Behavioral Asset Pricing Research Articles

Lottery factor and stock returns: Evidence from India

This study examines important aspects of lottery behavior in India using data from December 2001 to March 2021. We experiment with a new measure of lottery along with well-established measures. Our lottery measure focusing on more recent information is found to be appropriate for India. MAX has a unique role in predicting returns that is not subsumed by other risk measures. We find MAX, skewness, tail risk, and idiosyncratic volatility as relevant characteristics of lottery stocks. Using these, we construct a lottery factor representing investors’ risk-seeking behavior. This behavior is an outcome of mis-weighing probability of extreme gains or losses and leads to overreaction to attention-catching events. We augment the Fama-French five-factor model with our lottery factor. Our six-factor behavioral asset pricing framework is found to be an appropriate performance benchmark. Lottery behavior is mainly the result of retail investor actions and is linked to several behavioral biases.

Behavioral asset pricing under expected feedback mode

We develop an enhanced DSSW model of behavior asset pricing by introducing the expected feedback mode. Through numerical simulation, it has been theoretically proved that risky asset price is jointly determined by trend extrapolated effect of expected feedback traders and the creating space effect of noise traders, and that price fluctuation depends on the expected feedback coefficient. As expected feedback traders' expectation return has been realized and the above two effects are obviously imbalance, their confidence will deteriorate prior to price collapse, and eventually achieve self-fulfilling expectation of price reversal in the process of price momentum. This model sheds lights on financial anomalies of trade size clustering and the formation of stock bubble, besides it provides a new perspective for avoiding the “bewitching” trading and reducing market volatility.

Explaining abnormal returns in stock markets: An alpha-neutral version of the CAPM

This paper develops a behavioural asset pricing model in which traders are not fully rational as is commonly assumed in the literature. The model derived is underpinned by the notion that agents’ preferences are affected by their degree of optimism or pessimism regarding future market states. It is characterized by a representation consistent with the Capital Asset Pricing Model, augmented by a behavioural bias that yields a simple and intuitive economic explanation of the abnormal returns typically left unexplained by benchmark models. The results we provide show how the factor introduced is able to absorb the “abnormal” returns that are not captured by the traditional CAPM, thereby reducing the pricing errors in the asset pricing model to statistical insignificance.

Bayesian Estimation of Agent-Based Models via Adaptive Particle Markov Chain Monte Carlo

Over the last decade, agent-based models in economics have reached a state of maturity that brought the tasks of statistical inference and goodness-of-fit of such models on the agenda of the research community. While most available papers have pursued a frequentist approach adopting either likelihood-based algorithms or simulated moment estimators, here we explore Bayesian estimation using a Markov chain Monte Carlo approach (MCMC). One major problem in the design of MCMC estimators is finding a parametrization that leads to a reasonable acceptance probability for new draws from the proposal density. With agent-based models the appropriate choice of the proposal density and its parameters becomes even more complex since such models often require a numerical approximation of the likelihood. This brings in additional factors affecting the acceptance rate as it will also depend on the approximation error of the likelihood. In this paper, we take advantage of a number of recent innovations in MCMC: We combine Particle Filter Markov Chain Monte Carlo as proposed by Andrieu et al. (J R Stat Soc B 72(Part 3):269–342, 2010) with adaptive choice of the proposal distribution and delayed rejection in order to identify an appropriate design of the MCMC estimator. We illustrate the methodology using two well-known behavioral asset pricing models.

Modeling Asset Pricing Using Behavioral Variables: Fama-Macbeth Approach

Investors generally make decisions based on risk and stock returns, and their decisions are influenced by two factors, namely macroeconomic variables and microeconomic variables. The behavioral factors affecting investment decisions are investigated in the area of behavioral finance. In other words, behavioral finance focuses on specific human behavior attributes and their utilization in asset pricing. Behavioral asset pricing is the result of applying behavioral finance theories within traditional asset pricing theories. Although there are many asset-pricing models, due to their weaknesses and incompleteness as well as the necessity of investigating behavioral factors, this study attempted to model asset pricing using behavioral models.The population of the study included all listed firms in Tehran Stock Exchange over the years 2008 to 2018, and the sample was selected through systematic elimination of the population. Given these conditions, 141 firms were selected as the sample. The hypotheses were then tested by designing multivariate regression models.The results showed that using Fama-Macbeth approach, accounting information risk, investors’ trading behavior, and investors' sentiments had a significant and direct impact on firms’ stock returns.Thus, it is argued that behavioral variables can play a significant role in Modeling Asset Pricing.

Underreaction to Information Reliability: Evidence from Sports Betting

Some behavioral asset pricing theories assume that investors pay less than optimal attention to implicit aspects of available information. We use a sports betting market as a laboratory to test this conjecture, analyzing the beliefs of professional bookmakers for outcomes of tennis matches, exploiting exogenous variation in an implicit yet material aspect of information, related to the length that different matches are played according to tournament regulations. We find that bookmakers’ beliefs do not fully reflect the effect of match length, which significantly reduces their profits. Our results provide causal evidence that limited attention leads to systematic biases in beliefs.

Does Social Media Sentiment Matter in the Pricing of U.S. Stocks?

This paper applies a recently developed social media-based sentiment proxy for the construction of a new risk factor for sentiment-augmented asset pricing models on U.S. equities. Accounting for endogeneity, autocorrelation and heteroskedasticity in a GMM framework, we find that the inclusion of sentiment significantly improves the performance of the five-factor model from Fama and French (2015, 2017) for di ff erent industry and style portfolios like size, value, profitability, investment. The sentiment risk premium provides the missing component in the behavioral asset pricing theory of Shefrin and Belotti (2008) and (partially) resolves the pricing puzzles of small extreme growth, small extreme investment stocks and small stocks that invest heavily despite low profitability.

International Factor Models

We evaluate the relative and absolute performance of eight competing neo-classical or behavioral asset pricing models in four regions (North America, Europe, Asia-Pacific and Japan). Our holistic analysis incorporates both right-hand-side tests (based on maximum squared Sharpe ratios as suggested in Barillas et al., 2020 and Fama and French, 2018) and lefthand-side tests (240 individual anomalies, two composite mispricing proxies). While recently proposed models as well as the behavioral approaches generally tend to perform better than classical models, there is no dominating approach. This finding and further results point to the need for new powerful global asset pricing models.

Time-Varying Risk Attitude and Behavioral Asset Pricing

Time-Varying Risk Attitude and Behavioral Asset Pricing

BEHAVIOURAL ASSET PRICING: A REVIEW

Neoclassical asset pricing is built on the premise investors are rational and there are unlimited arbitrage opportunities. Behavioural implications of irrational investors led to the development of the counter paradigm, behavioural asset pricing. This study systematically reviews the origin and evolution of behavioural asset pricing distinct to neoclassical asset pricing. It addresses the two pillars of behavioural asset pricing where; investors are not always rational and there are limits to arbitrage. The study captures investor irrationality in two perspectives; investors' beliefs and their preferences. It reviews psychological biases and heuristics adopted from experimental psychology to behavioural asset pricing in explaining beliefs and preferences of irrational investors. Furthermore, it lists key biases and heuristics recognised in behavioural asset pricing literature. It discusses theoretical behavioural asset pricing models that try to explain variation of stock returns through specific biases of investor psychology. Lastly, the study reviews aggregate investor sentiment studies that try to capture mass psychology of investors in financial markets. The significance of this study is that it attempts to develop a holistic view of the foundation and evolution of behavioural asset pricing.

Behavioral Finance: The Second Generation

Finance Behavioral finance presented in this book is the second-generation of behavioral finance. The first generation, starting in the early 1980s, largely accepted standard finance’s notion of people’s wants as “rational” wants—restricted to the utilitarian benefits of high returns and low risk. That first generation commonly described people as “irrational”—succumbing to cognitive and emotional errors and misled on their way to their rational wants. The second generation describes people as normal. It begins by acknowledging the full range of people’s normal wants and their benefits—utilitarian, expressive, and emotional—distinguishes normal wants from errors, and offers guidance on using shortcuts and avoiding errors on the way to satisfying normal wants. People’s normal wants include financial security, nurturing children and families, gaining high social status, and staying true to values. People’s normal wants, even more than their cognitive and emotional shortcuts and errors, underlie answers to important questions of finance, including saving and spending, portfolio construction, asset pricing, and market efficiency. Second-generation behavioral finance offers an alternative foundation block for each of the five foundation blocks of standard finance, incorporating knowledge about people’s wants and their cognitive and emotional shortcuts and errors. According to second-generation behavioral finance: 1. People are normal. 2. People construct portfolios as described by behavioral portfolio theory, where people’s portfolio wants extend beyond high expected returns and low risk, such as wants for social responsibility and social status. 3. People save and spend as described by behavioral life-cycle theory, where impediments, such as weak self-control, make it difficult to save and spend in the right way. 4. Expected returns of investments are accounted for by behavioral asset pricing theory, where differences in expected returns are determined by more than just differences in risk—for example, by levels of social responsibility and social status. 5. Markets are not efficient in the sense that price always equals value in them, but they are efficient in the sense that they are hard to beat.

Structural estimation of behavioral heterogeneity

SummaryWe develop a behavioral asset pricing model in which agents trade in a market with information friction. Profit‐maximizing agents switch between trading strategies in response to dynamic market conditions. Owing to noisy private information about the fundamental value, the agents form different evaluations about heterogeneous strategies. We exploit a thin set—a small sub‐population—to point identify this nonlinear model, and estimate the structural parameters using extended method of moments. Based on the estimated parameters, the model produces return time series that emulate the moments of the real data. These results are robust across different sample periods and estimation methods.

Environmental, Social and Governance (ESG) and Investment Decision in Bangladesh

As a key facet of sustainable development, environmental, social and governance (ESG) discretion on stock market investment decision is gaining prevalence following the global financial crisis. ESG considers the sustainable return, risk reduction, and accountability aspects of investments. This study is an exploration of the individual stock market investors’ preferences for ESG issues and the influence that purpose of investment has on investment decision-making, by testing the investment horizon as a moderator. The theoretical background was taken from the theory of planned behavior (TPB), goal setting theory (GST), and the behavioral asset pricing model (BAPM). The study uses the sequential mix method of research, starting with an interview followed by a survey, which was conducted among individual stock market investors in Bangladesh, using simple random sampling. Structural equation modeling (SEM) analysis was carried out using Warp PLS version 6.0. The key findings of this study delineate the effect of ESG issues and the purpose of investment on investment decision-making. The contribution of the study signifies the moderating role of the investment horizon, which confirms the importance of the long-term horizon as a time and risk diversification factor. The sparse utilization of the United Nations Global Compact (UNGC) (2004) and Thomson Reuters Corporate Responsibility Index (TRCRI) (2013) as measurement scales in this study is mentioned. This study has made practical contributions for managers, investors, and regulators.

Explaining Abnormal Returns in Stock Markets: An Alpha-Neutral Version of the CAPM

This paper develops a behavioural asset pricing model in which traders are not fully rational as is commonly assumed in the literature. The model derived is underpinned by the notion that agents’ preferences are affected by their degree of optimism or pessimism regarding future market states. It is characterized by a representation consistent with the Capital Asset Pricing Model, augmented by a behavioural bias that yields a simple and intuitive economic explanation of the abnormal returns typically left unexplained by benchmark models. The results we provide show how the factor introduced is able to absorb the “abnormal” returns that are not captured by the traditional CAPM, thereby reducing the pricing errors in the asset pricing model to statistical insignificance.

Economic policy uncertainty in China and stock market expected returns

AbstractWe investigate the impact of China's economic policy uncertainty (EPU) on the time series variation of Chinese stock market expected returns. Using the news‐based measure of EPU, we find that EPU predicts negatively future stock market return at various horizons. This negative relation between economic policy uncertainty and expected future return remains significant as we control for a number of economic and market uncertainty variables or conduct out‐of‐sample tests. Our findings are consistent with behavioural asset pricing models, in which high uncertainty amplifies behavioural biases and generates speculative mis‐pricing under short‐sales constraint.

All’s well that ends well? On the importance of how returns are achieved

We demonstrate that investor satisfaction and investment behavior are influenced substantially by the price path by which the final investor return is achieved. In a series of experiments, we analyze various different price paths. Investors are most satisfied if their assets first fall in value and then recover, and they are least satisfied with the opposite pattern, independent of whether the final return is positive or negative. Price paths systematically influence risk preferences, return beliefs, and ultimately trading decisions. Our results enable a much more holistic perspective on a wide range of topics in finance, such as the disposition effect, risk-taking behavior after previous gains and losses, and behavioral asset pricing.

Behavioural Asset Pricing: Review and Synthesis

This article presents an overview of literature on behavioural and experimental asset pricing theory. We systematically review the evolution and current development of behavioural asset pricing models as an alternate approach to asset pricing in financial economics literature. A review and synthesis of research carried out in behavioural finance spreading across theoretical, empirical and experimental approaches are presented to understand the behavioural dimension of pricing of financial assets. From theoretical perspective, behavioural asset pricing models try to adopt additional behavioural variables into asset pricing process. In terms of empirical investigation perspective, it is documented that econometric and computational advancement takes its biggest place ever in financial literature when compared with the other field. Our review underlines the fact that the direction of advancing a methodology is changing from financial literature to economics due to the fact that there is huge account of raw data available to analyze. Future research direction should be judging the empirical power of the asset pricing models and their role in practice for incorporating a new dimension to the model. The distinctiveness of the study is that this is the first attempt to review literature written on behavioural asset pricing models in the form of structural empirical review. In doing so, the historical perspective of the concept and the place it will take in future are clarified and the way further researches will be conducted are explored. JEL: E03, G02, G12

Individual stock crowded trades, individual stock investor sentiment and excess returns

Recent behavioral asset pricing models and the popular press suggest that investors may follow similar strategies resulting in crowded equity positions to push prices further away from fundamentals. This paper develops a new approach to measure individual stock crowded trades, and further investigates the joint effects of individual stock crowded trades and individual stock investor sentiment on excess returns. Specifically, our results show that the combined effect of individual stock crowded trades and individual stock investor sentiment on excess returns is positive and significant, which reveals the importance of “anomaly factors” in asset pricing. Furthermore, our results suggest that increasing individual stock buyer-initiated crowded trades will increase excess returns simultaneously; however, increasing individual stock seller-initiated crowded trades will decrease excess returns simultaneously. Collectively, our results highlight the importance of individual stock crowded trades and individual stock investor sentiment on the formation of stock prices.

An application of the information-adjusted noise model to the Shenzhen stock market

Purpose – The purpose of this paper is to: first, test if information-adjusted noise model (IANM) can be applied in China; second, quantify noise trader risk, overreaction, underreaction and information pricing errors in that market; and third, explain the relationship between noise trader risk and return. Design/methodology/approach – The authors use a behavioural asset pricing model (BAPM), CAPM, the information-adjusted noise model and model proposed by Ramiah and Davidson (2010). Findings – The findings show that noise traders are active 99.7 per cent of the time on the Shenzhen A-share market. Furthermore, our results suggest that the Shenzhen market overreacts 41 per cent of the time, underreacts 18 per cent of the time and information pricing errors occur 40 per cent of the time. Originality/value – Various methods have been applied to the Chinese stock market in an effort to measure noise trading activities and all of them failed to account for information arrival. Our study uses a superior and alternative model to detect noise trader risk, overreaction and underreaction in China.

Yield curve responses to market sentiments and monetary policy

Central banks recently started to target longer term interest rates. The empirical failure of the rational expectations theory of the yield curve, however, limits its applicability to monetary policy analysis. The success of agent-based behavioral asset pricing models and behavioral macroeconomic models in replicating statistical regularities of empirical data series motivates to apply them to yield curve modeling. This paper analyses how the interaction of monetary policy and market sentiments shape the yield curve in a behavioral model with heterogeneous and bounded-rational agents. One result is that the behavioral model replicates empirical facts of term structure data. Moreover, it overcomes one major deficiency of rational expectations models of the yield curve in explaining the empirically observed uncertain responses of longer term yields to changes in the central bank rate. These are explained by the behavioral model’s ability to generate different responses of market sentiments to shocks at different times which lead to a variety of interest rate responses. Further results of this paper can be used as policy advice on how central banks can target the level, slope and curvature of the yield curve by targeting market sentiments about inflation and the business cycle.