This paper investigates the effect of exchange-traded funds (ETFs) on the ESG performance of their underlying firms. Using data from China, we find that ETFs enhance the ESG performance of their underlying firms. This finding remains consistent after several robustness and endogeneity tests. Further, we show that the effect is more pronounced for non-SOEs, firms in low-polluting industries, and firms at growth and maturity stages. Studying the mechanisms behind these results, we find that ETFs mitigate the corporate agency problems, enhance the willingness of managers to invest in ESG, and improve the ESG performance.

This study attempts to shed new light on how the state as a minority shareholder can affect the tax planning of non-state-owned enterprises(non-SOEs). We examine publicly traded non-SOEs in China and find that non-SOEs are more tax avoidance when the government is a minority shareholder, indicating that minority state ownership has played a "shelter effect" on tax avoidance of non-SOEs. Further analysis shows that the sheltering effect of minority state ownership is more prominent for firms located in areas with more social burden, worse tax enforcement and firms with stronger incentive to avoid taxes. Furthermore, non-SOEs with minority state ownership increase excessive capital expenditure and employ redundant employees, but still have higher firm value. Overall, our findings suggest the state as a minority shareholder shapes the tax-planning activities of non-SOEs in a “two-way favor exchange” manner and it is beneficial for non-SOEs to maintain a close relationship with the government in China where the government controls key resources.

We examine a unique phenomenon among exchange traded funds (ETFs) in the Chinese stock market, finding that ETFs pervasively participate in initial public offerings (IPOs) to profit from underpricing. The ETF IPO participation passes primary market benefits to retail investors, providing benefits from hard-to-reach investment opportunities. These active moves showing ETFs are not entirely passive highlight the gains of the active management. However, we observe that this activity leads to increased non-fundamental volatility and short-term return reversals, as well as decreased investment-q sensitivity among ETF member stocks, presenting a negative externality. Using a policy shock as the quasi-natural experiment, we establish the causality of these effects, underscoring the dual nature of ETFs active management.

This paper shows that a New Year’s gambling preference of individual investors impacts prices and returns of assets with lottery features. January call options, especially the out-of-the-money calls, have higher retail demand and are the most expensive and actively traded. Lottery-type stocks outperform their counterparts in January but tend to underperform in other months. Retail sentiment is more bullish in lottery-type stocks in January than in other months. Furthermore, lottery-type Chinese stocks outperform in the Chinese New Year’s Month but not in January. This New Year effect pro- vides new insights into the broad phenomena related to the January effect.

Using data from 11 major international markets that celebrate six cultural New Year holidays that do not occur on January 1, we find that stock markets tend to outperform in days surrounding a cultural New Year. After controlling for firm characteristics, an average stock earns 2.5% higher abnormal returns across all markets in the month of a cultural New Year relative to other months of the year. Further evidence suggests that positive holiday moods, in conjunction with cash infusions prior to a cultural New Year, produce elevated stock prices, particularly among those stocks most preferred and traded by individual investors.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese public news articles to predict stock returns. Based on representativeness and influences, we consider seven LLMs: BERT, RoBERTa, FinBERT, Baichuan, ChatGLM, InternLM, and their ensemble model. We show that news tones and return forecasts extracted by LLMs from Chinese news significantly predict future returns. The value-weighted long-minus-short portfolios yield annualized returns between 35% and 67%, depending on the model. Building on the return predictive power of LLM signals, we further investigate its implications for information efficiency. The LLM signals contain firm fundamental information, and it takes two days for LLM signals to be incorporated into stock prices. The predictive power of the LLM signals is stronger for firms with more information frictions, more retail holdings and for more complex news. Interestingly, many investors trade in opposite directions of LLM signals upon news releases, and can benefit from the LLM signals. These findings suggest LLMs can be helpful in processing public news, and thus contribute to overall market efficiency.

Digital transformation (DT) is an ongoing and costly process that requires careful planning and the motivation of top executives (CEOs). This research analyze the CEOs compensation as a motivation to embrace DT by reducing agency issue. We determine the extent of DT through a textual analysis method and utilize data from Chinese publicly traded companies spanning the period between 2007 and 2020. Our study findings are threefold, (a) we observe a positive relationship between CEOs' pay gap and DT, highlighting the significant role CEOs compensation plays in encouraging CEOs to adopt digitalization, (b) we find that managerial shareholding significantly enhances this relationship, (c) we note that the relationship between CEOs pay gap and DT is more pronounced in state-owned enterprises compared to non-stateowned enterprises. Additionally, we discover through channel analysis that agency cost and audit quality mediate the relationship between CEOs pay gap and DT potentially by reducing the agency problem between CEOs and shareholders. These findings are vital for comprehending the pay practices and behaviors of corporate executives regarding digitalization in China. Importantly, the study results remain robust when considering instrumental variables (IV), propensity score matching (PSM), and alternative techniques.

This study investigates the relationship between trade policy uncertainty (TPU) and market diversification with risk-averse firms. We build a model to demonstrate how a risk-averse firm diversifies risks stemming from escalating TPU through entering new markets whose trade policies are negatively correlated with ones in its alreadyentered markets. The positive effect of TPU on market diversification is moderated if the firm has lower risk hedging ability and/or is less risk-averse. Conditional on the TPU in the already-entered markets, there is an inverted-U relationship between TPU in the new market and the probability of entering it. Using a unique firm-productlevel dataset on Chinese exporters, we find robust evidence supporting our theoretical predictions.

This paper develops an SOE (small open economy) dynamic general equilibrium model to study the impact of climate change on the current account. By calibrating the model to Chinese economy, we find the following results. First, the current account-output ratio improves in the first decade following an increase in global temperature caused by climate change. It then deteriorates in the following next three decades. Second, the overall current account-output ratio dynamics in response to climate change is neither affected by the types and stringency of climate policies, nor by the levels and growth rates of temperature increases. Third, the impact of an increase in temperature from 1.28 ℃ to 1.5 ℃ relative to the pre-industrial periods (1850-1900) on the current account-output ratio is equivalent to that of an approximate 0.14% permanent decline in TFP. Finally, although the current account-output ratio is likely to deteriorate in the first year when temperature increases instantly, it might not be true if the coefficient of relative risk aversion, or interest rate premium is larger, or debt sensitivity to interest rate is smaller.

Quantum theory provides a comprehensive framework for quantifying uncertainty, often applied in quantum finance to explore the stochastic nature of asset returns. This perspective likens returns to microscopic particle motion, governed by quantum probabilities akin to physical laws. However, such approaches presuppose specific microscopic quantum effects in return changes, a premise criticized for lack of guarantee. This paper diverges by asserting that quantum probability is a mathematical extension of classical probability to complex numbers. It isn’t exclusively tied to microscopic quantum phenomena, bypassing the need for quantum effects in returns.By directly linking quantum probability’s mathematical structure to traders’ decisions and market behaviors, it avoids assuming quantum effects for returns and invoking the wave function. The complex phase of quantum probability, capturing transitions between long and short decisions while considering information interaction among traders, offers an inherent advantage over classical probability in characterizing the multimodal distribution of asset returns.Utilizing Fourier decomposition, we derive a Schr¨odinger-like trading equation, where each term explicitly corresponds to implications of market trading. The equation indicates discrete energy levels in financial trading, with returns following a normal distribution at the lowest level. As the market transitions to higher trading levels, a phase shift occurs in the return distribution, leading to multimodality and fat tails. Empirical research on the Chinese stock market supports the existence of energy levels and multimodal distributions derived from this quantum probability asset returns model.