PDF Effective Stock Price Forecasting Using Machine Learning Techniques
the states of stock market. A framework is proposed which enables the selection of the best performing model with relevant inputs and which can also factor insensitivity of the stock‟s price to various states of the market. The initial simulations were run for 147 companies with 252 days out stock price forecasting, and further simulations ...
PDF Deep Learning for Stock Market Prediction: Exploiting Time-Shifted
stock market prediction is further facilitated by the availability of large-scale historic stock market information. As such information, e.g. on stock prices and volumes of stock trades, takes the form of time series, classical approaches to time series analysis are currently widespread within the investment industry (Clarke et al., 2001). This
PDF Stock Market Prediction Through Sentiment Analysis of Social-Media and
may yield more accurate predictions. This thesis proposes a method to predict the stock market using sentiment analysis and financial stock data. To estimate the sentiment in social media posts, we use an ensemble-based model that leverages Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models.
PDF Machine learning in stock indices trading
This thesis focuses on two fields of machine learning in quantitative trading. The first field uses machine learning to forecast financial time series (Chapters 2 and 3), and then builds a simple trading strategy based on the forecast results. The second (Chapter 4) applies machine learning to optimize decision-making for pairs trading.
(PDF) Stock Prediction Using Machine Learning
The research in this thesis focuses on the capture of the public's opinion on stocks and cryptocurrencies. ... Stock market price prediction is a difficult undertaking that generally requires a ...
Stock market prediction using machine learning
In this paper, I propose a machine learning approach that will be trained from the available stock data by using acquired knowledge for a prediction with accuracy. In this context, the study will use a machine learning technique called Support Vector Machine (SVM) and Long Short term memory (LSTM) to predict stock prices. Date.
Machine Learning for Financial Market Forecasting
Stock market forecasting continues to be an active area of research. In recent years machine learning algorithms have been applied to achieve better predictions. Using natural language processing (NLP), contextual information from unstructured data including news feeds, analysts calls and other online content have been used as indicators to ...
PDF StockPricePredictionUsingMachineLearning
The research on stock price prediction has never stopped. In the early days, many economists tried to predict stock prices. Later, with the in-depth research of ... and select real stocks in the stock market, perform modeling analysis and predict stock prices, andthenusetheroot
PDF Machine learning for financial market prediction
The usage of machine learning techniques for the prediction of financial time se- ries is investigated. Both discriminative and generative methods are considered and compared to more standard financial prediction techniques. Generative meth- ods such as Switching Autoregressive Hidden Markov and changepoint models are found to be unsuccessful ...
Digital Commons @ NJIT
Stock market prediction has attracted not only business but academia as well. It is a research topic, to which many computational methods have been proposed, but desirable and reliable performance is yet to be attained. This study proposes a new method for stock market prediction, which adopts the Gated Recurrent Unit a
PDF UNIVERSITY OF CALIFORNIA Los Angeles
the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector ... Financial time series forecasting, such as stock market prediction, is usually considered a challenging task due to its volatile and chaotic characteristics [3]. Precisely ...
Stock Market Forecasting Based on Artificial Intelligence Technology
LIANG, YUZHUN, "STOCK MARKET FORECASTING BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY" (2021). Electronic Theses, Projects, and Dissertations. 1324. https://scholarworks.lib.csusb.edu/etd/1324. This Thesis is brought to you for free and open access by the Ofice of Graduate Studies at CSUSB ScholarWorks.
PDF STOCK MARKET FORECASTING USING RECURRENT NEURAL NETWORK
using daily stock price data, we collect hourly stock data from the IQFEED database in order to train our model with relatively low noise samples. Nevertheless, based on the prediction results of LSTM model, we build up a stock database with six U.S market stocks from five different industries. The average test accuracy of these six stocks is
Stock Market Prediction with High Accuracy using Machine Learning
The paper also highlights some more efficient and robust techniques that are used to forecast trends in the stock market. In detail, the methodology followed, to acquire the results, has been talked about step-wise. Furthermore, a detailed comparative analysis of the performances of the aforementioned algorithms for stock price prediction has ...
Stock Market prediction using Artificial Neural Networks
Stock Market prediction using Artificial Neural Networks Master's thesis in Computer Systems and Networks RAFAEL KONSTANTINOU Department of Computer Science and Engineering CHALMERS UNIVERSITY OF TECHNOLOGY ... Stock market is so complicated and many things can affect the change in a price.
PDF PHD Thesis
this thesis we will go through all of these methodologies. The structure of the thesis is consist of three papers of the Author, published in the ISI journals about using technical and fundamental features for stock market prediction with different algorithms in the data mining as chapter 3 until chapter 5. The thesis exploits
Stock market prediction using artificial intelligence
This study builds a stock prediction network structure for a series of tests using the Python language on the Linux 16.4 environment. Although our experiment only employs a restricted number of stocks, it nevertheless yields competitive prediction results, which supports the conclusion analysis of this study.
Predicting the Stock Market Behavior Using Historic Data ...
Prediction of stock market performance is an impossible task, theoretically. Voluminous stock transactions and the spontaneity of the factors affecting the data bear testimony to the theory. Spanning from the wide range of the unexpected changes in the business climate to the mood outlandish swings of capricious CEOs, anything and everything ...
Shodhganga@INFLIBNET: PREDICTION OF FUTURE STOCK PRICES EXPLOITING
The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access. Shodhganga@INFLIBNET. Dr. M.G.R. Educational and Research Institute. Department of Mathematics.
Dissertations / Theses: 'Stock price prediction'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles. Consult the top 50 dissertations / theses for your research on the topic 'Stock price prediction.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to ...
PDF Stock Price Prediction Using Deep Neural Network
(IPO). The price of the stock market will go up or fall down depends on the market demand and supply. The more the investor willing to buy the stock price will go high and vice-versa. Although one can judge the number of sell and buy, it is very difficult to find out what factors contribute these transactions.
Analysis And Evaluation Of Technical Indicators For Prediction Of Stock
The aim given in this paper are: i) to look at the influence of short -term economic indicators on stock. prices ii) to research the impact of market indexes on oil and gas stocks; iii) to predict ...
Thesis Topics
Stock Market Prediction With Long Short-Term Memory Recurrent Neural Networks (BSc, A. Elizabeth Sanyal) ... (MSc, B. Jäger, Winner of Finance Award, best Master Thesis in Business Science) Stock Age as Proxy for Uncertainty of Parameters (MSc, S. Sturzenegger) The Influence of News Coverage on Stock Returns - Evidence from European Markets ...
Mom gives birth in car hours before getting PhD
A New Jersey mom becomes a mother and a doctor on the same day. She gave birth just hours before defending her dissertation. A New Jersey mom becomes a mother and a doctor on the same day. She ...
COMMENTS
the states of stock market. A framework is proposed which enables the selection of the best performing model with relevant inputs and which can also factor insensitivity of the stock‟s price to various states of the market. The initial simulations were run for 147 companies with 252 days out stock price forecasting, and further simulations ...
stock market prediction is further facilitated by the availability of large-scale historic stock market information. As such information, e.g. on stock prices and volumes of stock trades, takes the form of time series, classical approaches to time series analysis are currently widespread within the investment industry (Clarke et al., 2001). This
may yield more accurate predictions. This thesis proposes a method to predict the stock market using sentiment analysis and financial stock data. To estimate the sentiment in social media posts, we use an ensemble-based model that leverages Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models.
This thesis focuses on two fields of machine learning in quantitative trading. The first field uses machine learning to forecast financial time series (Chapters 2 and 3), and then builds a simple trading strategy based on the forecast results. The second (Chapter 4) applies machine learning to optimize decision-making for pairs trading.
The research in this thesis focuses on the capture of the public's opinion on stocks and cryptocurrencies. ... Stock market price prediction is a difficult undertaking that generally requires a ...
In this paper, I propose a machine learning approach that will be trained from the available stock data by using acquired knowledge for a prediction with accuracy. In this context, the study will use a machine learning technique called Support Vector Machine (SVM) and Long Short term memory (LSTM) to predict stock prices. Date.
Stock market forecasting continues to be an active area of research. In recent years machine learning algorithms have been applied to achieve better predictions. Using natural language processing (NLP), contextual information from unstructured data including news feeds, analysts calls and other online content have been used as indicators to ...
The research on stock price prediction has never stopped. In the early days, many economists tried to predict stock prices. Later, with the in-depth research of ... and select real stocks in the stock market, perform modeling analysis and predict stock prices, andthenusetheroot
The usage of machine learning techniques for the prediction of financial time se- ries is investigated. Both discriminative and generative methods are considered and compared to more standard financial prediction techniques. Generative meth- ods such as Switching Autoregressive Hidden Markov and changepoint models are found to be unsuccessful ...
Stock market prediction has attracted not only business but academia as well. It is a research topic, to which many computational methods have been proposed, but desirable and reliable performance is yet to be attained. This study proposes a new method for stock market prediction, which adopts the Gated Recurrent Unit a
the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector ... Financial time series forecasting, such as stock market prediction, is usually considered a challenging task due to its volatile and chaotic characteristics [3]. Precisely ...
LIANG, YUZHUN, "STOCK MARKET FORECASTING BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY" (2021). Electronic Theses, Projects, and Dissertations. 1324. https://scholarworks.lib.csusb.edu/etd/1324. This Thesis is brought to you for free and open access by the Ofice of Graduate Studies at CSUSB ScholarWorks.
using daily stock price data, we collect hourly stock data from the IQFEED database in order to train our model with relatively low noise samples. Nevertheless, based on the prediction results of LSTM model, we build up a stock database with six U.S market stocks from five different industries. The average test accuracy of these six stocks is
The paper also highlights some more efficient and robust techniques that are used to forecast trends in the stock market. In detail, the methodology followed, to acquire the results, has been talked about step-wise. Furthermore, a detailed comparative analysis of the performances of the aforementioned algorithms for stock price prediction has ...
Stock Market prediction using Artificial Neural Networks Master's thesis in Computer Systems and Networks RAFAEL KONSTANTINOU Department of Computer Science and Engineering CHALMERS UNIVERSITY OF TECHNOLOGY ... Stock market is so complicated and many things can affect the change in a price.
this thesis we will go through all of these methodologies. The structure of the thesis is consist of three papers of the Author, published in the ISI journals about using technical and fundamental features for stock market prediction with different algorithms in the data mining as chapter 3 until chapter 5. The thesis exploits
This study builds a stock prediction network structure for a series of tests using the Python language on the Linux 16.4 environment. Although our experiment only employs a restricted number of stocks, it nevertheless yields competitive prediction results, which supports the conclusion analysis of this study.
Prediction of stock market performance is an impossible task, theoretically. Voluminous stock transactions and the spontaneity of the factors affecting the data bear testimony to the theory. Spanning from the wide range of the unexpected changes in the business climate to the mood outlandish swings of capricious CEOs, anything and everything ...
The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access. Shodhganga@INFLIBNET. Dr. M.G.R. Educational and Research Institute. Department of Mathematics.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles. Consult the top 50 dissertations / theses for your research on the topic 'Stock price prediction.'. Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to ...
(IPO). The price of the stock market will go up or fall down depends on the market demand and supply. The more the investor willing to buy the stock price will go high and vice-versa. Although one can judge the number of sell and buy, it is very difficult to find out what factors contribute these transactions.
The aim given in this paper are: i) to look at the influence of short -term economic indicators on stock. prices ii) to research the impact of market indexes on oil and gas stocks; iii) to predict ...
Stock Market Prediction With Long Short-Term Memory Recurrent Neural Networks (BSc, A. Elizabeth Sanyal) ... (MSc, B. Jäger, Winner of Finance Award, best Master Thesis in Business Science) Stock Age as Proxy for Uncertainty of Parameters (MSc, S. Sturzenegger) The Influence of News Coverage on Stock Returns - Evidence from European Markets ...
A New Jersey mom becomes a mother and a doctor on the same day. She gave birth just hours before defending her dissertation. A New Jersey mom becomes a mother and a doctor on the same day. She ...