Chinese A-share Market Research Articles

Application of Multi-Factor Financial Models in Asset Pricing

This paper explores the theoretical underpinnings and practical applications of multi-factor models in stock portfolio management. It outlines the theoretical frameworks of multi-factor asset pricing models such as APT theory, Fama-French three-factor, and five-factor models, analyzing how various risk factors influence asset returns. The study then discusses methods for identifying core risk factors empirically, constructing portfolios, optimizing weight allocations, and evaluating investment performance. Empirical testing using data from the CSI 300 and CSI 800 indices confirms that optimized multi-factor portfolios achieve significant excess returns while managing risk effectively, showcasing the practical utility of this quantitative investment framework in the Chinese A-share market.

Excess return and tracking errors of Chinese ETFs

We adopt 72 passive equity exchange-traded funds (ETFs) from the Chinese A-share market to examine the size and time-varying characteristics of their tracking errors, identify their determinants, and gain insight into the persistent and positive excess returns. Net asset value traces index better than closing price does; surviving ETFs perform better than delisted ones. The common determinants are fund expenses, liquidity and volatility of the index. Many determinants asymmetrically influence the tracking errors in bull and bear markets. Further examination based on the capital asset pricing model indicates persistent positive excess return is a result of A-share market inefficiency.

Impact of Fintech on labor allocation efficiency in firms: Empirical evidence from China

Fintech has significantly influenced the traditional financial industry by introducing advanced technologies and innovative business models that have resulted in profound impacts. This study examines the effects of Fintech development on labor allocation efficiency and explores its underlying mechanisms. Using a set of Chinese A-share public firms from 2011 to 2020, we find that Fintech development plays a positive role in labor allocation efficiency, mainly by mitigating labor overinvestment. This positive effect is further reinforced by market competition. We also find that the primary pathways of this enhancement include lowering information asymmetry, mitigating agency issues, and substituting low-skilled labor. Moreover, we show that the dimensions of depth and digital integration are particularly important in improving labor allocation efficiency.

The Asymmetric Effect of Mutual Fund Herding on Price Deviation: Evidence from the Chinese Stock Market

Mutual funds, as significant participants in financial markets, play a crucial role in the formation of asset pricing. The relationship between herding behavior of mutual funds and mispricing has been a subject of intense debate among scholars. In this paper, we investigate the asymmetric impact of mutual fund herding on price deviation and explore the underlying mechanism. In terms of research design, this study selects relevant data on mutual fund holdings and stock trading in the Chinese A-share market from 2010 to 2023, we examine whether herding behavior leads to mispricing, specifically focusing on the asymmetric effects of herding buying and herding selling on stock price deviation from intrinsic value. This study distinguishes the trading direction of mutual fund herding behavior and separately examines the relationship between herding buying behavior and herding selling behavior with stock price deviation. Our empirical findings reveal that buying herding behavior is linked to positive price deviations from intrinsic value. However, selling herding behavior shows no significant relationship with price deviation. Furthermore, our results provide evidence that the herding buying of mutual funds stimulates similar trading sentiments among other market participants and contributes to the formation of market bubbles, while herding selling prompts counterbalancing trades, mitigating the impact of price deviation.

Does digital finance drive the green level of transportation companies? Coordination effects of governmental digital preference

This paper examines the influence of digital finance (DF) on the green level of transportation companies (GLTC, n = 112) listed on the Chinese A-share market from 2011 to 2021 through moderation and mediation models. According to the conclusions, first, a significantly positive correlation exists between DF and GLTC. Second, DF indirectly promotes GLTC by enhancing corporate value and alleviating financing constraints. Both enterprise value and financing constraints have a chain mediation effect. Third, the government's digital preference can enhance the role of DF in promoting GLTC and alleviating financing constraints. In addition, governmental digital preference can moderate the impact of financing constraints on GLTC. Fourth, the effect of DF on GLTC exhibits regional, scale, and ownership heterogeneities. Drawing on the conclusions of the empirical analysis, several pertinent recommendations are proposed from micro and macro perspectives.

Does the limelight foster the green transition of listed industrial companies? An empirical analysis from Chinese A-share market

This paper contributes current research by investigating the extent to which equity linkage networks impact enterprise green transition in sustainable development. By adopting a complex network approach, we constructed a common shareholding network based on the top ten shareholders of listed industrial enterprises in Shanghai and Shenzhen A-shares from 2013 to 2022. The empirical results indicate that enterprises closer to the centre of the equity linkage network tend to have higher degrees of green transition. This impact is facilitated through three mechanism channels of capital flow, information exchange, and knowledge transfer within the network. We find enterprises at the core of the network can effectively reduce their financing constraints on enterprises, mitigate risks associated with external environmental uncertainties and managerial myopia, and promote knowledge exchange and innovation cooperation between enterprises. We have also discovered that the centrality of equity network has a greater impact on promoting transition in state-owned, large-scale enterprises, and enterprises with less heavy pollution and the network effect can be enhanced by strong regional environmental regulations. The above findings not only provide policy makers with policy recommendations to guide the enterprises green transition, but also provide industry practitioners with practical paths and directions, which can help promote the green development process of the whole society.

The Impact of ESG Performance on Green Innovation among Traditional Energy Enterprises—Evidence from Listed Companies in China

To address escalating environmental challenges and the energy crisis, traditional energy companies must initiate green transformations and enhance green innovation. ESG (Environmental, Social, and Governance) performance is vital for gauging enterprises’ sustainable development. Therefore, this study explores the relationship between the ESG performance of traditional energy companies and their extent of green innovation. It aims to investigate whether improving ESG performance can lead to enhanced green innovation within these companies. Therefore, this paper employs a fixed effect model to analyze the impact of ESG performance on green innovation among traditional energy companies, specifically focusing on those listed in the Chinese A-share market from 2013 to 2022. The results indicate that ESG performance significantly promotes green innovation within traditional energy companies. The mechanism test’s findings reveal that ESG performance impacts green innovation via three key pathways: innovation investment, external monitoring, and government subsidies. Furthermore, further analysis reveals that the intense market competition environment positively moderates the effect of ESG performance enhancement on the extent of green innovation. This implies that, by improving their ESG performance, traditional energy companies can enhance their green innovation and green transformation efforts. Moreover, this impact is particularly pronounced among state-owned enterprises.

Deep factor asset pricing with policy guidance based on multi-source heterogeneous information

We propose a novel latent factor pricing model to extract latent pricing factors and corresponding factor loadings from multi-source heterogeneous information through a deep learning architecture. Notably, we pioneer the extraction of policy pricing factors from China’s national strategies (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using natural language processing and a dynamic topic model. The proposed mixed-frequency deep factor asset pricing (MIDAS-DF) model learns from mixed-frequency heterogeneous data and captures nonlinear joint patterns between inputs and outputs, providing more nuanced insights into asset pricing. The empirical analyses of the Chinese A-share market from January 1, 2003 to July 31, 2022 show that the MIDAS-DF model outperforms competing models in pricing individual stocks, various test portfolios, and investment portfolios. The results also demonstrate that low-frequency policy information anchors long-term pricing trends, while high-frequency market and sentiment information refine short-term pricing accuracy. They work together to enhance the pricing performance.

Analyzing the Efficacy of the Relative Strength Indicator of Capital Inflows and Outflows Based on Big Data Analysis in Achieving Abnormal Returns Evidence from the Chinese Stock Market

This study originates from an analysis of market microstructure and introduces a novel statistical indicator of relative strength in capital flows through the application of big data analytics. This indicator effectively captures the impact of capital movements on future stock prices by integrating variations in stock prices with the volume of transactions within a corresponding timeframe. Building upon this foundation, the research develops an innovative momentum investment strategy based on the relative strength indicator of capital inflows and outflows, extending beyond the traditional fixed holding period momentum strategy framework. Empirical analysis reveals that this new indicator significantly demonstrates momentum effects in the Chinese A-share market; notably, in portfolios of stocks with smaller market capitalizations, the momentum strategy grounded on the new indicator achieves higher excess returns compared to traditional momentum strategies. These findings not only validate the efficacy of the new indicator but also underscore the critical role of big data analytics in enhancing the analysis of financial markets and the efficiency of investment decision-making.

Anatomy of recent value premium's travails

The recent underperformance of the value effect has prompted investors to question whether the value effect has lost its potency. In the Chinese A-share market where the value effect has vanished like in the U.S. market, there is a negative relationship between value and composite profitability, a metric comprising profitability levels and earnings growth. This steadily intensifying relationship, coupled with the positive and stable composite profitability premium, causes the weakening effect of composite profitability on the value premium to grow more pronounced over time. This substantial dampening effect, the primary driver of the time variations of the value premium, is precisely why the value premium, although impressive in its early stages, remains ineffective over the nearly two decades of the entire sample period. When the interference of composite profitability is removed, the value effect experiences a significant resurgence, boosting returns to an astonishing 1.97% per month.

An Index of Skilled Analysts and Stock Market Returns

ABSTRACT This study constructs a monthly index of skilled analysts (ISA) regarding the coverage by such analysts and explores their predictive ability on the future excess returns of Chinese A-share market. The empirical results reveal that ISA significantly and positively predicts excess stock market returns, with R 2 s of 4.39% and 4.13% in- and out-of-sample respectively, which surpasses the predictive ability of most commonly used economic variables. High ISA precedes a higher investor sentiment index (ISI) and lower idiosyncratic volatility (IVOL) at firm level. The conclusion remains valid after controlling for a series of other predictor variables and performing robustness tests.

Can Investor Sentiment Explain the Abnormal Returns of Volatility-Managed Portfolio Strategy? Evidence from the Chinese Stock Market

ABSTRACT This study develops a theoretical model to capture volatility-managed portfolios’ risk-adjusted returns affected by investor sentiment, and uses Chinese A-share market data to analyze the volatility-managed effect based on the small-minus-big (SMB) factor and explain the abnormal returns of volatility-managed portfolios from the investor sentiment perspective. Our results show that the Sharpe ratio of the SMB factor significantly increases after volatility management, especially in low-sentiment periods. Moreover, the mechanism analysis shows that in low-sentiment periods, small-cap stock investors overreact to volatility compared to large-cap stock investors, making the SMB factor significantly positively correlated with lagged volatility. Additional analyses show that volatility-managed portfolios constructed through stocks with gambling characteristics can achieve higher abnormal returns, and the abnormal returns of volatility-managed portfolios cannot be corrected by arbitrage traders as arbitrage is ineffective, reflecting the immaturity of China’s emerging capital market.

Management risk appetite, internal control and corporate financialization

This paper examines the impact of management risk appetite on corporate financialization among non-financial firms listed on the Chinese A-share market over the period 2009–2021. The empirical analysis shows that there is a significant positive relationship between management risk appetite and corporate financialization. In addition, the moderation effect results highlight that firms' internal controls play a negative moderating role between management risk appetite and financialization. These findings have far-reaching implications for regulators, standard setters and corporate governance bodies, as they highlight the need for effective internal control systems to ensure financial stability.

The Incentive Effect of Fintech Policies on Corporate Green Technological Innovation: A Study Based on the Chinese A-Share Market

Against the backdrop of the rapid development of global fintech, the formulation and implementation of China's fintech policies have attracted widespread attention. These policies not only affect the financing efficiency and market competitiveness of enterprises but also have a profound impact on their green technological innovation. This paper analyzes data from Chinese A-share listed companies between 2007 and 2022 to explore how fintech policies specifically influence corporate green technological innovation. The research findings indicate that fintech policies have significantly promoted the green technological innovation of enterprises. This discovery remains robust after various robustness tests, including parallel trend tests, variable and model changes, sample adjustments, and propensity score matching methods. Further analysis reveals that the impact of fintech policies on green technological innovation is particularly significant for enterprises with weaker profitability, shorter establishment time, non-Big Four accounting firm audits, and smaller scales. Mechanism analysis further points out that fintech policies effectively reduce the costs and agency problems in the green technological innovation process by alleviating financing constraints. This study not only provides theoretical support for the economic effects of fintech policies but also offers empirical evidence for policymakers on how to use fintech policies to promote corporate green technological innovation, which has important practical value for promoting corporate sustainable development and addressing global climate change.

Does stock liquidity affect expropriation behavior by controlling shareholders? Evidence from China

When a firm is controlled by a large shareholder, the principal agency problem arises from expropriation by controlling shareholders of other shareholders. Using a sample of 3776 publicly traded firms in the Chinese A-share market over the period 2007–2020, this study investigates whether stock liquidity impedes or enhances the expropriation behavior of controlling shareholders. I demonstrate that (1) a liquid stock market generally encourages expropriation behavior by controlling shareholders, (2) the positive effect of stock liquidity on expropriation by controlling shareholders is mitigated when active investors hold a large stake in the firm, and (3) active investors’ monitoring of expropriation by controlling shareholders is mitigated at state-owned enterprises and firms that have a large gap between ownership and control. However, higher competition among blockholders strengthens the discipline of active investors with respect to expropriation. This study builds a new link between a market factor (stock liquidity) and a governance problem (expropriation by controlling shareholders) and reveals some new characteristics in the relationship between stock liquidity and corporate governance in an emerging market.

Exacerbate or alleviate? Impact of controlling shareholders' share pledging on over-investment

This paper investigates the relationship between controlling shareholders' share pledging and firm level over-investment. Utilizing Chinese A-share market data spanning 2007–2020, we find that firms with share pledging by controlling shareholders are more prone to over-invest, and the over-investment degree becomes severer as the pledging percentage increases. In addition, we observe this effect more pronounced in firms characterized by more concentrated ownership structure. Our findings still hold after the endogeneity disposal with a firm-fixed effect regression, Heckman two-stage regression with instrumental variables, the propensity score matching (PSM) and differences-in-differences (DID) approach. Furthermore, we observe that firms with controlling shareholders' share pledging tend to be concerned more with additional investments while neglecting maintenance investments and R&D investments. Combined with the motivation of controlling shareholders to gain substitute cash flow rights and convey good signals to the market, as well as the feasibility by using their dominate voting rights and pledged funds, it properly illustrates the mechanism through which controlling shareholders' share pledging impact over-investment, which meanwhile aggravates the principal-principal (PP) conflicts between controlling shareholders and minority shareholders. Consequently, our findings empirically demonstrate that signaling costs in signaling theory could be explicit conflicts in the agency theory, adding new empirical evidence for the relationship between signaling theory and agency theory.

Research on the Amplification Effect of Trading Volume on Mispricing in the Chinese A-share Market

With the continuous growth of the economy, the residents' demand for preserving and appreciating family wealth has gradually become prominent. The stock market has become the primary choice for investors pursuing asset appreciation. Investors expect to achieve higher returns through scientific methods, and factor models, as well as mispricing, have become the focus of attention, aiming to obtain excess returns through market anomalies. However, with technological advances, the complexity of the market is increasing, making it increasingly difficult to find excess returns through pricing models. As trading volume is the most fundamental indicator in the market, containing many effective market insights and capturing investor disagreements, it is necessary to delve into the role of trading volume in the impact of mispricing on expected returns. This research selects all A-share stocks from 2000 to 2022, excluding the Sci-Tech Innovation Board, measuring mispricing through the anomaly in pricing models. Then, by independently double-sorting mispricing and trading volume into 5x5 portfolios, constructing market-weighted investment portfolios, and holding for one month, we observe the predictive ability of mispricing for future returns at different levels of trading volume. The results reveal that in stocks with high trading volume, the predictive ability of mispricing is stronger. Specifically, in stocks with high trading volume, the difference in future returns between the undervalued stock portfolio and the overvalued stock portfolio is greater than the difference in returns in low trading volume stocks. In other words, trading volume plays a moderating role in the impact of mispricing on expected returns, enhancing the predictive ability of mispricing on returns.

Research on Problems and Countermeasures for the Chinese A-share Market

Nowadays, the A-share market plays an important role in Chinese society and promotes China’s economic development. It has strengthened the public's awareness of investment and financial management, promoted the progress and growth of many listed companies, and increased the government's fiscal revenue and credibility. Although the A-share market has accumulated lots of wealth and provided development opportunities for China, it also faces great challenges. This paper will introduce the current situation of China's A-share market, several existing problems and corresponding countermeasures and suggestions, in order to create healthy and sustainable development of the A-share market. The proposed problems are the high volatility of stock market, large number of inferior listed companies and serious illegal phenomena. Finally, the research can help investors protect their rights effectively and facilitate the work of the market supervisor.

Testing the Validity of the Multi-Factor Model in the Chinese Stock Market

In the wake of the burgeoning field of quantitative finance, factor models have risen to considerable prominence within the Chinese financial landscape. This research undertakes a comprehensive analysis employing data spanning the years 2014 to 2022, encompassing a total of 108 monthly observations sourced from the Chinese A-share stock market. The principal objective of this study revolves around the evaluation of the effectiveness of an array of common factors. These factors include the market risk premium, market value, book-to-market ratio, profitability, investment, momentum, and liquidity factors. Moreover, our inquiry extends beyond the conventional boundaries of the Fama-French three-factor and five-factor models. It introduces the pivotal elements of momentum and liquidity factors, effectively formulating an enriched model poised to offer a more robust framework for understanding and explaining returns. The discerned findings shed valuable light on the model configurations most apt for dissecting the excess returns exhibited within the sample stocks. Notably, the amalgamation of the three-factor model with the strategic incorporation of the liquidity factor emerges as the most comprehensive explanatory model for the observed excessive returns in the context of the Chinese A-share stock market. It is important to highlight that the introduction of the momentum factor, while explored, does not impart a significant augmentation in the model's capacity to clarify excess returns, a noteworthy departure from expectations within the Chinese A-share market. These insights not only advance our understanding of the Chinese financial landscape but also underscore the intricate dynamics at play in the realm of quantitative finance.

Replicating and Digesting Anomalies in the Chinese A-Share Market

We replicate 469 anomaly variables similar to those studied by Hou et al. (2020) using Chinese A-share data and a reliable testing procedure with mainboard breakpoints and value-weighted returns. We find that 83.37% of the anomaly variables do not generate significant high-minus-low quintile raw return spreads. Further adjusting risk increases the failure rate slightly to 84.22% based on CAPM alphas and 86.99% based on Fama–French three-factor alphas. We show that the conventional procedure using all A-share breakpoints with equal-weighted returns for the anomaly test is indeed problematic as it assigns too much weight to microcaps and has a very limited investment capacity. The CH3-factor, CH4-factor, and q-factor models show the best performance over the whole sample period. The q-factor model is the best performer in the post-2007 subsample period after significant improvements occurred in China’s financial market environment, such as the completion of the split-share structure reform and the implementation of new accounting standards conforming to the International Financial Reporting Standards. The non–state-owned enterprise subsample in the post-2007 period is a cleaner sample in which the CH4-factor and q-factor models are the best performers. This paper was accepted by Lukas Schmid, finance. Funding: Z. Li acknowledges financial support from the National Natural Science Foundation of China [Grant 72103043] and the Fundamental Research Funds for the Central Universities in UIBE [Grants 19QN01 and 22PY053-72103043]. L. X. Liu acknowledges financial support from the National Natural Science Foundation of China [Grants 71872006 and 72273006]. L. X. Liu and X. Liu acknowledge financial support from the Guanghua Thought Leadership Platform of Peking University. K. C. J. Wei acknowledges partial financial support from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant 15507320]. The authors acknowledge financial support from the Guanghua School of Management, Peking University; the School of Banking and Finance, University of International Business and Economics; and the Non-PAIR Research Centre “Research Centre for Quantitative Finance” at Hong Kong Polytechnic University. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4904 .