Forecasting Fund Flows in Indian Equity Mutual Funds Market using Time Series Analysis: An Empirical Investigation
DOI:
https://doi.org/10.18311/jbt/2021/25970Keywords:
Fund Flows, Feedback-Trading, Time Series, ARIMA Modelling, ForecastingJEL classification
, C23, G12, G23Abstract
Mutual Funds are the second most preferred financial investment option in India amongst households, corporate and private investors alike. Managed funds bring with them the expertise of fund managers along with the benefits of diversification and lower costs. The sensitivity of fund flows defines the ability of the fund manager in offering expected future returns. Mutual fund flows exhibit time series characteristics, it being financial data collected at regular intervals over a time period. This paper studies the dynamics of mutual fund flows by utilising time series regression modelling. Monthly fund flows data for a sample of 142 equity open-ended growth orientation across major marketcap categories – Large Cap, Large and Mid Cap, Multi Cap, Mid Cap, and Small Cap have been analysed using ARIMA Modelling in the R software package. Appropriate lag length and the presence of a unit root have been investigated with the help of established techniques coupled with suitable checks of robustness. Model of best fit has been used to forecast monthly fund flows for a lag length of 60. Our study leads us to two major outcomes. One, unlike many developed and emerging markets, fund flows in the chosen sample do not confirm to positive feedback trading hypothesis. This lends credible support to the absence of irrational exuberance in mutual fund investments. Second, equity-based funds in Large Cap, Large and Mid Cap, and Multi Cap category exhibit strong trend component while funds in Mid Cap and Small Cap category have a strong random component. Beginner investors can take advantage of alpha offered by fund managers possessing effective market -timing skills, an indicator of trend-investing strategy. Funds belonging to these categories are also lesser prone to market volatility in comparison to Mid Cap and Small Cap funds, being more suitable for experienced investors
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All the articles published in JBT are distributed under a creative commons license. The journal allows the author(s) to hold the copyright of their work (all usages allowed except for commercial purpose).Accepted 2021-07-13
Published 2022-08-18
References
Alexakis, C., Dasilas, A., and Grose, C. Asymmetric dynamic relations between stock prices and mutual fund units in Japan. An application of hidden cointegration technique. International Review of Financial Analysis. 2013; 28:1–8.
Watson, J., andWickramanayake, J. (2012). The relationship between aggregate managed fund flows and share market returns in Australia. Journal of International Financial Markets, Institutions and Money. 2012; 22(3):451–472.
Alexakis, C., Niarchos, N., Patra, T., andPoshakwale, S. The dynamics between stock returns and mutual fund flows: empirical evidence from the Greek market. International Review of Financial Analysis. 2005; 14(5):559–569.
Warther, V. A. Aggregate mutual fund flows and security returns. Journal of Financial Economics. 1995; 39(2–3):209–235.
Kopsch, F., Song, H. S., and Wilhelmsson, M. Determinants of mutual fund flows. Managerial Finance. 2015; 41(1):10–25.
Franklin Fant, L. Investment behavior of mutual fund shareholders: The evidence from aggregate fund flows. Journal of Financial Markets. 1999; 2(4):391–402.
Zhang, X., and Edwards, F. R. Mutual Funds and Stock and Bond Market Stability. SSRN Electronic Journal. Published. 1998.
Chevalier, J., and Ellison, G. Risk Taking by Mutual Funds as a Response to Incentives. Journal of Political Economy. 1997; 105(6):1167–1200.
Ippolito, R. A. Consumer Reaction to Measures of Poor Quality: Evidence from the Mutual Fund Industry. The Journal of Law and Economics, 1992; 35(1):45–70. https://doi.org/10.1086/467244
Sirri, E. R., andTufano, P. Costly Search and Mutual Fund Flows. The Journal of Finance. 1998; 53(5):1589–1622.
Ivkovi?, Z., Sialm, C., and Weisbenner, S. Portfolio Concentration and the Performance of Individual Investors. Journal of Financial and Quantitative Analysis. 2008; 43(3):613–655.
Guercio, D. D., and Tkac, P. A. The Determinants of the Flow of Funds of Managed Portfolios: Mutual Funds vs. Pension Funds. The Journal of Financial and Quantitative Analysis. 2002; 37(4):523.
Gruber, M. J. Another Puzzle: The Growth in Actively Managed Mutual Funds. Investments and Portfolio Performance. 2010; 117–144
Chen, Y., Ferson, W., and Peters, H. Measuring the timing ability and performance of bond mutual funds. Journal of Financial Economics. 2010; 98(1):72–89.
Perold, A. F., & Salomon, R. S. The Right Amount of Assets under Management. Financial Analysts Journal. 1991; 47(3):31–39.
Chernenko, S., andSunderam, A. Frictions in Shadow Banking: Evidence from the Lending Behavior of Money Market Mutual Funds. Review of Financial Studies. 20104; 27(6):1717–1750.
Ferreira, M. A., Keswani, A., Miguel, A. F., and Ramos, S. B. The Determinants of Mutual Fund Performance: A CrossCountry Study*. Review of Finance. 2013; 17(2):483–525.
Stein, J. C. Internal Capital Markets and the Competition for Corporate Resources. The Journal of Finance. 1997; 52(1):111–133.
Scharfstein, D. S., and Stein, J. C. The Dark Side of Internal Capital Markets: Divisional Rent-Seeking and Inefficient Investment. The Journal of Finance. 2000; 55(6):2537–2564.
Johnson, W. T. Predictable Investment Horizons and Wealth Transfers among Mutual Fund Shareholders. The Journal of Finance. 2004; 59(5):1979–2012.
Khanna, T., andYafeh, Y. Business Groups and Risk Sharing around the World. The Journal of Business. 2005; 78(1):301–340.
Gopalan, R., Nanda, V., andSeru, A. (2014). Internal Capital Market and Dividend Policies: Evidence from Business Groups. Review of Financial Studies. 2014; 27(4): 1102–1142.
Ferson, W. E., and Kim, M. S. The factor structure of mutual fund flows. International Journal of Portfolio Analysis and Management. 2012; 1(2):112.
Rakowski, D., and Wang, X. The dynamics of short-term mutual fund flows and returns: A time-series and cross-sectional investigation. Journal of Banking & Finance. 2009; 33(11):2102–2109.
Oh, N. Y., andParwada, J. T. Relations between mutual fund flows and stock market returns in Korea. Journal of International Financial Markets, Institutions and Money. 2007; 17(2):140–151.
Khorana, A., Servaes, H., andTufano, P. Explaining the size of the mutual fund industry around the world. Journal of Financial Economics. 2005; 78(1):145–185.
Lakonishok, J., Shleifer, A., andVishny, R. W. The impact of institutional trading on stock prices. Journal of Financial Economics, 1992; 32(1):23–43.
Bohl, M. T., andSiklos, P. L. Empirical evidence on feedback trading in mature and emerging stock markets. Applied Financial Economics. 2008; 18(17):1379–1389.
Dean, W. G., and Faff, R. Feedback trading and the behavioural ICAPM: multivariate evidence across international equity and bond markets. Applied Financial Economics. 2011; 21(22):1665–1678.
Shieh, M. F., Yang, T. Y., Yang, Y. T., and Lee, J. S. Evidence of herding and positive feedback trading for mutual funds in emerging Asian countries. Quantitative Finance. 2011; 11(3):423–435.
Jank, S. Mutual fund flows, expected returns, and the real economy. Journal of Banking & Finance. 2012; 36(11):3060–3070.
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