Behavioral Finance and Asset Prices The Influence of Investors Emotions.epub
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There are nine chapters in all, organized into two parts. The first part, called Asset Pricing, has three chapters. Chapter 1, “Oil Price Uncertainty: Panel Evidence from the G7 and BRICS Countries,” is coauthored by Apostolos Serletis (University of Calgary, Canada) and Libo Xu (Lakehead University, Canada). In this chapter, the authors analyze oil price dynamics for a large sample of countries including the G7 and the BRICS, underscoring the high level of oil price uncertainty and oil price shocks. They find that oil price fluctuations as well as oil price uncertainty have a statistically negative effect on the overall factor productivity growth of the countries under consideration. Interestingly, the impact of oil price shocks is felt asymmetrically and nonlinearly.  Investigations on the impact of oil price shocks on the real economy have pitted several authors against one another, including Hamilton (1983, 1996) and Kilian (2009). Indeed, while Hamilton’s work concluded that oil price shocks may provoke and explain an economic recession, Kilian (2009) contested this conclusion. The study by Apostolos Serletis and Libo Xu reframes the research question, examining oil price uncertainty with reference to the real options theory that is underpinned by investment under uncertainty. Following a panel data investigation, the authors show the negative impact of oil price shocks on economic activity, which is in line with Hamilton’s work. The authors also suggest that oil price uncertainty and unexpected economic events are factors that attract the attention of both policymakers and market participants. Indeed, since oil is a key input for the real economy as a whole, oil volatility is likely to induce scrutiny and even uncertainty with respect to different consumption and investment decisions.
Chapter 2, entitled “Climate Risk and the Volatility of Agricultural Commodity Price Fluctuations: a Forecasting Experiment,” is coauthored by Rangan Gupta (University of Pretoria, South Africa) and Christian Pierdzioch (Helmut Schmidt University, Germany). The contribution also analyzes commodity asset prices and their volatility, with the authors highlighting the extreme volatility that characterizes agricultural commodity prices. This volatility has been central to a range of investor decisions, increasing the financialization of agricultural commodities since 2008. Further, the authors assess whether taking climate risk factors into consideration could help to improve the modeling and forecasting of agricultural price volatility or not, using high-frequency data and recent findings from the heterogeneous autoregressive realized volatility (HAR-RV) model. They conclude that the inclusion of climate risk factors improves agricultural commodity volatility forecasts. The authors explin the effects of climate risk factors on agricultural commodity prices through their impact on financial stress and market jitters. Further, investors and householders obviously keep a close eye on the evolution of agricultural commodity prices as this class of commodities represents a major component of household consumption and is a significant factor in their volatility, which could have considerable implications for food security.
Chapter 3, entitled “Linking the COVID-19 Epidemic and Emerging Market OAS: Evidence Using Dynamic Copulas and Pareti Distributions,” is coauthored by Imade Chitou (Aix-Marseille University, France), Gilles Dufrenot (Sciences Po Aix and Institut Louis Bachelier, France), and Julien Esposito (Aix-Marseille University, France). The authors examine asset pricing, with a particular focus on the dependence of the option-adjusted spread (OAS) for several emerging markets on the COVID-19 outbreaks over the period March 2020–April 2021. To this end, the authors apply different classes of copulas, showing a significant correlation between spreads and epidemiological variables (i.e., number of new cases, reproduction rate, and death rate). They analyze this result with reference to investor sentiment, viewing the recent pandemic crisis to be a key factor in the behavioral biases that lead to uncertainty. This uncertainty is a key driver in emotional experiences, characterized by panic selling or regret over decisions made, leading to behavior that is inconsistent with the EMH and investor rationality. Overall, the chapter puts forward a novel framework to highlight the influence of epidemiological variables on financial asset returns during COVID-19. The link is not explained by fundamental variables as would be assumed by the investor rationality hypothesis which indirectly reflects the impact of investor sentiment.
The second part of the volume is called Behavioral Financeand includes six chapters. Chapter 4, entitled “ is coauthored by Hamza Bahaji (Université Paris-Dauphine, France) and Jean-François Casta (Université Paris-Dauphine, France). The chapter is at the intersection between asset pricing and behavioral finance. In fact, the authors propose a behavioral analysis for employee stock options (ESO) using the cumulative prospect theory, the rank-dependent expected utility theory, etc. The authors also discuss the implications of these models for the valuation of ESO, the design of optimal ESO contracts, and the assessment of employee sentiment. Taking the complexity associated with ESO into account, especially the complexity related to the nature of their payoffs, which in turn depends on the behavior exercised by the employee, the authors show that these stock options are always long-dated nontransferable call options and are therefore held for a long time. In addition, holding such stock options can sometimes result in hedging restrictions and even less diversification. Accordingly, the exercise decisions of these stock options can be driven by different factors compared to unrestricted option holders: employee endowment, risk preferences, etc. Accordingly, the authors show that employee exercise behavior obeys both rational and psychological drivers. These rational factors are in line with the standard framework of the expected utility theory and include liquidity shocks, risk diversification, etc. Psychological factors correspond to a set of behavioral biases that affect investors’ beliefs and preferences, e.g., anchoring, overconfidence, miscalibration, and mental accounting. Thus, these psychological factors reflect Tversky and Kahneman’s (1992) cumulative prospect theory. Overall, the authors conclude that this behavioral framework is more than helpful to explain employees’ stock option patterns.
Chapter 5, entitled “The Term Structure of Psychological Discount Rate: Characteristics and Functional Forms,” is coauthored by Aboudou Ouattara (Centre Africain d’Etudes Supérieures en Gestion, Senegal) and Hubert de La Bruslerie (Université Paris Dauphine, France). The authors focus on the discounted utility theory and its usefulness in intertemporal asset pricing. In particular, they investigate the psychological discount function hypothesis that underlies intertemporal choices. Using data provided by experimentation, the authors show the limitations of classical functional forms for the well-known discounted utility model (i.e., exponential form). Instead, other psychological factors such as impatience appear to be useful in building an alternative framework to better understand individual time preferences. Accordingly, the authors point to the relevance of knowledge of the psychological discount function that underlies intertemporal choices, investigating the shape and parameters of psychological discount functions. They calibrate the answers to different functional forms and compare various forms for each agent, allowing for heterogeneous time discounting functions. They mainly show the violation of the standard discounted utility theory, rejecting the assumption of an exponential discount function, and concluding that the population under consideration is characterized by significant heterogeneous psychological discount functions.
Béatrice Boulu-Reshef (LEO - University of Orleans, France), Catherine Bruneau (CES - University of Paris 1 Panthéon-Sorbonne, France), Maxime Nicolas (CES - University of Paris 1 Panthéon-Sorbonne, France), and Thomas Renault (CES - University of Paris 1 Panthéon-Sorbonne, France) are the coauthors of Chapter 6entitled “An Experimental Analysis of Investor Sentiment.” The chapter proposes an analysis of investor sentiment based on an experiment with a sample of professional investors to investigate the impact of languages and emojis on investment decisions. To this end, the authors study the possible causal link between the linguistic corpus employed in work to assess investor sentiment and investor behavior or the decisions they make. They thus contribute to the related literature by investigating the impact of images and visualization on investor behavior. The authors find that while text has a significant statistical effect on investors’ decisions, its magnitude is too small (around 1%) to conclude that there is a significant economic impact. Further, emojis have no impact on investment decisions. Accordingly, the authors conclude that investment decisions appear to be driven more by fundamental information (expected return, related risk, etc.) than by investor sentiment when the decision’s payoff and probability are known. Their conclusion remains unchanged, even when gender, age, education, political preferences and belief effects, the credibility of the user sending the message (professional or individual), etc. are included. However, it is important to mention that these findings are dependent on the capacity of the social media information (i.e., emojis) to capture investor sentiment.
Analysis of investor sentiment is also at the center of Chapter 7, entitled “On the Evolutionary Stability of the Sentiment Investor,” coauthored by Andrea Antico (Institute of Economics and EMbeDS Department, Scuola Superiore Sant’Anna, Italy), Giulio Bottazzi (Institute of Economics and EMbeDS Department, Scuola Superiore Sant’Anna, Italy), and Daniele Giachini (Institute of Economics and EMbeDS Department, Scuola Superiore Sant’Anna, Italy). The chapter examines the role of investor sentiment in line with Barberis et al. (1998). However, while Barberis et al. (1998) modeled the behavior of both underreaction and overreaction to news under the assumption of a representative agent framework characterized by an imperfect learning model, the authors question whether the heuristic mechanism proposed by Barberis et al. (1998) is stable in evolutionary terms. Accordingly, they investigate this question and compare the performance of the agent described in Barberis et al. (1998) with an agent as a pure Bayesian competitor. In particular, the authors test whether the biased probability updating the process in Barberis et al. (1998) can survive when competing against a Bayesian. Overall, the authors conclude that investor sentiment supplants the Bayesian agent for some combinations of parameter values, while it is driven out of the market for others.
Chapter 8is entitled “Institutional Investor Field Research: Company Fundamentals Driven by Investor Attention” and is coauthored by Fateh Saci (Institut Paul Bocuse and University of Montpellier, France) and Boualem Aliouat (University Côte d’Azur, France). The chapter studies the relationship between the on-site research of institutional investors and information about the company’s fundamentals, with a focus on investor attention. In particular, the authors analyze the relationship between the three concepts of institutional investor field research, company fundamentals, and investor attention. The authors find that investor attention drives institutional investors to conduct research in addition to some specific corporate fundamentals.
Chapter 9, entitled “What drives the US stock market in the context of COVID-19, fundamentals or investors’ emotions?,” is coauthored by David Bourghelle (IAE Lille University School of Management, France), Pascal Grandin (IAE Lille University School of Management, France), Fredj Jawadi (IAE Lille University School of Management, France), and Philippe Rozin (IAE Lille University School of Management, France). The study investigates the excessive volatility characterizing the US stock market during the different waves of the COVID-19 pandemic, with the authors questioning whether this volatility might be explained by further changes in market fundamentals (dividend, profit, interest rate, etc.),changes in macroeconomic variables (unemployment rate, inflation rate, interest rate), or whether it is instead due to the impact of certain behavioral factors. To this end, the authors propose a sequential econometric model. They propose a nonlinear multifactorial model that reproduces the dynamics of the US market in which financial factors play a key role whatever the regime, while the impact of behavioral factors appears more significant only in the second regime when investors’ anxiety exceeds a given threshold. Further, the forecasting performance analysis shows the superiority of the nonlinear multifactorial model in forecasting the dynamics of the US stock market.