Analytical modeling reveals optimal portfolio rules on the efficient frontier under estimation risk, highlighting superior performance over standard benchmarks.
Key Points
To determine how investors can analytically optimize the out-of-sample Sharpe ratio when asset return parameters must be estimated from limited historical data.
Derived exact analytical expressions for the expectation and variance of the out-of-sample Sharpe ratio for portfolio strategies along the sample mean-variance efficient frontier in a nonasymptotic framework.
Evaluated the proposed portfolio rules against multiple standard benchmark allocation strategies using numerical simulations and empirical financial market data.
Identified the optimal location along the sample efficient frontier that explicitly accounts for parameter estimation risk.
Demonstrated via simulations and empirical testing that the proposed analytical portfolio rules achieve superior out-of-sample risk-adjusted performance relative to traditional benchmark models.