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.

金融资产因子结构映照着风险与收益的权衡，因子模型能否同样描绘期权收益？期权合约存续时间极短、风险敞口变化频繁，难以应用传统因子模型进行定价。工具主成分分析方法（IPCA）提供了新的解决方案，动态风险载荷形式与期权风险特征高度吻合。本文尝试采用IPCA模型揭示上证50ETF期权的因子结构。研究结果表明，三因子IPCA模型能够解释超过87%的单个期权收益变化和超过99%的投资组合收益变化，表现优于现有的期权因子模型以及静态PCA模型。IPCA因子与期权在值状态偏度、剩余期限斜率以及Gamma价值紧密联系，能够解释40%至60%的因子变化。本文的研究对于优化投资组合风险管理具有重要意义，有助于监管者提高期权市场定价效率，促进衍生品市场稳健发展。

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.

This paper employs a difference-in-differences approach to study whether a Chinese audit firm improves its competencies through organizational learning after one of its audit teams has a client cross-listed in the US. Among a group of companies that are only listed in China, we define those audited by Chinese audit firms that are not international Big 4 affiliates and have cross-listed clients as the treatment group, and companies audited by other audit firms as the control group. We find an improvement in audit quality for the treatment group after their audit firms have cross-listed client experience in the US, and this improvement is not attributable to the effect of joining an international accounting firm network, registration with the PCAOB, or the consolidation in the audit market. A survey of auditors corroborates these findings and provides evidence on audit firms’ specific actions to facilitate learning. Our findings shed light on the benefits of auditing cross-listed clients in the US and its positive externality on improving the audit quality of non-US-listed companies in China.

投资者对宏观经济风险的评估如何影响资产价格一直是实证资产定价的难点之一。已有研究指出新闻文本会改变投资者对宏观经济的判断和预期进而影响股价，但如何有效提取与宏观经济风险相关的文本叙事信息来解释或预测资产价格变动，尚未达成共识。本文基于2007-2021年中国七家专业财经媒体的新闻报道数据，首次结合人工智能前沿领域的BERT大语言模型来测度新闻叙事注意力信息，然后利用稀疏工具主成分（Sparse IPCA）估计影响基本面的状态变量和影响资产价格的叙事定价因子。基于A股市场的实证检验发现：第一，本文利用新闻文本估计的状态变量对于消费、产出、国债收益率等宏观经济指标具有显著的预测效果，这表明新闻叙事蕴含着影响经济运行的信息。第二，相比CAPM、三因子等基准模型，基于新闻文本构建的叙事因子模型能更好地解释资产错误定价现象，并对未来资产价格的变化有更强的预测能力。第三，与基于关键词的文本分析方法（如LDA主题模型）相比，利用BERT提取文本信息可在保证因子模型简约性的基础上获得更优异的定价效果。本文的研究结论对于解释资产横截面收益差异有新的启示，同时为应用大语言模型于经济金融学研究抛砖引玉。

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.

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 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.