Journal of Behavioral and Experimental Finance最新文献

We examine the effect of overconfidence bias on VC firms’ investment. Using a sample of U.S. venture capital exits by IPOs and M&As between 2000 and 2019, we construct an overconfidence index and find a strong positive relationship between the follow-on funds and the degree of overconfidence. We also find that the higher the VC’s overconfidence, the shorter the time to raise new capital. Further, we show that overconfident VCs are more likely to exit their investments via IPOs rather than M&As and that the degree of overconfidence negatively and significantly affects the time to exit.

We investigate the impact of ambiguity attitudes on the willingness-to-pay (WTP) for index insurance among female smallholders in Kenya. We gauge incentive-compatible measures of ambiguity aversion and insensitivity in the domain of gains and losses, as well as loss aversion. Next, we setup a framed experiment to measure WTP for insurance with basis risk. For a random subsample we introduce an alternative ‘rebate’ insurance, comparable to an insurance purchased through a loan – repaid in good years and deducted from payout in bad ones – that is expectedly more palatable for the loss averse. We find that ambiguity aversion significantly increases WTP for the standalone insurance, while loss aversion reduces it as expected. The former result is seemingly at odds with previous evidence from the field, but is consistent with a setting in which insurance ambiguity engenders relatively less disutility compared to the vagaries of weather. We show that this apparent divergence is not caused by differences in the method used to estimate ambiguity aversion compared to existing field studies. Rather, we exploit exogenous variation in the familiarity with insurance within our sample to show that it is explained away by the role of experience with the novel technology—a previously underestimated mediator. Ambiguity aversion hinders adoption at early stages but increases when the insurance is better understood. The rebate scenario, instead, all but cancels the effect of loss aversion on WTP, but the increased contractual ambiguity results in significantly lower bids by the ambiguity averse. In the lab, the WTP for rebate-type insurance-credit bundles is not different from that of the actuarially equivalent standalone insurance, implying that evidence from the field on greater uptake for the former may be attributable to liquidity constraints and time discounting effects, rather than to behavioural traits.

We examine how the return predictability of deep learning models varies with stocks’ vulnerability to investors’ behavioral biases. Using an extensive list of anomaly variables, we find that the long-short strategy of buying (shorting) stocks with high (low) deep learning signals generates greater returns for stocks more vulnerable to behavioral biases, i.e., small, young, unprofitable, volatile, non-dividend-paying, close-to-default, and lottery-like stocks. This performance of deep learning models for speculative stocks becomes pronounced when investor sentiment is high, and when new information is delivered through earnings announcements. Moreover, our nonlinear deep learning signals are negatively associated with analysts’ earnings forecast error especially for speculative stocks, implying that analysts’ forecasts are too low for speculative stocks with high deep learning signals. These results suggest that deep learning models with nonlinear structures are useful for capturing mispricing induced by behavioral biases.

We examine the effect of economic policy uncertainty (EPU) on sell-side analysts’ forecasts, the allocation of attention, and the stock market reaction to earnings news. We find that periods of high EPU are associated with higher analyst disagreement and a decrease in forecast accuracy. Specifically, we show that analysts issue on average more pessimistic forecasts when EPU is high. Second, we show that higher levels of EPU are associated with higher attention to the overall stock market and lower firm-level attention, in line with category learning behavior. Forecast errors also have a larger firm-specific component during periods of high EPU. Finally, we show that the trading volume and price responses to earnings announcements hardly depend on EPU. Hence, investors tend to follow analyst forecasts, thereby failing to incorporate a predictable bias.

In this study, we address the ongoing debate about the relative importance of the three dimensions of the ESG framework and whether they are sufficient to capture the full scope of sustainability. We propose a new dimension, the Future Generations pillar (F-pillar), which aims to account for intergenerational equity and sustainability. Our online experiment explores how retail investors make investment decisions when presented with different combinations of financial and ESG information, including the F-pillar. Our findings suggest that retail investors try to balance their financial objectives with sustainability considerations. Moreover, the E-pillar appears to be most relevant when investors adopt a sustainability perspective, while the S-pillar is most relevant when investors consider the financial perspective. Interestingly, our results show that an explicit F-pillar is somewhat redundant, as individuals believe that the three existing ESG pillars already indirectly address the sustainability towards the future generations. This study contributes to the ongoing debate on the relevance of the ESG framework and highlights the need to further explore the interplay between financial and sustainability considerations in retail investment decision-making.

This study analyzes the interaction between sustainability preferences and investment behavior, particularly in the context of mutual fund investments. Based on survey data from a representative sample of Swedish mutual fund investors, we observe that while a majority of respondents express a willingness to sacrifice returns for more sustainable investments, only a minority claim to have actively invested in sustainable funds. This highlights a discrepancy between preferences and behavior, which we show can be understood by (in)attentiveness in the financial decision-making process. We reveal that sustainability-motivated investors are less attentive than those motivated by returns, leading to potential misalignment with their preferences. This finding emphasizes the significance of banks taking (in)attentiveness into account when communicating with customers. Information is effective for return-focused investors, while nudges and boosts may better facilitate decisions for sustainability-focused investors.

This study examines the impact of female CEOs and their power on corporate debt usage. Adopting the framework of Finkelstein (1992), we distinguish different power dimensions to analyze a sample of 418 CEOs of non-financial U.S. listed firms over the time period of 2007–2015. We find that the leverage of firms run by female CEOs is not different from that of firms run by male CEOs. However, when firms experience a transition of male-to-female CEOs, we find some evidence of an increase in debt. Further analysis shows that the gender effect is associated with the type of power CEOs possess. Female CEOs with structural power (reflected by low frequency of board meetings) and prestige power (reflected by education from an elite university) tend to use more debt than their powerful male peers. We recommend that the gender effect needs to be studied together with the power CEOs hold.

Using 10 significant cryptocurrencies, we construct emotional spillovers and provide inferences about the transmission of fear and greed shocks. The results show intense emotional bonding signifying a high level of cryptocurrency interdependence through the sentiment channel of crypto traders and investors. The amplitude of idiosyncratic (own) connectedness increases significantly for most cryptocurrencies in their greed phase, implying that positive investor sentiment is more substantial than negative sentiment in driving the trading of individual cryptocurrencies. Though market conditions and the pandemic alter the role of most cryptocurrencies, some cryptocurrencies display persistence. Ethereum cash and, to some extent, Ripple remain considerably isolated, whereas Ethereum and Dash remain highly interlocked with the rest of the cryptocurrencies. Dash is the leading net transmitter, whereas Ripple is the leading net receiver of fear and greed shocks.

This research explores the impact of individual investor sentiment derived from social networks on stock market returns. Using keyword-based techniques, we collect and analyze Sina Weibo posts related to COVID-19, extracting daily influential weighted sentiment indexes from a dataset of over 2.4 million posts in 2020. Empirical tests utilizing a sentiment-augmented three-factor model reveal that individual investor sentiment exerts an independent influence on Chinese financial markets, after controlling for market risk, size, and value effects. We further find that negative sentiment carries a stronger impact on stock returns, which is in line with the loss-averse behavior commonly observed among individual investors. We also find an asymmetric pattern in the sentiment-return relation across different industry types. While positive sentiment affects both types of industries that suffer or benefit from COVID-19, negative sentiment affects only the industries that suffer from the pandemic. Overall, our empirical results provide robust support for the significance of individual investor sentiment in explaining the behavior of the Chinese financial markets.

The gender legislation enacted around the world has put enormous pressure on companies to increase the number of women on their boards. Employing US firm-specific data, we document a significant negative relationship between CEO-director ties and female representation on the board, suggesting that socially connected directors are detrimental to gender parity in senior management. We find that the situation improves in firms with female directors with valuable attributes while being moderated by CEO characteristics. Cross-sectional analyses reveal that the association is more pronounced during the low board gender diversity periods and for firms that are led by male CEOs or have weak monitoring mechanism. We rule out endogeneity concerns by performing a battery of analyses. The findings remain robust in a range of sensitivity tests. Our study offers practical implications for regulators and top management teams to improve board effectiveness, thus engendering lasting transformational change in the boardroom.