Mutual fund portfolio optimization using clustering and particle swarm optimization: evidence from Indian markets

E. S. Suhaira, Natasha Pankunni

Abstract

The large and correlated universe of mutual fund schemes poses challenges in identifying representative funds and constructing efficient portfolios. This study proposes a heuristic framework that combines correlation-based hierarchical clustering with Particle Swarm Optimization (PSO) to construct a diversified fund-of-funds portfolio comprising equity mutual funds. Clustering reduces dimensionality and enhance diversification by selecting representative schemes from a large investment universe. Portfolio weights are then obtained using PSO under long-only, full-investment and bounded-weight constraints, incorporating transaction costs and periodic rebalancing. Using a seven-year estimation period and a three-year out-of-sample evaluation window, the optimized portfolio demonstrates comparatively strong performance relative to Mean–Variance, Minimum-Variance, Equal-Weight and benchmark portfolios, with higher Sharpe, Sortino and Treynor ratios and a positive Jensen’s alpha, without a commensurate increase in volatility or drawdown. The evidence from Indian equity mutual funds indicates that the proposed framework provides a competitive and implementable approach to mutual fund portfolio construction.

Source: semanticscholar · PDF

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