This paper studies how regulatory changes affect investors’ reactions at shareholder meetings in China. The objective of this paper is twofold: first, to analyse the information content transmitted to the shareholders of the largest Chinese companies listed on the China Securities Index 300 when an Annual General Meeting is held. A distinction is made between ordinary and extraordinary general meetings. Second, to find out if regulatory changes related to the Company Law of China and online voting in Annual General Meetings affect the information content of those meetings. The abnormal return obtained is examined through an event study using the Fama-French five-factor model. The results of our study indicate that the release of information and involvement of minority shareholders in general meetings during the research period led to higher return volatility and traded volume.

This paper studies the Level, Slope, and Curve factor model under different tests in the Chinese stock market. Empirical asset pricing tests reveal that the slope factor in the model represents either reversal or momentum effect for the Chinese stocks. Further tests on individual stocks demonstrate that the Level, Slope, and Curve model using effective predictor variables outperforms other common factor models, thus a filter in virtue of multiple hypothesis testing is designed to identify the effective predictor variables. In the filter models, the cross-section anomaly factors perform better than the time-series anomaly factors under different tests, and trading frictions, momentum, and growth categories are potential drivers of Chinese stock returns.

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.

There is mixed evidence of a positive relationship between the stock market risk and return. We reexamine this critical implication of asset pricing theory using fresh data from China's stock market, which is largely segmented from the rest of the global financial market. Using formal variable selection methods and a comprehensive set of predictor variables, we identify conditional market variance, scaled market prices, and inflation as crucial determinants of equity premiums. The estimated simple risk-return relationship exhibits downward omitted variable bias, which underlines the importance of considering multiple factors to explain the variation in equity premiums. We cannot wholly attribute the three-factor conditional equity premium model to data mining, as Guo, Sanni, and Yu (2022) select the same model for the U.S. stock market. These findings challenge existing asset pricing models and provide valuable guidance for future theoretical research.

We investigate the cross-sectional factors of Chinese corporate bond returns via the reducedrank regression analysis (RRA) proposed by He et al. (2022). We collect 37 individual bond characteristics in the extant literature using a new dataset and construct 40 factor portfolios. Empirically, we find that the four-factor models created by RRA outperform the traditional factor models, PCA, and PLS factor models, both in-sample and out-of-sample. Among the 40 factors, the bond market factor is the most substantial predictor of future bond returns. In contrast, other factors provide limited incremental information for the cross-sectional pricing. Therefore, it is necessary to find more new bond factors. We further find that stock market anomalies do not improve the explanatory power of the RRA factor models. In particular, stock market anomalies can only partially explain the systematic part of bond returns in the RRA framework and have almost no explanatory power for the idiosyncratic component.

In this paper, we identify cross-sectional anomalies in excess returns of industrial bonds at the issuer and secondary market levels, and find that liquidity, risk, and historical return variables can generate cross-sectional excess returns that cannot be explained by traditional bond factors. We also introduce a risk premium factor that is economically and statistically significant in industrial bonds based on the risk characteristics prevalent in credit bonds and that cannot be explained by long-standing bond market factors. We show that the newly identified risk factor outperforms the other anomalies considered in this paper in explaining the cross-sectional returns of industrial bonds.

We propose a Capital Asset Pricing Model where investors exhibit prospect preferences. In equilibrium, we find that agents seek an optimal trade-off between expected returns, variance, and skewness. All assets in the economy are then priced by a three-factor model, which augments the security market line with two factors that respectively capture positive and negative coskewness with the market portfolio. Using U.S. stock market data, we find evidence consistent with these predictions. In additional tests, we find that the results are stronger among stocks traded by less sophisticated investors. Overall, prospect preferences have a substantial effect on stock prices.

We have developed two novel salience factors — PMOR and PMOV based on the stock’s salient return and salient trading volume (as proposed by Cosemans and Frehen, 2021, and Sun et al., 2023). Notably, these factors cannot be accounted for by existing factor models in China. When we integrate the salience trading volume factor — PMOV into Liu et al. (2019)’s Chinese three-factor model, the resulting four-factor model outperforms other models including the Chinese four-factor model in explaining 33 significant anomalies in China.

In the era of big data, asset pricing is influenced by various factors, which are extracted from multi-source heterogeneous information, such as high frequency market and sentiment information, low frequency firm characteristic and macroeconomic information. Especially, low frequency policy information plays a significant role in the long-term pricing in China but it is barely investigated due to its textual form. To this end, we first extract policy variables from major national development plans (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using Natural Language Processing (NLP) technique and Dynamic Topic Model (DTM). However, traditional models are inadequate for mixed frequency data modeling and feature extraction. Then, we propose a mixed frequency deep factor asset pricing model (MIDAS-DF) that solves the asset pricing problems under the mixed frequency data environment through mixed data sampling (MIDAS) technique and deep learning architecture. Time-varying latent factors and factor loadings can be modeled from mixed frequency data directly in a nonlinear and data-driven way. Thus, the MIDAS-DF model is able to learn the nonlinear joint-patterns hidden in multi-source heterogeneous information. Our empirical studies of 4939 stocks on the Chinese A-share market from January 2003 to July 2022 demonstrate that low frequency policy information has profound impacts on asset pricing, which anchors the long-term pricing direction, and high frequency market and sentiment information have significant influences on stock prices, which optimize the short-term pricing accuracy, they together enhance the pricing effects. Consequently, pricing effects the MIDAS-DF model outperform the five competing models on individual stocks, various test portfolios, and investment portfolios. Our research about heterogeneous information provides implications to the government and regulators for decision-support in policy-making and our investment portfolio is of great importance for investors’ financial decisions.

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.