Financial Markets and Portfolio Management最新文献

This study investigates the impact of firm-level investor sentiment derived from Twitter and news media on herding behavior among contributors on Estimize, a leading platform for crowdsourced earnings forecasts. The findings show that sentiment gleaned from tweets and news media content positively influences herding among Estimize contributors. Notably, herding intensifies when Twitter and news sentiment polarities align, while divergent sentiment polarities diminish this herding effect. Additionally, the analysis indicates that firms with investment-grade ratings and those characterized by low valuation uncertainty are particularly prone to sentiment-driven herding. Importantly, positive sentiment is identified as having a more potent influence on herding behavior than negative sentiment. By focusing on Estimize contributors, this study offers insights into how firm-level sentiment cues shape the crowd’s herding behavior, offering new perspectives on how different media sources shape the wisdom of the crowd.

A fundamental implication of asset pricing theory is that investors must earn risk-premiums for bearing exposure to systematic risk. The two-pass cross-sectional regression is a popular approach for risk-premium estimation. The empirical literature has found that this approach often delivers estimates that significantly differ from their time-series counterparts. The paper explores a test of model misspecification that exploits the difference between cross-sectional and time-series risk-premium estimates. The suggested approach complements traditional misspecification tests and may be applied as an alternative to the deployment of misspecification-robust standard errors to test risk-premium significance.

Various factor models extended by Jensen’s (J Financ 23:389–416, 1968) alpha have been used to measure the retail investors’ portfolio (under-) performance compared to the market portfolio. The previous studies tried to explain this anomaly in behavioral finance by examining retail investors’ cognitive biases that induce irrational trading behavior. While operationalizing these cognitive biases in trading is not trivial, researchers still have found measures to proxy for biases and prove their statistical and economic significance. However, these studies only focused on linking one or a subset of behavioral biases and their effect on portfolio performance. In addition, different measures of biases across studies complicate the comparability of results. Therefore, this paper provides a structured overview of the current state of the literature regarding behavioral biases and their measurements to design a behavioral factor model that should help to explain the performance alpha from a behavioral finance perspective. The paper presents an overview of 11 behavioral bias factors and 29 corresponding measurements to consider inputting in such a model. With an application-oriented focus, it is recommended to include the most researched bias factors in a factor model, which are also the most detrimental to portfolio performance, as well as to include the most frequently used and least complex measures, which results in the primary inclusion of the following eight behavioral bias factors: disposition effect, under-diversification, home bias, local bias, lottery stock preference, trend chasing, overtrading, and trade clustering.

In March 2023, Credit Suisse (CS) was bailed out based on the implementation of emergency law to the exclusion of all shareholder rights of the involved banks, likely violating basic principles of monetary order. However, this paved the way for a support plan amounting to 209 billion Swiss francs and the implementation of a state-orchestrated emergency merger with UBS. By the end of August 2023, UBS had fully paid back the support plan and reported the biggest-ever quarterly profit for a bank, amounting to 29 billion US dollars. UBS also started to absorb CS’s domestic business, thereby abandoning the branding of an institution with a history of 167 years. Popular accounts claim the plan could be considered a success and that there was no cost because the money was repaid. We critically evaluate the CS bailout, shedding light on key issues such as bailout-induced wealth transfers, the “too-big-to-fail” challenge, the likelihood of bank bailouts, the optimal level of bank equity, the doctrinal separation of solvency and liquidity, and the benefits of ex-ante market-based bank fragility indicstors rather than ex-post accounting indicators. We infer a financial economist’s perspective, in which supervision is expanded by ex-ante market-based risk indicators, unweighted capital ratios are increased to adequately reflect large bank risks, and ex-ante paid liquidity options are introduced. Finally, we call for a public debate on the willingness of taxpayers to implicitly finance the too-big-to-fail risk of large banks.

Abstract

This paper investigates the informativeness of short sales on detecting firm investment inefficiency. Neoclassical and agency theory suggest that investment inefficiency destroys firm value by allocating resources to less-valued uses. This paper finds that short-sellers adjust their short positions before the announcement of a financial statement, to use their information advantage on firm investment inefficiency. The relation between the short positions in a firm and its future investment inefficiency is both statistically and economically significant, and robust to a broad set of control variables. Subsample analyses show that the informativeness of short sales positions about future investment inefficiency is concentrated on overinvestment firms, firms with little board independence, and firms with low CEO incentive pay.

This paper aims to investigate the influence of financial technology (FinTech) on sustainable finance. The sample for this study spans from 2010 to 2021, encompassing data from 89 countries. The study employed a two-stage least-square regression approach with the instrumental variables and confirmed the findings using a two-step system generalized method of moments. The findings show that FinTech has a significant favorable impact on sustainable finance. Other factors such as institutional quality, socioeconomic condition, and renewable energy have a significant and beneficial influence on the trajectory of sustainable finance, except the impact of globalization, which is positive but insignificant. Furthermore, FinTech is crucial to driving the transition toward a sustainable future distinguished by a lower carbon economy. The study found that FinTech has extensive application across various sectors of sustainable finance and has substantial potential to create long-term positive effects in this regard. FinTech can further integrate with other technologies to facilitate diversified growth in sustainable finance. Additionally, this study highlights FinTech-related trends and research opportunities in sustainable finance, demonstrating how they can help each other advance worldwide with significant policy implications for countries seeking to advance sustainable finance through technology.

Daily leveraged exchange traded funds amplify gains and losses of their underlying benchmark indexes on a daily basis. The result of going long in a daily leveraged ETF for more than one day is less clear. Here, bounds are given for the log-returns of a daily leveraged ETF when going long for more than just one day. The bounds are quadratic in the daily log-returns of the underlying benchmark index, and they are used to find sufficient conditions for outperformance and underperformance of a daily leveraged ETF in relation to its underlying benchmark index. Of note, results show promise for a 2x daily leveraged S&P 500 ETF. If the average annual log-return of the S&P 500 index continues to be at least .0658, as it has been in the past, and the standard deviation of daily S&P 500 log-returns is under .0125, then a 2x daily leveraged S&P 500 ETF will perform at least as well as the S&P 500 index in the long-run.

Goal-based investing is concerned with reaching a monetary investment goal by a given finite deadline, which differs from mean-variance optimization in modern portfolio theory. In this article, we expand the close connection between goal-based investing and option hedging that was originally discovered in Browne (Adv Appl Probab 31(2):551–577, 1999) by allowing for varying degrees of investor risk aversion using lower partial moments of different orders. Moreover, we show that maximizing the probability of reaching the goal (quantile hedging, cf. Föllmer and Leukert in Finance Stoch 3:251–273, 1999) and minimizing the expected shortfall (efficient hedging, cf. Föllmer and Leukert in Finance Stoch 4:117–146, 2000) yield, in fact, the same optimal investment policy. We furthermore present an innovative and model-free approach to goal-based investing using methods of reinforcement learning. To the best of our knowledge, we offer the first algorithmic approach to goal-based investing that can find optimal solutions in the presence of transaction costs.