Assessment of mutual fund performance based on Ensemble Empirical Mode Decomposition
Zhaojun Yang () and
Physica A: Statistical Mechanics and its Applications, 2020, vol. 538, issue C
This study analyzes mutual fund performance in three different time scales. The mutual fund return time series is decomposed by ensemble empirical model decomposition method, which is a data analysis method, especially for processing nonstationary and nonlinear time series, into three time-scales, namely, short cycle, long cycle and trend, which have different meaning on mutual fund management. Short cycle represents the temporary volatility of the market and long cycle represents the operation circle of the mutual fund and trend represents the development tendency of the fund. The mutual funds are also divided into equity, bond, and mixture funds according to portfolio types. The performances of the three fund types are analyzed. The data set, having 2600 mutual funds, in this study is relatively large compared with that in other researches. Result shows that the bond and mixture funds have different management strategies from that of the equity fund, which means that, to seek excess profit, the equity fund focuses on short-cycle management and tends to ignore the long-cycle management, whereas the bond and mixture funds focus on long-cycle management and take less care on short-cycle management. In short cycle, all three sorts of funds are making excess profit through taking market system risk and have no significant performance on α return; in long cycle and trend, they seek excess profit through acquiring more α return. The assessment indices used to assess fund performance confirm the differences in the three fund’s management strategies.
Keywords: Capital asset pricing model; Ensemble Empirical Model Decomposition; Mutual fund performance; Fund management strategy (search for similar items in EconPapers)
JEL-codes: C15 G12 G17 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:538:y:2020:i:c:s0378437119315900
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