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Equity fund flows, market returns, and market risk: evidence from China

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Abstract

We examine contemporaneous and dynamic relationship among equity fund flows, market returns, and market risk in China by applying structural vector autoregression (SVAR) and reduced-form VAR models using monthly and quarterly data over the period of 2005–2016. Results from the reduced-form VAR suggest that equity funds can play important role in reducing market risk by actively participating in the equity market. Moreover, adverse market conditions can cause equity funds to refrain from active participation in trading activities. The results from the structural VAR show that market risk and stock returns are contemporaneously related to fund flows, suggesting that concurrent relationships are important in studying the linkages between aggregate equity fund flows and stock market variables. We also discuss the policy implications of findings in the context of recent downturn in the Chinese stock market.

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Fig. 1

Source V. Stone Financial Products Research Center, http://en.vstone.com.cn/view

Fig. 2

Source V. Stone Financial Products Research Center, http://en.vstone.com.cn/view

Fig. 3

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Notes

  1. Mutual funds provide numerous advantages to individual investors who either lack professional expertise or do not have enough time to manage their own portfolios. Some of the advantage of mutual funds include professional management of portfolios, diversification, economies of scale, and liquidity.

  2. See Edelen (1999), Friesen and Sapp (2007), Greene et al. (2007), Rakowski and Wang (2009) and Rakowski (2010).

  3. See Warther (1995), Fortune (1998), Fant (1999), Oh and Parwada (2007), Cao et al. (2008), Cha and Kim (2010), Ben-Rephael et al. (2011, 2012), Jank (2012), and Kopsch et al. (2015), among others.

  4. Mutual funds influence stock prices in short and long term due to size of their investments. The short-term affects include immediate rise or fall of share prices triggered by sales or purchase of a stock by mutual funds. Trading activities of mutual funds are likely to be followed by other institutional and individual investors resulting in herding patterns. The resultant herding behavior can further move prices up or down. The long-term affects include bullish or bearish trends associated with buy and hold strategy by mutual funds. A buy or sell decision by mutual funds is considered to be based on rigorous or well wetted analysis; therefore such decisions are more likely followed by the individual investors further reinforcing the trend in stock prices.

  5. See also Fama (1965), Grier and Albin (1973), Reilly (1977), Reilly and Wachowicz (1979), Daigler and Wiley (1999).

  6. Also see the study by Narayan et al. (2014) conducted in India.

  7. There are couple of studies on mutual funds in China but their focus is essentially limited to micro aspects i.e., fund performance. See for instance, Chen et al. (2013), Kiymaz (2015), Gang and Qian (2016), Vidal-García et al. (2016), Chen and Chen (2017), Weng and Wang (2017),Yang and Liu (2017), and Choi et al. (2017). The detailed discussion of the mutual fund industry in China and the reason behind studying Chinese market is discussed in “Overview of China’s mutual fund industry” section.

  8. See, for example, Qureshi et al. (2016, 2017a, b). Jank (2012) find that there is contemporaneous relationship between equity fund flows and market returns supporting information response hypothesis, however, with the limitation that the study applies only reduced-form VAR model in the context of USA.

  9. Cao et al. (2008) examine all three variables together to avoid biased and spurious results. For further details see Qureshi et al. (2016).

  10. Limitation of reduced VAR model is discussed in “Structural VAR” section.

  11. Statistics on China’ mutual fund industry has been extracted from various sources including V. Stone Financial Products Research Center, http://en.vstone.com.cn/view; China Securities Regulatory Commission (CSRC), http://www.csrc.gov.cn/pub/csrc_en/; The Shanghai Stock Exchange (SSE), http://english.sse.com.cn/; Asset Management Association of China (AMAC), http://www.amac.org.cn/cms/site/preview?ID=23.

  12. For further readings related to Chinese stock market, see Feng and Chan (2016), Li et al. (2016a, b), Cai et al. (2017), and Xue and Zhang (2017). For recent studies on risk modeling in China and other countries from different perspectives, see, among others, Lee and Jeong (2016), Zhang et al. (2016), Liu and Wu (2017), Elenjical et al. (2016), Batten and Vo (2016), Díaz et al. (2017), Jeon et al. (2017), and Moula et al. (2017).

  13. http://money.cnn.com/2015/07/09/investing/china-crash-in-two-minutes/.

  14. https://www.theguardian.com/business/2015/jul/08/china-stock-markets-continue-nosedive-as-regulator-warns-of-panic.

  15. https://www.theguardian.com/world/2015/jul/09/china-bans-major-shareholders-from-selling-their-stakes-for-next-six-months.

  16. https://www.nytimes.com/2016/01/08/business/dealbook/china-shanghai-stocks-fall.html?_r=0.

  17. Several other studies such as Fang and You (2014) and Moore and Wang (2014) also apply SVAR in time series context to identify contemporaneous relationships among macroeconomic and stock exchange variables. Moreover, all variables under this study are stationary at level I(0) which is suitable for SVAR application. For co-integration and error correction model, the variables need to be integrated at level one I(1). For details on SVAR, See Sims (1986).

  18. Additionally, we also employ Cholesky identification scheme with similar ordering of variables. However, the results are similar under both identification approaches.

  19. Data on TNA and returns for individual equity fund have been separately extracted from Bloomberg database. There are 818 equity funds for which data are available over the period of 2005–2016. However, majority of the funds were introduced in later years therefore, we have to drop mutual funds for which data are not available. Each mutual fund has been matched for availability of TNA and returns. A mutual fund qualifies for inclusion in the sample if it has data on both TNA and returns. These screening criteria leave us with 768 mutual funds.

  20. Alternatively, stock market capitalization can also be used in denominator of Eq. 6 as in Warther (1995) and Lee et al. (2015) among others. We also calculate equity flows using market capitalization; however, the results from both measures are qualitatively similar. For brevity purpose, we only report results from equity flows obtained through Eq. 6.

  21. We choose 2 lags for reduced-form VAR model based on Akaike Information Criteria (AIC) and Schwarz Bayesian Criteria (SBC).

  22. Different measures of aggregate equity flows i.e., total, expected, and unexpected equity flows, are independently included in the model. Estimation results from daily data are reported in panel A and those from quarterly data in Panel B.

  23. Results from monthly and quarterly data are reported in vertical panels X and Y, respectively. The horizontal panels A, B, and C, respectively, report the results when total, expected, and unexpected equity flows are separately used in structural model.

  24. Since our focus is on response of aggregate equity flows to the contemporaneous shocks in market volatility and market returns, we only report relevant responses in the figure.

  25. Following earlier studies i.e., Lee et al. (2015), we only report results of VDC over a period of 12 months due to convergence within this period.

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Authors and Affiliations

  1. Institute of Business Administration, University of Sindh, Jamshoro, Pakistan

    Fiza Qureshi & Saba Qureshi

  2. Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL, 62026-1102, USA

    Ali M. Kutan

  3. Faculty of Management Sciences, International Islamic University, Islamabad, Pakistan

    Habib Hussain Khan

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  1. Fiza Qureshi
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Qureshi, F., Kutan, A.M., Khan, H.H. et al. Equity fund flows, market returns, and market risk: evidence from China. Risk Manag 21, 48–71 (2019). https://doi.org/10.1057/s41283-018-0042-3

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