 2014 ; Hingoro and Nawaz 2021 ). The ranking algorithm employs various factors. While most of these factors are published in Google guidelines, Footnote 10 the exact role in ranking and the method to attain page ranking have not been precisely specified (Matošević et al. 2021 ). These ranking factors are kept confidential by Google (Khan and Mahmood 2018 ), and only some of them are revealed, e.g. website popularity, the density of keywords, quality of content, page speed and website security. Numerous efforts have been made to reveal the important ranking factors of the search engine. The study conducted by Su et al. ( 2014 ), aims to determine the important ranking factors influencing the search engine to rank the webpages. To conduct their study, they collected the top 20 pages against 60 keywords and analyzed 17 ranking features for each webpage. They use the SVM-rank implementation with linear and polynomial kernels to train the ranking functions. The dominant ranking factors they revealed are page rank, keyword in the title tag, keyword in the meta description, keyword in the hostname and keyword in the URL’s path. A similar study has been conducted to investigate the contributing factors that help to increase organic traffic through various SEO factors. The authors analyzed 171 cultural heritage websites along with their keyword ranking performance and user experience. The five focused factors that they explored in their research are website size, loading speed (LD), SEO crawling, website security (HTTPs), organic traffic (TF) and user behavior. They developed a diagnostic exploratory model derived from linear regression to analyze the cause-and-effect relationship between the five factors. This statistical approach was developed to understand the impact of each factor on the organic traffic change. Their analysis concluded that SEO crawling, website security, website size, and user behavior significantly impact the increase in organic search traffic. Among all these influencing factors, user behavior seems to have the highest impact on increasing organic search traffic. A detailed literature analysis was conducted by (Ziakis et al. 2019 ) on SEO factors that influence rankings on SERP. They recorded the features of 24 websites, with the most significant factors involving on-page and off-page SEO factors. They gathered on-page SEO factors from previous research. They conducted a statistical analysis on each factor through the Spearman correlation coefficient and concluded that the most significant factors were the quantity and quality of backlinks, the bounce rate, and the SSL certificate. Moreover, their study also confirmed that the website’s loading time (LD), URL length (ULC), and use of target keywords in the title (KWT) do not participate in the ranking of the website. Moreover, the study (Matošević et al. 2021 ) shows that machine learning can be used to classify the sample webpages and detect those that need improvements to comply with SEO guidelines. They extracted the target on-page factors through expert knowledge and machine learning. The importance of features was estimated through different statistical methods, including correlation, information gain, chi-square, relief, and random forest. The classification methods used in their research to evaluate the results were decision tree, naive Bayes classifier, k-nearest neighbor, support vector machine, and logistic regression. The results of this study concluded that the important factors that webmasters must consider while preparing a webpage are keywords in the meta title, keywords in the meta description, keywords in the heading tags and keywords in the webpage’s body.

3 Dataset collection & characterization

We collected the latest dataset for voice search analysis since we only found a dataset from one preceding literature related to the information about featured snippets (Strzelecki and Rutecka 2020a ), which couldn’t help us identify the SEO trend. Moreover, Google started rolling out the May 2020 core update (04/05/2020), and it was the second hottest update after the August 2018 medic update. The recent May core update was significant, and many websites were affected by Google SERPs. Therefore, we are motivated to collect the latest dataset and analyze its ranking factors. Each time Google updates, its algorithm improves the user experience and shows the most relevant results to the searcher. Below are images taken from the SEMRUSH tool that show the down traffic of affected sites after the Google May 2020 core update. The x-axis in Figs.  2 and  3 shows the months, while the numbers in the y-axis show the amount of traffic, and the line trend shows the prominent drop in traffic after the May 2020 core update.

Effected Site (mediaite.com)

Effected Site (rfi.fr)

3.1 Voice query generation

Existing literature (Strzelecki and Rutecka 2020a ) confirms that voice queries have a long tail structure. Voice search queries are more extended than text search queries and involve question words, e.g. (What, When, How, Why, Where, Did, Who, etc.). The first step of this research is the formation of voice search queries. The primary list of keywords associated with each of the 16 domains is collected from search engine suggestions and auto-completion tools. Later, we used AnswerThePublic Footnote 11 and keyword.io Footnote 12 to formulate extended search queries with the seed keyword. These tools generate long-tail queries relevant to our target seed keywords and retain a structure similar to the voice search query. The list of search queries comprises common questions and keywords selected from the well-known areas under the technology domain (see Table  1 ). The long-tail queries were transformed/synthesized from text-to-speech to simulate the procedure of asking questions for voice search queries. We added “Hey Google” command with each query, fed the queries to Google Text-to-Speech, and then converted the output into an audio file. In our setup, Google Assistant took 5–10s to process each query. Therefore, we modified the audio file using Audacity’s audio editor and added a 10-seconds silent buffer after each query, ensuring that each query audio was correctly aligned with the Google Assistant. By the end of this process, a list of voice search queries was finalized and ready to be used for the data acquisition.

3.2 Voice search and data acquisition

The process of asking queries and acquiring data involves the following steps.

3.2.1 Device setup for voice search

In this research, the device that we use for asking questions to Google Assistant is “Google Home Mini,” a smart speaker that is powered by Google Assistant. After the Google Home Mini device’s setup, we automate the process by feeding audio files (voice queries) into the Android phone.

The audio file is then played on the Android phone, which programmatically asks all the queries one by one to Google Home Mini. We extract the search results answered by Google Assistant and URLs of webpages displayed against the voice search queries in Google SERPs. The whole process is executed by logging through Google My Activity. Footnote 13

3.2.2 Dataset

The dataset of URLs is collected through the extraction bookmarklet code. After collecting URLs, we formulated the dataset into a CSV structure containing feature estimation for each item (page) in the dataset along with the query. For each query, we collected 20 URLs segregated into the top 1-10 and bottom 91-100 webpages. A total of 80 webpages were dropped due to Google Assistant's inability to interpret some of the voice queries correctly. Finally, the dataset consists of 2960 webpages, extracting 31 features for each webpage. To collect this dataset, approximately the top 50% of queries were selected. We asked 148 voice search queries to Google Assistant to build a benchmark dataset. The quality of data is essential when training a machine learning model. Therefore, we calculated the percentage of outliers in the data to understand the data quality. We use Interquartile Range (IQR) as a statistical measure to assess the outliers in the dataset. It is defined as the difference between the third quartile (Q3) and the first quartile, i.e. \(IQR = Q3 - Q1\) . Where Q1 and Q3 are the first ( \(25^{th}\) percentile) and third ( \(75^{th}\) percentile) values in the dataset, respectively, sorted in ascending order. Lower Bound (LB) and Upper Bound (UB) Outlier values are defined as \(Outlier_{LB} = Q1 - 1.5\times IQR\) and \(Outlier_{UB} = Q3 + 1.5\times IQR\) , respectively. Estimating outliers was not straightforward. We split the data into two parts; top-10 is separated from the bottom-10 ranked pages. Intuitively, the feature values would have a visible gap between these two groups, resulting in greater outliers. However, these data points are equally important for the learning process of classifiers. Therefore, removing outliers from the data may lead to incorrect conclusions in this particular scenario. Outliers from each group are estimated separately. The complete data with 31 features has a total of 4.688% outlier values. However, in the final/filtered feature set, the outliers have reduced to 2.67%. Moreover, Meta Description Length in Characters (MDLC), Traffic, and backlinks have the highest outlier values 12.11%, 9.73%, and 7.87%, respectively. The outliers values in these features are comprehensible because several webpages optimize MDLC with extensive descriptions for including relevant keywords. On the other hand, top-ranked webpages have massive traffic; therefore, the outliers are relatively high in these features. Similarly, backlinks were once considered an essential factor for SEO; therefore, some webpages use backlinks in huge numbers. However, our feature selection methodology proved that backlinks are not among the top influential factors for improving page ranking. Further details about the dataset are shown in the Table  2 and publicly available on GitHub repository. Footnote 14

3.3 Features collection

The features were collected using a Python script where we implemented the BeautifulSoup library and used SEO tools, including Alexa, Google page speed insight tool, Moz, ahref, Semrush and Netspeak Spider. While collecting the features, we selected the top-10 organic results from Google SERP and the results from the bottom page. For feature analysis, we collected the ranking position of each page in the Google SERP. Table  3 shows the list of features for each webpage we contained against the queries to conduct our research. The existing features (EF) are taken from the literature (Khan and Mahmood 2018 ; Drivas et al. 2020 ; Matošević et al. 2021 ; Su et al. 2014 ). Additionally, a list of newly analyzed features (NAF) was also collected to investigate their influence on Google ranking.

4 Methodology

To address the research question (RQ1), the following section describes the methodology derived through the framework shown in Fig.  4 . We used top keywords related to 16 different domains and used them as seeds to formulate extended/long-tail questions like queries (see Sect.  3.1 ) through a systematic process. On average, each domain’s top 20 questions are voice synthesized and recorded as queries. Later, these voice queries are simulated to ask questions from Google Assistant. The retrieved results through the search engine are segregated according to their ranks. A detailed feature analysis process is conducted to collect features set for the resultant webpages (see Table  3 ). The feature selection methods based on Information Gain and SelectKBest (Chi-square as a scoring function) are used to identify the expressive features. An ensemble feature selection approach ranks and filters top features (see Sect.  5.2 ), later used for experimental design. Two existing feature designs were compared as baselines with four newly analyzed feature designs (see Sect.  5.6 ). We then used well-known machine learning approaches (SVM, Logistic Regression, Naive Bayes, K-Nearest Neighbours, Decision Tree, and Random Forest) to identify whether the selected feature set contributes to a higher page rank. Finally, to cross-verify the impact of outperforming feature design, we optimized the identified factors of an online blog over four months and retested the rank results (see Sect.  7 ). The convincing improvement in page ranking verifies that the identified factors should be prioritized when optimizing for SEO.

The proposed Framework for the research methodology including the data acquisition process

5 Experimental setup

This section presents a detailed experimental setup, including various combinations of feature design with data normalization techniques. The section also discusses the results of the proposed research methodology.

5.1 Data preprocessing

Concerning the statistical tests and classification, we employ a 2-steps process in data preprocessing:

Data normalization

Label Encoding

Before we apply any classification algorithm, preprocessing through normalization was performed on our dataset because the features we collected had different numerical ranges. There is high variance in the dataset, i.e. some of the features such as Traffic , Backlinks , and CWC have high numeric ranges compared to other features such as WCIMD , LD , and TLC . To correct this bias and get better results for the classification, we employed min-max normalization in our dataset. Normalization changes the value of the dataset to a standard scale. Our dataset uses min-max normalization with a scale ranging from 1 to 100.

In Eq.  1 , \(z_i\) is the normalized value in the dataset, \(x_i\) is the \(i^{th}\) value in the dataset, minimum Min and maximum Max value range is defined as 1 and 100. respectively, for continuous value type features. Furthermore, label encoding (Cerda and Varoquaux 2020 ) is used to transform the categorical features into numeric values.

5.2 Feature selection

The feature engineering reveals the importance of various features in the dataset. The two approaches we use in our research are based on information gain (IG) and univariate selection. By measuring the results of these tests, we identify the important features and drop those with low IG. To calculate the IG, we first calculated the entropy of the entire dataset and every single feature. Entropy measures disorder in the data and ranges between 0 and 1. If data is entirely homogeneous concerning the target class, entropy is 0. On the other hand, if data is uniformly divided concerning the target class, the entropy is 1. Information Gain estimates how much information a feature tells about the target class.

Where \(p_i\) is the probability of the element i in the data we can select the features in descending order of their score once we calculate the IG. It is measured by subtracting the entropy of particular features from the entropy of the entire dataset by comparing the entropy before and after the data split using a particular feature, as shown in Eq.  2 .

Univariate feature selection is also widely used in machine learning. It selects the top features with the most vital relationship with the output variables to better understand the data. We apply a univariate selection to the normalized dataset. SelectKBest (see Algorithm 1) is used to choose the top- K features according to the highest scores. We have a multi-variable classification problem; therefore, the chi-square scoring function is used to rank the continuous (independent) variables according to categorical (dependent) variables.

Ranked list of all features based on SelectKBest algorithm

Ranked list of all features based on Information Gain

SelectKBest Algorithm for Feature selection and ranking

The features are sorted from highest to lowest values concerning Information Gain and SelectKBest, as shown in Figs.  5  and 6 . We identified these expressive features for their estimated values in the dataset. Moreover, some of them are also recommended in previous studies (Drivas et al. 2020 ; Matošević et al. 2021 ; Su et al. 2014 ). Although we used two different approaches, the feature scores in both analyses are correlated. The features with the highest scores are ST (Snippet type), MS (Mobile speed), MDLC (Meta description length in characters), KWMD (Keyword in Meta description), KWURL (Keyword in URL), DS (Desktop Speed), and KWT (keyword in title). We drop the less important features based on their scores and literature review (Khan and Mahmood 2018 ). We exclude AR and SEMRUSH Rank from the feature set because the rank evaluated by these two is based on the top-level domain instead of the distinctive webpage (Khan and Mahmood 2018 ), which means the information about the distinctive pages is not inherent to these features. Moreover, DSX and WC features are also dropped because they are based on nominal values. DSX tells about domain suffixes like.com,.org,.edu, etc. The feature WC gives information about website categories, e.g., blogs, services, eCommerce, etc. However, we plan to use these features in our future work.

5.3 Feature filteration

Feature filtration is based on aggregation. The aggregated method fuses the ranking score from IG and SelectKBest to get a ranked feature set, as shown in Fig.  7 and Table  5 . Furthermore, during the experiments, we successively dropped features from the tail-end of the ranked list until the classifiers’ performance (accuracy) started degrading. The features we reduced during this process are CWC, OL, IMGL HTTP(s), RTS, DA, PA, WCIMD, SMU, Traffic, IL, and QWURL.

Feature Filtration using Aggregated Method

5.4 Machine learning algorithms

In our research, we use the supervised machine learning approaches that are widely used for classification problems. Classification is the process of predicting the target class (dependent variable) based on the given data points (independent variables). In this study, we have used six different classifiers to evaluate the effectiveness of various feature designs by comparing the results based on new and existing feature sets. The list of classifiers used in this study is given in Table  4 . Python’s Scikit-learn library Footnote 15 was used for all the classification algorithms. The k-fold method is used for cross-validation with the value of K = 10 for reliable assessment and avoiding overfitting.

5.5 Evaluation measures

Evaluating the feature design is an essential part of this research. The evaluation is performed by comparing the accuracy of classification methods. A classification model's accuracy (as shown in Eq.  4 ) is a ratio between the number of correct predictions and all the predictions. It is defined as the ability to correctly identify all true cases and reject all false cases. Finally, the average accuracy for each classifier is estimated to compare and evaluate the performance against each featured design.

where TP and TN are positive and negative instances respectively that are correctly classified. However, FP and FN are positive and negative instances that are incorrectly classified.

5.6 Feature design

We designed six combinations to evaluate and compare the impact and contribution of the selected feature set. The feature design for the experimental setup is defined as follows:

Complete Feature set (CF)

Complete Feature set Normalized (CFN)

Filtered Feature set (FF)

Filtered Feature set Normalized (FFN)

The complete feature set (CF) includes all the newly analyzed features and existing features (Drivas et al. 2020 ; Matošević et al. 2021 ; Su et al. 2014 ). In comparison, the Complete Feature Set Normalized (CFN) is the normalized version of CF. Through the feature selection process, We drop the less important features according to their scores and literature review (Khan and Mahmood 2018 ) and then perform classification on various feature sets. The list of selected and dropped features is listed in Table  5 .

The selected features are marked as “ \(\checkmark\) ”, while the dropped features are marked as “✗”. As for the FF, the features we exclude from the evaluation are AR (Alexa Rank), CWC (webpage content word count), DSX (Domain suffix), SMR (SEMrush Rank) and WC (Website category) from the newly analyzed features and OL (outgoing link) from the previous features (see Sect.  5.2 for detail). In this research, the following two settings of the existing feature sets (i.e., EF and EFN) collected through the literature were considered as baselines for the comparison with four settings of the new feature sets (i.e. CF, CFN, FF, and FFN).

Existing Feature set (EF)

Existing Feature Set Normalized (EFN)

We formed a list of existing features based on previous studies (Drivas et al. 2020 ; Matošević et al. 2021 ; Su et al. 2014 ) (see Table  5 ). The six configurations of the feature sets discussed above are used as the classifier’s input. Each feature set incorporates a different number of features. We use the classification results to understand the role of the newly analyzed features we extracted in this research. Hence, we compare the performance of each classifier for different combinations of feature sets. The performance of each feature set is measured through a confusion matrix and average accuracy. Through the above feature design, we evaluate the SEO trend by comparing the results of CF, FF, and EF.

6 Results and discussion

In this section, we compare the newly analyzed and existing features using machine learning. Figure  8 compares the feature set (CF, CFN, FF, and FFN) with the feature set (EF and EFN) using SVM, LR, NB, KNN, DT, and RF. The feature sets CF, FF, CFN and FFN comprise our newly analyzed features as defined earlier, while the EF and EFN contain the features recommended by existing studies. The x-axis and y-axis represent the classification methods and their performance in terms of accuracy, respectively.

6.1 Performance comparison

The results show that the RF outperforms all other classifiers using CF and CFN, with an accuracy of 0.86 and 0.87. The performance of SVM, LR and DT is comparable using CF (0.80, 0.82, 0.82) and CFN (0.80, 0.82, 0.82), respectively. However, NB and KNN have the lowest accuracy using CF (0.64, 0.73) and CFN (0.64, 0.73), respectively. On the other hand, if we compare the performance of classifiers using FF and FFN, the results show that RF outperforms all other classifiers using FF and FFN, with an accuracy of 0.87 and 0.82, respectively. The performance of SVM, LR and DT is comparable using FF (0.79, 0.81, 0.82) and FFN (0.80, 0.81, 0.82), respectively. NB and KNN remain the lowest accuracy using FF (0.65, 0.73) and FFN (0.65, 0.73), respectively. Figure  8 compares the results of all classifiers against different feature sets. The results show a significant improvement with the newly analyzed features compared to baseline feature sets. The average accuracy results using newly analyzed features (CF, CFN, FF, and FFN) significantly outperform baseline feature sets (EF and EFN). However, no significant difference was observed among the CF, CFN, FF, and FFN results. Figure  9 compares the average accuracy using each feature set. The x-axis and y-axis represent the feature set and average accuracy, respectively. The comparison of average accuracy using (CF, FF, EF) and (CFN, FFN, EFN) shows (77.79%, 77.98%, 68%) and (77.82%, 77.12%, 69%), respectively, leading to a conclusion that normalization of data does not have a significant effect on the performance of classifiers. The average accuracy using FF (77.98%) remains the best among all other feature sets.

Comparison of newly analyzed feature sets (CF, CFN, FF, and FFN) with baseline feature sets (EF and EFN)

Average accuracy comparison using newly analyzed (CF, FF, CFN, and FFN) and baselines (EF and EFN) feature sets

The average classification accuracy we achieved for CF, CFN, FF and FFN is 77.79%, 77.98%, 77.82% and 72.12%, respectively, while the average classification accuracy for EF and EFN was 68% and 69%, respectively. The results emphasize that FF consists of important factors that webmasters must consider while designing webpages. It confirms the trend that the newly analyzed feature set (FF) is the most essential and will affect the webpage’s ranking, hence satisfying the research question (RQ2).

6.2 Heatmap

To achieve a high-level comparison of the results for the proposed feature design, we utilized heatmaps for the side-by-side comparison (see Fig.  10 ). A heatmap provides a visual summary of all feature designs and their level of performance using different classifiers. There are 20 unique classes (ranks); each heatmap visualizes 20 \(\times\) 20 confusion matrix. The scale range is between 0 (blue) and 1 (red), reflecting the percentage of correctly classifying the page to their actual ranks. The heat signature of existing features (EF, EFN) is scattered in all classifiers. The signature is scattered because most webpages are classified incorrectly (true negative). While in the decision tree, the heat signature is relatively better than others, but after the \(10^{th}\) position, the signature is scattered. Overall, the signature against the filtered feature (FF) set shows better results as the heat signature is concentrated on the diagonal, which shows more accurate predictions. There is a clear distinction and segregation in the heat signals; the top-10 page rank is predicted more accurately by most of the classifiers compared to the bottom-10 pages. In FF, the narrow signature of the top-10 pages guarantees that the FF feature design is correctly optimized and is significant. The heatmap of CF and FF are distinct from the EF. CF and FF separate the top-10 and bottom-10 more accurately. That guarantees the correct rank position of the top-10. However, the signature of CF is a bit scattered in some classifiers. Hence, it shows that FF produces relatively better results. Intuitively, we can conclude that these features are optimized in the top-10 results, which is why Google ranks them at the top. However, the bottom-10 pages are not optimized properly, which is why the signature of the heatmap is scattered at the bottom. The heatmap signature concluded two things: First, the features contained by FF are significant. Second, if these factors are correctly optimized, the overall page rank could improve, and the page can get more visibility in the user search.

Heatmap: A visual summary of the classifiers’ performances for different feature sets

6.3 Statistical significance

Furthermore, we applied the independent two-sample t-test to compare the significance of feature design. A two-sample t-test uses the data points (results) and estimates the statistical difference in the sample mean (in either direction) of the data distribution(Easterling 2015 , Chapter 3), as shown in Eq.  5 . We use the average accuracy results of each feature design as a group sample across all classifiers.

Where \(\mu _1\) , and \(\mu _2\) are the sample means of groups (feature designs) in comparison. s is the sample size. \(\sigma _p\) is the pooled standard deviation calculated using Eq.  6 with the degree of freedom \(df = 2n - 2\) .

Here, \(\sigma _1^2\) and \(\sigma _2^2\) are the variances of average accuracy for each feature design in comparison. The confidence interval is set to 95%; therefore, p-value \(< 0.05\) shows a significant difference between the results of feature designs.

We test the significance of all the feature designs. However, we only discuss the test results of FF with the baselines (EF and EFN) to show the significance of the proposed features. The null hypothesis ( \(H_0\) ) and alternative hypothesis ( \(H_a\) ) for the t-test are described as:

“The performance of the FF feature design is equal to or lower than the baselines EF and EFN; hence, the newly analyzed factors are not significant.”

“The proposed FF feature design performs consistently better than the baseline (EF and EFN) and significantly improves webpage ranking.”

Table  6 compares p-values for all the pairs of feature sets. The newly analyzed feature designs FF and FFN show significance compared to the existing features EF and EFN with p-values < 0.05.

Similarly, we performed these tests to compare the significance of classifiers used across each feature design. Our primary focus was to analyze the performance based on various feature designs; nevertheless, the results of the significance test show that random forest (RF) performs better in most cases. (see Appendix- A Table  9 ) with greater accuracy (see Fig.  8 ).

7 Case study of a blog

To address the research question (RQ3), we conducted a longitudinal study to optimize identified SEO factors and verify the impact by analyzing their visibility in Google SERP. Over four months, the improvements in the keywords rank position led to an increment in the number of users and sessions. Table  7 details the experimental blog we designed intending to increase the website’s visitors and rank position in Google SERP.

7.1 Checklist for technical SEO setup

The SEO activities started from 2 February 2021 to 15 May 2021. Before jumping into keyword-based research, we perform the technical SEO (see SEO Checklist in Fig.  11 ) to ensure that the website structure meets the guidelines of the Google SE Algorithm.

Checklist for technical SEO

7.2 Keywords ranking in Google SERP

In January 2021, a blog Footnote 16 was analyzed for its influential ranking. The blog ranking was very low, which caused low traffic. To increase the blog’s organic traffic, 23 keywords against 16 different webpages were selected with the help of Google Keyword Planner, Footnote 17 and the search volume was found through the Ahref tool. Footnote 18 The keyword research was conducted by considering the short-tail and long-tail queries, including question words for voice search. Table 8 shows the keywords’ rank position in Google SERPs comparison for the last six months. The keyword rank position report is generated by the Google search console Footnote 19 and by searching for them in the Google search engine. The rank position of the previous six months is zero because the site was new at that time, and no SEO activity was performed. However, the rank position improves gradually.

7.2.1 Number of users and sessions

The results show the process of technical SEO setup to get the change in the number of users and sessions in Google Analytics. The period of SEO is from 2 February 2021 to 15 May 2021. The ranking and the number of users were observed through Google Analytics Footnote 20 and Google webmaster Footnote 21 on different dates. After implementing the identified SEO factors, there has been a prominent improvement in the number of users and sessions. Figure  12 ) shows the number of users on different dates. In February 2021, the number of users who visited the website was only 82, while in November 2021, the number of users increased to 857. Figure  13 ) shows the number of sessions that increased from 119 to 846. The statistics from the Google Analytics tool show the increment in the number of users and sessions.

Increasing traffic on the Blog designed for the case study

Increasing user sessions on the Blog designed for the case study

8 Conclusion

The emergence of more advanced mobile devices and voice search is incredibly increasing. Voice search is a natural addition and provides a new way to search the web. With this fast adoption of voice search technology, a need arises to understand the ranking factors that influence the Google search engine to rank webpages higher against voice search queries. SEO involves different techniques; the preceding literature attempts many types of research to identify some essential factors that affect ranking in search engines. There was a need to analyze additional ranking factors contributing to the search engine ranking against voice queries. Moreover, such studies must be updated due to continuous changes in Google’s ranking algorithm. This research explored a machine learning approach to identifying the most dominant factors. We proposed a framework that can be adopted anytime Google’s ranking algorithm updates by acquiring new webpage ranks and re-evaluating significant factors contributing to SEO. Four novel feature sets were designed and compared with existing ones. We used six widely used classifiers and trained on these features extracted from 2960 webpages. A unique feature design (i.e., FF) significantly improves webpage ranking prediction. FF feature set outperforms all other feature designs with an average accuracy of 77.98%, and CFN is the second best with 77.82%. The existing feature sets EF and EFN remain 68% and 69%, respectively. Furthermore, a longitudinal study on a blog over four months confirms that the proposed factors improve the webpage visibility and increase organic traffic. In conclusion, the SEO factors used in FF design will help webmasters achieve a higher ranking in search engines.

9 Future direction

Further experiments will be conducted to investigate the more influencing factors. For example, in this research, we only checked the quantity of backlinks. In the future, we would like to analyze the quality of backlinks with a scoring weight. In future, we aim to consider other domains for voice query generation and extend the benchmark dataset. Moreover, with the rapid growth of voice search technology, there is a great need to track voice search queries against a specific webpage and introduce a profile-based scoring function for weighing the voice queries against particular domains.

www.google.com

www.yahoo.com

www.bing.com

https://web.archive.org/web/20230904051047/https://googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html Access Date: 07-02-2024.

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Acknowledgements

This research study was partially supported by the project FAIR - Future AI Research (PE00000013), spoke 6 - Symbiotic AI( https://future-ai-research.it/ ), under the NRRP MUR program funded by the NextGenerationEU.

Open access funding provided by Università degli Studi di Bari Aldo Moro within the CRUI-CARE Agreement. This research study was partially supported by the project FAIR - Future AI Research (PE00000013), spoke 6 - Symbiotic AI( https://future-ai-research.it/ ), under the NRRP MUR program funded by the NextGenerationEU.

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ZS and FA conceived this research and designed experiments; ZS and AG contributed in developing the research idea; FA implemented the idea and conducted experiments; MU and IR worked on the problem formulation and presentation; ZS, FA, AG, MU, and IR wrote and edited the paper.

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Saeed, Z., Aslam, F., Ghafoor, A. et al. Exploring the impact of SEO-based ranking factors for voice queries through machine learning. Artif Intell Rev 57 , 144 (2024). https://doi.org/10.1007/s10462-024-10780-9

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MicroStrategy, Indian Companies Among Additions to MSCI Indexes

FILE PHOTO: The MSCI logo is seen in this June 20, 2017 illustration photo. REUTERS/Thomas White/Illustration/File Photo

(Reuters) -Index provider MSCI will count U.S. bitcoin investor MicroStrategy among 42 additions to its widely tracked world index while more firms from India join its emerging markets index, following a quarterly review.

They are closely watched as billions of dollars invested in exchange-traded funds track MSCI indexes and they are used as a performance benchmark for fund managers.

In total, there are 42 securities to be added and 121 cut from the all-country world index.

The three largest additions will be U.S. companies: software firm and bitcoin investor MicroStrategy, data storage company Pure Storage and mechanical and electrical construction firm Emcor.

"All represent the direction of new economy development," said Kenny Ng, a strategist at China Everbright Securities International in Hong Kong.

MSCI reviews its indexes to reflect moves in market value of constituents, among other factors. The changes, which will take effect at market close on May 31, usually drive a spike in turnover.

Traders are alert this time for possible disruption as the rebalance falls only a few days after the U.S. shortens its stock-trading settlement cycle from two days to one on May 28.

Elsewhere, India's Canara Bank, Indonesian chemical firm Chandra Asri Pacific and Indian power generator JSW Energy will be the three largest additions to the MSCI emerging markets index.

The three largest additions to the frontier markets index will be Kazakhstan carrier Air Astana, Croatia's electrical-equipment maker Koncar Distributivni I and Romanian telco Digi Communications.

New York-based MSCI said it will add six and delete 70 securities from the China A Onshore Index. The three largest additions will be steel manufacturer CITIC Pacific Special Steel, energy firm CNOOC and forklift manufacturer Hangcha Group.

The company said foreign listings will become eligible for the MSCI Sweden Indexes starting from the November quarterly index review as the market has met the foreign listing materiality requirements.

MSCI will also resume review of Egyptian and Kenyan securities after hearing from market participants that liquidity and foreign exchange access had improved there.

Special treatment for equities listed in Bangladesh in the MSCI indexes will remain due to accessibility issues, MSCI said.

(Reporting by Arasu Kannagi Basil in Bengaluru; Editing by Shounak Dasgupta, Shailesh Kuber and Subhranshu Sahu)

Copyright 2024 Thomson Reuters .

Tags: United States , India , Indonesia

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