Individual institutions marginal contributions to the systemic risk contain predictive power for its potential future exposure and provide early warning signals to regulators and the public. We use higher-order co-skewness and co-kurtosis to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

This paper proposes a conditional factor model embedded with heterogeneous group structure, called grouped Instrumented Principal Component Analysis (Grouped IPCA) model, to study the enhancement of industry classifcations on the pricing power of frm characteristics. We derive an inferential theory on the alternating least square (ALS) estimators of the grouped IPCA model under an unbalanced panel data. Based on this, we use two BIC-type information criteria to determine the number of latent factors. We further examine the group heterogeneity with a bootstrap test statistics. Simulations are conducted to evaluate both our asymptotic theory and test statistics. In the empirical study, we show that the in-sample performance of Grouped IPCA model excels the IPCA model, and fnd a strong evidence on the incremental pricing power of industries.

We apply various test portfolios and alternative statistical methodologies to evaluate the performance of eleven prominent asset pricing models. To compile the test portfolios, we construct 105 anomalies in China and apply the 23 significant anomalies as test assets for model comparison. The results indicate that in the time-series test and anomalies explanation, the Hou et al. (2019) five-factor q model exhibits the best overall performance. The pairwise cross-sectional R^2s and the multiple model comparison tests affirm that the Hou et al. (2019) five-factor q model, the Fama and French (2018) six-factor (FF6) model and the Kelly et al. (2019) five-factor Instrumented Principal Component Analysis (IPCA5) model stand out as the top performers. Notably, the performance of the five-factor q model is insensitive to variations in experimental design.

Using a comprehensive factor zoo, we document a notable factor MAX premium in the Chinese market. Factors with high maximum daily returns consistently outperform those with low maximum returns by 0.82% per month in the future, on a risk-adjusted basis. This premium remains robust controlling for various factor characteristics, and is not sensitive to the selection of factors. The factor MAX anomaly stands apart from lottery-type stock anomalies and contributes to elucidate most of these anomalies. The factor MAX premium concentrates in high-eigenvalue principal component factors, shedding light on the prevalent lottery preferences for factor investing in China’s A-share market. We document pronounced existence of factor MAX anomaly in the United States and other G7 countries.

Using a comprehensive factor zoo, we document a notable factor MAX premium in the Chinese market. Factors with high maximum daily returns consistently outperform those with low maximum returns by 0.82% per month in the future, on a risk-adjusted basis. This premium remains robust controlling for various factor characteristics, and is not sensitive to the selection of factors. The factor MAX anomaly stands apart from lottery-type stock anomalies and contributes to elucidate most of these anomalies. The factor MAX premium concentrates in high-eigenvalue principal component factors, shedding light on the prevalent lottery preferences for factor investing in China’s A-share market.

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.

We analyze the latent factors for the Chinese market through the recently proposed risk premium principal component analysis (RP-PCA). Our empirical research covers 95 firm characteristics. We demonstrate that the RP-PCA on the Chinese market can identify factors that capture co-movements and explain pricing. Compared to the traditional PCA approach, it explains a larger proportion of return variation in both double-sorted and single-sorted portfolios. The Sharpe ratios of the tangency portfolios are significantly higher than those of the standard PCA. Additionally, we show that the RP-PCA loadings are more closely associated with factor returns.

Regional financial development has greatly supported the rapid growth of Chinese municipal corporate bonds. This study introduces the concept of regional financial resources and constructs an informative measure of regional financial development by using principal component analysis (PCA), incorporating 13 indicators from three primary financial industries, including bank, security and insurance. Using a sample of municipal corporate bonds (MCBs) issued in China from 2009 to 2019, we find that an increase in regional financial development is associated with significant MCB credit spreads narrowing. This effect can be realized by improving fiscal stability and debt sustainability. Additionally, this narrowing varies among cities and provinces with different fiscal conditions and economic development. The results are also verified through a series of robustness tests. This study proposes possible policy suggestions for improving the Chinese fiscal management and MCBs market.

The macroeconomic risk associated with climate change potentially results in a risk premium on asset prices. Using a sample of 11,468 Chinese quasi-municipal bonds from 2014-2021 in 267 cities, this research investigates the impact of climate risk on the credit spreads of quasi-municipal bonds. We employ principal component analysis (PCA) to construct a climate risk index and find that climate risk significantly increases credit spreads by increasing the local government fiscal gap and debt burden. The effect of climate risk is more remarkable for bonds that have shorter maturity and lower corporate ratings, issued by smaller city investment companies and corporations located in regions with stronger environmental regulation, stronger climate risk perception, and better green financial development. A significant relationship is also observed in the eastern regions but not the western regions. This study broadens the scope of quasi-municipal bond credit spread determinants from traditional financial to climate indicators.

In this paper, we examine the prediction performance using a principal component analysis (PCA). In particular, we perform a PCA to identify significant factors (principal components) and then use these factors to form predictions of stock price movements. We apply this strategy on the Chinese stock markets. Using data from January 2, 2019 till September 16, 2021, the empirical results show substantial out-performances from the PCA-based predictions against a naïve buy-and-hold strategy and also single time-series predictions of individual stocks. Next we examine if the factors retrieved from PCA are indeed important contributing factors in explaining stock price movements. To do this, we adopt a machine learning technique popular in studying stock performances – random forest. We discover that, comparing to widely used descriptive factors such as industry sector, geographical location, and market types (known as “board” or “ban” in Mandarin), principal components rank very highly among those descriptive factors.