This study examines whether corporate relationship spending through business entertainment expenses (BEEs) affects future stock price crash risk. Stakeholder theory suggests that expenditure on relationship building with external stakeholders enhances trust, firm reputation, and transparency, potentially lowering future crash risk. However, agency theory suggests that excessive relationship spending is associated with greater information opacity and managerial opportunism, contributing to greater future crash risk. Our results are more aligned with the agency perspective, showing that BEEs relate positively to future crash risk. China's 2012 anti-corruption campaign significantly moderated the effect of BEEs on stock price crash risk, particularly for firms having weak political connections, weak information transparency, and weak external monitoring mechanisms. The positive BEE-crash relation persists after the anti-corruption campaign for high financial risk firms.

Siblings are important sources of support. Male siblings, in particular, are valuable extended family resources in patriarchal societies such as China. This paper examines the effects of the number of brothers on household stock market participation in China. We find that having more brothers increases both the probability of stock market participation and the portfolio share in stocks. This positive effect is more pronounced for individuals who face high income risk, suffer from poor health, lack private insurance, and reside in areas with low financial development and high gender discrimination. In addition, the brother effect persists in recent periods. This evidence highlights the importance of informal risk-sharing networks in household investment decisions. Our results imply that demographic changes such as fertility decline might have unnoticed but sizable impacts on household portfolio choice, especially in countries with strong family ties.

We show that increases in oil prices, rather than changes in oil prices, can predict stock returns. The revealed stock return predictability is both statistically and economically significant. The forecasting performance of oil price increases is not affected by changes in the choice of subsample, a considerable advantage over other popular predictors. We obtain greater forecasting gains by adding oil price increases as an additional predictor to univariate macro models. This forecasting improvement is also present when using multivariate information methods. The success of oil-macro models in forecasting stock returns is robust to a large battery of robustness tests. Oil price increases predict stock returns by affecting future industrial production and discount rates.

This paper shows that politicians’ pressure to climb the career ladder increases bank risk exposure in their region. Chinese local politicians are set growth targets in their region that are relative to each other. Growth is stimulated by debt-financed programs which are mainly financed via bank loans. The stronger the performance pressure the riskier the respective local bank exposure becomes. This effect holds for local banks which are under some control of local politicians, it has increased with the release of stimulus packages requiring local co-financing and it is stronger if politicians hold chairmen positions in bank boards.

Analyzing a sample of Chinese firms, we find that when companies receive more coverage from FinTech advisory firms, their stock prices move less in tandem with the overall market. This suggests that FinTech coverage improves how accurately stock prices reflect firm-specific information. Our results hold true across multiple testing methods and alternative ways of measuring stock price synchronicity. Further analysis shows that FinTech coverage reduces stock price synchronicity primarily by addressing information gaps between companies and investors. Additionally, greater FinTech coverage improves stock liquidity and lowers the costs of raising debt or equity financing. When examining the topics covered by FinTech firms, we find that diverse coverage topics are linked to lower stock price synchronicity, with discussions on finance, corporate governance, and negative sentiment playing a particularly effective role. Finally, a textual analysis reveals that FinTech coverage includes significantly more firm-specific information than traditional analyst reports.

Using a unique proprietary dataset of daily mutual fund trading records and the COVID-19 pandemic-triggered lockdown in Wuhan (China) as a natural experiment, we find that individual mutual fund investors in Wuhan significantly reduced their daily trading frequency, total investment of their portfolios, and risk level of their invested funds during the lockdown period as compared to investors in other cities. The results suggest that the elimination of face-to-face interaction among individual investors during the lockdown reduced their information sharing, which led to more conservatism in their financial trading. We rule out alternative explanations of salience bias due to limited investor attention and temporary changes in personal circumstances such as depression and/or income reduction, during the lockdown period. Finally, consistent with the theory of naïve investor trading, we also find that investors received higher trading returns during the lockdown as they reduced trading aggressively in the absence of face-to-face interactions.

We propose a new nonparametric kernel (NPK) mean-lower partial moments model for portfolio construction that includes transaction costs. In the theory section, we study the properties of the solution to this model. We use simulated financial returns to demonstrate that the NPK model outperforms the traditional moment (MOM) model in terms of estimation accuracy, portfolio performance and transaction costs. We then empirically test our model using actual hedge fund returns, because holding these assets can expose investors to substantial downside risk. Portfolios formed using our NPK model significantly outperform those formed using the MOM model and other conventional investment strategies, regardless of the performance metric examined. Our NPK model will therefore be useful in a wide variety of contexts requiring downside risk management

Taking advantage of the institutional difference in capture between local and non-local media in China, we examined the association between media capture and post-earnings announcement drift (PEAD). Using both portfolio and regression analyses, we found that, for the same firms, non-local media coverage is negatively associated with PEAD; however, there is no association between local media coverage and PEAD, except for non-state-owned firms. Given that in China, non-local media are less captured or more independent than local media, the negative association observed for non-local media coverage can be interpreted as an indication that media independence plays a role in reducing PEAD or improving informational efficiency in the stock market.

We construct a unique political connection index to capture variations in the strength of firm political relations in China. The index incorporates various channels through which a firm's executives, chairperson, directors, and other senior officers are politically connected with government officials and bureaucrats. Overall, there is a negative relation between our index and firm value for the full sample, but such negative relation mainly exists for state-owned enterprises (SOEs) and it becomes positive for non-SOEs. Furthermore, close examination shows an inverted U-shaped relation between political connections and firm value for the full sample in general and for non-SOEs in particular: Firm value increases initially at a lower level of connections and then begins to decrease at a higher level. The findings are consistent with the different business objectives and motivations of SOEs and non-SOEs in seeking political connections. Finally, our findings are robust after controlling for potential endogeneity and using an alternative headcount index construction method.

We reinvestigate the classic portfolio optimization problem where the notion of portfolio risk is captured by the “Foster–Hart risk”—a new, bankruptcy-proof, reserve based measure of risk, extremely sensitive to left tail events (Foster and Hart, 2009). To include financial market frictions induced by market microstructure, we employ a general, ex-ante transaction cost function with fixed, linear and quadratic penalty terms in the objective function. We represent the US equity market by the Dow Jones Industrial Average (DJIA) index and study the performance of the Foster–Hart optimal DJIA portfolio. In order to capture the skewed and leptokurtotic nature of real life stock returns, we model the returns of the DJIA constituents as an ARMA–GARCH process with multivariate “normal tempered stable” innovations. We demonstrate that the Foster–Hart optimal portfolio’s performance is superior to those obtained under several techniques currently in use in academia and industry.