Journal of Behavioral and Experimental Finance最新文献

In an important contribution, Lei et al. (2001, Econometrica) argue that speculation is not the driver of bubbles in the absence of common knowledge of rationality, suggesting a focus on mistakes and confusion. We revisit Lei et al.’s (2001) design, confirming the existence of bubbles. However, we argue that, although their design removes the ability to speculate, it introduces several unintended design artifacts. We discuss four possible behavioral implications of the design that may put upward pressure on transaction prices. The first is extreme initial asymmetric endowments. Second, cash to asset ratio increases with each transaction. Third, the combination of a high cash to asset ratio and removal of cash and assets from the market with each transaction impact perceived scarcity of assets more than cash. Lastly, actual scarcity of assets is present in these markets. We argue that these factors individually or in combination lead to the observed bubbles despite prohibiting speculative behavior.

Social comparisons and rank-based endowments impact risk-taking decisions. We provide experimental evidence indicating that rank-based endowments have differential impacts on risk-taking decisions based on the aspect used to rank individuals. We observe the largest rank-based differences when such endowments are determined based on individuals’ effort or skill. Compared to individuals who rank first, individuals who rank last in these settings increase their risk-taking by, on average, 17.55 percentage points (pp). In the case of luck-based endowments, the effect size is significantly smaller (11.34 pp). We conduct an additional treatment where rank and endowment are independent and find that differences in risk-taking are driven mainly by participants’ endowments rather than by the ranking itself.

This paper studies how the Chinese stock market reacts to new COVID-19 infections under the zero-COVID policy. Consistent with the literature, we find that a COVID-19 outbreak within a city adversely affects the performance of local firms, but further unveils the effect’s nonlinearity. More importantly, we document an unexpected pattern of spatial spillover of the COVID-19 shock, which is likely to be caused by the policy itself. In addition, firms with significant retail exposure are most vulnerable, while firms with low pandemic exposure, larger size, state-owned enterprise (SOE) status, and robust finances demonstrate greater resilience to the COVID-19 shock. Mechanism analysis indicates that the COVID-19 effects are realized through both cash flow and discount rate channels. Supplementary back-of-the-envelope calculations illustrate the substantial economic consequences of these phenomena, which shed light on the debate on the optimal policy in response to a future pandemic.

Banking crises have recurrently emphasized the crucial need for establishing effective mechanisms to prevent bank runs, and different organizations are exploring a range of potential measures. With the aim of contributing to this debate, we run a laboratory experiment to study the effectiveness of two untested devices: Stability funds that automatically limit depositors’ possibility of withdrawing their assets, and voluntary individual insurance against the risk of default. Depositors start the interaction with a monetary endowment deposited in a bank. They can then withdraw money before and after the bank suffers a liquidity loss. Such a loss can be either permanent or temporary, but its nature will only be discovered at the end of the interaction. The bank defaults if the desired withdrawals exceed its available liquidity. Our results show that the only effective mechanism in reducing bank defaults, compared to the baseline, is the stability fund with high coverage. When groups have a high share of female depositors, there is a significant reduction in the likelihood of bank runs, which can be explained by women’s higher propensity to buy insurance. When a critical liquidity signal is issued, indicating a dangerous situation, women’s lower propensity to withdraw disappears, bringing it to levels similar to that of men.

With three experiments, we study the design of financial markets to help spread knowledge about solutions to the 0-1 Knapsack Problem (KP), a combinatorial resource allocation problem. To solve the KP, substantial cognitive effort is required; random sampling is ineffective and humans rarely resort to it. The theory of computational complexity motivates our experiment designs. Complete markets generate noisy prices and knowledge spreads poorly. Instead, one carefully chosen security per problem instance causes accurate pricing and effective knowledge dissemination. This contrasts with information aggregation experiments. There, values depend on solutions to probabilistic problems, which can be solved by random drawing.

Using a unique sample of retail impact investors, this study evaluates how investors deal with the challenge of aligning their financial and their nonfinancial goals. We find that investors with stronger nonfinancial motives are more likely to expect the overperformance of an impact investment and the underperformance of traditional equity and bond investments than investors with weaker nonfinancial motives. This cross-asset relationship between nonfinancial motives and expected performance indicates that investors form expectations that fit with the investment decisions that their nonfinancial motives are likely to motivate. We also find that after experiencing losses, investors with stronger nonfinancial motives are less likely to revise their expectation that the impact investment will underperform and more likely to expect that the impact investment will overperform than other investors. Our findings provide further evidence that preferences can affect expectations, and challenge conclusions drawn from observed behavior regarding investors’ willingness to pay for impact.

This paper examines the effect of air pollution on individual lenders' decisions. Using unique data from the leading peer-to-peer lending platform, we find evidence that for every 10 units increase in the air quality index (AQI), the lender would lend RMB 5.47 yuan more in one single bid and 6% earlier in the fund-raising period. Moreover, the impacts from PM2.5 and CO pollutants are serious. The older people and people that are not well-educated would significantly suffer from air pollution. We further explore three potential mechanisms: physical channel, physiological channel as well as psychological channel, and show that the first two are valid. Overall, our findings shed light on the influence of environmental factors on online lending participants.

We investigate the role that weather-induced mood plays in underperformance of lottery stocks. We hypothesize that during pleasant weather conditions, investors are more likely to become risk-taking and optimistic, and invest more in lottery stocks. This results in overpricing and lower expected return for these stocks. We use sky cloud cover as a proxy for the weather condition and show that the MAX effect mainly exists following sunny weather; however, there is less evidence for the MAX anomaly following cloudy weather. Our finding is robust to using alternative constructions of MAX and skewness measures as well as other proxies for weather condition such as rain, wind and temperature.

This paper documents that fund investors’ behavior is biased by the physical attractiveness stereotypes that they hold about fund managers. In an experimental setting, we find that although there is no information about highly attractive fund managers outperform their peers, respondents still perceive highly attractive managers to possess superior management skills and are willing to allocate more money to their managed funds. We further find that provision of information in contrast to the assumptions associated with physical attractiveness stereotypes diminishes the bias in investor behavior. When less attractive managers have historical returns that are 4% higher than more attractive managers, investors’ preference for attractive faces is overridden. Empirical evidence obtained using fund market data is consistent with our experimental results.

The study develops an original interdisciplinary approach, leveraging complex networks through which it identifies groups of investors and projects in equity crowdfunding, investigates whether clientele effects arise resulting in specific investor-entrepreneur matching, and explores which investor-entrepreneur combinations can lead to the emergence of collective behaviors. Data about campaigns and investors are gathered from Crowdcube to identify investors and company types that populated this leading UK platform during its early years (2011–2016). Results show that the clientele effect exists only between specific investors and project types: serial investors are attracted to innovative companies, whereas high-value and small investors, representing the largest group in the crowd, prefer mature companies in the consumer product industry. Moreover, the study reveals that information exchange in certain matching drives the clientele effect, resulting in collective behavior on specific segments: small investors engage in collective behaviors only when targeting high-tech innovative companies. These findings provide a new view on the clientele effect in equity crowdfunding platforms and the financing of innovative companies.