International Review of Financial Analysis最新文献

This paper studies the potential effect of deviation from Benford’s law on stock return prediction. Departures from the anticipated pattern can act as early indicators of irregular market behavior or possible fraudulent activities, both of which have the potential to impact future price trends. In this study, the deviation is measured by chi-squared test statistics over the first significant digit. Preliminary results indicate no compliance between daily stock returns data from Euronext Paris and Tunisian stock markets with Benford’s distribution. Then, the impact on the returns is explored via several models: linear regression and smooth transition models with various transition variables. Empirical results show the nonlinear effect of Benford’s law on stock returns prediction. We illustrate that this law can detect and predict abnormal returns generated by fraudulent or abnormal activities in both developed and emerging markets. By using Benford’s Law to analyze leading digits, investors and analysts can effectively identify irregularities and gain valuable insights into market dynamics.

The aim of index tracking is to mimic the performance of a benchmark index via minimizing the tracking error between the returns of the market index and the tracking portfolio. Lately, various deep learning solutions have been proposed to perform stock prediction or active investment. However, there remains a gap in literature to explore the application of deep learning to index tracking. In this paper, the one-dimensional Pointwise Convolutional Autoencoder is proposed to capture the main market characteristics and the Shapley Additive Explanations feature importance ranking is applied to select stocks to implement the partial replication index tracking with and without Covid-19 data. Moreover, portfolios with different holding periods and with different rebalancing frequency are created on different financial markets to check the effectiveness of the proposed strategy. Compared with different benchmark stock selection strategies, including Pearson correlation, mutual information, and Euclidean distance, the proposed strategy achieves state-of-the-art performance on different financial markets.

In examining return prediction strategies in China’s stock market, we find that the chronological return ordering is ineffective within a one-month window. To overcome this limitation, we introduce a more robust measure, named chronological turnover ordering (${\text{CTO}}_3$), calculated using turnover in the past three months. As anticipated, ${\text{CTO}}_3$ demonstrates statistically significant predictability for returns, indicating a tendency among investors to overvalue stocks with high recent and low distant turnover. Bivariate portfolio analysis reveals that ${\text{CTO}}_3$ performs more effectively during high-sentiment periods and on stocks with high investor attention. This research contributes significantly to understanding investor behavior and market dynamics in China.

In the context of China's aging process, optimizing financial asset allocation of middle-aged and elderly households is an essential way to meet the diversified old-age security needs and do a good job in aging finance. To enrich the understanding of the “limited participation puzzle” in the financial market of middle-aged and elderly households in China, this paper takes the long-term care insurance (LTCI) pilot policy as a quasinatural experiment; takes the China Health and Retirement Longitudinal Study (CHARLS) data from 2011 to 2018 as the research sample; and empirically tests the influence, internal mechanism and heterogeneity of LTCI on the financial asset allocation of middle-aged and elderly households using difference-in-differences (DID) and intermediary effect models. The findings are as follows. First, LTCI can significantly promote middle-aged and elderly households’ participation in risky financial markets, and this finding still holds under a series of robustness tests. Second, LTCI can promote the financial asset allocation of middle-aged and elderly households by improving their heath capital. Third, the effects of LTCI pilot policies on the income level and household registration of middle-aged and elderly households significantly differ. The effect of LTCI is more obvious in the sample of high-income and urban households than in the sample of low- and middle-income and rural households. These results help provide a policy basis for realizing the innovation of the economy and financial market products in a deeply aging society and facilitating organic connections between these products.

Given the significant political and economic frictions between China and the US, which bring high uncertainty to the global economy, it is crucial to understand how these tensions led to tail risk events and potentially destabilize the stock markets. We construct a multilayer network to examine tail risk spillovers between the stock markets of the two countries and find that (i) the value of total connectedness, an index measuring the overall spillover, rises amidst the tensions and declines during reconciliations; (ii) compared to the intralayer effects, the interlayer tail risk spillovers mostly generate in a form of extreme points rather than steady overflows, which implies that interlayer transmissions occur irregularly, i.e., a sudden surges in distressed sectors; and (iii) in terms of sector, the out-strengths are more concentrated in contrast with the in-strengths, which suggests that some sectors may play the role of major transmitters in the interlayer tail risk spillovers. To summarize, we quantify tail risk spillovers via the multilayer networks, which provides evidence on how tail risk spillovers materialize between the Chinese and the US stock markets during different stages of bilateral relations.

We investigate the impact of mutual fund herding on fund performance. Using a novel and dynamic measure of fund-level herding that captures the tendency of a fund manager to imitate the trading decisions of the institutional crowd based on a sample of 3490 mutual funds in China for 21 years between 2003 and 2023, we find that mutual fund herding is negatively related to fund performance. Our empirical results still hold when we employ a battery of methods to mitigate endogeneity issues. Additionally, we find that herding behavior becomes more detrimental to performance when the portfolio managers are older, male and more experienced.

This study evaluates the relationship between establishing environmental courts and corporate environmental information disclosure quality. We use panel data from 28,718 Chinese listed companies between 2008 and 2022 and investigate whether the quality of environmental information disclosure by companies improves owing to the establishment of regional environmental courts. The results indicate that the quality of environmental information disclosure increase significantly after establishing environmental courts in the region. Factors such as corporate green culture, public environmental awareness, and local environmental regulatory strength play intermediary roles in this relationship. In the heterogeneous analysis, we find that the effect of environmental court construction on improving the quality of environmental information disclosure is more significant for highly polluting industries and state-owned enterprises. Additionally, environmental courts are more effective in enhancing the quality of corporate environmental information disclosure in regions with the low level of legalization and the high level of environmental information disclosure. Our research contributes to environmental court construction and corporate environmental information disclosure literature and provides decision-making references for businesses and governments.

Whether households’ consumption level in an Inflation-Targeting (IT) regime, which steers inflation and its expectations, differs from that of a non-IT regime remains empirically unexplored. Using IT as a treatment and acknowledging the potential for country heterogeneity and material confounding factors, we leverage several treatment methods to study the causal, dynamic IT effect on aggregate household consumption across developed and developing countries. We find significant heterogeneous IT effects on aggregate consumption levels in two-thirds of the IT sample. The heterogeneity does not support the notion of an isomorphic average causal IT effect on household consumption across countries. The government’s willingness to meet IT policy adoption preconditions and the country’s fiscal profile are critical drives of this parameter heterogeneity in developing and developed countries. However, the central bank’s appetite for restricting exchange rate movements contributes to this heterogeneity in only developing countries. We find that macroeconomic uncertainty proxied by the inflation level, inflation variability and output growth variability are essential channels in developing countries. In addition, financial development and savings are crucial channels across development status.

The financial sector has undergone a significant transformation with the advent of financial technology (FinTech), which has put pressure on the business models of incumbents and has raised several concerns, including some related to instances of misbehavior. This article examines the existing body of literature on misconduct in the FinTech industry, encompassing a range of unethical behaviors, the underlying causes that contribute to such activities, and viable strategies to alleviate associated concerns. We employ a bibliometric and scientometric analysis, as well as topic modeling, a machine learning approach applied to natural language. Through a comprehensive examination of academic output, we highlight noteworthy patterns and promising areas of study.

We also discuss the possible advantages and disadvantages linked to the integration of developing technologies such as artificial intelligence and machine learning in the realm of fraud prevention and detection.

We examine the impact of climate-risk exposure on accounting conservatism, and document a significant positive association. Utilizing a firm-level climate-exposure measure (CRI) for a large international sample covering 46 countries from 2002 to 2021, we find that firms facing climate risk tend to adopt more conservative reporting practices. This effect is notably stronger during pivotal periods of climate awareness, such as that around the Paris Climate Agreement, and in jurisdictions governed by common law. Our results remain valid under a series of robustness tests and after addressing endogeneity concerns. Further analyses indicate that this positive association is more pronounced in well-governed firms and in firms with high levels of environmental and social performance, but less pronounced in firms with high degrees of complexity and information asymmetry. Additional tests reveal that firms in environmentally sensitive industries and those operating in developed countries adopt more conservative reporting than counterparts in non-environmentally sensitive industries or in developing countries. Our final analysis suggests that regulatory risks have a greater effect on accounting conservatism than physical risks. Our paper contributes to the ongoing discourse on climate change, documenting the ‘bright side’ of firms' exposure to climate risk.