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.

This study introduces a novel factor model based on self-attention mechanisms. This model effectively captures the non-linearity, heterogeneity, and interconnection between stocks inherent in cross-sectional pricing problems. The empirical results from the Chinese stock market reveal compelling ffndings, surpassing other benchmarks in terms of profftability and prediction accuracy measures, including average return, Sharpe ratio, and out-of-sample R2. Moreover, this model demonstrates both practical applicability and robustness. These results provide valuable evidence supporting the existence of the three aforementioned properties in crosssectional pricing problems from a theoretical standpoint, and this model offers a powerful tool for implementing profftable long-short strategies.

In contrast to previous literature, our study not only examines the communicative value of Key Audit Matters (KAMs) through the capital market reaction to KAMs but also analyses the content and reporting format of KAMs, which vary based on the intrinsic risk of business activity. Using a sample of Chinese firms from 2017 to 2020, we find that more M&A-related KAMs are reported and they are disclosed through less boilerplate language when M&As are accompanied with the Performance Commitment contracts (PCs), an indicator as high possibility of overpayment during M&As thus inducing the high risk of the goodwill impairment and high litigation risk. Additionally, we find that the negative impact of PCs on boilerplate language is amplified when the benchmark in PCs is precisely achieved or when the firm has been sued in recent years. In other words, the disclosure of M&A-related KAMs is more tailored to the client firm when auditors observe a high risk for accountability. Consequently, capital market participants, as well as other recipients of auditing reports, such as regulators and analysts, perceive non-boilerplate M&A-related KAMs as informative for their decision-making process.

We develop a machine learning (ML) approach to establish new insights into how memory affects ffnancial market participants’ belief formation processes in the field. Using analyst forecasts as proxies for market beliefs, we extract analysts’ mental contexts and recalls that shape forecasts by training an ML memory model. First, we find that long-term memories are salient in analysts’ recalls. However, compared to an ML benchmark trained to fit realized earnings, analysts pay more attention to distant episodes in regular times but less during crisis times, leading to recall distortions and therefore forecast errors. Second, we decompose analysts’ mental contexts and show that they are mainly shaped by past earnings and forecasting decisions instead of current firm fundamentals as indicated by the ML benchmark. This difference in contexts further explains the recall distortion. Third, our comprehensive memory model reveals the significance of specific memory features and channels in analysts’ belief formation, including the temporal contiguity effect and selective forgetting.

China’s housing purchase restriction (HPR) policy imposes administrative restrictions on households’ home purchase eligibility to curb speculative demand. We quantify households’ willingness to pay (WTP) to re-acquire such eligibility. The empirical results based on the staggered DID specification suggest that when local governments implement the HPR policy, the transaction prices of judicial housing auctions legally exempted from HPR increase by 18.91%. This HPR-exempted qualification premium can be converted to an estimate of 22.48% of the transaction price as buyers’ WTP for home purchase eligibility. The heterogeneity analysis also suggests that the WTP significantly increases when speculative incentives are stronger in the housing market. If policymakers in mainland China consider replacing the HPR policy with an additional buyer transaction tax like that in Singapore and Hong Kong, China, the WTP estimates can serve as the benchmark in setting the tax rate.

A deep learning method is applied to predict stock portfolio allocation in the Chinese stock market. We use 6 original price and volume series as benchmark model settings and further explore the model's predictive performance with social media sentiment. Our results show that our model can achieve a high out-of-sample Sharp ratio and annual return. Moreover, social media sentiment could increase the performance for both Sharp ratio and annual return while reducing annual volatility. We provide an end-to-end stock portfolio allocation model based on deep neural networks.

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.

This paper uses ensemble learning to build a predictive model to analyze the short selling mechanism of short institutions. We demonstrate the value of combining domain knowledge and machine learning methods in financial market. On the basis of the benchmark model, we use three input data: stock price, financial data and textual data and we employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. In specific methods, we use LSTM-AdaBoost and CART-AdaBoost for model prediction. The results show that the model we train have strong prediction ability for short-selling and the company' s financial text data is more likely to have an impression of whether it would be shorted or not.

We identify both dynamic and long-run relationships between each of the level, slope and curvature factors of the Treasury and corporate bond markets yield curve in China. We aim at determining which market plays a leading role in the discovery of each factor of the yield curve. We obtain three main results. First, we document for the first time the presence of a long-run relationship between the corporate and Treasury bond markets in China both for the level and the slope of their yield curve. Second, such a long-run relationship appears to be stable between the slopes over the full sample 2006-2017, but shows a break for the level factor in 2012. Third, the source market for price discovery varies with the parameters of the yield curve. While the corporate bond market is the source of price discovery for the level factor, this function is fulfilled by the government bond market for the slope parameter. The finding that the Treasury bond market is not fully dominant in level bond-pricing may not come as a surprise. Although China’s corporate bond market has developed rapidly in the past fifteen years, there were few default cases during that period. It is believed investors treat the default risk of corporate bonds as similar to that of Treasury bonds, and benefit from the high corporate spread. Our results for the slope parameter imply that market-oriented reform has progressed enough for the Treasury bond market to already provide a benchmark slope for the yield curve of corporate bonds. When the reform progresses further, we would expect corporate bonds to be priced according to their risk profile which should make the Treasury market lead in price discovery also for the level of the yield curve.

The adequate evaluation of mutual fund performance and of the fund managers’ ability to add value is an issue to which it has been given special attention in the recent financial literature. One of the traditional evaluation measures most commonly used is Carhart's alpha. However, one of the main problems of the evaluation methods that use the beta of the portfolios as a measure of risk and, therefore, Carhart's alpha is its sensitivity to the definition of the market portfolio. In this work we study the importance of defining the market portfolio using Carhart's alpha for a sample of UK mutual funds, and the influence of this market portfolio in the funds´ excess returns and in the performance ranking classification of the fund sample.