Investors’ Biases & Stock Return Volatility: A Systematic Literature Review

Purpose: Literature is scarce on the possible relationship between investors’ biases, risk tolerance attitude, and stock return volatility. The researcher investigated what are the investor biases, and how they contribute to the risk-averse and risk-seeking attitudes and developed a taxonomy model of investors’ biases in the form of a causal framework that impacts stock return volatility. Methodology: The study employs a systematic literature review approach. The analysis of literature includes 65 articles from impact factor journals including three seminal papers in the fields of traditional and behavioral finance. The time frame ranges from 2008 to 2022. Findings: The findings suggest that investors encounter certain biases such as cognitive, emotional, cultural, religious, financial, macroeconomic, demographic, etc. Literature has identified positive, negative, and mixed impacts of investors' biases on stock return volatility. The systematic analysis of literature helps in identifying recently evolving biases such as individualism, uncertainty avoidance, a religious adherence, investor mood, weather bias, fear sentiments, sports sentiments, power distance, masculinity, social media sentiments so on and so forth. Conclusion: The study has proposed an integrated taxonomy model comprised of possible investors’ biases as independent variables along with mediating, controlling, and moderating variables that impact stock return volatility. Moreover, investors’ risk tolerance profile is also constructed which indicates the role of behavioral biases in shaping investors’ attitudes as risk seekers and risk-averse

CEOs’ Managerial Ability and Stock Market Crash Risk: Empirical Evidences from the Business Environment in Saudi Arabia

The main objective of the study is to examine the impact of CEOs’ managerial ability on stock market crash risk faced by non-financial firms listed in Saudi stock exchange. The study is based on an analytic approach that is an analysis of annual reports from Saudi companies. A sample of 112 non-financial companies listed in Saudi stock exchange from 2018 to 2020 with total 336 views is examined. This sample is examined for the purpose of evaluating the impact of the managerial ability on crash risk of the stock market. In addition, multiple regression method is applied to test the hypotheses of the study. The findings of this paper show that CEOs’ managerial ability is associated with a negative correlation, and also reveals stock market crash risk. Based on these findings, the study recommends that companies necessarily take into consideration CEOs’ managerial ability (such as accounting background, expertise, office hours, personal skills, reputation, and communication skills) because of their positive effects on the company’s economy. The study also recommends rules and regulations under which CEO should work, his/her overconfidence is reduced, employees are prevented from withholding bad news, and transparency is ensured. All of this reduces stock market crash risk. The findings of the study must be handled according to the sample size and methods of measuring the variables used. The study focused on the analysis of the impact of CEOs’ managerial ability on stock market crash risk in non-financial companies listed in Saudi stock exchange. The findings of this study may be a matter of concern to boards of directors. The findings help them make the decisions to recruit CEOs, and stakeholders when they evaluate the role of managerial skills in reducing stock market crash risk. The study explains the role of the managerial ability in reducing crash risk of the stock market in the Saudi business environment as an example of the economy of developing countries

Extrapolative bubbles and trading volume

We propose an extrapolative model of bubbles to explain the sharp rise in prices and volume observed in historical financial bubbles. The model generates a novel mechanism for volume: because of the interaction between extrapolative beliefs and disposition effects, investors are quick to not only buy assets with positive past returns but also sell them if good returns continue. Using account-level transaction data on the 2014–2015 Chinese stock market bubble, we test and confirm the model’s predictions about trading volume. We quantify the magnitude of the proposed mechanism and show that it can increase trading volume by another 30%

Revisiting Overconfidence In Investment Decision-Making: Further Evidence From The U.S. Market

Investor overconfidence leads to excessive trading due to positive returns, causing inefficiencies in stock markets. Using a novel methodology, we build on the previous literature by investigating the existence of overconfidence by studying the causal relationship between return and trading volume covering the COVID-19 period. We implement a nonlinear approach to Granger causality based on multilayer feedforward neural networks on daily returns and trading volumes from 2016 to 2021, covering 1424 daily observations of the S&P 500 index. The results provide evidence of overconfidence among investors. Such behavior may be linked to the increase in the number of investors. However, there is a decline in the rate of returns during the study period, implying uncertainty caused by the COVID-19 pandemic

Revisiting Overconfidence In Investment Decision-Making: Further Evidence From The U.S. Market

nvestor overconfidence leads to excessive trading due to positive returns, causing inefficiencies in stock markets. Using a novel methodology, we build on the previous literature by investigating the existence of overconfidence by studying the causal relationship between return and trading volume covering the COVID-19 period. We implement a nonlinear approach to Granger causality based on multilayer feedforward neural networks on daily returns and trading volumes from 2016 to 2021, covering 1424 daily observations of the S&P 500 index. The results provide evidence of overconfidence among investors. Such behavior may be linked to the increase in the number of investors. However, there is a decline in the rate of returns during the study period, implying uncertainty caused by the COVID-19 pandemic

Impact of the COVID-19 pandemic on the relationship between uncertainty factors, investor behavioral biases and the stock market reaction of US Fintech companies

Object: This article investigates the impact of the COVID-19 pandemic on the relationship between uncertainty factors (Equity Market Volatility–  Infectious Diseases, Economic Policy Uncertainty and Financial Stress) and investor behavioral biases (Herding Behavior, Loss Aversion, Mental  Accounting and Overconfidence) with the US Fintech stock market abnormal returns.Methodology: we analyze this relationship by using Johansen cointegration test, Granger causality test and the Ordinary least square method for  the period from July 20, 2016 to December 31, 2021.Results: The Empirical results indicated the presence of a long-run equilibrium relationship between all the studied variables, before and during the  COVID-19 pandemic period. In fact, the obtained results indicated that the COVID-19 pandemic is a crucial source for resulting abnormal  returns in the US Fintech market. Especially, during the COVID-19 pandemic, the Fintech market under-reacted to the common signal of financial stress. Moreover, behavioral biases, especially, overconfidence and herding, have a power positive effect on the abnormal reaction of US Fintech stock market, comparatively to the pre COVID-19 period.Originality: This study is one of the few studies which have compared the effect of uncertainty factors and the investor’s behavioral biases on the  US Fintech stock market reaction before and during the COVID-19 pandemic.   French title: L’impact de la pandémie de COVID-19 sur la relation entre les facteurs d'incertitude, les biais comportementaux des investisseurs et la réaction oursière des Fintech américaines Objectif : Le but de l’étude est d’identifier l’impact de la pandémie COVID-19 sur la relation entre les facteurs d’incertitudes (volatilité des marchés  boursiers -maladies infectieuses, incertitude de la politique économique et le stress financier) et les biais comportementaux des investisseurs (lecomportement grégaire, l’aversion aux pertes, la comptabilité mentale et l’excès de confiance) avec les rendements anormaux du marché Américain de la Fintech.Méthode : Pour parvenir à cet objectif, cet article fait recours au test de cointégration de Johensen, test de causalité de Granger et méthode des moindres carrés ordinaires pour la période allant du 16 Juillet 2016 au 31 décembre 2021.Résultats : Les résultats obtenus démontrent qu’il existe une relation à long terme entre les variables étudiées avant et durant la période de la  pandémie COVID-19. En fait, ces résultats indiquent que cette pandémie est une source cruciale pour résulter des rendements anormaux dans le marché boursier américain de la Fintech. En particulier, pendant l’épidémie de COVID-19, le marché Fintech a sous-réagi au signal commun de stress financier. De plus, les biais comportementaux, en particulier l'excès de confiance et le comportement grégaire, ont un effet positif sur la réaction  anormale du marché boursier américain de la Fintech, comparativement à la période avant COVID-19.Originalité/ Pertinence: Cette étude est l'une des rares études qui ont comparé l’effet des biais comportementaux et des facteurs d'incertitude sur  la réaction du marché américain de la Fintech avant et pendant la pandémie COVID-19. &nbsp

Herding evidence in Chinese stock market : a study of the relationship between stock price index and trading volume based on behavioral finance theory

Over the last couple decades, more evidence has been found supporting the notion that investors are not always rational. Herding behaviors have been observed in both the stock market crash1 and financial bubbles2, which were beyond the understanding of modern finance theory. In this paper, the herding phenomenon was explored in the Chinese stock market by the study of the relationship between stock prices and trading volume over the past 7 years. It was found that the change of price is statistically significant to have caused the change of trading volume, but the reverse is not true. Theoretically, this identifies persistent herding phenomenon in the Chinese stock market. The findings provide useful investment guidance for investors and new considerations in financial reform for the government

Investors’ Biases & Stock Return Volatility: A Systematic Literature Review

Purpose: Literature is scarce on the possible relationship between investors’ biases, risk tolerance attitude, and stock return volatility. The researcher investigated what are the investor biases, and how they contribute to the risk-averse and risk-seeking attitudes and developed a taxonomy model of investors’ biases in the form of a causal framework that impacts stock return volatility. Methodology: The study employs a systematic literature review approach. The analysis of literature includes 65 articles from impact factor journals including three seminal papers in the fields of traditional and behavioral finance. The time frame ranges from 2008 to 2022. Findings: The findings suggest that investors encounter certain biases such as cognitive, emotional, cultural, religious, financial, macroeconomic, demographic, etc. Literature has identified positive, negative, and mixed impacts of investors' biases on stock return volatility. The systematic analysis of literature helps in identifying recently evolving biases such as individualism, uncertainty avoidance, a religious adherence, investor mood, weather bias, fear sentiments, sports sentiments, power distance, masculinity, social media sentiments so on and so forth. Conclusion: The study has proposed an integrated taxonomy model comprised of possible investors’ biases as independent variables along with mediating, controlling, and moderating variables that impact stock return volatility. Moreover, investors’ risk tolerance profile is also constructed which indicates the role of behavioral biases in shaping investors’ attitudes as risk seekers and risk-averse

A Political Theory of the Chinese Stock Market

The Chinese stock market crash of 2015 attracted much attention from both the media and academia. Yet it was not a unique incident. The Chinese stock market fluctuates more frequently and drastically than most mature stock markets. The purpose of this work is to explain these unusual stock market fluctuations through a political lens. Traditional financial models and behavioral finance cannot sufficiently explain the unusual fluctuations of the Chinese stock market. Traditional financial models find that economic forces cannot explain all fluctuations in China’s stock market. Behavioral finance attributes the fluctuations to investor’s irrational behavior without explaining why investors behave more irrationally than other investors. Other explanations, like financial knowledge and the immature market arguments cannot sufficiently explain the fluctuations of Chinese financial markets. A common characteristic of previous literature is a lack of real political explanations. This work develops a political explanation of the Chinese stock market, with an emphasis on biased financial institutions. Biased financial institution are the result of state-owned enterprises’ interest and political influence, and cause behavioral changes in investors and the market environment. Drastic market fluctuations serve as a channel for market forces to input their interests into political system. In reaction to these unusual market fluctuations, the Chinese government adjusts institutions to make concessions to private capital and to stabilize the market. Market fluctuations are the key force behind the Chinese government’s institutional development. Three case studies will illustrate this theory: non-tradable share reform, circuit-breaker institution, and the international board. These cases demonstrate how Chinese institutional design conforms to the interest of state-owned enterprises, introduces bias, and shows how the government uses the reform process to make concessions