Abstract
Mutual funds are efficient investment vehicle for small investors. The buy and hold strategy is the prevailing way of investing in mutual funds because of the trading cost and tax considerations. Since the emergence of online trading platforms, the trading cost has come down significantly. Now it is the time to evaluate strategies of more actively managed portfolios of mutual funds. In this study, we show how to use mean-variance portfolio selection methods to construct and manage portfolios of mutual funds, with the focus on funds categorized as foreign large blend by Morningstar. There are two reasons we choose this category of mutual funds. First, total foreign equity markets are as large as the US equity market now, and mutual funds are still the best way to get exposures to it. Second, this category of mutual fund is under-studied. Most researchers focus on the relative performance of US equity mutual funds. We report that: (1) The performance predictive variables that work for US equity mutual funds can also work for foreign large blend mutual funds; (2) the mean-variance approach can effectively diversify the risk of portfolios for this category of mutual funds too. The risk of the minimum variance portfolio could be 6 percentage points less than the risk of the expected-return maximizing portfolio while the realized return is only about 2 percentage points less; (3) the mean-variance approach can produce portfolios with higher Sharpe ratios than the Sharpe ratio of either the index funds or the category average which are the benchmark of this study. Some efficient portfolios can outperform these two benchmarks by more than 2 percentage points while having the same risk levels even after transaction cost.
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Acknowledgements
The authors would like to thank Dianne Toombs, Chunrong Pan for preparing the data used in this study.
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Xu, G., Markowitz, H., Wang, M., Guerard, J.B. (2017). Constructing Mean Variance Efficient Frontiers Using Foreign Large Blend Mutual Funds. In: Guerard, Jr., J. (eds) Portfolio Construction, Measurement, and Efficiency. Springer, Cham. https://doi.org/10.1007/978-3-319-33976-4_14
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DOI: https://doi.org/10.1007/978-3-319-33976-4_14
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