Beat Equal Weighting : a Strategy for Portfolio Optimisation

We propose an estimate of the optimal portfolio weights under parameter uncertainty. In theory, we prove that our new estimate is close to the true value with an upper bound. Empirical evidence shows that our new estimate can consistently outperform the naive diversification method (1/N Rule), more specifically, our method considerably improves the out-of-sample Sharpe Ratio by 48% on average, compared to the 1/N Rule

Year of publication:	2018
Authors:	Jin, Yong
Other Persons:	Wang, Lie (contributor)
Publisher:	[2018]: [S.l.] : SSRN
Subject:	Portfolio-Management \| Portfolio selection \| Theorie \| Theory \| Mathematische Optimierung \| Mathematical programming

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