Behavioral Finance and Asset Prices

This chapter investigates the effects of oil price uncertainty and oil price shocks on total factor productivity growth in the G7 and BRICS countries. It shows that oil price uncertainty has negative and statistically significant effects on total factor productivity growth and that positive (negative) oil price shocks decrease (increase) the TFP growth rate in the G7 countries but have the opposite effects in the BRICS countries. Moreover, oil price shocks have in general asymmetric effects on total factor productivity growth in the G7 countries but symmetric effects in the BRICS countries.

Using the Heterogeneous Autoregressive Realized Volatility (HAR-RV) model as a modeling platform, we study whether climate-risk factors help to predict the realized volatility of movements of agricultural commodity prices. Our main finding is that climate-risk factors improve the predictive performance of the HAR-RV model mainly at longer prediction horizons (month or beyond). Our main finding is robust to estimating the HAR-RV model by the ordinary least squares technique, and to using various shrinkage estimators. We discuss the implications of our results for policymakers and investors.

This chapter investigates the dependence of the option-adjusted spread (OAS) for several ICE BofA Emerging Markets Corporate Plus Indexes on the Covid-19 pandemic outbreaks between March 1, 2020, and April 30, 2021. We investigate whether the number of new cases, reproduction rate, death rate, and stringency policies have resulted in an increase/decrease in the spreads. We study the bivariate distributions of epidemiological indicators and spreads to investigate their concordance using dynamic copula analysis and estimate the Kendall rank correlation coefficient. We also investigate the effects of the epidemiological variables on the extreme values of the spreads by fitting a tail index derived from a Pareto type I distribution. We highlight the existence of correlations, robust to the type of copulas used (Clayton or Gumbel). Moreover, we show that the epidemiological variables explain well the extreme values of the spreads.

The behavioral framework has proven successful in explaining several puzzling aspects of executive-stock-based compensation contracts. Recent literature suggests that both the cumulative prospect theory and the rank-dependent expected utility theory lead to better predictions of employee stock option (ESO) exercise decisions. The aim of this chapter is to provide an overview of those behavioral ESO models and discuss their implications in (1) the valuation of ESOs, (2) the design of optimal ESO contracts, and (3) the assessment of employee sentiment.

This chapter relates to knowledge of the psychological discount function that underlies intertemporal choices. Exponential, proportional, hyperbolic, and generalized hyperbolic functional forms have been cited in the literature. The chapter empirically compares different functional forms on a given population. Based on data collected through an experimental study, a violation of the discounted utility theory is confirmed, which means that time preferences could not be characterized by an exponential discount function. This finding supports other previous empirical studies and shows that individuals are characterized by impatience which decreases with the time horizon.

We use an experiment with a sample of professional investors to study the impact of text and emojis on investment proportion. We find that texts—provided as a supplementary information—have a statistically significant on investors’ decisions. However, the magnitude of the impact is too small (around 1%) to conclude that investor sentiment has an economically significant impact on investment decisions. We also find that emojis have no impact on investment decisions. Overall, our results are consistent with the efficient market hypothesis: in an experimental setting where the payoff and the probability of each decision are known, investment decisions of sophisticated traders are driven mostly by the type of asset, the level of risks, and the associated return of each investment and not by investor sentiment.

This chapter investigates whether a behaviourally biased agent is able to persistently maintain a positive consumption share when trading in the market with a Bayesian agent. The question is addressed by recasting a popular model of investor sentiment in a general equilibrium framework. Our evolutionary stability analysis complements standard Behavioural Finance studies, where a biased representative agent is usually considered to explain deviations from rational pricing. In fact, if the biased agent asymptotically disappears from the market, then misvaluation patters generated by its behaviour do not survive in the long term. We find that, despite the existence of generic cases in which the biased agent succumbs, the learning process with behavioural biases displays a good degree of evolutionary stability.

Based on data from all listed companies on the Shenzhen Stock Exchange from 2014 to 2018, this chapter examines the relationship between the on-site research of institutional investors and information about the company’s fundamentals as well as investor attention. The study found that investor focus drives institutional investors to conduct research, and this role has strong industry differentiation. In addition to this, specific corporate fundamentals also drive investors to list companies as research objects, and profitability indicators do not produce such an effect. The conclusion is still valid after replacing the profit indicator and checking the multicollinearity between variables. This chapter studies the possible relationship between the three variables from a new perspective and has a certain significance for institutional investors and companies.

The US stock market has displayed considerable excess volatility during the different waves of the COVID-19 pandemic. Notably, while most US indexes fell abruptly and lost about 20–30% during the first wave and in times of lockdowns, unlike the global financial crisis of 2008–2009, the correction was rapid, and most stock indexes subsequently exceeded their pre-COVID levels. Accordingly, it is important to assess whether this dynamic is driven more by a switch in fundamentals or whether it is simply due to a conversion of investors’ emotions. This chapter aims to analyze the dynamics of the US (S&P500) stock index, both before and during the ongoing coronavirus pandemic. Our findings point to three interesting results. First, US stock returns are driven by both macrofinancial and behavioral factors. Second, a two-regime multifactorial model reproduces the dynamics of the US market in which financial factors play a key role whatever the regime is, while the impact of behavioral factors appears more significant only in the second regime when investors’ anxiety exceeds a given threshold. Third, our in-sample forecasts point to the superiority of our nonlinear multifactorial model to forecast the dynamics of the US stock market.