This study investigates Chinese equity mutual funds’ performances while holding those that are well behaved in financial disclosure (transparent) companies, so-called peace of mind investing. This study uses detailed semi-annual data on mutual funds from 2011 to 2020, and finds that holding these transparent companies’ stocks is profitable for mutual funds and trusted by investors, thereby boosting their inflows. However, there is no significant evidence that mutual funds can beat the market portfolio when fees are considered. The study then provides possible explanations for the above findings from mutual fund managers’ skills and mutual holdings between institutional shareholders of fund management and transparent companies.

This paper investigates the intertemporal relation between expected aggregate stock market returns and conditional variance considering periodic trading breaks. We propose a modified version of Merton’s intertemporal asset pricing model that merges two different processes driving asset prices, (i) a continuous process modeling diffusive risk during the trading day and, (ii) a discontinuous process modeling overnight price changes of random magnitude. Relying on high-frequency data, we estimate distinct premia for diffusive trading volatility and volatility induced by overnight jumps. While diffusive trading volatility plays a minor role in explaining the expected market risk premium, overnight jumps carry a significant risk premium and establish a positive risk-return trade-off. Our study thereby contributes to the ongoing debate on the sign of the intertemporal risk-return relation.

This paper investigates the trading behavior of ambiguity-loving investors and the corresponding impacts on asset price. The ambiguity-loving attitude increases investors' willingness to participate in the risky asset market. Their rising participation gradually crowds out ambiguity-averse and sophisticated investors, extending their nonparticipation region. When the market supply is small, the discontinuous and non-unique properties of ambiguity-loving investors' demand mapping can cause flat ranges in the equilibrium price. When the market supply is moderate or large, an increase in the fraction of ambiguity-loving investors or ambiguity level reduces equity premium. We find the effect of ambiguity-loving attitudes remains with short-sales constraints except for ambiguity-loving investors' positions and the equity premium. Their positions shrink, and equity premium decreases when the market supply is small. Besides, the rising fraction of ambiguity-level investors and ambiguity level increases equity premium when ambiguity-loving investors with heterogenous opinions only sell the risky asset.

We establish a framework to study the factor structure in stock variance under a high-frequency and high-dimensional setup. We prove the consistency of conducting principal component analysis on realized variances in estimating the factor structure. Moreover, based on strong empirical evidence, we propose a multiplicative volatility factor (MVF) model, where stock variance is represented by a common variance factor and a multiplicative lognormal idiosyncratic component. We further show that our MVF model leads to significantly improved volatility prediction. The favorable performance of the proposed MVF model is seen in both US stocks and global equity indices.

In 2013, the Shanghai Futures Exchange (SHFE) introduced a night session in Chinese metal futures markets. Using high-frequency data of gold, silver, and copper futures, we investigate the impact of night trading on intraday return predictability in Chinese metal futures markets. Firstly, we find the intraday return predictability has changed after introducing night trading: before the launch of night trading, the first half-hour daytime returns show significant predictability, whereas the first half-hour night returns exhibit forecasting power after that. Such changes can be explained by the immediate reactions of domestic investors to international news released in the evening. Secondly, the market timing strategy outperforms the always-long and buy-and-hold benchmark strategies. Thirdly, the predictability of night return is stronger on days with higher volatility and volume. Furthermore, stronger intraday predictability is associated with global news releases and positive news sentiment, suggesting enhanced connectedness of Chinese and international metal futures markets after the launch of night trading.

数字金融迅速发展如何影响孩子的学习成绩？本文基于社会资本理论和物质资本理论探究数字金融发展对孩子学习成绩的影响及内在机制。基于中国家庭追踪调查（CFPS）和中国数字普惠金融指数数据，本文发现数字金融发展降低了孩子的学习成绩。 通过分析数字金融对孩子成绩的影响机制，发现数字金融发展促进了父母的就业，增加了父母工作时间， 引起家庭“社会资本”降低，使得父母陪伴和监督孩子的时间减少， 导致孩子的学习成绩下降。祖父母的陪伴、 参加课外辅导班及住校能在一定程度上弥补父母对孩子陪伴时间的减少，缓解数字金融发展对孩子学习成绩的负向影响。 数字金融虽能提升家庭的“物质资本”，但并没有通过显著增加对孩子的教育投资而提升孩子成绩。 本文结论对家长和政策制定者具有一定的启示。

We propose a behavioral signaling framework to explain the positive announcement effects of stock splits. (Retail) investors view stock splits as good news and are loss averse. Thus, a stock split can boost investors’ expectations of the firm’s growth potential and its stock price, but may also cause disproportionally larger price declines if the firm cannot meet investors’ high expectations. In equilibrium, only managers with favorable information use stock splits to signal. Empirical analyses of stock splits in China find supporting evidence for this explanation: (1) investors become more optimistic after stock splits; (2) higher split ratios are associated with stronger market reactions; (3) splitting firms have better future performance than non-splitting firms; and (4) they experience larger price declines when falling short of investors’ expectations. These findings, along with the unique institutional features of the Chinese market, help differentiate our behavioral explanation from alternative explanations within the rational framework.

This study investigates the effect of institutional cross-ownership on stock price crash risk using a sample of Chinese listed companies during the period 2011–2021. We find that institutional cross-ownership can significantly reduce stock crash risk. After a series of robustness tests, the above findings still hold. In addition, we find that the relationship is more pronounced for non-state-owned listed companies and those in less-developed regions. The study finds that the quality of corporate disclosure and financing constraints have the mediating effect. This paper provides new empirical evidence on how to reduce stock crash risk in emerging financial markets.

This paper compares Chinese firms’ IPO performance both in the short- and the long-run on domestic and overseas markets and investigates what factors determine the IPO destinations of Chinese firms. We find China’s domestic IPO market performs well over both time horizons, while some listings in the overseas market perform well in the long run except for small- and mid-cap listings in the US. Analysis based on a capital asset pricing model reveals IPO premiums and short-term returns are less affected by three common risk factors, while longer term returns are mainly driven by market fundamentals. Investigation of the drivers for Chinese firms’ IPO destinations using the binary choice model shows that firm specifics, institutional setups, and market characteristics influence the choice of IPO destinations. The prospect of a high IPO premium and strong trading in IPO shares are substantial drivers for firms to list their shares onshore. On the other hand, indicators of market size and profitability appear to have the highest predictive power for the likelihood of overseas listings, followed by firm’s ownership structure, IPO offering size and IPO underwriting costs. Institutional setups have the least predictive power for overseas listings. These results are in general robust to domestic delisting and IPO suspension events.

In this study, we adjust the hierarchical risk parity (HRP) model by introducing hierarchical information on assets to help manage portfolio risk. The adjusted HRP model with hierarchical information considers both correlation and hierarchical information. Compared with other models, the HRP model with hierarchical information has better out-of-sample robustness for simulation data. Moreover, this model achieves better out-of-sample performance using Chinese industry indices data. The results reveal that the adjusted HRP model is an efficient tool to control out-of-sample portfolio risk.