Mutual funds behavior and risk-adjusted performance in Nigeria
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DOIhttp://dx.doi.org/10.21511/imfi.18(3).2021.24
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Article InfoVolume 18 2021, Issue #3, pp. 277-294
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The paper investigates the behavior of mutual funds and their risk-adjusted performance in the financial markets of Nigeria between April 2016 and May 31, 2019, using descriptive statistics, as well as CAPM, Jensen’s alpha, and other risk-adjusted portfolio performance measures such as Sharpe and Treynor ratios, as well as Fama decomposition of return. The descriptive tests revealed that 80.77% of the funds were superior to market returns, while 13.46% were riskier. The market and the fund returns behaved abnormally with asymptotic and leptokurtic characteristics as their skewness and kurtosis varied from the normal requirements. Diagnostically, the normality test by Jacque-Berra showed that the return was not normally distributed at a 1% significance level. The market was more aggressive relative to the funds. The average risk-free rate was 6.75% above the market’s return. The risk-adjusted portfolio returns measured by Sharpe and Treynor ratios showed that 67.31% of the funds underperformed the market compared to 40.38% that outperformed the market using Jensen’s alpha. Fama decomposition of return revealed that the fund managers are risk-averse with 48% superior selection ability and rationally invested over 85% of investors’ funds in schemes with fixed income securities at a given risk-free return that cushioned the negative effects of the systematic and idiosyncratic risks and consequently threw the total returns into positive territories. Overall, the fund managers possessed 52% of inferior selection abilities that only earned 33% of superior risk-adjusted returns and hence, failed to achieve the desired diversification in the relevant period.
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JEL Classification (Paper profile tab)G11, G12, G14, G41
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References43
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Tables9
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Figures1
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- Figure 1. Distribution of investment in mutual funds in Nigeria
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- Table 1. Fund managers, schemes, and net asset value as at May 31, 2019
- Table 1 (cont.). Fund managers, schemes, and net asset value as at May 31, 2019
- Table 2. Descriptive statistics of the mutual funds in Nigeria (April 2016 – May 2019)
- Table 3. Risk-adjusted performance of the schemes in Nigeria (January 2016 – May 2019)
- Table A1. Descriptive statistics of mutual funds in Nigeria (January 2016–May 2019)
- Table B1. Risk-adjusted returns of mutual funds in Nigeria (January 2016–May 2019)
- Table B1 (cont.). Risk-adjusted returns of mutual funds in Nigeria (January 2016–May 2019)
- Table C1. Fama decomposition of mutual funds returns (January 2016–May 2019)
- Table C1 (cont.). Fama decomposition of mutual funds returns (January 2016–May 2019)
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- Babar, S., Nawaz, S., & Ashraf, S. (2013). A Comparative Study on Performance Evaluation of Pakistani Mutual Funds. International Journal of Business and Management Review, 1(1), 151-165.
- Barua, S. K., Varma, J. R., & Venkiteswaran, N. (1991). A Regulatory Framework for Mutual Funds. Economic & Political Weekly, Review of Management & Industry, 26(21), 55-59.
- Bhosle, M., & Adhikary, U. (1994). Risk-Return Analysis of Mutual Fund Growth Scheme. Indian Management.
- Carhart, M. M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance, 52(1), 57-82.
- Elton, E., Gruber, M., & Blake, C. (2012). Does mutual fund size matter? The relationship between size and performance. The Review of Asset Pricing Studies, 2(1), 31-55.
- Fama, E. F. (1972). Components of Investment Performance. The Journal of Finance, 27(3), 551-567.
- Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427-465.
- Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56.
- Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics, 116(1), 1-22.
- Friend, I., Brown, F. E., Herman, E. S., & Vickers, D. (1962). A Study of Mutual Funds. University of Pennsylvania.
- Grinblatt, M., & Titman, S. (1992). The persistence of Mutual Fund Performance. The Journal of Finance, 47(5), 1977-1984.
- Grinblatt, M., & Titman, S. (1994). A Study of Monthly Mutual Fund Returns and Performance Evaluation Techniques. Journal of Financial and Quantitative Analysis, 29, 419-444.
- Henriksson, R. D., & Merton, R. C. (1981). On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills. The Journal of Business, 54, 513-533.
- Ilo, B. M., Yinusa, O. G., & Elumah, L. O. (2018). Performance of Mutual Funds in Nigeria. Aestimatio, the Ieb International Journal of Finance, 17, 8-25.
- Investment Company Institute. (2015). Quarterly Statistics on Worldwide Regulated Open-End Fund Assets and Flows.
- Investment Company Institute. (2019). Quarterly Statistics on Worldwide Regulated Open-End Fund Assets and Flows.
- Ippolito, R. A. (1989). Efficiency with costly information: A study of mutual fund performance, 1965–1984. Quarterly Journal of Economics, 104(1), 1-23.
- Isiaka, M. A., & Okoh, L. (2019). An Empirical Analysis of the Performance of Collective Investment Schemes in Nigeria (2014-2018). International Journal of Research and Scientific Innovation, 6(2).
- Jaideep, S., & Sudipta, M. (1994). Performance Evaluation of Mutual Funds in India. NMIS Management Review, 6(2), 64-78.
- Jensen, M. C., Black, F., & Scholes, M. S. (1972). The Capital Asset Pricing Model: Some Empirical Tests. In M. C. Jensen (Ed.), Studies in the Theory of Capital Markets. Praeger Publishers Inc.
- Kaura, M. N. & Jayadev, M. (1995). Performance of Growth Oriented Mutual Funds: An Evaluation. The ICFAI Journal of Applied Finance, 1(1), 1-14.
- Kong, Y., Owusu-Akomeah, M., Antwi, H. A., Hu, X., & Acheampong, P. (2019). Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC). Financial Innovation, 5, 10.
- Kothari, S. P., & Warner, J. B. (2001). Evaluating Mutual Fund Performance. The Journal of Finance, 56(5), 1985-2010.
- Kundu, A. (2009). Stock Selection Performance of Mutual Fund Managers in India: An Empirical Study. Journal of Business and Economics Issue, 1(1).
- Lee, C. F., & Rahman, S. (1990). Market timing, selectivity, and mutual fund performance: An empirical investigation. The Journal of Business, 63(2), 261-278.
- Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
- Markowitz, H. M. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons Inc.
- Musah, A., Senyo, D. B., & Nuhu, E. (2014). Market Timing and Selectivity Performance of Mutual Funds in Ghana. Management Science Letters, 4(7), 1361-1368.
- Narayanasamy, R., & Rathnamani, V. (2013). Performance Evaluation of Equity Mutual Funds (On Selected Equity Large Cap Funds). International Journal of Business and Management Invention, 2(4), 18-24.
- Oduwole, O. (2015). The performance of Nigerian mutual funds in the period 2011–2014. Mathematical theory and modelling, 5(3), 85-96.
- Omokehinde, J. O., Abata, M. A., Russell , O., Migiro, S. O., & Somoye, C. (2017). Asymmetric Information and Volatility of Stock Returns in Nigeria. Journal of Economics and Behavioral Studies, 9(3(J)), 220-231.
- Patel, J., Hendricks, D., & Zeckhauser, R. (1994). Investment Flows and Performance: Evidence from Mutual Funds, Cross-Border Investments, and New Issues. In R. Sato, R. M. Levich, & R. Ramachandran (Eds.), Japan, Europe, and the International Financial Markets: Analytical and Empirical Perspectives (pp. 51-72). New York: Cambridge University Press.
- Prasad, K., & Prasad, C. V. (2012). Performance of selected mutual funds (A comparative study on public and private balanced and sector funds). Asian Journal of Research in Social Sciences and Humanities, 2(9), 267-278.
- Rahman, A. B., Qiang, F., & Barua, S. (2012). Mutual Fund Performance: An Analysis of Monthly Returns of an Emerging Market. Research Journal of Finance and Accounting, 3(4).
- Rao, K. V., & Venkateswarlu, R. (1998). Market Timing Abilities of Fund Managers – A Case Study of Unit Trust of India. Quest Publications.
- SEC. (2019). Weekly Net Asset Value Data for Collective Investment Schemes.
- Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442.
- Sharpe, W. F., & Cooper, G. M. (1972). Risk-Return Classes of New York Stock Exchange Common Stocks, 1931–1967. Financial Analysts Journal, 28(2), 46-81.
- Tobin, J. (1958). Liquidity Preference as Behavior Towards Risk. Review of Economic Studies, 25(2), 65-86.
- Treynor, J. L. & Mazuy, K. (1966). Can mutual funds outguess the market? Harvard Business Review, 44(4), 131-136.
- Treynor, J. L. (1962). Toward a theory of market value of risky assets. SSRN.
- Tripathy, N. P. (2006). Market Timing Abilities and Mutual Fund Performance - An Empirical Investigation into Equity Linked Saving Schemes. Vilakshan, XIMB Journal of Management, 127-138.
- Ugwoke, R. O., & Onyeanu, E. O. (2013). An Examination of the Unit Trust Scheme/Mutual Fund as a Veritable Vehicle of Investment in the Nigerian Stock Market. Research Journal of Finance and Accounting, 4(6), 176-184.
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Estimating systematic risk for the best investment decisions on manufacturing company in Indonesia
Zarah Puspitaningtyas
doi: http://dx.doi.org/10.21511/imfi.14(1).2017.05
Investment Management and Financial Innovations Volume 14, 2017 Issue #1 pp. 46-54 Views: 1761 Downloads: 803 TO CITE АНОТАЦІЯEstimation of systematic risk is one of the important aspects of the best investment decisions. Through systematic risk prediction will be known risks to be faced by investors, because systematic risk is a measure of investment risk. In addition to returns, investors always consider the risk of investment, because investors are rational individuals, ie individuals who always consider the trade-off between return and risk. At a certain level of return, investors will tend to choose investments with the lowest risk level. Conversely, at a certain level of risk, investors tend to choose investments with the highest return rate. The purpose of this paper is to analyze the influence of the financial information on the systematic risk of stock manufacturing companies listed on the Indonesia Stock Exchange over a period of five years from January 2011 to December 2015. The financial information is measured in four accounting variables, i.e. financial leverage, liquidity, profitability, and firm size. The results of data analysis using multiple linear regression method to prove that at the 0.05 level only variable sized companies that significantly influence systematic risk. Meanwhile, the variable financial leverage, liquidity, and profitability does not affect the systematic risk. The results showed inconsistencies with the results of several previous studies. This inconsistency may be due to measurement problems variable accounting, the implementation period of the study, and the use of different research samples.
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On the causality analysis of the correlation between financial leverage and systematic risk: evidence from Indonesian Stock Exchange
Ibnu Qizam
doi: http://dx.doi.org/10.21511/imfi.14(4).2017.08
Investment Management and Financial Innovations Volume 14, 2017 Issue #4 pp. 73-89 Views: 1256 Downloads: 358 TO CITE АНОТАЦІЯThis research is aimed at analyzing the causality puzzle on the correlation between financial leverage and systematic risk (beta). Financial leverage and beta are usually considered as two proxies of risk derived from different domains: one ends at financial decision outcome, and the other points to market. Cross-sectionally, this result does not support the moderating-variable impact of size on the relation between financial leverage and systematic risk. On the other hand, however, the moderating-variable impact of industry and operating leverage (to some extent) on the relation between financial leverage and systematic risk were well documented. Inter-temporally, financial leverage is significantly and symmetrically related to beta, not moderated by size and operating leverage. This means that the two variables show bidirectional causality. This study contributes to the new insight that financial leverage and beta are the two variables with bidirectional causality, showing that in the long run, risks from fundamental (financial/micro-economy) and from market (macro-economy) are tightly linked to each other inter-temporally.
