Chinese A-share Stock Research Articles

A three-level nested portfolio optimization model with position allocation

Existing portfolio optimization models cannot well capture the real position allocation requirement, leading to limited impact in practice. To overcome this challenge, we propose a three-level nested portfolio optimization model with position allocation. Within this model, the inner and middle levels collaboratively determine the optimal portfolio, while the outer level focuses on optimizing the holding proportion of each stock in the optimal portfolio. Compared with existing models with portfolio weights, the proposed model imposes the position allocation constraint that precisely characterizes the limitations on holding each stock. This constraint is crucial for investors to obey securities trading regulations involving position limitations and to mitigate the potential impact of market risks. To address the nonlinear and nonconvex nature of the novel model, we develop an intelligent optimization algorithm by effectively hybridizing the support vector regression and the enhanced grey wolf optimizer. We comprehensively evaluate its performance using eight metrics, including accumulative return, annual return, Sharpe ratio, maximum drawdown, absolute and relative win ratios, predictive precision and accuracy. The experimental results indicate that (i) the proposed model can achieve more excess returns than those stock selection models not considering position allocation, especially for the large-cap stocks; (ii) compared with other state-of-the-art meta-heuristics, the enhanced grey wolf optimizer can yield better portfolio in conjunction with the support vector regression; (iii) in the context of the Chinese A-share stock market, specific financial indicators such as return on equity, inventory turnover rate, net income growth rate, and debt-to-equity ratio should be given greater consideration compared to other financial metrics.

Analysts’ forecast anchoring and discontinuous market reaction: evidence from China

Based on a comprehensive dataset of Chinese A-share stocks, we find significant evidence for analysts’ anchoring in China. This anchoring behavior exists only when forecasts are lower than the industry median and correlates to a series of analysts’ and corporate characteristics. The following market reactions to analyst anchoring are discontinuous: only forecasts without analysts’ anchoring achieve higher and positive returns in the next period, while those with anchoring bias have no influence on the following returns. Further analyses based on the earnings response coefficient reveal that analysts’ anchoring inhibits earnings information dissemination, and discontinuous stock performance is mainly from professional institutions. Overall, this paper provides evident results about analyst anchoring and its impact, and the findings are beneficial for investors to efficiently use analysts’ forecast information.

Do Eastern Religious Traditions Stifle or Encourage Corporate Innovation? Evidence from China

ABSTRACT Although the relationship between Western religiosity and innovation is widely investigated, the effect of Eastern religious traditions on corporate innovation remains unexplored. Using a sample of firms listed in Chinese A-share stock exchanges, we find that firms registered in areas with stronger Eastern religious traditions (Buddhism and Taoism) are generally more innovative than firms registered in areas with weaker Eastern religious traditions. This result suggests that Eastern religiosity promotes rather than stifles corporate innovation. We find additional evidence suggesting that Eastern religiosity promotes corporate innovation mainly through the following three channels: (1) fostering nonexclusivity and openness to science and innovation, (2) mitigating agency conflicts, and (3) nurturing commitment and fortitude. JEL Classifications: N35; D81; O31.

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.

In search of distress premium in the Chinese energy sector

We explore the link between the Chinese firms' default probabilities and their stock returns with an emphasis on the energy sector. We show that energy stock portfolios sorted by probability of default (PD) demonstrate a return reversal pattern, in which the distressed energy portfolio earns a higher return than its adjacent portfolio. In contrast, PD-sorted portfolios for all Chinese A-share stocks demonstrate a strong distress anomaly puzzle. We find the energy sector distress premium, measured by buying distressed energy stocks and selling distressed stocks in other sectors, earns a significant return, and is not explained by the standard or energy factors. However, this premium is vulnerable to extreme market conditions. In a structural framework of credit premium, we exploit specific properties of the Chinese energy sector, such as the high proportion of state-owned enterprises (SOEs) and their importance to China's energy transition goals, to explain our empirical results.

The asymmetric effect of information shock on overnight and intraday expected returns: Evidence from Chinese A-share stock market

This study examines the impact of jumps in stock prices on the expected return and its components, overnight and intraday return. The findings reveal that a long-short jump return strategy generates positive risk-adjusted alphas for the close-to-close and overnight periods, but a negative alpha for the intraday period. These results are confirmed by Fama-MacBeth regressions. The positive premiums for the overnight and close-to-close periods are particularly evident for illiquid stocks and small firms. Further analysis indicates that it is positive jump returns, rather than negative jump returns, that primarily drive the positive (negative) overnight (intraday) return, as investors in the Chinese stock market tend to prefer buying winners. Lastly, limited investor attention is identified as a factor contributing to the heterogeneous effects of jumps on overnight and intraday return responses to information shock.

Organizational stakeholders and environmental sustainability investment: does China’s regional heterogeneity matter?

PurposeGiven the regional diversity in China, this study aims to provide an empirical evaluation of how organizational stakeholders (i.e. customers, employees, suppliers and shareholders) affect corporate environmental sustainability investment (ESI).Design/methodology/approachTo empirically investigate the influence of organizational stakeholders on ESI, this study used regional-level data consists of Chinese A-share stocks for the years 2009–2019.FindingsThis study’s findings show that pressure from customers, employees and suppliers has a significant effect on corporate ESI, with customers being the most important stakeholder group. Shareholders, by contrast, have no significant influence on ESI. The influence of these pressures is more pronounced in developed regions (the east) than in less developed (the west) localities of China.Research limitations/implicationsThis study complements the stakeholder–institutional perspective by implying to consider the differentiated logics of the contesting stakeholders in the nonmarket operations.Practical implicationsPractically, this study poses that managers must realize the heterogeneity of pressures from stakeholders and the differentiated impact of these pressures keeping in view the institutional differences in different regions.Originality/valueOur study reports initial empirical evidence that shows how regional differences influence the role of stakeholders in determining corporate environmental strategy.

Dynamic graph construction via motif detection for stock prediction

Stock trend prediction is crucial for recommending high-investment value stocks and can strongly assist investors in making decisions. In recent years, the significance of stock relationships has been gradually recognized for trend prediction, and graph neural networks (GNNs) have been introduced to capture useful features from relationships. However, applying GNNs to stock relationship analysis still faces numerous challenges, including inappropriate distance algorithms, non-dynamic stock graphs, and over-fitting. To address these challenges, we propose a dynamic graph construction module. The module offers the following advantages: (1) A dynamic graph construction module is introduced. (2) A novel stock distance algorithm based on motif detection is proposed to reduce the distance between stocks with similar trends. (3) A dynamic graph-based LSTM is proposed to aggregate the changes in historical graphs. We have conduct numerous experiments on 4503 Chinese A-share stocks, spanning 1218 trading days. Our model demonstrates 8.65% and 1.02% relative improvements in accumulated return and accuracy, respectively. In addition, the trading simulation validates that our algorithm outperforms the state-of-the-art (SOTA) algorithms in terms of profitability.

Blockchain implementation and shareholder value: a complex adaptive systems perspective

PurposeBlockchain is a distributed ledger technology that uses cryptography to ensure transmission and access security, which provides solutions to numerous challenges to complex supply networks. The purpose of this paper is to empirically test the impact of blockchain implementation on shareholder value varying from internal and external complexity from the complex adaptive systems (CASs) perspective. It further explores how business diversification, supply chain (SC) concentration and environmental complexity affect the relationship between blockchain implementation and shareholder value.Design/methodology/approachBased on 138 blockchain implementation announcements of listed companies on the Chinese A-share stock market, the authors use event study methodology to evaluate the impact of blockchain implementation on shareholder value.FindingsThe results show that blockchain implementation has a positive impact on shareholder value, and this impact will be moderated by business diversification, SC concentration and environmental complexity. In addition, environmental complexity exerts a moderating effect on SC concentration. In the post hoc analysis, the authors further explore the impact of blockchain implementation on long-term operational performance.Originality/valueThis is the first research empirically examining the effect of blockchain implementation on shareholder value varying from internal and external complexity from the CASs perspective. This paper provides evidence of the different effects of blockchain implementation on short- and long-term performance. It adds to the interdisciplinary research of information systems (IS) and operations management (OM).

Revealing stock market risk from information flow based on transfer entropy: The case of Chinese A-shares

According to the efficient-market hypothesis, asset prices can reflect all available information in an efficient stock market. This paper focuses on the transmission of information flow between different equities during a financial crisis to reveal the potential risk spreading in the stock market. The information flow and corresponding time-varying stock networks (i.e., information flow networks and industry networks) are constructed using the transfer entropy with the intra-day data of the Chinese A-Share stock market around the 2008 subprime mortgage crisis. The empirical results show that the information flow can reflect the market condition and the financial crisis stage well. The information flow is more apparent when it falls than when it rises. Similarly, it is more apparent in the crash period than in the downturn period. Furthermore, the evolution of information flow networks, markedly shows that several crucial topological parameters, indicates the crash period. We identify four key stocks, through which the whole stock market tends to exchange market information. Finally, we transform the information flow networks into coarse-grained industry networks, and find that the stock market is more likely to be driven by a master or two industries. Through the core industries, the entire stocks are more like to exchange market information, resulting in the increasing system risk in the crash period. Our work provides an interesting insight for regulators to manage market risks and for market participants to avoid troubles.

Do Financial Liabilities Matter in “Size Effect”? Evidence from the Chinese A-Share Market

Financial liabilities, as an important part of the capital structure, are closely related to the value creation and scale of growth of a company. To test whether financial liabilities affect the “size effect” of company value, this paper employs the enterprise value factor, i.e., a size factor increasing financial liabilities, to study the performance of the company in the Chinese securities market. Taking the stocks of listed companies in the Chinese A-share stock market from 2000 to 2019 as samples, this paper applies the asset pricing models for an empirical study of enterprise value. These results show that: (i) the excess return of the portfolio constructed by the enterprise value is negative and significantly different from zero, which presents the robustness of “size effect” on the attribute of financial liabilities; (ii) additionally, the “size effect” also exists in the subsamples after the financial events occurred; (iii) the results still robust while controlling the risk factors of the asset pricing models respectively.

The informativeness of the top holdings of Chinese equity mutual funds

PurposeThis study aims to determine whether the stock holdings of equity mutual funds are informative for predicting future stock performance in the Chinese market. It is a puzzle that actively managed mutual funds underperform passive benchmarks, whereas retail investors still delegate investment decisions to the fund managers. The present study sheds light on whether mutual fund managers possess security selection skills in their top ten holdings.Design/methodology/approachBy regression analysis and portfolio sorting, this study focuses on 830 Chinese A-share stocks in the industry research reports from the Guotai Junan Securities Company. It collects mutual fund's top ten holdings data from the Wind Financial Terminal between 2019Q1 and 2021Q1. As robustness checks, the result holds for the fixed-effect model, an additional measure of ranks in the top ten holdings, the predictability test based on the confusion matrix and two stage least square (2SLS) regression.FindingsThe authors find that the top ten holdings by equity mutual funds are informative for predicting stock performance and can provide valuable information for investors to support their decision-making.Practical implicationsThe findings of this study provide insightful guidance for retail investors in making investment decisions and support the hypothesis that active fund management adds value.Originality/valueFirstly, the authors find that the top ten holdings of Chinese mutual funds show significantly positive signals for future stock excess returns, indicating the selection skills of fund managers. Secondly, the above positive relationship exhibits a diminishing marginal effect with more funds holding this stock. Thirdly, the authors find that the predictability horizon of the number of overweighing funds is up to three quarters and then diminishes in the fourth quarter. Finally, investors have a 59% prediction accuracy for the whole stock sample and an 85% precision conditional on the predicted positive subsample to outperform the market. The authors also address the endogeneity and reverse causality issues.

ESG investment in China: Doing well by doing good

This paper provides comprehensive evidence that ESG investors in China can get rewarded by doing good. We construct composite firm-level ESG scores in China covering all the A-share stocks with China-specific ESG-related issues and NLP technologies. We provide strong evidence that stocks with high ESG scores outperform those with low ESG scores in the Chinese A-share stock market, and the premium cannot be reduced by common pricing factors. We find that ESG scores are positively related to firm future fundamentals and the return predictability is more pronounced in stocks with low investor attention and high arbitrage costs. We suggest that current ESG premium is dominated by investors' underreaction to ESG-implied cash flow information rather than risk compensation or investors' preference. Our findings are consistent with the ESG pricing theories, and they can help promote the ESG investment in China.

Dynamic analysis and community recognition of stock price based on a complex network perspective

In the context of the 2008 financial crisis, this paper focuses on the Chinese A-share stock prices in ten years (from January 1, 2003 to December 31, 2012) to explore some possible cautionary phenomena based on a complex network perspective. Firstly, with the help of the threshold method and taking monthly and fixed time (20 days, 40 days and 60 days) as time windows, this paper establishes highly correlated networks of daily opening price and backward answer authority closing price respectively. Notice that the correlation between stock price and its category in Citic One-level industry index is misty. And then, it is interesting to discover some special behaviors of stock prices by the evolution of different network features and community identification, so as to provide guidance for crisis identification and control. The results show that the size of time windows has a certain influence on the evolution, and network parameters appear obvious particularity near the 2008 crisis for the networks with fixed times of monthly and 20 days. For example, the average path length reaches a maximum around May 2009; the average degree is low and stable around May 2005 to December 2006 but fluctuates greatly around May 2008 to September 2008; the clustering coefficient fluctuates greatly and reaches the maximum value 0.78 during May 2008 to August 2008; the assortativity coefficient reaches its maximum value at a certain time and fluctuates relatively greatly. In addition, the community identification results indicate that near the crisis, cluster communities with high network density, which contain a relatively large number of nodes, appear.

Does higher investments necessarily reduce stock returns?

Previous studies find corporate investments negatively predict firm performance and stock returns. Using data from the Chinese A-share stock market, we find firms that substantially increase their investments have higher, rather than lower, subsequent stock returns. Such effect persists after controlling for important price factors such as size, value, momentum and turnover. Further analysis shows that this effect is more pronounced among large, low BM, profitable firms and stocks in high-growth industries. We offer a unified explanation for the contradictory findings: The correlation between investment and stock returns depends on whether return to capital is decreasing or increasing. Moreover, we find evidence of increasing returns to capital in last two decades in China with abundant investment opportunities, which implies more investment leads to higher rather than lower profits and consequently higher stock returns.

Environmental, social, and governance ratings and firm performance: Evidence from the Chinese stock market

Environmental, social, and governance (ESG) evaluation criteria, which measure a company’s comprehensive performance in three dimensions of environmental, social responsibility, and corporate governance based on the company’s operational stability and long-term economic value, have developed rapidly. We report a statistical study that investigated whether there were differences in ESG ratings of listed companies in different industries and whether there were differences in market value and financial performance of listed companies with different ESG ratings. We selected all listed companies that have ESG ratings in the Chinese A-share stock market on January 31, 2021 and used Huazheng ESG ratings to reflect their comprehensive performance and sustainable development ability of listed companies. We found significant differences in ESG ratings of listed companies in different industries and significant differences in valuation and financial indicators of listed companies with different ESG ratings. Overall, the ESG ratings of companies in the financial industry are higher and more balanced than in other industries. Except for the finance industry, all other industries had lower overall ESG ratings with E ratings ranking the lowest among the 3 scores. Moreover, we found that listed companies with higher ESG ratings are prone to perform better financially.

Do Government R&D Subsidies Stimulate Corporate R&D Investment? Evidence from a Quasi-natural Experiment

ABSTRACT This paper examines the impact of Guangdong provincial R&D subsidies policy on corporate R&D investment.1 1 Guangdong has ranked first in terms of GDP in all Chinese provinces for many years. There are 643 Chinese A-share stock market listed firms in Guangdong in 2019. Using a large sample of Chinese firms for the 2011–2017 period and DID methodology, we find that government R&D subsidies have a negative and significant effect on corporate R&D investment. Our findings are robust to a battery of sensitivity tests. Our study supports the ‘crowd out effect’ of government R&D subsidies.

Stock market liberalization and institutional herding: Evidence from the Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connects

Using the Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connects (the Connects) as exogenous shocks to the liberalization of China's A-share markets, we study the impact of stock market liberalization on the herding behavior of domestic institutional investors. We find a robust and negative impact of the Connects on the level of institutional herding in connected Chinese A-share stocks. The impact is more pronounced for firms with smaller market capitalizations, less analyst coverage, or higher stock illiquidity, i.e., firms with higher information asymmetry. Moreover, the Connects improve the transparency of connected firms by improving audit quality, decreasing earnings management, and reducing stock price synchronicity. The improvement is greater for stocks that are more intensively traded by Hong Kong investors. Finally, the Connects amplify the price impact of institutional herding without causing price reversals. Overall, our results suggest that the Connects reduce the institutional herding in connected A-share stocks by reducing information asymmetry.

Does the price of natural resources affect firms’ total factor productivity? Evidence from a natural experiment in China

This study uses a natural experiment on a market reform implemented in China in 2006 to examine how the marketization of the pricing of natural resources affects the total factor productivity (TFP) of resource-oriented firms, focusing on Chinese A-share stocks. We adopt the difference-in-differences (DIDs) approach and show that the reform has significantly improved firms’ TFP. This study uses propensity score matching and dynamic DIDs and finds consistent results. Cross-sectional tests indicate that the TFP improvement is stronger for firms with greater misallocation of resources, lower political expropriation risk, and greater financial constraints. We also document that firms’ TFP has a catch-up effect and show that the reform has narrowed TFP gaps between firms. Our study provides clear policy suggestions and offers evidence of the economic consequences of the reform of the natural resources market.

Heterogeneous Beliefs and the Beta Anomaly in the Chinese A-share Stock Market

ABSTRACT The beta anomaly indicates that high-beta stocks earn low future returns. We confirm the existence of the beta anomaly in the Chinese A-share stock market and propose that heterogeneous beliefs and arbitrage limits play important roles in explaining the beta anomaly. We provide a new proxy for aggregate disagreement based on analysts’ earnings forecasts and find that the beta anomaly does not exist in stocks with low arbitrage limits and periods with low aggregate disagreement. Furthermore, a higher aggregate disagreement period leads to a more concave Security Market Line.