Positional Portfolio Management
研究经理以投资组合收益的横截面排名为目标函数的策略，使用非线性不可观测因子模型分离资产收益的分布与排名动态，发现该策略优于传统动量、反转和均值方差策略。
Abstract We study positional portfolio management strategies in which the manager maximizes an expected utility function written on the cross-sectional rank (position) of the portfolio return. The objective function reflects the manager’s goal to be well-ranked among competitors. To implement positional allocation strategies, we specify a nonlinear unobservable factor model for the asset returns which disentangles the dynamics of the cross-sectional distribution and the dynamics of the ranks of the individual assets. Using a large dataset of stocks returns we find that positional strategies outperform standard momentum, reversal and mean-variance allocation strategies, as well as equally weighted portfolio for criteria based on position.