| 主讲人 |
王学钦 |
简介 |
<p>The expanding number of assets offers more opportunities for investors but poses new challenges for modern portfolio management (PM). As a central plank of PM, portfolio selection by expected utility maximization (EUM) faces uncontrollable estimation and optimization errors in ultrahigh-dimensional scenarios. Past strategies for high-dimensional PM mainly concern only large-cap companies and select many stocks, making PM impractical. We propose a sample-average approximation-based portfolio strategy to tackle the aforementioned difficulties with cardinality constraints. Our strategy bypasses the estimation of mean and covariance, the Chinese walls in high-dimensional scenarios. Empirical results on S&P 500 and Russell 2000 show that an appropriate number of carefully chosen assets leads to better out-of-sample mean variance efficiency. On Russell 2000, our best portfolio profits twice more than the best mean-variance portfolio but reduces the maximum drawdown by 47%. While no more than 30 assets can form diversified portfolios for S&P 500, near 100 assets are needed for Russell 2000 due to higher volatility and lower signal-noise ratios. Our strategy balances the trade-off among the return, the risk, and the number of assets with cardinality constraints. Therefore, we provide a theoretically sound and computationally efficient strategy to make PM practical in the growing global financial market.</p> |
