The paper builds, in the first part, a benchmark index based on the optimal mix of indices for the global asset classes of equity, fixed-income securities, real estate, commodities, and currencies including cryptocurrencies so as to maximize the ex-post Sharpe ratio. The objective of the first part is to help investors across the globe compare portfolio performance with a uniform benchmark. In the second part, a comparison of portfolio performances is based on five methods of portfolio construction viz; 1) historical returns and variance matrix used along with Markowitz model to discover optimal weights for portfolio components, 2) modification to this approach by using autoregressive integrated moving average (ARIMA) based predicted returns in place of historical returns, 3) global minimum volatility (GMV) portfolio, 4) global market weight portfolio and 5) equal weight portfolio. The objective in the second part is to explore an easy-to-use and at the same time conceptually sound method to build portfolios for any investor worldwide even if such an investor does not have access to or does not wish to rely upon the views and opinions of investment experts. The ex-post performance of portfolios based on these five methods is compared with the ex-post performance of 207 global active and passive funds. This comparison suggests that an equal-weighted portfolio with periodical rebalancing gives the best Sharpe ratio for a global investor.

Mutual fund performance evaluation has seen an ever-growing interest for research amongst industry and academicians alike. In this paper an attempt has been made to compare and correlate global actively managed equity mutual funds’ performance across time intervals, to evaluate and establish how predicting future performance can be made meaningful for investors using analysis of historical data based on monthly net asset values (NAVs) (March 2009–March 2021). Of the top 500 global equity mutual funds based on market-cap (on March 31, 2021), the paper evaluated 180 actively managed funds adding up to approximately USD 5 trillion of the fund assets as of March 31, 2021. The research gap which the paper aims to fill is to bring under one umbrella, prediction analysis using performance measures, downside risk measures, style factor analysis, and market timing models. For sampled equity funds various performance ratios and style attributes were computed and compared across periods for their relative performance. Relative performance was found to be stable (at 1% significance level) across periods and hence predictable. A portfolio of funds constructed optimally using historical performance was seen to be in the top quartile ex-post performance in the subsequent period. However, it was found that the market timing abilities of fund managers were unstable across periods and could not be used for predicting performance. Based on the study findings, it would be appropriate for investors to use the relative past performance of the funds and their style attribute analysis for the future allocation of investible surplus across these funds