Chinese Fund Market Research Articles

Fund Performance Driven by ChatGPT: Evidence from Chinese Fund Market

Fund Performance Driven by ChatGPT: Evidence from Chinese Fund Market

Can mutual fund investors benefit from volatility managing? Evidence from China

Using a comprehensive sample of Chinese mutual funds from 2004 to 2021, we find strong evidence that managing the intertemporal total (downside) risk of actively managed equity mutual funds significantly improves the performance in terms of alphas, Sharpe ratios, and Sortino ratios. Each volatility-managed strategy studied in this paper can survive transaction costs. By testing the relation between risk and return of mutual funds, we find the gains from managing the total (downside) volatility are attributed to both volatility timing and return timing. Empirical results are robust to alternative measurements of total (downside) volatilities, leverage constraints, rebalancing frequency, factor models, sample filters, and a portfolio of all mutual funds. There is no obvious relationship between fund flows and past fund total volatility in the Chinese fund market. However, after dividing the total volatility into upside and downside volatility, the fund flows are positively (negatively) related to the upside (downside) volatility, and these relations are more pronounced for institution-dominated funds.

Price limit hits in the Chinese fund market: Determinants and post-hit performance

This article examines the predictors of price limit hits and post-hit market behaviors in the Chinese fund market. We show that macroeconomic news, past market returns, lagged limit hit dummies, and lagged price deviations have significant predictive powers for limit hits. We find that there is asymmetry between upper and lower limits, and between positive news and negative news. Furthermore, our tests of post-hit market dynamics provide evidence of volatility spillover after news upper-limit hits and no-news lower-limit hits. We find delayed price discovery except for no-news lower-limit hits, and a strong short-lived trading interference effect for news lower-limit hits. The price deviation is larger after news lower-limit hits but smaller after news upper-limit hits.

Research on the Investment Value of Hybrid Funds in the Chinese Capital Market based on Factor Analysis

Factor analysis is a cutting-edge and effective data processing method that has a wide range of uses in the field of finance, as it can reduce the numerous indicators in finance into a few valid common factors in the form of factor analysis. The researcher can name the factors by the percentage contribution of each variable in the different factors. After that, the scores of financial products (e.g., stocks, funds) on different factors give a clear picture of how well the product performs in these aspects. The main research direction of this paper is to reduce the dimensionality of each index of hybrid funds in the Chinese fund market through the factor analysis technique to get reasonable public factors. And to rank these funds with factor scores to get the best funds among these ninety hybrid funds to make recommendations for investors. Finally, this paper successfully selects four excellent hybrid funds, which have excellent performance in terms of size, earnings, and debt-servicing ability, which can provide some reasonable suggestions for investors who want to invest in Chinese hybrid funds.

A new measure of fund window dressing and its application to Chinese mutual fund market

In this paper, we propose a new window dressing index, Simulation Return Rank Gap, and show it improves over the two popular fund window dressing indexes, Backward Holding Return Gap and Rank Gap. First, we conduct a simulation analysis to confirm the dominance of the new index. Next, we use the Chinese market data to compare the three indexes empirically. We find all three indexes confirm the existence of window dressing in the Chinese fund market. The new index maintains a high consistency ratio and a large Spearman correlation coefficient with other indexes. It also has a smaller probability of misidentifying the window dressing funds. The results support the new index as a superior alternative to the current method.

An Improved DEA Prospect Cross-Efficiency Evaluation Method and Its Application in Fund Performance Analysis

It is well known that a traditional data envelopment analysis (DEA) cross-efficiency evaluation model assumes that the decision-makers are completely rational, which causes the evaluation results to be inconsistent with the actual situation. To remedy this, in this paper, we propose an improved DEA prospect cross-efficiency evaluation method called EPCE model. The EPCE model captures the risk attitude of decision-makers and retains the decision information in the evaluation process. In particular, this new approach generates a more practical, realistic weighting scheme to measure the cross-efficiency and provides a reliable technique for ordering the decision-making units (DMUs) from the perspective of multi-criteria decision analysis. Finally, to demonstrate the validity and reliability of the proposed approach, we show an empirical analysis of mutual fund investment selection from Chinese fund market.

The green effects of fund market – analysis based on institutional investors’ preference

PurposeAs Chinese stock market develops and grows, mutual fund plays an increasingly important role for strong financial strength and good social resources. This paper aims to present an overview of fund investment effect, identify the investment preference of institutional investors and explore the socially responsible investment value of mutual fund, especially green fund.Design/methodology/approachIn order to test the green effects in the fund market, especially from the perspective of institutional shareholding, the authors build three panel data models to examine the attention, promotion and network effect respectively.FindingsIn general terms, the authors obtain a positive relationship between mutual funds and green firms (attention effect). This positive relationship is conducted by the comparison of institutional shareholding level between green firms and non-green firms by using two-sample t-test. The authors find that although all the coefficients of mutual funds' shareholding variables are positively related to social performance, only the number of mutual fund shareholders shows slight significance. The authors observe a stronger promotion effect of green funds on social performance than the other mutual funds (promotion effect). From a network perspective as well as previous social capital research, the mutual funds shareholding network shows a significant and positive effect on the firm's social performance in the firm–fund network (CFClose) as well as in the firm–firm network (CCClose), while the coefficients of closeness in the firm and green fund network are positive but non-significant (network effect).Research limitations/implicationsAlthough some valuable findings have been documented, there is a great potential to be further explored. For example, how to guide more green funds to solve the finance problem of middle and small-sized firms may be another crucial task for the point of view of government or industry level. While at the mutual fund level, it is essential to positively participate in and fulfill the environment duty of listed firms which can not only satisfy the expectation of environment value investment.Practical implicationsThe fund market has witnessed unprecedented growth in China and plays an important role in promoting the development of green industries. This indicates the practice of mutual funds as socially responsible investment in China. Some effective measures should be taken to incentive the mutual funds to continually invest in environment-friendly firms, which would benefit to the promotion of social performance driven by financing innovation.Social implicationsThe ESG issue is a global one that needs the participation of the countries worldwide. As one of the most important emerging markets, regulators in China should consider taking more action in promoting ESG concerns. To some extent, institutional investments can actually reflect how the responsible investments are going in this market.Originality/valueThis paper provides a systematic empirical analysis of Chinese fund market in the view of institutional investor preference. Three green effects, namely attention, promotion and network effects, are put forward, which have not been employed in previous studies. Our work is useful for understanding sustainable finance which has been elevating into national strategy.

Chinese securities investment funds: the role of luck in performance

Purpose The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry. Design/methodology/approach This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models. Findings The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills. Practical implications The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)]. Originality/value The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.

The Effect of Social Media on Fund Returns in China : Empirical Research Based on Rongtong Healthcare Mixed A/B

Social media exerts as an influential force where fund investors can exchange views in real time. The influence may affect fund returns. Focusing on Chinese fund market, this paper examines the influence of social media, eastmoney.com, on the returns of Rongtong Healthcare Mixed A/B fund. We adopt a VAR model, perform the Granger causality test, and analysis the impulse response results. The results highlight the positive influence of social media on fund returns in short term, and there is a unidirectional causality running from the weekly activation and the weekly influence of posts as a whole to fund returns. The paper also reveals that the weekly activation, compared with the weekly influence of posts, has a greater influence on the returns of Rongtong Healthcare Mixed A/B fund.

Self-similarity and network perspective of the Chinese fund market

By testing 88 different funds of the Chinese fund market (CFM), we find fractal behavior and long-range correlations in the return series, which are insensitive to the kind of funds. Meanwhile, a power-law relationship between the deviation D of prices and the Hurst exponent H has been obtained, which may be useful for predicting the price time series. In addition, with funds being viewed as nodes, and the connections among the funds being determined by the cross-correlation coefficients, using a winner-takes-all approach, we investigate the topological properties of the fund network. Our analysis reveals that, during different time periods, the cumulative degree distributions of the fund network all obey the double power-law format. Moreover, the small-world property is also found for the fund network.

ON THE APPLICATION OF THE CROSS-CORRELATIONS IN THE CHINESE FUND MARKET: DESCRIPTIVE PROPERTIES AND SCALING BEHAVIORS

On the basis of the relative daily logarithmic returns of 88 different funds in the Chinese fund market (CFM) from June 2005 to October 2009, we construct the cross-correlation matrix of the CFM. It is shown that the logarithmic returns follow an exponential distribution, which is commonly shared by some emerging markets. We hereby analyze the statistical properties of the cross-correlation coefficients in different time periods, such as the distribution, the mean value, the standard deviation, the skewness and the kurtosis. By using the method of the scaled factorial moment, we observe the intermittence phenomenon in the distribution of the cross-correlation coefficients. Also by employing the random matrix theory (RMT), we find a few isolated large eigenvalues of the cross-correlation matrix, and the distribution of eigenvalues exhibits the power-law tails. Furthermore, we study the features of the correlation strength with a simple definition.

NONLINEAR PROPERTIES, FRACTAL BEHAVIOR AND LONG-RANGE CORRELATION ANALYSIS OF THE CHINESE FUND MARKET

Applying the statistical hypothesis testing, we investigate several nonlinear properties embedded in the return series of the Chinese Fund Market (CFM), which suggests the series is non-normal, auto-correlative and heteroskedastic. We hereby analyze the Hurst exponent of the return series in different timescales on the basis of the detrended fluctuation analysis (DFA) algorithm, and discuss the fractal behavior of the CFM. Furthermore, by studying the correlation of different weights in the volatility, we find the persistent long-range power-law correlation exists in the time series. We also provide hints that the above statistical properties are insensitive to the funds kind, and may be irrelevant to the market phases. Our work may reveal the self-similarity characteristics of the financial market and show a better understanding of the CFM.

A new mean reversion model of close-end fund

On the basis of fractal theory, one of the nonlinear theories, this paper studies the validity of Chinese fund market fractal time sequence through Hurst exponent, calculates the H value and proposes a new close-end fund mean reversion model. Meanwhile, this paper validates the mean reversion time sequence for consecutive 54 week data of fund market. The result indicates that this model can effectively prove that Chinese close-end fund market follows the biased random walk. The research also proves that the fund discount does have mean reversion tendency and averagely the fund with high discount has a higher excess yield than that of the fund with low discount. The mean excess yield and the ratio between discount rate deviation and standard deviation demonstrate a descending relationship. The optimum investment period based on “mean reversion” is one month. Consequently this model provides a new arbitrage method through the discount of close-end fund.