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.

在“上市”成为我国商业银行发展趋势的背景下。本文选取2007年我国13家上市银行，根据其2007年年报数据，利用主成分分析方法及构建上市银行效率水平模型，测度各银行的效率值。研究发现目前我国上市银行的效率较低，成长性状况较好，盈利水平有待于提高，银行间效率差距水平显著。最后本文就此提出简短的对策建议。