Behavioral Finance

The following sections are included:

• Dedication
• Preface
• About the Editors
• List of Contributors
• Acknowledgments
• Contents

Our study investigates how the existence of short interest affects the Post Earnings Announcement Drift (PEAD). When extremely negative or positive earnings are announced, investors with large short positions can either cover their short positions immediately or continue to hold and/or increase their short positions. Our combined evidence show that investors typically trim down short positions after earnings announcements, regardless of the direction of the earnings surprise. Also, investors seem to increase their short positions if they had low levels of short positions, especially for firms that reported positive earnings surprises. Investors seem to behave in a manner inconsistent with the PEAD, and support an explanation that PEAD is the result of investor underreaction to earnings news.

This chapter adopts a behavioral approach to explain why firms prefer dividends over stock repurchases (a tender offer auction) as a payout mechanism despite the significant tax disadvantage that dividends yield. We suggest that different shareholders might have different preferences toward stock repurchases, which may stem from differences in their financial literacy, their diverse discount factors, or from similar other idiosyncratic preferences. This divergence of behavior may lead to differences in the number of shares the various groups of shareholders would agree to sell under the tender offer. If the shareholders cannot coordinate so that they all buy the same number of shares, and if the firm makes an underpriced offer, then a value transfer would occur from those who bought more to those who purchased fewer shares. The stockholders, not knowing a priori to which category they belong, will object to this cash disbursement mechanism and may prefer dividends. This chapter develops a formal two-stockholder model that proves the above assertions.

This study investigates whether energy futures provide the ability to hedge against inflation. Using a Markov-switching vector error correction model (MS-VECM), we find that the Brent crude oil futures index is the only index that exhibits significant inflation hedging capability, among the subindexes of energy futures. Moreover, its inflation hedging capacity exhibits substantial variation over time, with most of the hedging power emerging under the relatively longer and more common regime. Results are robust to include common stocks and bonds in the model. We do not find evidence that unleaded gas, heating oil, gas oil, and natural gas futures have inflation hedging ability. Overall, our results suggest that crude oil futures are alternative candidates for well-diversified investment portfolios with inflation protection ability.

I extend the analysis of Kaplanski (2023) to explore arbitrage activity following the discovery of cross-sectional anomalies. After anomaly discovery, arbitrage capital reshapes out-of-sample returns, thereby creating a contrarian effect on top of the general decay in returns. As a result, the monthly first-day return is responsible for more than 10% of the portfolio value, which increases to 30% in case of monthly based anomalies. The continuous arbitrage activity suggests that arbitrageurs find anomalies profitable in the long run. This implies mispricing, which is difficult to reconcile with full rationality but can be explained via investors’ behavioral biases.

We examine the effects of behavioral biases among professional investors using the case of structured product (SP) investments. We outline key features embedded in SP and associate each with a specific behavioral bias. Our findings reveal that, to varying degrees, the biases examined affect professional investors. As the findings show that even professional investors are not immune to biases, we try to ascertain whether there are certain personal characteristics that influence the magnitude of bias within this group of professionals. Using logit, probit, and linear probability models, we show that the behavioral patterns examined are so deeply rooted in human behavior that they are difficult to overcome, and that this holds true with regard to any of the personal characteristics analyzed. By demonstrating these behavioral biases’ impact on investors, our results lend support to applying specific regulation to SP investments, aimed at improving investor protection. The regulation should apply to both professional and non-professional investors.

Surprisingly, analysts initiate unfavorable coverage nearly as often as they initiate positive coverage. We show that analysts do this to build their reputation. First, they are more likely to initiate unfavorable recommendations when their reports include more discussions about firm uncertainty. This suggests that analysts are dissuaded from issuing favorable forecasts when they perceive the firm’s future prospects to be unclear. Second, we find that analysts gain credibility from investors after they begin coverage with unfavorable recommendations. Specifically, the market reactions to subsequent upgrade recommendations are significantly stronger when analysts initiate unfavorable coverage than benchmark returns.

Despite the link between smoking and reduced lifespan, the effect of smoking on retirement savings withdrawal remains understudied. We investigate the impact of various leaflets on smokers’ retirement choices: (a) smoker vs. non-smoker life expectancies, (b) smoking’s health effects, (c) health impact with government’s advice to consider health upon retirement, or (d) combined smoker’s life expectancy and health info. Analyzing data from 2,000 smokers, we find that those informed about smoking’s lifespan impact, alone or with health consequences, were likelier to opt for lump sum over annuity. This suggests that informing smokers about smoking’s harms can influence their retirement choices.

This study explores the impact of relative scarcity on borrowing decisions through experiments involving a Brick Breaker computer game. Participants receiving messages about others’ superior achievements exhibited a greater inclination toward larger loans. Gender differences were observed with females showing a heightened tendency to borrow when informed about peers’ accomplishments. These findings underscore the significance of upward social comparisons in loan choices. Notably, high-income inequality may deplete household savings, increasing the risk of debt crises. Effective credit market regulation assumes greater importance in addressing income inequality’s implications.

We replicate the results of a previous study about the effect of intelligence and financial resources on the repayment of High- and Low-Consequence Debts (HCD and LCD, respectively), and extend the scope of the individual differences that are examined to include personality characteristics, and particularly the big-five personality dimensions. Our results from the first study are replicated showing that intelligence is more strongly (negatively) related to HCD repayment difficulty than to LCD repayment difficulty, whereas financial resources tend to be more strongly (negatively) related to LCD repayment difficulty than to HCD repayment difficulty. We also find that personality has a stronger effect on HCD than LCD repayment difficulties. These results are explained by the positive relationship between involvement and quality of financial decision-making in general, and debt-taking decisions in particular. The relationships between the big five and HCD and LCD payment difficulties are also explained by the relationship between involvement and decision quality. Of special interest in this set of findings were the more positive [negative] effect of conscientiousness [neuroticism] on the repayment of HCD [LCD]. These results are consistent with the idea that the self-discipline and deliberation associated with conscientiousness on the one hand, and impulsivity and emotionality associated with neuroticism on the other hand, affect people’s debt-taking decision making.

Tipping behavior continues to intrigue economists, psychologists, and hospitality management researchers primarily because tipping is a voluntary payment that does not buy something real in return. Yet, it is still a major source of income for millions of workers. This chapter offers a glimpse into the main reasons why people tip. Since consumers’ psychological and social motivations can better account for tipping behavior than economic considerations, the following sections present key studies on the behavioral factors that affect tips. Future directions are discussed.

This study analyzes market reactions to firms’ “NFT” or “Non-Fungible Token” disclosures filed with the SEC. The analysis distinguishes “Clear” disclosures, indicating actual NFT engagement, from “Vague” ones, referring to vague future intentions. Both types exhibit similar non-significant CAAR behavior upon announcement, followed by a significant decline. Positive cash flow firms experience initial positive CAAR, reversing by day 4. Segmenting by traits shows varied CAAR patterns, but overall, the study suggests skepticism toward NFTs. Firms in or entering this space face consistent negative CAAR over 30 days post-publication, indicating market mistrust.

Consumption of plant-based meat has been booming over the past few years, accompanied by surging interest on the part of investors, traditional meat producers, and the media. But is the alternative meat industry a true disruptive force in the meat industry, or is it just a fad? The case sets out to explore this question. Following an introduction of the issue at hand, it provides an overview of the reasons for the increase in popularity of meat substitutes. Next, industry structure and competition are explored. An examination of the different types of investors is warranted since behavioral considerations might govern some of their decisions. The case concludes with a discussion of the challenges facing the industry and potential policy issues.

Artificial Intelligence has become an inseparable part of civilization and rapidly changes every field of human activities. In addition to the positive impact of AI on humankind, it also may have negative consequences or unintentionally harm businesses, individuals, or parts of society. Through this chapter, we explain how the embedded human nature biases may become inherited in AI systems. Then, we raise the attention of AI practitioners to the dangers of ignoring human and machine biases and provide actionable recommendations on how to mitigate this phenomenon and thus contribute to increasing trust and adoption of AI systems.

The following section is included:

• Index