ORIGINAL RESEARCH article

The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states.

\r\nSobia Naseem

  • 1 School of Economics and Management, Shijiazhuang Tiedao University, Shijiazhuang, China
  • 2 School of Business, Hunan University of Humanities, Science and Technology, Loudi, China

A highly transmittable and pathogenic viral infection, COVID-19, has dramatically changed the world with a tragically large number of human lives being lost. The epidemic has created psychological resilience and unbearable psychological pressure among patients and health professionals. The objective of this study is to analyze investor psychology and stock market behavior during COVID-19. The psychological behavior of investors, whether positive or negative, toward the stock market can change the picture of the economy. This research explores Shanghai, Nikkei 225, and Dow Jones stock markets from January 20, 2020, to April 27, 2020, by employing principal component analysis. The results showed that investor psychology was negatively related to three selected stock markets under psychological resilience and pandemic pressure. The negative emotions and pessimism urge investors to cease financial investment in the stock market, and consequently, the stock market returns decreased. In a deadly pandemic, the masses were more concerned about their lives and livelihood and less about wealth and leisure. This research contributes to the literature gap of investors’ psychological behavior during a pandemic outbreak. The study suggests that policy-makers should design a plan to fight against COVID-19. The government should manage the health sector’s budget to overcome future crises.

Introduction

The terminology of “Corona” is not newly invented in science. This single-stranded RNA virus’ primary roots were observed in 1960, belonging to the Corona viridae family in the order Nidovirales ( Galante et al., 2016 ; Kanwar et al., 2017 ; Guo et al., 2020 ; Mohsin et al., 2020b ). The taxonomic naming comes from the virus’ structure, which gives the appearance of crown-like spikes on the virus’ outer surface ( Azam et al., 2020 ; Sarfraz et al., 2020c ; Shereen et al., 2020 ). The prey of the first coronavirus species was chicken and pig; there was no human–human transmission. From 1960 to 2020, different allied versions of the same family of viruses have been observed: the common cold in adults (COV 229E and COV OC43 in mid-1960); severe acute respiratory syndrome coronavirus (SARS-CoV-2003); human coronavirus with common cold, bronchitis, and asthma; chronic obstructive pulmonary disease (COPD) exacerbations; pneumonia (HCOV NL63-2004 and CoV-HKU1); Middle East respiratory syndrome (MERS CoV-2012); and severe acute respiratory syndrome coronavirus-19 (SARS-CoV-2019 or SARS-CoV-2), displaying unmatched intensity and severity compared to the previous species of corona ( Van Der Hoek et al., 2004 ; Kahn and McIntosh, 2005 ; Woo et al., 2005 ; Esper et al., 2006 ; Zaki et al., 2012 ). At the start of the virus breakout, the virus name was 2019-nCOV as per the International Committee on Taxonomy of Viruses (ICTV), and the Chinese Center for Disease Control and Prevention (CCDC) changed it into SARS-CoV-2 on January 7, 2020 due to its structure and symptoms. COVID-19 was first discovered in Wuhan’s wet market, Hubei Province, China, in early December 2019, and this aroused global attention in late January 2020. The virus has been spreading exponentially, using human-to-human transmission through respiratory droplets, i.e., sneezing and coughing ( Azam et al., 2020 ; Li et al., 2020 ; Sarfraz et al., 2020a ; Shereen et al., 2020 ). During this incubation period, researchers focused on exploring, preventing, and treating patients. Still, the pandemic’s psychological impact is the other side of the disease (mental illness). The global quarantine announcement has sparked several concerns: fear of separation from family, fear of illness and death, avoidance of medical facilities due to threat of infection, fear of unemployment, the threat of racism against people who live in or are perceived to be from the affected areas, fear of losing near and dear ones because of the virus, maintained space from minors and disabled or elderly family members due to infection, isolation, and recalling the severity of the treatment of infected people. These have become originators of anxiety, stress, and grave concern globally. These mental health aspects of the COVID-19 outbreak have affected individual lives as well as the financial markets.

Human Psychology and COVID-19

The current pandemic of SARS-CoV-2 has seriously influenced human psychology through a notable mental state of “anxiety.” The term “anxiety” covers the population’s reaction toward the epidemic to all media, whether the information is authentic or erroneous, e.g., inappropriate behavior of people concerning the abandonment of animals and panic buying of other foods. The panic attacks are not properly defined without linkage to anxiety disorder in the medical sense. Anxiety is a combination of different psychiatric disorders both internal (phobias, panic attacks, and panic disorder) and external (worry, stress, fear, painful experiences, or events). The psychological effect of COVID-19’ has led to mass hysteria, post-traumatic stress disorder (PTSD), panic attacks, obsessive-compulsive disorder (OCD), and generalized anxiety disorder (GAD). The behavioral immune system (BIS) theory, stress theory, and perceived risk theory explain that negative emotion (anxiety, aversion) and negative cognitive assessment of human beings are developed for self-protection. People tend to develop avoidant behavior and strictly follow the social norms due to the pandemic’s severe effects and the potential threat of disease ( Cao et al., 2020 ; Lai et al., 2020 ; Sarfraz et al., 2020b ). The anxiety, stress, and panic attacks of people due to COVID-19 have created two etiologies. The first is the identification of symptoms of acute respiratory distress syndrome (ARDS), such as cough and dyspnea, at high frequency ( Preter and Klein, 2008 ; Javelot and Weiner, 2020 ). The second one is “false alarming” ( Klein, 1993 ) as a psychopathological link to the catastrophic interpretation of physiological sensation (respiration rate). The recurrence of panic attacks has increased the respiration rate and has become the reason for excessively avoidant behaviors and blind conformity ( Li et al., 2020 ; Mohsin et al., 2020a ). Psychopathology is a keen concern for this study because it has an intense effect on investor behavior. Stock market investors and business people generally spend most of their time in the workplace. However, they are currently mostly homebound; the present situation of the stock markets, investment decision pressure, and family members’ psychological health now put pressure on investor psychology.

Investors’ Psychology (Sentiments), Stock Market, and COVID-19

The COVID-19 outbreak has threatened every individual field of life to influence public health. The sustainability of the global stock market and financial markets also carries significant repercussions ( Ali et al., 2020 ; Huang and Zheng, 2020 ). Being a part of the societal system, investor psychology (sentiments) and their optimism or pessimism about future stock prices can also change. A sharp decrease has been observed in Shanghai, Dow Jones, and Nikkei’s stock prices due to investor sentiment volatility during the pandemic outbreak (see Figure 1 ). The visual presentation of Figure 1 has shown a sudden downward trend in stock markets after the outbreak of the pandemic.

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Figure 1. Impact of COVID-19 on stock markets: Source Bloomberg.

Existing literature has focused on the relationship between stock prices and investor sentiment. Lee et al. (2002) and Brown and Cliff (2004) explained that the past market returns are important sentiment determinants, while investor sentiment changes significantly correlated with the contemporary market return. The positive relationship between stock markets and sentiment will confirm that investor sentiment is a contrarian predictor for consequent market returns. Meanwhile, the sentiment impact is stronger if easy (hard) to value stocks are negatively (positively) influenced by sentiments ( Baker and Wurgler, 2007 ; Xiang et al., 2020 ). Using the component of market index return, which is avoidant of fundamental macroeconomic factors, Lan et al. (2020) observed robust evidence that the pre-announcement abnormal return derives from investor sentiment. The sentiment determined overvaluation corrects within 1 month in the post-announcement period. The market timers tackle this sentiment situation and take advantage of issuing season shares. The stock price sensitivity in terms of the good news of earning is higher during a high sentiment period. In contrast, in a low sentiment period, the stock price sensitivity behaves negatively. As per analysis suggestions, the investor sentiment becomes the reason for the general mispricing of stock because of sentient-driven mispricing of earning contributions ( Schmeling, 2009 ; Zouaoui et al., 2011 ; Mian and Sankaraguruswamy, 2012 ; Cheema et al., 2020 ). The high market competition indicated that sentiments and returns are positively related to each other, and this relationship disappears in low market competition. Although the financial crisis changes the situation irrespective of market competition, a positive relationship exists between sentiments and returns ( Ryu et al., 2020 ). Investors can accept psychological pressure more sensitively and intensively than the lay person. Apart from the pandemic’s rapid spread, the financial news, media, and amplifiers have worked as fear spreaders about COVID-19. Tetlock (2007) elucidated that spread of news about the stock market strongly affects investor psychology and sociology. The high media pessimism leads to downward pressure on market prices and vice versa. The investor sentiment theory also confirmed the consistent relationship between media content and individual investor behavior with disproportionately small stocks. This research is based on a new ideology of investor psychology and the stock market during the pandemic. There have been few studies in this area, but a bulk of research centers on both human psychology and COVID-19 as well as the stock market and COVID-19. Under the caption of investor psychology, the stock market, and COVID-19, we tried to explain this research’s nature and relationship.

The psychological pressure negatively impacts investors and investing decisions, which can decline any individual country’s economy. This study analyzed investor psychology and stock market behavior during COVID-19—a comparatively new debate about COVID-19. This research will contribute to the existing literature and open up new dimensions in understanding investor sentiment toward investment decisions in the stock market under special circumstances during the outbreak of pandemics and times of intense anxiety. Our research differs from previous studies in the use of proxies of investor sentiment as indicators of the stock market and COVID-19. The strong theoretical upbringing of psychological behavior and the dynamic process of stock price fluctuation will deepen the understanding of readers, investors, and researchers. A sample of three different stock markets will help elaborate on investors’ psychological and geographical sensation during investment decisions in a pandemic.

Data Description and Methodology

Data description.

Our research includes daily observations of three different stock markets, i.e., Shanghai stock market, Nikkei, and Dow Jones, from January 20 to April 27, 2020. The market selection is based on two reasons. The first is the impact of COVID-19 on investor sentiment during the pandemic, i.e., the Shanghai Stock Market (China). The second is to check the global impact by using Nikkei and Dow Jones. The reason behind the selected data span is the global spread of COVID-19. The sample period starts from the data declaration of all sample markets because the synchronized data lead to accurate results. The data are collected from stock markets in China, Japan, and the United States. The analyzed data are secondary and publically available on mentioned databases, i.e., Bloomberg for stock markets data and WHO for COVID-19.

Methodology

The Sentiment Index (SMI) model used in this research is presented below:

In eq. 1, SMI m,t indicates the first principal component estimated by eq. 1’s linear combination of the standardized variables. Stock exchange turnover ratio (STURN) is the turnover of the respective stock exchange, MFI is the Money Flow Index, RSI is the Relative Strength Index, Δ CC is the change in daily confirm cases, and Δ CD is the daily confirmed deaths.

Stock Exchange Turnover Ratio

The stock market’s trading activity can be measured by turnover ratio; subsequently, it is used in the primary measurement model. Ying (1966) and Rehman et al. (2017) have explained that more considerable turnover is an indication of a rise in stock prices (Bullish Market), while small turnover reflects a fall in stock prices (Bearish Market). The stock exchange turnover ratio is calculated by using the following equation:

where VM Daily is used for daily volume, VM Monthly is the average volume of the month, and STURN is calculated using a running or moving basis, which means the previous dropping value and adding the next one.

Money Flow Index

The MFI comprises daily stock prices and turnover information. An increase in money flow indicates the market trend. The rising trend in the MFI increases the buying pressure, whereas the rise in the falling trend increases the selling pressure. The following formula is used to calculate the MFI:

When the current day price is higher than the previous day, the money flow is positive, while there is a comparatively lower current day price than the previous day, the money flow is negative ( Tolonen, 2011 ; Wang et al., 2015 ; Marek and Marková, 2020 ). The daily MFI has been calculated as follows:

Relative Strength Index

The technical analysis used the RSI, a momentum indicator that measures the magnitude of recent price changes, to evaluate the oversold or overbought condition in stock or other asset prices ( Russell, 1978 ; Wilder, 1978 ; Russell and Franzmann, 1979 ; Ivascu and Cioca, 2019 ). An oscillator is a display board of RSI between two extremes (low and high with a range of 0–100). Suppose the oscillator shows an upward trend with RSI ≥ 70 value, meaning that a security is overbought or overvalued. In that case, a positive but downward trend with RSI ≤ 30 value indicates an oversold or undervalued condition.

Change in Daily Confirmed and Death Cases

The changes in daily confirmed and death cases of COVID-19 are used to capture investor mood swings regarding the spreading pandemic. This term was used by Chen et al. (2010 , 2014) to check the impact of market index change on investor mood. The changes in daily confirmed cases and daily death cases are calculated as follows:

Relationship Between Stock Market Index and Investor Sentiment

The SMI regressed on the stock market volatility series during COVID-19. The following regression equation checks the respective sentiment and market return relationship:

Y m,t–1 is the market return of the stock market’s indicator concerning time, while SMI m,t–1,i is the Sentiment Index. The calculation of Y m,t–1 is done by the following equation:

In this equation, P t is the current market price (closing), and P t–1 is the preceding market price (closing).

Principle Component Analysis

The principle component analysis (PCA) is employed to extract meaningful information from multivariate data (orthogonal linear transformation) and present the information in the form of a set of new variables by use of scalar projections, which are called principal components (PC). The total number of PCs is less than or equal to the original number of variables. That is why the new variables or PCs are known as a linear combination of actual variables. The PCs are used as direction identifiers and correspond to the total variation of the data set. The multivariate data dimensionality reduces by using PCA with minimal loss of information. The eigenvalues explained that every PC retains the amount of variation. The division of variation between PCs as the eigenvalues is large for first PCs and small for subsequent ones. The first PC, or the PC with more than one eigenvalue, was used to check the correlation because of the increased variation retention of the data set.

Shanghai Stock Market

The principal component analysis of the selected variable for the Shanghai Stock Market is presented in Tables 1 , 2 . According to Kaiser Criterion, the principle component with an eigenvalue not less than 1 will be used ( Yeomans and Golder, 1982 ; Braeken and Van Assen, 2017 ; Rehman et al., 2017 ). The eigenvalue of PC-1 is 2.0545, which meets the criteria of maximal variation. The numeric presentation of PC-1 shows 41.09% of the Shanghai Stock Market relationship, which is the highest value compared to other principal components. The following index was created by using the first principle component:

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Table 1. Principle component analysis (PCA) of the variables (China).

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Table 2. The eigen vector loadings (Japan).

The relationship between Shanghai stock returns and the created SMI is graphically presented in Figure 2 .

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Figure 2. Relationship between Shanghai stock returns and created Sentiment Index.

The correlation matrix results are displayed in Table 3 , which is employed to check the multicollinearity among independent variables. The multicollinearity check is essential for the accuracy of results because inter-correlation among independent variables in a multiple regression model can mislead the results. When the regressor shows a value of more than 0.80, then the data series shows multicollinearity. The correlation matrix range for the Shanghai Stock Market is from −0.0991 to 0.8018, ensuring the data series is free from multicollinearity.

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Table 3. Ordinary correlations (United States).

Nikkei 225 Stock Market

The PCA of the selected variable for the Nikkei 225 stock market is presented in Tables 4 , 5 . According to Kaiser Criterion, the principle component with an eigenvalue not less than 1 will be used ( Yeomans and Golder, 1982 ; Braeken and Van Assen, 2017 ; Rehman et al., 2017 ). The eigenvalue of PC-1 for the Nikkei 225 stock market is 1.8735, which captures maximum variation and gets the full support of Kaiser Criterion. The cumulative proportion value of PC-1 shows 37.47% of the Nikkei 225 stock market relationship with a set of selected variables. The following index was created by using the first principle component:

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Table 4. Principle component analysis (PCA) of the variables (China).

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Table 5. The eigen vector loadings (Japan).

The relationship of Shanghai stock returns and created SMI is graphically presented in Figure 3 .

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Figure 3. Relationship between Nikkei 225 market returns and created Sentiment Index.

The correlation matrix results are displayed in Table 6 , which is employed to check the multicollinearity among independent variables. The range of correlation matrix for the Nikkei 225 stock market is between −0.0991 and 0.8008, which rejects the existence of multicollinearity.

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Table 6. Ordinary correlations (United States).

Dow Jones Stock Market

The PCA of the selected variable for the Dow Jones stock market is presented in Tables 7 , 8 . The eigenvalue of PC-1 is 1.7291, which captures maximum variation and gets the full support of Kaiser Criterion ( Yeomans and Golder, 1982 ; Cioca et al., 2014 ; Braeken and Van Assen, 2017 ; Rehman et al., 2017 ). The cumulative proportion value of PC-1 shows 34.58% of the Dow Jones stock market relationship with chosen variables, which is the highest value among all principal components. The following index was created using the first principle component:

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Table 7. Principle component analysis (PCA) of the variables (China).

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Table 8. The eigen vector loadings (Japan).

The relationship between Dow Jones stock returns and the created SMI is graphically presented in Figure 4 .

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Figure 4. Relationship between Dow Jones stock market returns and created Sentiment Index.

The correlation matrix results are displayed in Table 9 , which is employed to check the multicollinearity among independent variables. The importance of multicollinearity is observed because inter-correlation among independent variables in a multiple regression model can betray the results. The range of the correlation matrix for the Dow Jones stock market is from −0.0367 to 0.6135, which guarantees the data series free from multicollinearity.

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Table 9. Ordinary correlations (United States).

The regression results are presented in Table 10 , which shows the coefficient (β) −0.2532, −0.2532, and −0.0264 as the value of the SMI with 0.0056, 0.0056, and 0.0000 probability values for Shanghai, Nikkei 225, and Dow Jones stock markets, respectively. According to this study, the SMI is negative and significantly related to stock returns at a 1% level of significance ( Baker and Wurgler, 2007 ; Chen et al., 2014 ). The importance of SMI explicated that investor sentiments are strongly affected by volatility and investment decision of the stock market during the pandemic. The results have also shown the negative impact of COVID-19 on investor sentiment and stock market returns. The spreading pandemic disturbs the general public’s daily routines and interrupts stock markets, financial markets, and investor psychology toward investment decisions.

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Table 10. The relationship between stock exchange and Sentiment Index during COVID-19.

After the pandemic outbreak and the WHO’s classification of it as a public health emergency, investors’ psychological pressure and response showed a downward trend in stock markets. The sudden reduction in Shanghai (1.6%), Dow and Jones (9.5%), and Nikkei (10.6%) can be observed in Figure 1 . The numerical facts of stock markets are collected from Bloomberg’s official site, and the investor sentiment is generated using different proxies. The selected proxies represent different circumstances that might be affected the psychological behavior and decision power of investors. In this research, daily data of stock markets and increase in COVID-19 (death and confirmed cases) are used, accurately covering investors’ daily psychological pressure. The PCA is employed due to its useful features: correlation removal, improved algorithm performance, repaired overfitting among variables, and reduction of high dimensions into low dimensions for clear visualization of every single component. The research results elucidated a negative and significant relationship between investor psychology and investment decision under pandemic outbreaks for all selected markets. The stock market movement along the investor SMI in Figures 2–4 has shown the beneficial relationship between investor psychology and stock market returns. The investors or business people were generally outbound for 10–15 h per day. During pandemic, however, they were homebound, which affected their psychology adversely. This research provides some precautionary measures for releasing the pandemic and investment pressure. Investors should adopt behavior therapy—home-based relaxation exercises to control their anxiety and depression. The small-scale version of their official stock market setup should be established in their homes, and visits to the offices should be reduced. The global paramedical staff and scientists are continually struggling to elucidate the vaccines. Until they succeed, everyone should follow the precautions, i.e., wearing a mask, sanitizing, and maintaining distance in workplaces and the like. The less psychological control or pressure can help investors invest money and keep stock markets and economies on track.

The origin of the current “COVID-19” pandemic is considered to be the wet market of Hunan, Hubei Province, China. Within 1 month from its evolution, COVID-19 has spread to 109 countries, and the pandemic has gained intense global attention. The sudden outbreak of the pandemic and the rapid increase of its spread have left a significant impact on human physiology and psychology. The psychological effect disrupts the psychology of the general public and investor psychology toward stock market investment decisions. The increasing number of cases and deaths worldwide due to COVID-19 has made the economic situation more uncertain and unpredictable. A sudden and dramatic downward trend in financial markets is observed in Chinese and global financial markets, such as Shanghai, Nikkei 225, and Dow Jones, which are down by −1.6, 10.6, and −9.5% points, respectively. There are no promising clinical treatments or prevention strategies developed against COVID-19 until now, threatening human psychology. At the same time, healthcare workers are searching for a solution to the question of vaccination against COVID-19 and psychiatrist-designed psycho-therapeutic strategies to cope with the threat, stress, and anxiety of the pandemic, which have a devastating effect on daily life.

This research paper examined the relationship between the stock market and investor psychology regarding stock market investment decisions during the pandemic. By employing PCA, this research observed a downward trend in stock markets and the pandemic’s negative impact on investor sentiment. This investigation confirmed the economic crises in the Shanghai, Nikkei 225, and Dow Jones stock markets during the pandemic. The results have pointed out that the threat of health strongly affected the psychology of investors. The created SMI behaved negatively with a significance of 1% for three selected markets. The three selected markets represented three different world areas with diverse geographical backgrounds, financial positions, cultures, resources, and traditions to check global investor behavior. The significant relationship between the SMI and the stock market during a pandemic confirmed that the behavior of almost every nation fighting COVID-19 and investor financial behavior is the same across China and other developed countries. This study concluded that health crises and psychological disorders among the general public affect the economic condition and financial position of individual and global investors.

Limitations and Suggestions

The pandemic is still under discussion, and healthcare workers are trying to find a solution to the issue of vaccination. It is doubtlessly tough to run global systems, such as the stock market, from the workers’ individual homes, but to stop working due to anxiety or psychological threat is also not the solution to the problem. Investor sentiment creates tremendous uncertainty for stock markets and commensurate with a potential crisis of scale and speed. The governments and policy-makers should have to set some domestic and international policies for this unpredictable situation for workplaces. The pandemic is a worldwide issue, but the courageous actions of governments, global citizens, policy-makers, healthcare workers, scientists, and investors can enable us to overcome this global crisis.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : COVID-19, investor psychology, stock market behavior, financial sustainability, masses psychology

Citation: Naseem S, Mohsin M, Hui W, Liyan G and Penglai K (2021) The Investor Psychology and Stock Market Behavior During the Initial Era of COVID-19: A Study of China, Japan, and the United States. Front. Psychol. 12:626934. doi: 10.3389/fpsyg.2021.626934

Received: 07 November 2020; Accepted: 04 January 2021; Published: 10 February 2021.

Reviewed by:

Copyright © 2021 Naseem, Mohsin, Hui, Liyan and Penglai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Muhammad Mohsin, [email protected] ; Kun Penglai, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Exploring behavioural bias affecting investment decision-making: a network cluster based conceptual analysis for future research

International Journal of Industrial Engineering and Operations Management

ISSN : 2690-6090

Article publication date: 25 November 2022

Issue publication date: 6 December 2022

This study systematically explores the patterns and connections in the behavioural bias and investment decisions of the existing literature in the Scopus database published between 2007 and 2022. The purpose of this paper is to address this issue.

In the article it was determined which contributed documents were the most significant in this particular subject area along with the citations, publications and nations that were associated with them. The bibliographic coupling offered more in-depth insights into the papers by organizing them into distinct groups. The pattern of the publications has been brought to light, and the connection between different types of literature has provided insight into the path that future studies should take.

Research limitations/implications

This study considered only articles from the Scopus database. Future studies can be based on papers that have been published in other databases.

Originality/value

The outcome of this study provides valuable insights into the intellectual structure and biases of investors and adds value to existing knowledge. This review provides a road map for the future trend of research on behavioural bias and investment decisions.

  • Behavioural biases
  • Emotional bias
  • Cognitive bias
  • Systematic literature review
  • Investment decision-making

Bihari, A. , Dash, M. , Kar, S.K. , Muduli, K. , Kumar, A. and Luthra, S. (2022), "Exploring behavioural bias affecting investment decision-making: a network cluster based conceptual analysis for future research", International Journal of Industrial Engineering and Operations Management , Vol. 4 No. 1/2, pp. 19-43. https://doi.org/10.1108/IJIEOM-08-2022-0033

Emerald Publishing Limited

Copyright © 2022, Anshita Bihari, Manoranjan Dash, Sanjay Kumar Kar, Kamalakanta Muduli, Anil Kumar and Sunil Luthra

Published in International Journal of Industrial Engineering and Operations Management . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and no commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The traditional economic theory assumes that investors behave rationally while making various kinds of decisions ( Muhammad and Maheran, 2009 ). Standard financial practice consists of various concepts and theories, e.g. the expected utility theory ( Bernoulli, 1738 ), Markowitz portfolio principles ( Markowitz, 1952 ), the capital asset pricing model ( Treynor, 1961 ) etc., to explain the efficiency of the market that contains all the available information while taking financial decisions; investors are assumed to be rational ( Zahera and Bansal, 2018 ; Kumar and Goyal, 2015 ). Although all of the above theories are widely accepted by researchers, they have failed to provide answers to some questions, such as the causes of market bubbles and crashes and which variables are to blame for unpredictable situations ( Sharma and Kumar, 2019 ; Zahera and Bansal, 2018 ). After analysing the results, they found that the role of behavioural finance that contradicts all the standard theories of concepts and assumptions of rational investors always leads to an optimal financial decision. Human emotions, attitudes and psychological biases that influence investors' decisions tend to be inefficient and irrational ( Kahneman and Tversky, 1982 ; Kapoor and Prosad, 2017 ). Thus, in the 1980s, a new perspective on finance, i.e. behavioural finance, emerged from psychology, finance and sociology; this deals with the psyche of investors and also attempts to describe the impact of psychological errors on their decision-making process ( Kahneman and Tversky, 1979 ; Kishor, 2020 ). Behavioural finance examines the puzzling behavioural aspects of individuals operating in financial markets while considering their emotions, psychology, sociology and other related factors. The emphasis is on the various behaviours shown by investors and how these behaviours influence the investors ( Yoong and Ferreira, 2013 ). The introduction of the prospect theory, which replaces the expected utility traditional theory, is one of the major steps in the shift from the traditional concept to the modern concept of behavioural finance ( Kahneman and Tversky, 1979 ). There have been many empirical studies conducted to solve the questions raised about traditional finance. Examinations have been conducted to identify the relationships between behavioural factors and classical factors and how they influence investment decision-making. The prior literature has found that the behavioural factors are affected by two major areas, i.e. the prospect theory and heuristics. The characteristics of these areas are explained through different kinds of biases, including loss aversion bias, risk aversion bias, regret aversion bias, mental accounting, overconfidence, self-control bias, herding behaviour, etc. Studies have explored and shown that the biases of overconfidence, expert bias and self-control bias have a positive significant impact on individual investors' behaviour, influencing their financial satisfaction levels ( Sahi, 2017 ). A recent study, conducted with the help of an analytical hierarchy process, analysed behavioural factors such as overconfidence bias, representative bias, regret aversion, mental accounting and herd behaviour affecting investment decisions. The result concluded that bias overconfidence and regret aversion impact were much stronger than other biases. A similar study was conducted by Almansour and Arabyat (2017) .

What is the publication trend in the area of behavioural biases and investment decisions? Year-wise, what was the greatest contribution made by authors, journals and countries?

Based on citations (co-authorship, co-occurrence and bibliographic coupling), what are the most important documents?

What are the emerging research themes on behavioural biases?

What are the directions for future research?

Explore how documents about behavioural biases and how they affect investment decisions are linked.

Understanding the adoption of systematic literature review (SLR) methodology by searching different databases and classifications.

Explain the research gap, findings and recommendations for a future research road map in behavioural finance.

In this article, the introduction is followed by the literature review in Section 2 ; this reviews previous studies on behavioural bias, investment decision-making and the factors influencing the investment decision-making behaviour of investors. Section 3 provides a brief discussion of the methodology adopted in this research. Section 4 discusses the results of bibliometric analysis, followed by Section 5 that reports on emerging research agendas. Section 6 discusses the key findings and implications of the study. Concluding remarks are provided in Section 7 . Section 8 presents the limitations and future direction of this study.

2. Literature review

Scholars have used a variety of methods to review literature, i.e. meta-analysis, weight analysis, scoping review, SLR and narrative review. The present research study is an integration of SLR and bibliometric analysis techniques, facilitating the identification of intellectual structure. The paper has looked at an SLR of the past 33 years regarding biases that impact investment decision-making. The main purpose is to analyse behavioural biases like herding, home bias, overconfidence and disposition effects of investors and their impact on returns, volatility and portfolio selection. This will provide a road map for future research.

The purpose of this research is to gain an understanding of the various behavioural biases that influence investors' investing decisions as well as the socio-economic factors that are taken into consideration; this is done by utilizing SLR and meta-analysis. Overall, 17 types of bias and 15 socio-economic items were identified in the outcome data set. The findings revealed that there was an interrelationship between behavioural biases and common biases like disposition and overconfidence ( Sharma and Nandi, 2018 ). A few studies also found that there is a strong correlation between behavioural biases and socio-economic variables in different geographic areas ( Calzadilla et al ., 2021 ; Quaicoe and Eleke-Aboagye, 2021 ). Research by Fischer and Lehner (2021) focusses on behavioural finance and its development by systematic literature from 36 finance journals published between 2009 and 2019; they set out to explore the field of neuro-finance and the human brain. Their research also analyses the impact of behavioural bias on rational investment decisions. The research work by Clark et al. (2019) focusses on foreign and domestic bias and portfolios in a bond market. They observe that foreign investors have a good return policy only because of their behaviour. Foreign investors have enough ideas to avoid the uncertainty and controlled volatility of the bond market. This paper provides insights into emerging markets so that investors can observe the behaviour of foreign investors and make wise decisions. Scholarly works by Pradhan (2021) and Kishor (2020) examined the impact of both financial literacy and behavioural biases like heuristic bias, herd mentality, framing effect and cognitive illusion on investment decisions. Their findings confirmed the existence of a significant positive association between heuristic bias and investment decisions. The study also found that there is a negative link between the framing effect, the herd mentality, and cognitive illusions and investment decisions. It came to the conclusion that financial literacy has a significant effect on investment decisions in the stock market.

A recent research paper by Bhatia et al. (2021a, b) analysed the theories of traditional finance and behavioural finance that are generated based on behavioural aspects. The authors' objective is to make all investors aware of the psychological factors and their impact on rational decision-making. Studies by Sharma et al. (2021) as well as Dangi and Kohli (2018) focus on the investment decisions of women entrepreneurs and attempt to explore the way women's behaviour affects their investment decisions. Kappal and Rastogi (2020) examined behavioural biases such as disposition effect, herding effect, and overconfidence bias and their impact on investment decisions using a moderating role, i.e. the investor type. Their results indicated that the moderating role is positive in overconfidence bias and negative in herding effect while making investment decisions. Research work by Lather et al. (2020) focusses on biases like overconfidence bias, reference point bias, self-attribution bias, framing effect bias, regret avoidance bias and overreaction amongst male and female investors and their impact on investment decisions. The study observed that the impact of gender was significant on overconfidence bias, self-attribution bias and regret avoidance bias. A study by Hassan et al . (2013) explains how the theory of traditional finance is different from the modern theory after observing the behaviour of people. The study explores positive and negative aspects of biases and also the way they are related to the financial satisfaction of individuals. The results were found to be positive and significant in the case of biases like overconfidence, reliance on expert bias and self-control. A study by Sahi (2017) considered both individual investors and professional investors and investigated their investment decisions by comparing them using different techniques and tools. They discovered that long-term investment decisions have 1.5 times the impact of short-term decisions. Short-term investors are being affected by heuristic biases. Heuristic bias affects investors in analysing their current financing situation and previous experience that shows how to deal with the behavioural biases for making rational decisions ( Rauwerda and De Graaf, 2021 ).

We have utilized an organized procedure in order to locate the relevant literature. The intellectual dynamics of the research area can be better understood after science mapping has been carried out with the assistance of bibliometric tools. In addition, investors, analysts, practitioners and academics may find the comprehensive analysis of the content leadership, research gaps and future research directions to be of great value.

3. Methodology

SLR explores and offers a model for summarizing and critiquing literature to enhance the future research agenda. It is used as a standard for a road map to potential documents ( Davis et al. , 2014 ; Livinski et al. , 2015 ). The systematic review adopts a common protocol and is carefully documented to be transparent to other researchers, enabling them to access results and thus helping to maintain external validity. It combines findings from various studies and reveals the trends, patterns and themes in the current literature from the available body of knowledge ( Davis et al. , 2014 ). This provides reliable outcomes by reducing the existing bias ( Liberati et al. , 2009 ). SLR is a means of consolidating knowledge about a specific topic or research question. It has been used in exploring relevant research based on a research question on a particular research method systematically. This provides the most clarity, validity and reliability in relevant studies in the area. It tries to reduce the biases that occur during the review of research evidence. SLR clarifies documents by defining their structure, document methods and search process. SLR removes potential bias, e.g. selection and publication. In order to avoid publication bias, we have included a wide range of reviews, including the years 2007–2022. Accurate records are kept in order to provide an auditability feature that enables the review to be consistent and systematic throughout. Articles published in English have more citations than articles in other languages; they are dominant in the area of behavioural finance and also make English accessible to a wider audience. Many researchers communicate their results to the global community in English.

The period considered has shown a high growth in published articles and also citations, all of which have increased over the time period. Identifying the trends and topics may give vital insight to researchers in the domain of behavioural finance. A number of researchers discussed the selection of different databases, e.g. Scopus and Web of Science (WoS), for bibliometric analysis. Each database has its own advantages. As per the rule of thumb, all databases should be used, but this demands huge data cleaning and merging of databases. There are several reasons for the choice of Scopus databases. One is that the coverage of Scopus is relatively larger, with more citations of peer-reviewed articles. Scopus had more weightage than Google Scholar databases and more extensive research, whereas Google Scholar provides limited bibliometric information for bibliometric analysis ( Corbet et al. , 2019 ; Kumar et al. , 2020 ; Levine-Clark and Gil, 2008 ).

The systematic review helps to create questions or hypotheses and collect and analyse data from studies to find solutions; it also identifies paths to further research. This study focusses on the SLR process and bibliometric analysis; this implies structured, transparent, reproducible and iterative development in nature ( Fischer and Lehner, 2021 ). Some keywords are required in the SLR process for the extraction of research articles. Primarily, data were provided by Scopus, the largest database that consists of abstracts and citations in the pertinent literature. To create the most relevant study, the searching process used a combination of a title, an abstract and a keyword. Furthermore, to understand the intellectual structure of this study, bibliometric analysis was used. To perform this bibliometric analysis, a variety of different pieces of software have been used. For this study, we used VOSviewer to perform bibliometric analysis.

During the process of searching, we will only take into consideration papers that have been published in Scopus journals and are written in English. The overall selection process that the researchers used in order to carry out the relevant investigations is depicted in Figure 1 . From the published articles, the ones that do not have a full-text version, do not pertain to the subject at hand, or are articles such as conference papers, proceedings, and book chapters which are not included in this research. The detailed criteria for inclusion/exclusion of papers are summarized in Table 1 . The records that were imported in the “.csv” and “.bibtex” formats were disregarded for the sake of the subsequent study. In addition, selected articles were taken into consideration for additional bibliometric research.

4. Bibliometric analysis

4.1 data extraction process.

It could be observed from Table 2 , that there are a total of 63 authors who have contributed to the publication of 27 articles in 20 reputable journals that have been subjected to peer review. These authors come from a wide variety of fields, including business, management, accounting, economics, econometrics, finance plus arts and humanities. Overall, 60 authors have contributed to multiple documents. In addition, there are just three authors who have a single research document.

The study activity that was associated with this topic began in 2007. From 2019, an upward trend was observed in the publication frequency. This results in a collaboration index of 2.5 between the authors. Annual scientific productivity is increasing at a rate of 27.54% per year. Bibliometric analysis was used to obtain this information.

Behavioural biases and their influence on financial decisions are increasingly becoming the focus of attention amongst researchers and other experts, who endeavour to better understand both. In both of the years 2020 and 2021, there are a total of seven articles published that could be observed from Figure 2 . It was also observed that in addition, three articles were published in 2019. This indicates a greater emphasis being placed on research in this sector beginning in 2019.

In this field, most of the research work was done in India ( n  = 51) followed by Iraq and Pakistan ( n  = 3). But if we observe citations of the paper, then the USA, i.e. ( n  = 2) with 215, has the highest number of citations compared to India (119) and Pakistan (5); refer to Figure 3 . In contrast, the publications from China and other remaining countries are not able to create sufficient citations. It is essential to understand how behavioural biases influence investors in making investment decisions.

4.2 Most influential works

These chosen papers are listed in Table 3 , in ascending order of the number of citations they received. In addition, the author's name, the title of the work, the journal in which it was published and the h index of the publication are given. This knowledge is very important to pave the way for new research directions for those working on this subject. The material also sheds light on the author's contributions to their work in this field and provides insights into those contributions. In addition, the name of the journal which contains highly referenced works is also stated. When it comes to obtaining useful information, one of the best sources is found in highly referenced publications. Knowledge regarding behavioural biases and investing choices can be gained by analysis of those papers which have received a significant number of citations. The authors highlight the illogical investment decisions that are made by investors due to their biases ( Chen et al. , 2007 ). As a result, this creates a direction for researchers to explore more deeply with the assistance of existing frameworks and even tries to extend the framework with a new construct after identifying a gap that is presented in the previous paper ( Kumar and Goyal, 2015 ). A number of researchers then improved and broadened the scope of the study by incorporating both primary and secondary data, as well as a wide variety of tools, methods and more advanced levels of statistical analysis. This was done in an effort to validate the underlying framework and theory ( Singh et al. , 2016 ; Sharma and Nandi, 2018 ; Zahera and Bansal, 2019 ; Kappal and Rastogi, 2020 ; Abdullah and Naved Khan, 2021 ; Barber and Odean, 2001; Fischer and Lehner, 2021 ). The scope of the research was expanded to include looking into how investors think and how they do business. This helped to develop a theory and build a structure using advanced statistical skills.

To analyse the relationships between co-author, co-occurrence, co-citation analysis and bibliographic coupling, we used VOSviewer. With the help of VOSviewer, a co-occurrence map can be created based on network data, allowing us to understand the flexibility and integrity of collected information. WoS, Scopus, Dimensions, Lens and PubMed are the databases that are taken into account for visualization.

4.3 Analysis of the author co-authorship network

A study was made of the relationships amongst authors, organizations and countries. We used VOSviewer to examine the relationship between authors' works and how collaborative work provides a new direction and better quality of research papers.

To analyse this, a minimum number of documents was set; the threshold was at least one research paper co-authored and the number of citations at least four times between 2007 and 2022. This resulted in 25 authors in nine clusters ( Figure 4 ) with 26 links between them. Each cluster is represented by a different colour. Goyal N. and Singh S. have the strongest co-authorship network with three documents followed by Jain who has a co-authorship network of two documents.

Figure 5 represents the co-authorship network amongst affiliated countries; the minimum number of documents is set at one. This found that there are 11 countries present which have at least one paper; the countries are India, China, Hong Kong, Pakistan, Saudi Arabia, the USA, Ghana, France, Indonesia, Iraq, Norway, Bangladesh and Oman. India has the highest number of documents, 19, with the strongest co-authorship network, followed by China and Pakistan both having two documents.

4.4 Co-occurrence of author's specific keyword

Here co-occurrence means the situation occurs at the same time, or one thing has a connection with others. In the literature review, we have to understand how the keywords of all the authors are connected, so we can clearly understand the connection. This is essential for further analysis. To analyse more deeply and examine strong bonding between the papers, we have considered author keyword analysis. We used the VOSviewer of co-occurrence analysis with the help of author keywords. We set a minimum of three keywords of occurrences. The result found that out of the 92 keywords, ten keywords met the threshold criteria.

Figure 6 shows the keywords network that is frequently used by authors in their papers. The result found that the disposition effect had the strongest link and highest occurrence, i.e. 6, with behavioural biases, herding, heuristics and investment decisions. This modern model can better investigate the topic with regard to behavioural biases and investment decisions.

4.5 Co-occurrence network of abstract

To analyse more strongly the connection of authors' work, we consider abstract in conceptual structure in biblioshiny for bibliometrix. Figure 7 shows 49 nodes, with these nodes grouped under three clusters. Cluster 1 (red colour) is the largest cluster having 22 nodes. Out of 49 nodes, 22 nodes are grouped. These nodes have a strong connection with each other. This includes biases, investment, research, data, decision-making, etc. Cluster 2 (blue) is the second-largest cluster with 18 nodes. In this cluster, nodes such as the individual, cognitive, questionnaire, finance, impact etc. are prominent. Cluster 3 , in green, contains nine nodes; this includes findings, implications, model, influence etc. As there are so many authors, researchers who have adopted the process of SLR to validate new models and hypotheses for this new concept of behavioural finance must establish which theories are different from traditional theories. Our examination shows that much more research work on the modern concept of behavioural biases has been carried out in India recently.

4.6 Co-citation of cited references

When two documents are cited together by another one, the frequency of citation is known as co-citation. The high strength of co-citation means that they are more semantically linked. The citing literature motivates the author as it generally occurs when the document contains academically relevant material.

For analysis, the citation of reference used the VOSviewer of co-citation and full counting method. Further, the minimum number of citations set as three, results in the 18 thresholds classified into three clusters. Each cluster is represented by a different colour ( Figure 8 ). Cluster 1 (in red) consists of highly co-cited references and is the largest cluster. Odean, T., Shefrin, H., Fama, E.F., Nofsinger, J.R. and Sataman, M. have more citations in Red Cluster 1. Similarly, Cluster 2 (in green) includes Ritter, J.R., Glaser, M., Barberis, N., Shiller, R.J. and Prosad, J.M. with the second highest number of citations. Cluster 3 (in blue) has 5 references, i.e. Kahneman and Tversky (1979 , 1982) , Waweru et al. (2008) . This co-citation of references has 133 total link strengths.

4.7 Author level – co-citation analysis

Co-citation analysis was also carried out by considering all the authors with the help of VOSviewer and presented in Figure 9 ; this gave co-citation of the full counting process. There are 2,144 authors present. A minimum number of citations of ten produced 25 thresholds grouped into three clusters. Cluster 1 (red) includes authors like Kahneman, D. who has the highest number of citations (71) with 1,573 link strengths; Odean, T. has 53 citations with 1,342 strengths; Tversky, A. has 58 citations with 1,287 strengths. Cluster 1 has the strongest bonding. Similarly, Cluster 2 (in green) consists of Shleifer, A. who has 17 citations with 429 link strengths and Subrahmanyam, A. who has 14 citations with 416 strengths; Cluster 3 (in blue) includes the author Hair, J.F. who has ten citations with 196 link strengths. Overall, the three clusters have a total of 300 links and 6,886 total link strengths.

4.8 Thematic map of authors abstract

A thematic map is known as a statistical map. A thematic map describes the dimensional variability of a specific theme including the major information. Here, we used all the abstracts of selected documents and analysed the information by biblioshiny for bibliometrics. Here the thematic map provides four elements: a base map, a network diagram, statistical data and clusters’ information.

Figure 10 shows that there are four clusters present. There are 195 nodes present, and these nodes are divided into four clusters. Cluster 1 (red colour) shows the strongest bonding; words like investment, biases, behaviour, decisions, etc. have a connection with each other and provide major information. Cluster 2 (purple) has a connection and information like findings, implications, publishing, practical, methodology approach etc. Cluster 3 (green) shows words like research, decision-making, individuals, analysis, understanding, etc. Cluster 4 (blue) contains factors, trading, future, risk, etc.

4.9 Bibliographic coupling of countries

Bibliographic coupling occurs when two research work references are common in a third one. When one document cites another document, the strength of other documents also increases. There is a similar strategy in the form of documents, authors, organizations and countries. To see the connection between countries we used the VOSviewer of bibliographic coupling of the full accounting method, and results shown in Figure 11 . As per Figure 1 , this showed that India had 19 documents with 142 citations and 530 link strengths, China had two documents with 215 citations and 413 link strengths while Pakistan had two documents with five citations and 250 strengths. It could be observed from Figure 11 that Cluster 1 (red colour) includes India, Indonesia, France, Ghana and Iraq. It has the strongest bonding. Cluster 2 comprises Saudi Arabia, Pakistan, Oman and China. Cluster 3 has two countries – Hong Kong and the USA. This clustering provides future directions for collaboration and academic research network development.

4.10 Factorial analysis in the field of titles

We used multiple correspondences and factorial analysis by biblioshiny of bibliometrics to develop the conceptual structural map. The factorial analysis was administered by considering the field of titles of papers. The number of terms is set as 20. Dim. 1 and Dim. 2 showed 40.23% and 18.85% correlated variables, respectively.

Figure 12 shows the word map of factorial analysis. This factorial analysis was also explained with the help of dendrogram parameters ( Figure 13 ).

5. Emerging research theme on biases

In the behavioural finance literature, the consensus view is that influence on investors makes for poor judgment and pricing in the practical field ( Barber and Odean, 2001 ; Fischer and Lehner, 2021 ). The bounded rationality theory describes how individuals have limited rationality while making an investment decision; the limitation includes the cognitive capability of the mind to make satisfactory solutions rather than making the best possible solution. When influenced by behavioural bias, people make irrational decisions. Behavioural biases consist of cognitive errors or emotional biases. When reasoning is faulty or errors are present in the memory or information processing, this defect is known as a cognitive error. Whereas some bias, e.g. emotional bias, is influenced by feelings which are very difficult to change; these include biases such as regret aversion, overconfidence, loss aversion and many more.

5.1 Mental accounting

We need to examine the role of mental accounting in the decision-making process.

We need a strategy development to identify the bias and monitor the investor's decision-making process in investment.

5.2 Herding behaviour

Influential barriers should be identified to facilitate personal research and strategies in the process of investment decision.

We need to investigate the linkage between herding behaviour and investment decision-making, targeting how to control bias and avoid an irrational investment decision.

5.3 Representative bias

Proper criteria that measure the bias must be set before making any investment decision.

A computation method must be proposed that helps in an empirical experiment by identifying the representative behaviour to control the bias and provide optimized portfolio selection for better returns.

5.4 Availability bias

We need to explore and analyse the role of this bias and how it affects the investment decision.

We need to devise a strategy to measure the actual conditions and reality of the market before making an investment decision, ignoring any irrelevant information without verification.

5.5 Emotional bias

Emotional bias is the kind of impulse or intuition which is based on feelings that are very difficult to change. This emotional bias includes loss aversion, regret aversion and overconfidence etc.

5.5.1 Overconfidence bias

We propose a strategy through which an investor can think of the consequences, reflect on personal limitations and give attention to feedback before any kind of investment is made.

We need to examine the role of overconfidence in investment decisions.

5.5.2 Loss aversion

We need to examine the role of loss aversion and its effect on the decision-making process.

We must prepare an innovation portfolio approach, appropriate frameworks and methods in the investment project.

5.5.3 Regret aversion

Before investing, we must prepare a diversified portfolio approach and strategy to avoid bias.

We need to analyse the role of this bias and its effect on investment decisions by procuring the help of unbiased analytics and financial planners.

5.6 Research gap

There is a requirement for more advanced statistical techniques that can identify the biases present in investors and provide the most suitable and optimized solutions.

Behavioural bias is a modern concept that is different from traditional concepts. Nowadays it is essential to understand all biases before making any kind of investment. There is no specific organization or procedure available to make investors aware of the impact of biases. Theory-based quantitative research needs to be thoroughly addressed in future research.

The quantity of research on different biases like emotional bias, status quo, representative bias etc. is lacking. So, more work is needed in this area to observe these biases and their consequences.

6. Discussions

An SLR and a bibliometric analysis were conducted. This allowed us to analyse the research trends in behavioural biases and investment decisions and to segregate documents into different clusters to observe their themes and the association between them; this identified gaps for further research. For bibliometric analysis, VOSviewer and bibliometrics software were employed. To address the first objective, the relevant literature published between 2007 and 2022 was reviewed to gain a thorough understanding of the characteristics of identified behavioural biases and their impact on decision-making. We considered 27 articles for analysis. SLR was conducted in an effort to understand how existing research work is connected and to provide valuable information for future study. It was observed that research work on behavioural biases and investment decisions around 2007 was very limited as it was a relatively new concept then. However, after 2009, research has steadily demonstrated an upward swing. India has the highest number of documents under study, i.e. 19, with the strongest co-authorship network. This is followed by China and Pakistan with 2 documents. The most influential journal is Journal of Behavioural Decision Making with authors, Chen et al. (2007) having the highest number of citations – 215. We set up the network map with the help of co-authorship, co-occurrence, the thematic map through VOSiewer and biblioshiny of bibliometrix. This creates interesting patterns and themes of behavioural biases and investment decision-making and provides the intellectual structure of this study through clusters. This was accomplished by forming a thematic map by biblioshiny of bibliometrics. The thematic map considered the abstracts of all the authors and observed the dimensional variability present in the research study. Finally, a demonstration was made with four clusters with different colours showing how their abstract information bonded with each other. The SLR helps in understanding the role and effect of cognitive and emotional biases on investment decisions. The cognitive error generated from memory includes biases like mental accounting, herding behaviour, representativeness and availability bias. Biases based on feelings known as emotional bias include overconfidence, loss aversion and regret aversion. From the review and information gathered, the study found that investors are influenced by these biases resulting in irrational investment decisions being made.

6.1 Implication

Behavioural biases play a vital role in the cognitive thinking process of investors. This creates a perceptual map of biases in the minds of investors, influencing irrational decisions. Findings from the study reveal both academic implications and business implications. For academic purposes, behavioural bias provides key insights of biases affecting all investors, suggesting how outcomes will be shaped. The study also identifies the progress made over the years from traditional concepts to modern concepts. It is recommended that financial plans of individual investors should include a proper strategy to ensure a quality investment. The study suggests that institutional investors or financial brokers should create a strategic retention programme, building a portfolio by considering the impact of biases and all risk factors. This will guarantee that the decision for investment will be rational and returns from the portfolio will be optimum. Governments can make a societal contribution by running various programmes of “investor awareness” to increase the knowledge base regarding biases, financial markets and important practical insights into this concept.

7. Conclusion

The main contribution of this article is to give an insight into the themes and associations present in the subject of behavioural biases and investment decisions by analysing those highly cited research documents published by top contributing authors from top journals and a range of countries. To achieve this, the study has conducted a review of the literature from Scopus indexed journal publications that investigate behavioural biases and investment decision-making to identify the existing gaps and provide optimized solutions. From these observations, we have found that most of the research has been contributed by authors from India, China, Pakistan and the USA as per the country-wise scientific production analysis. The analysis conducted in this area, with the help of various frameworks like VOS viewer and biblioshiny of bibliometrix gives an overview of the researched topics and identifies the proposed dimensions. Under bibliometric analysis, we have identified the trends in this area, classifying the topmost authors' publications with their citations, journals, countries and bibliographic coupling by clustering these documents. The dimensions cover most related articles on behavioural biases and investment decisions. These findings will help decision-makers to create a framework that helps individuals to identify their biases and make rational investment decisions. The proposition of biases includes the emerging research themes of biases that give direction for future study. With the help of this study, some propositions are made by analysing the biases for future study and developing the best framework and strategy for rational investment. Moreover, future researchers can explore the areas identified and give more effort and understanding to creating models or tools to identify the characteristics of biases. This will provide a feasible solution to every kind of investor – individual investors, financial advisors and experts – to avoid irrational decisions by mitigating the biases.

8. Limitations and scope of future work

This study has some limitations. It has considered only Scopus database journal publications, excluding conference publications, editorials and book chapters. For reviewing and analysing the literature, only the tools of VOSviewer and bibliometrics were used. Through these tools, we get to know the connection between authors, documents, organizations and countries; this reveals the relationships between authors' works, and how collaborative working provides a new direction and better quality of research papers. Further, the study can be extended by practitioners as well as academicians to explore how behavioural biases impact investment decisions. Future research could also expand this study by considering other databases like WoS and Institute of Electrical and Electronics Engineers (IEEE) Xplore. We can extend this study to different types of tools like Cite space, Bib excel, Histocyte, Gephi, etc., rather than VOSviewer and bibliometrics; this may give better understanding and review. More study is required on a global basis. Cross-cultural considerations can better understand behavioural biases like recency, house money effect, self-attribution and status quo and all biases that affect investors. Advancing the present models will provide optimized solutions for investors. Future research can progress in the direction of stock markets and investors who are more inclined towards investment in the stock market aligned to the derivatives market. Work can be extended to empirical research based on primary data in analysing the biases impacting investors' decision-making. Further, along with cognitive and emotional biases, other biases should be taken into consideration. A comparative analysis can be carried out amongst institutional and retail investors with the moderating and mediating effect of demographic variables impacting the biases in the investment decision-making process.

investor behaviour in stock market research paper

Flow diagram [Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)]

investor behaviour in stock market research paper

Article publication per year

investor behaviour in stock market research paper

Country-wise scientific production and citations

investor behaviour in stock market research paper

Co-authorship network

investor behaviour in stock market research paper

Co-authorship network amongst affiliated countries

investor behaviour in stock market research paper

Keyword co-occurrence map

investor behaviour in stock market research paper

Co-occurrence network of abstract

investor behaviour in stock market research paper

Co-citation map

investor behaviour in stock market research paper

Co-citation analysis at the author level

investor behaviour in stock market research paper

Thematic map of authors abstracts

investor behaviour in stock market research paper

Coupling of countries

investor behaviour in stock market research paper

Conceptual structural map

investor behaviour in stock market research paper

Topic dendrogram

Criteria for inclusion/exclusion of papers

Descriptive data regarding scientific papers retrieved from Scopus Database

Highly cited research papers in the field of behavioural biases and investment decision-making (2007–2022)

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  • Open access
  • Published: 12 October 2020

An empirical examination of investor sentiment and stock market volatility: evidence from India

  • Haritha P H 1 &
  • Abdul Rishad 2  

Financial Innovation volume  6 , Article number:  34 ( 2020 ) Cite this article

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Understanding the irrational sentiments of the market participants is necessary for making good investment decisions. Despite the recent academic effort to examine the role of investors’ sentiments in market dynamics, there is a lack of consensus in delineating the structural aspect of market sentiments. This research is an attempt to address this gap. The study explores the role of irrational investors’ sentiments in determining stock market volatility. By employing monthly data on market-related implicit indices, we constructed an irrational sentiment index using principal component analysis. This sentiment index was modelled in the GARCH and Granger causality framework to analyse its contribution to volatility. The results showed that irrational sentiment significantly causes excess market volatility. Moreover, the study indicates that the asymmetrical aspects of an inefficient market contribute to excess volatility and returns. The findings are crucial for retail investors as well as portfolio managers seeking to make an optimum portfolio to maximise profits.

Introduction

There has been growing academic attention in the past decade on investors’ sentiments and their potential impact on market performance. Investor sentiment is the expectation of market participants about the future cash flows (returns) and investment risk (De Long et al. 1990 ). Because traditional stock market theories comprehended market dynamics under the theoretical framework of the efficient market hypothesis (EMH) and random walk theory, they did not consider investor sentiment as an important aspect. However, they failed to explain the heterogeneous behaviour of investors in the capital market. Investors’ sentiment is a vital aspect of the capital market, as it contributes to frequent fluctuations in the stock price and thus creates uncertainty about future returns on investments. In the past few decades, there have been radical changes in the Indian financial environment, especially in the basic structure—for example, shifting from a savings-oriented economy to an investment-oriented economy. These changes have increased heterogeneity in the composition of participants and impacted investors’ risk-taking behaviour.

As per the EMH in classical financial theory, market participants exhibit rational risk aversion. Moreover, the information efficiency of the market does not allow participants to outperform the market (Fama 1965 ). The classical theory fails to explain the presence of systematic mispricing in the capital markets resulting from sentimental factors. Behavioural financial theories claim that irrational behaviour of noise traders and arbitrators causes a disparity in asset prices from their intrinsic (fundamental) values. Recent theoretical advances in behavioural finance and empirical evidence both have rejected the hypotheses of classical financial theory because of its assumption of rationality of agents in capital markets. In the previous decade, rational participants did not seem to have played a leading role in bringing the value of assets up to the then current value of anticipated cash flows (Baker and Wurgler 2007 ). Behavioural finance offers an alternative model that claims that economic phenomena can be better understood if the investors are accepted to not be entirely rational. In this context, the asset pricing not only includes the risk-related anticipated rates but also the impact of investor expectations on the returns. Behavioural finance explains the relationship between investment and the investor’s psychology. Investor behaviour is reflected in the stock prices, and market fluctuations, which ultimately shape the market, are themselves shaped by the psychology of the investors. Baker and Wurgler ( 2006 ) argued that market sentiment creates a tendency for investors to be optimistic or pessimistic while speculating prices instead of deciding on fundamental factors.

Previous studies sought to detect the predictability of sentiments as a systematic risk factor valued as per certain conditions in the market. Studies from developed economies like the USA are far ahead in understanding the sentiment-related market dynamics (Barberis et al. 1998 ; Lee et al. 2002 ; Neal and Wheatley 1998 ). Academic study of investor sentiment in developing economies with rapidly growing capital markets is still in infancy. Previous research has mainly focused on the influence of investors’ sentiment on investment returns, whereas the effect of sentiment on the conditional volatility structure of the market is less explored. Also, even among those studies that consider sentiment as a critical factor influencing the time-varying stock return, volatility and potential profitability relating to noise traders were the main aspects of focus. During the periods of high sentiment and low sentiment, noise traders act differently to keep their positions secure. During the high sentiment episodes, their participation and trading is more aggressive compared to that during a low sentiment episode. This is caused by naive and unaware noise traders’ misjudgement of potential risk. Past academic studies about emerging economies have not explored such factors in-depth. The present study is an attempt to address the above-mentioned issues by using a market-oriented sentiment index. We developed an investors’ sentiment index by using multiple sentiment yardsticks mentioned by Baker and Wurgler ( 2006 ). Considering the investors’ sentiments’ contribution to volatility in emerging markets, the current study aimed to establish new empirical evidence to add a more comparative dimension to the existing literature. The findings can help market participants to understand the role of investor sentiment in the determination of conditional volatility of the market and to take decisions to optimise the portfolio.

This study developed the aggregate sentiment index (ASI) from market-oriented sentimental factors such as trading volume, put-call ratio, advance-decline ratio, market turnover, share turnover and number of initial public offers (IPOs) in the period. The use of a constructed sentiment index under the GARCH framework to estimate the association between stock market volatility and investor sentiment makes this study different from existing studies. The findings indicate the persistence of volatility in market indices. Such persistent connection between the sentiment index and stock volatility suggests that investor sentiment is one of the most crucial determinants of Indian stock market volatility.

Theoretical background

According to the conventional theory of ‘market noise’ proposed by the Black ( 1986 ), noise traders operate on noisy signals in financial markets and balance both the systematic and non-systematic risk of an asset. According to this theory, noise makes markets inefficient to some extent and prevents investors from benefitting from inefficiencies. The significance of sentimental factors in asset pricing theories is substantiated by empirical literature from developed economies. The question of how irrational beliefs held by investors affect the market through asset pricing and expected returns is explained in behavioural finance theories. The theoretical model developed by De Long et al. ( 1990 ) explained this phenomenon as ‘Some investors, denominated noise traders, were subject to sentiment – a belief about future cash flows and risks of securities not supported by economic fundamentals of the underlying asset(s) – while other investors were rational arbitrageurs, free of sentiment. The irrational beliefs were caused by noise, interpreted by the irrational traders as information, thus the term noise traders.’

Theoretically, noise is part of irrational behaviour; the irrational traders consider noise as information. Interestingly, proponents of an efficient market claimed that noise traders were exploited by rational arbitrageurs who drove prices towards fundamental equilibrium values. Thus, noise was a reaction of noise traders to the activities of rational arbitrageurs that caused overpricing or underpricing of stocks during periods of high and low sentiment (Lemmon and Portniaguina 2006 ; Baker and Wurgler 2006 ). Researchers have been unable to satisfactorily explain the interaction between rational and irrational investors. The continuing debate on this issue significantly contributes to the literature but concentrates mainly on the role of noise traders in anticipated asset yields and volatility of return. It is not understood how the market reacts to noise, which is caused by a large number of small events. This behaviour can be observed among investors from advanced economies because they believe that systematic risk and return anomaly is associated with irrational investment behaviour (Brown and Cliff 2004 ; Qiu and Welch 2006 ; Lemmon and Portniaguina 2006 ). With this theoretical background, our study examines the role of irrational feelings of investors and their impact on the volatility of the Indian capital market.

Literature review

The development of behavioural finance theories triggered a discussion on the impact of investor sentiment on asset returns in the integrated stock market. According to theoretical and empirical research, investor sentiment strongly influences stock prices with inevitable consequences on portfolio selection and asset management, as psychological differences of heterogeneous investors have implications on the pricing of assets in the market. The influence of investor’s sentiment in asset price volatility is widely described as a combination of investors’ reaction to the current market situation and unjustified expectation of the future cash flows (Baker and Wurgler 2006 , 2007 ).

As a psychological factor, it is not easy to estimate investors’ sentiment because of their subjective and qualitative nature. However, different proxies have been used to measure sentiment. These indicators of the sentiment index are classified as indirect and direct measures. In direct measures, researchers measure the individual investor sentiment via surveys and polling techniques. They are highly sampling-dependent, and the chances of sampling errors are high. Moreover, they may not be able to give a broad picture of the prevailing sentiment. Indirect measures use market-determined sentiment proxies, such as trading volume, turnover volatility ratio, put-call ratio, advance-decline ratio, market turnover and share turnover for measuring the same. They posit that investors’ sentiment are reflected in the structure and breadth of the market and understanding these dynamics helps to capture the irrational aspects of the market. The consistent and theoretically comprehensible nature of the sentiment index has led to its wide adoption (Baker and Wurgler 2006 ; Brown and Cliff 2004 ; Chen et al. 1993 ; Clarke and Statman 1998 ; DeBondt and Thaler 1985 ; Elton et al. 1998 ; Fisher and Statman 2000 ; Lee et al. 2002 ; Neal and Wheatley 1998 ; Sias et al. 2001 ). According to Zhou ( 2018 ), investor sentiment indicates the distance of the asset’s value from its economic bases. This can be measured from different sources, such as official documents, media reports and market surveys. Mushinada and Veluri ( 2018 ) used trading volume and return volatility for understanding the relationship between sentiments and returns. Their findings showed that post-investment analysis was essential to correct errors in previous behavioural estimations. Market participants’ behaviour is heterogeneous because of the risk-return expectation, and it creates noise in the market. These findings contradict with the premises of the efficient market hypothesis that postulate that markets turn information efficient when investors behave rationally.

In the past few decades, empirical studies across the globe have investigated the connection between investors’ sentiment and stock returns for understanding and substantiating theories of market inefficiency (Brown and Cliff 2004 ; Fisher and Statman 2000 ). Chi et al. ( 2012 ) examined the impact of investor sentiment on stock returns and volatility by using mutual fund flows as an investor sentiment proxy in the Chinese stock market. They found that investor sentiment has a great impact on stock returns. The relationship between stock market volatility and investor sentiment has also been reported as statistically significant. Supporting these findings, Zhou and Yang ( 2019 ) stated that the construction of a theoretical model of stochastic investor sentiment influences investor crowdedness and also affects asset prices. Their result indicated that optimistic (pessimistic) expectations of investors can move asset prices above (below) the basic value. By examining the long-term association between investor sentiment in the stock and bond market, Fang et al. ( 2018 ) showed that the index of investor sentiment is positively associated with market volatility. Contradicting the fundamental tenets of the efficient market hypothesis, Shiller ( 1981 ) argued that investors are not completely rational, which could affect market prices aside from fundamental variables. Wang et al. ( 2006 ) noted that the sensitivity of investor sentiments to the information flow affected both market return and volatility. Chiu et al. ( 2018 ) found a positive relationship between investor sentiment, market volatility and macroeconomic variables. Jiang et al. ( 2019 ) constructed the fund manager’s sentiment index as a predictor of aggregate stock market returns. They found that when managers had a high level of sentiment, it caused a reduction in overall income surprises from total investment. Li ( 2014 ) pointed out that the sentiment index has strong predictive power for Chinese stock market returns. Retail investors’ attention will help to mitigate the crash risk, as the retail investors’ attention will not allow any irrational or noise traders to overrun the rational market participants (Wen et al. 2019 ).

Verma and Verma ( 2007 ) studied the role of retail and noise traders in price volatility to yield similar results. Verma and Soydemir’s ( 2009 ) empirical examination of the rational and irrational investors’ impact on market prices also supported the previous finding. They discovered that individual and institutional investors’ feelings influenced the market. Further evidence shows that the response of the market to volatility is not homogeneous; it is heterogeneous depending on the variations in shareholder sentiment. These findings are validated by Gupta ( 2019 ) who found that sentiments of fund managers are a stronger predictor than the returns, when it comes to forecasting volatility. Yang and Copeland ( 2014 ) found that the investor sentiment index has a long-term and short-term asymmetrical impact on volatility. They concluded that bearish sentiment is associated with lower returns than bullish sentiment, which accelerates market return. This shows that the bullish feeling has positive effects on short-term volatility, whereas in the long-term, it has a negative effect on volatility. These findings agree with the findings discussed by Qiang and Shue-e ( 2009 ), namely that positive and negative sentiment create different impacts on stock price variation. Baker et al. ( 2012 ) constructed investor sentiment indicators of six nations but because of the disintegration of different markets owing to the heterogeneous behaviour of investors across the globe, the indicators were not viable. Other studies have reported that investors’ sentiments are driven by overall funding patterns irrespective of the investor being individual or institutional (Baker and Wurgler 2000 ; Henderson et al. 2006 ). Investors’ sentiment has a mutual relationship with the expected return of public bonds and the expected return from the stock market (Bekaert et al. 2010 ).

In the context of the Indian stock market, Sehgal et al. ( 2009 ) discussed the fundamental aspects of investor sentiment and its relationship with market performance. They identified several factors that might act (individually and together) as indicators of market behaviour and investor sentiments’ influence on market behaviour. The authors used macroeconomic factors such as real GDP, corporate profits, inflation, interest rates and liquidity in the economy and market-based factors such as the put-call ratio, advance-decline ratio, earnings surprises, the price to earnings ratio and price to book value as potential factors to explain the underlying investor sentiment at the aggregate market level. They also suggested the development of a sentiment index based on these macroeconomic and market indicators. Using some of these indicators, Dash and Mahakud ( 2013 ) examined the explanatory power of an index of investor sentiment on aggregate returns. They found a significant relationship between the investor sentiment index and stock returns across industries in the Indian stock market. Rahman and Shamsuddin ( 2019 ) studied the excess price ratio and its influence on investor sentiment and found that the price to earnings ratio increased with a rise in investors’ sentiment. Kumari and Mahakud ( 2016 ) and Chandra and Thenmozhi ( 2013 ) studied the impact of investor sentiment in the Indian capital market. They found a positive relationship between investor sentiment and market volatility. Verma and Verma ( 2007 ) showed that investor sentiment has a positive impact on asset return, but it makes an adverse impact on individual and institutional investors owing to market volatility. Aggarwal and Mohanty ( 2018 ) studied the impact of the investor sentiment index on the Indian stock market and found that there is a positive relationship between stock returns and investor sentiments. However, most of these studies focused on the general effect of investors’ sentiment on stock returns. Such an approach restricts our understanding of the phenomenon of investors’ sentiment and its influence on market dynamics to a single dimension. In the present study, we explored the role of investor sentiment in determining excess market returns and volatility.

Data and variables

Being a qualitative factor, it is not easy to quantify the market behaviour of investors. Past studies have used multiple ways to measure investors’ sentiment. Some studies have relied on media reports, events and other publicly available documents to collect information on investor behaviour, and other studies have conducted surveys among investors for the same. Some other researchers have used market-based indicators such as price movements and trading activities for constructing sentiment indexes. A few researchers have used single variables as an indicator of investor sentiment. For instance, Mushinada and Veluri ( 2018 ) used trading volume as an indicator of investor sentiment. Using a single variable may not be sufficient to explain market sentiments because there are multiple factors that cause variation in these single variable proxies. Latest studies have constructed the sentiment index by using multiple market-based indicators that directly reflect the participants’ behaviour. Following Baker et al. ( 2012 ), this study employed multiple market-based indicators for constructing the sentiment index for the period from January 2000 to December 2016. We used the monthly average closing price of the NIFTY 50 (Nifty) stock index to measure market volatility and return. The diversified market representation of the Nifty index over the other benchmark BSE SENSEX (Sensex) motivated us to select the former. The study used monthly data because of the scarcity of high-frequency data on market-related indicators. The data was collected from the official websites and various reports of the National Stock Exchange, Reserve Bank of India and Securities and Exchange Board of India.

The study employed Bollerslev ( 1986 ) generalized autoregressive conditional heteroskedastic model (GARCH) to measure volatility using the conditional variance equation and to capture the dynamics of volatility clustering. This helped us to examine how the investors’ sentiment reacts to market volatility. This model helps verify whether the investors’ shocks are persistent or not. For the serial correlation, this study used the autoregressive conditional heteroskedasticity-Lagrange multiplier (ARCH-LM) of Engle and Ng ( 1993 ), autoregressive conditional heteroskedasticity (ARCH) test of Engle ( 1982 ) and Mcleod and Ll ( 1983 ) tests for the estimation of models. We also employed the Granger causality test to check the direction of causality between sentiment and market volatility.

Construction of investors’ sentiment index

The present study adopted the framework developed by Baker et al. ( 2012 ) to construct the investors’ sentiment index. It considered six variables: trading volume, put-call ratio, advance-decline ratio, market turnover, share turnover and the number of IPOs. The number of IPOs was defined as the total number of IPOs during the period. Baker et al. ( 2012 ) argued that firms try to procure more capital when the market value of the firm is high and repurchase their shares when the market value is low. The intention is to take advantage of the market sentiment until it reaches the fundamental value. In a bullish market, new issue of shares will transfer wealth from new shareholders to the company or to the existing shareholders. This market timing hypothesis suggests that higher (lower) value or number of IPOs means that the market sentiment is bullish (bearish) (Baker and Wurgler 2006 ). The number of IPOs reflects the market pulse; hence, they can be considered as an important component of the sentiment index.

The share turnover ratio is one of the conventional yardsticks for measuring the liquidity position, which reflects the active participation of traders and investors in the market. It is the ratio of the total value of shares traded during the period to the average market capitalization for the period. Turnover is vital in gauging investors’ sentiment in the market. Irrational investors actively participate in the market when they are optimistic and accelerate the volume of turnover (Baker and Stein 2004 ). Theoretically, the relationship between market returns and turnover is expected to be negative (Jones 2001 ). The presence of high turnover ensures liquidity and reduces the chances of abnormal returns.

Market turnover (MT) is the ratio of trading volume to the number of shares listed on the stock exchange. Market sentiment can be sensed from the turnover of the market because turnover will be low in bearish markets and high in bullish markets (Karpoff 1987 ). Small turnover is usually preceded by a price decline, whereas high turnover is associated with an increase in price (Ying 1966 ). Thus, the turnover information is a significant component of measuring the sentiment of market participants.

The advances and declines ratio (ADR) is a market-breadth indicator that analyses the proportion between the number of advancing shares and declining shares. The increasing (decreasing) trends in the ADR confirm the upward (downward) trend in the market (Brown and Cliff 2004 ). Generally, the ADR ratio is expected to be positive because investors’ sentiment makes the market active. Thus, the ADR ratio helps to recognise the recent trend and can be used as an indicator of market performance.

The put-call ratio (PCR) is another indicator to measure the dynamics of the secondary market. This sentiment indicator is measured as the ratio between transactions on all the put options and the call options on Nifty. A higher (lower) ratio indicates a bullish (bearish) sentiment in the market. Incorporating PCR to measure the aggregate sentiment index yields accurate results because it reflects the expectations of market participants. When market participants expect a bearish trend, they try to shield their positions. When trade volumes of put options are higher relative to the trade quantity of call options, the ratio will go up (Brown and Cliff 2004 ; Finter and Ruenzi 2012 ; Wang et al. 2006 ). This derivative market proxy is considered as an indicator of a bullish trend because the bearish market PCR will be small (Brown and Cliff 2004 ).

The trading volume (TV) is a key variable for constructing the sentiment index. It is measured as the monthly average of the Nifty daily trade volume. Frequent trades in an active market increase the volume and create liquidity in the market. Therefore, researchers have used market turnover as a proxy for investor sentiment (Qiang and Shue-e 2009 ; Zhu 2012 ; Li 2014 ; Chuang et al. 2010 ). The present study considered TV as one of the indicators of market sentiment.

Macroeconomic factors that are often flashed in the media tend to influence investor sentiment quite significantly. Factors like the levels of inflation, corporate debt, economic growth rate and foreign exchange rate and reserves tend to affect the behaviour of market participants to a certain extent. Therefore, this study used variables such as the exchange rate, Wholesale Price Index (WPI), Index of Industrial Production (IIP), Net Foreign Institutional Investment (FII) and Term Spread (TS) to measure the intensity of aggregate investor sentiment on market volatility.

Unit root tests

Ensuring the stationarity of the variables is necessary for consistent estimators. This study used the augmented Dickey-Fuller test (ADF) (Dickey and Fuller 1981 ) to analyse the presence of unit roots in the time series properties of each variable. Table  1 shows the results of unit root analysis using the ADF test. Unit root tests were run with the linear trend and at levels and intercept. The result shows that all variables expect MT are stationary at level. MT was converted to stationarity by taking the first difference.

  • Principal component analysis

Principal component analysis (PCA) is a multivariate method in which several interconnected quantitative dependent variables describing the observations are analysed. PCA aims to find and extract the most significant information from the data by compressing the size and simplifying the data without losing the important information (Abdi and Williams 2010 ). It consists of several steps for conducting the linear transformations of a large number of correlated variables to obtain a comparatively few unrelated elements. In this way, information is clustered together into narrow sets and multicollinearity is eliminated. The principal goal of PCA is to summarize the indicator data through a common set of variables as efficiently as possible.

First, the six orthogonal sentiment proxies and their first lags were used as factor loadings to calculate the raw sentiment index. The study started with estimating the initial principal component of the six indicators and their lags, which gave a first-stage index with 12 loading factors, namely the six proxies and their lags. Then, we calculated the correlation between the initial index and the current and lagged values of the indicators. Finally, we estimated the sentiment as the first principal component of the correlation matrix of six variables, which were the respective proxy’s lead or lag. We chose whichever had a higher correlation with the first-stage index to rescale the coefficients so that the index had unit variance (Table 2 ). This process yielded a parsimonious index.

Investors’ sentiment and stock market volatility

Following the theoretical and empirical models proposed by Baker and Wurgler ( 2007 ), Brown and Cliff ( 2004 ) and Baker et al. ( 2012 ), this study used market-related indicators for the construction of the investor sentiment index in the initial stage. This study used six indirect proxies to create the sentiment index by considering the first principal component and the lagged components of the variable. The first principal component explains the sample’s variance. Researchers have argued that certain proxies take longer periods to reflect the investors’ sentiment. Therefore, the present study followed the approach of Baker and Wurgler ( 2006 ) and Ding et al. ( 2017 ) to reflect the investors’ sentiment accurately and to assess the PCA with levels as well as their lags to find the main factors.

The GARCH (1,1) model was used to estimate the impact of sentiment on market volatility and stock returns. The GARCH model helps analyse the volatility characteristics of the datasets, especially for financial data, as it has the unique characteristics of heteroscedasticity and volatility clustering (Fig.  1 ). The specific character of financial time series data limits the use of conventional econometrics models to estimate the parameters. The GARCH model helps to capture volatility clustering and to manage issues of heteroskedasticity.

figure 1

The rational aggregate sentiment index

Stock market volatility can be estimated in two ways: with the help of market-determined option prices or by time series modelling. Non-availability of option prices led us to choose the time-series method. There are multiple indices available to measure the dynamics of the Indian capital market. Among them, Nifty, which consists of 50 companies from different sectors, and Sensex, which covers 30 companies, are prominent. Inclusion of diversified sectors and wider market coverage (market capitalisation) motivated us to select Nifty as the indicator for measuring market volatility. The indicator of market returns and the aggregate sentiment index showed volatility clustering (Fig.  2 ), and the heteroskedastic behaviour was confirmed through the ARCH-LM test. This satisfied the prerequisites for estimating the GARCH model.

figure 2

Investors’ sentiment and stock index return

The GARCH ( p, q ) model, introduced by Engle ( 1982 ) and Bollerslev ( 1986 ), can be expressed as follows:

where r t is the log Nifty return (the positive value of r t indicates a bullish trend in the market, and the negative value shows a bearish trend in the market). It is calculated by.

\( {r}_t=\frac{P_1-{P}_0}{P_0} \) , where P 0 and P 1 represent the price at time t-1 and t. γ is the coefficient of the lagged value of the Nifty return ( r t  − 1 ).  c 0 is the constant of the mean equation; ω is the constant in the variance equations; and  ε t is the error term.  I t  − 1 represents the information available to the market participants. \( {\varepsilon}_{t-1}^2 \) is the ARCH term and \( {\sigma}_{t-1}^2 \) is the GARCH term that explains the instantaneous variance at time t − 1. α +β > 1β ≥ 0, which shows the persistence of volatility. A value close to 1 indicates the persistence of volatility and indicates a low level of mean reversion in the system. By increasing the number of the ARCH and GARCH terms, the model can be generalized to a GARCH (p,q) model. For a well-specified GARCH model, ω > 0, α > 0 and  β  ≥ 0 should be satisfied.

We modified the basic GARCH model by incorporating a sentiment variable in the equation,

where δ represents the coefficient of the sentiment index.

Empirical results

The estimated result of the GARCH (1,1) model is presented in Table  3 . The coefficients of the ARCH (α) and GARCH terms (β) are statistically significant and different from zero. In addition, the sum of α + β is close to unity. This indicates the high persistence of volatility, that is, the mean reversal process is very slow because of the persistent shocks. The result of the ARCH-LM test indicates the absence of further ARCH effects, which means the model captures the ARCH effects. The statistically significant coefficient of Q and Q 2 at the 20th lag indicates the absence of further autocorrelation in the model.

Sentiment is a crucial element that directly influences market behaviour. The conventional capital asset pricing model theory states that investors should be rewarded according to their risk-taking behaviour. However, the impact of sentiment on market volatility may cause market uncertainty and lead to less returns. If the market participants fail to earn a market risk premium for their expected volatility, they will move away from the market, which further causes volatility in the market. This vicious circle may cause a bearish trend and languid growth and development of the market. The conditional volatility graph shows that the impact of negative sentiment is higher than that of positive sentiment. This indicates that when sentiments are positive, investors actively participate in the market with the expectation of higher returns. However, this causes more speculative activities in the markets and may cause overvaluation of scrips. In contrast, during the dominance of negative sentiments, investors move away from the market because of the negative expectation of market returns. Therefore, it can be theorised that during positive sentiment, companies explore the opportunity to enter the market through IPOs. Similarly, dividend declaration, bonus issue and a rights issue also trigger positive sentiments.

Conditional volatility

The conditional variance graph from the GARCH (1,1) model shows the dynamics of market volatility of the Nifty returns (Fig.  3 ). Up to May 2008, volatility was high, though it can be deemed as moderate when compared to that during the subprime crisis period. During this period, volatility increased exponentially, and this trend continued up to February 2010. Later, the volatility reduced substantially.

figure 3

  • Granger causality test

The Granger causality test examines the direction of cause among different series (Granger 1969 ). A time series x t Granger-causes another time series y t if series y t can be predicted with better accuracy by using the past values of x t rather than by not doing so. This study examined the causal relationship between the sentiment index (S ent ) and stock market return. Tests between the aggregate sentiment index and stock returns were modelled for understanding the leading and the lagging variables. We found that investors’ sentiment leads to volatility of the market returns. However, volatility in the returns does not cause sentiment (Table  4 ).

De Long et al. ( 1990 ) pointed out that noise traders’ pressure in a market with a strong bullish sentiment on the price to move beyond the fundamental value causes a drop in the expected return. However, if bullish noise traders dominate the market, it causes a rapid upward movement in the market prices because of the upsurge in demand for the high-risk scrips. The expected level of market risk will be higher, creating a ‘hold more’ effect because of the expectation of higher returns. The intensity of sentiments on stock returns closely depends on the effect that dominates the market expectation. The unidirectional causality of sentiments to volatility indicates that the price-pressure effect (noise traders’ pressure on prices reduces the expected return) dominates the market and that noise traders benefit during episodes of a high sentiment index. This way, sentiment leads to volatility. However, once the noise traders start making profit, their expectation on return and risk will increase. Thus, it may not create a reverse causality in a developing market because of information inefficiency. In another way, it can be explained that when investors’ irrational sentiment is positive, their expectation on return is also positive. This may lead to speculative activities on their part to exploit the situation, exciting them to invest more. This leads to volatility in the market. On the other hand, market uncertainty causes withdrawal of market makers and encourages investors to stay inactive because of the uncertain expectation on the return in a risky market. Moreover, in such a situation, investors are always concerned about fundamentally induced equilibrium prices that give the fair value of assets. Following the arguments of Wen et al. ( 2019 ), retail investors should be more attentive in collecting information to minimise their information asymmetry for managing their potential risk.

This research provides a comprehensive examination of the impact of investor sentiment on stock market volatility. The study constructed a sentiment index by using a linear combination of different-market oriented proxies weighted using principal component analysis. The study found an asymmetrical relationship when the sentiment index was decomposed into positive and negative sentiment. The positive sentiment index has a positive effect on excess market return, but the intensity of negative sentiment is less on negative returns. These results imply that when investors are more optimistic about the market generating excess returns, their extreme optimism leads to more speculative activities that tempt them to invest even more. The study also found persistency of market volatility and the sentiment index, which shows the contemporaneous impact on sentiment and excess market returns. The findings reveal that investors consider the market as weak-efficient. This shows that the efficient market hypothesis may not be sufficient in explaining the market behaviour of emerging markets like India. The results indicate the scope for arbitration in the Indian market and thus invalidate the explanation of efficient market volatility in India. This further indicates a deviation from a random walk, but it is difficult to predict the volatility of the market sufficiently to produce excess returns.

The results help to understand the role of non-fundamental factors in driving the Indian equity market away from a fundamentally oriented equilibrium and in influencing the risk-return perception. They also show that sentiment is relatively correlated with unexpected stock returns, and the correlation differs significantly over time. This contradicts with the traditional capital market theories and supports the behavioural theories on capital markets. Proper examination of the market sentiment helps investors and fund managers decide their entry and exit points for investment. By taking the investor sentiment into account as a significant determinant of stock market volatility in asset price models, investors can enhance their portfolio performance. The results can also help policymakers’ efforts to stabilize stock market volatility and uncertainty in order to protect investors’ wealth and attract more investors. Therefore, future research should aim to develop investors’ sentiments from available high-frequency data by incorporating additional comprehensive investor sentiment factors to reflect real-time information.

Availability of data and materials

Not applicable.

Abbreviations

Indian Aggregate Sentiment Index

Gross domestic product

Price to earnings ratio

Generalized autoregressive conditional heteroskedastic model

Autoregressive conditional heteroscedasticity-Lagrange multiplier

Put-call ratio

Initial public offer

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Central University of Kerala, Kasaragod, India

Haritha P H

Central University of Himachal Pradesh, Dharamsala, Himachal Pradesh, India

Abdul Rishad

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The authors analyze the role of irrational investor sentiment in determining Indian stock market volatility using monthly information from India’s National Stock Exchange between June 2000 and December 2016. Sentiment index with the assistance of principal component analysis was developed using market-related implicit indices. Further, this sentiment index was modelled in the GARCH and Granger Causality framework to analyses its contribution to volatility. The results show that irrational sentiment significantly causes excess market volatility. Moreover, the study reveals that the asymmetrical aspects of an inefficient market contribute to excess volatility and returns. The findings reveal that investors consider the market as weak-efficient. This shows that the efficient market hypothesis may not be sufficient in explaining the market behaviour of emerging markets like India. The author(s) read and approved the final manuscript.

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P H, H., Rishad, A. An empirical examination of investor sentiment and stock market volatility: evidence from India. Financ Innov 6 , 34 (2020). https://doi.org/10.1186/s40854-020-00198-x

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  • Investor sentiment
  • Stock market volatility

investor behaviour in stock market research paper

Investor Behavior in the October 1987 Stock Market Crash: Survey Evidence

Questionnaires were sent out at the time of the October 19, 1987 stock market crash to both individual and institutional investors inquiring about their behavior during the crash. Nearly 1000 responses were received. The survey results show that: 1. no news story or rumor appearing on the 19th or over the preceding weekend was responsible for investor behavior, 2. investors' importance rating of news appearing over the preceding week showed only a slight relation to decisions to buy or sell, 3. there was a great deal of investor talk and anxiety around October 19, much more than suggested by the volume of trade, 4. Many investors thought that they could predict the market, 5. Both buyers and sellers generally thought before the crash that the market was overvalued, 6. Most investors interpreted the crash as due to the psychology of other investors, 7. Many investors were influenced by technical analysis considerations, 8. Portfolio insurance is only a small part of predetermined stop-loss behavior, and 9. Some investors changed their investment strategy before the crash.

  • Acknowledgements and Disclosures

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Robert Shiller, Market Volatility, MIT Press, 1989

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