Using “the Measures for the Administration of Bank Wealth Management (henceforth BWM) Funds Sales” as an exogenous shock in fund distribution channels in Chinese BWM industry, we investigate the impact of non-affiliated distribution on fund performance. We find that the adoption of non-affiliated distribution brokers has a positive effect on BWM fund performance. We further find that the effect is more pronounced when the non-affiliated distribution broker has more market power and when the fund issuer has better governance. We interpret our findings to indicate that non-affiliated distribution brokers alleviate the agency problems of fund managers by introducing both ex-ante and ex-post monitoring, highlighting the role of non-affiliated distribution brokers as an external governance mechanism in wealth management industry.

In the face of severe climate change, researchers have looked for assistance from financial instruments. They have examined how to hedge the risks of these instruments created by market fluctuations through various green financial derivatives, including green bonds (i.e., fixed-income financial instruments designed to support an environmental goal). In this study, we designed a green bond index option contract. First, we combined an autoregressive moving-average model (AMRA) with a generalized autoregressive conditional heteroskedasticity model (GARCH) to predict the green bond index. Next, we established a fractional Brownian motion option pricing model with temporally variable volatility. We used this approach to predict the closing price of the China Bond–Green Bond Index from 3 January 2017 to 30 December 2021 as an empirical analysis. The trend of the index predicted by the ARMA–GARCH model was consistent with the actual trend and predictions of actual prices were highly accurate. The modified fractional Brownian motion option pricing model improved the pricing accuracy. Our results provide a policy reference for the development of a green financial derivatives market, and can accelerate the transformation of markets towards a more sustainable economic development model.

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.

Prior literature shows that implied volatility spreads between call and put options are positively related to future underlying stock returns. In this paper, however, we demon- strate that the volatility spreads are negatively related to future out-of-the-money call option returns. Using unique data on option volumes, we reconcile the two pieces of evidence by showing that option demand by sophisticated, firm investors drives the posi- tive stock return predictability based on volatility spreads, while demand by less sophis- ticated, customer investors drives the negative call option return predictability. Overall, our evidence suggests that volatility spreads contain information about both firm funda- mentals and option mispricing.

This article proposes a semi-martingale approximation to a fractional Lévy process that is capable of capturing long and short memory in the stochastic process together with fat tails. The authors use the semi-martingale process in option pricing and empirically compare its performance to other option pricing models, including a stochastic volatility Lévy process. They contribute to the empirical literature by being the first to report the implied Hurst index computed from observed option prices using the Lévy process model. Calibrating the implied Hurst index of S&P 500 option prices in a period that covers the 2008 financial crisis, they find that the risk-neutral measure is characterized by a short memory in turbulent markets and a long memory in calm markets.

Using three natural experiments, we test the hypothesis that investor overconfidence produces overpricing of high idiosyncratic volatility stocks in the presence of binding short-sale constraints. We study three events: IPO lockup expirations, option introductions, and the 2008 short-sale ban on financial firms. Consistent with our prediction, we show that when short-sale constraints are relaxed, event stocks with high idiosyncratic volatility tend to experience greater price reductions, as well as larger increases in trading volume and short interest, than those with low idiosyncratic volatility. These results hold when we benchmark event stocks with non-event stocks with comparable idiosyncratic volatility. Overall, our findings suggest that biased investor beliefs and binding short-sale constraints contribute to idiosyncratic volatility overpricing.

Many corporate executives believe blockchain technology is broadly scalable and will achieve mainstream adoption, yet there is little evidence of significant shareholder value creation associated with corporate adoption of blockchain technology. We collect a broad sample of firms that invest in blockchain technology and examine the stock price reaction to the “first” public revelation of this news. Initial reac- tions average close to +13% and are followed by reversals over the next 3 months. However, we report a striking differ- ence based on the credibility of the investment. Blockchain investments that are at an advanced stage or are con- firmed in subsequent financial statements are associated with higher initial reactions and little or no reversal. The results suggest that credible corporate strategies involving blockchain technology are viewed favorably by investors.

This paper examines the effect of financial shared service centers (FSSCs) on corporate misconduct. Using a sample of Chinese public companies with hand-collected FSSC data, we find that the adoption of FSSCs is negatively associated with the likelihood and frequency of corporate misconduct. The results hold to a battery of robustness tests. Moreover, we show that the negative association between FSSCs and corporate misconduct is more pronounced in firms that have no management equity ownership, disclose internal control weaknesses, and have more subsidiaries. Additional analyses indicate that FSSCs can help mitigate both disclosure-related and nondisclosure-related misconduct.

This paper explores the ethical dimensions of firm-level digitalisation and its impact on ESG metrics during a decade (2010-2020) of rapid technological progress, focusing on Chinese-listed companies. Utilising a text-based index to measure digitalisation, we find that while digitalisation positively influences ESG ratings, supporting resource-based and dynamic capability theories, its relationship with corruption reveals complex dynamics. Surprisingly, corruption strengthens digitalisation’s positive impact on ESG, raising concerns about technology being used to enhance ESG appearances artificially. A distinct difference emerges between state-owned enterprises (SOEs) and non-SOEs; SOEs use digitalisation more ethically and are less influenced by corruption, indicating a more responsible approach to technology adoption. Through examining cash holdings, internal controls, and audit fees, we unpack how corruption influences the digitalisation-ESG nexus. These insights underscore the need for policy that encourages ethical digitalisation and highlight the potential role of SOEs in leading the charge towards sustainable and ethical digitalisation.

This paper studies the impact of AI technology on the mutual fund industry. I develop a new measure of AI adoption based on hiring practices and find that this measure can predict fund performance. The funds with high AI ratio outperform non-AI funds, after I controlling for standard factors and fund characteristics. Further empirical evidence shows that funds with a high AI ratio tilt their portfolios toward high information intensity stocks, indicating that mutual funds benefit from AI technology adoption by improving their information capacity. Consistent with this channel, I find that the outperformance of these mutual funds mainly comes from better stock picking skills. Finally, AI technology adoption has a negligible effect on fund manager turnover.