Optimal Portfolio Choice Under Decision‐Based Model Combinations
提出一种密度组合方法，权重基于各模型过去预测表现通过效用函数确定，用于预测股票收益，相比现有方法在统计和经济指标上更准确。
Summary We propose a density combination approach featuring combination weights that depend on the past forecast performance of the individual models entering the combination through a utility‐based objective function. We apply this model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecasting performance relative to a host of existing combination methods, both within the class of linear and time‐varying coefficients, stochastic volatility models. Overall, we find that our combination scheme produces markedly more accurate predictions than the existing alternatives, both in terms of statistical and economic measures of out‐of‐sample predictability. Copyright © 2016 John Wiley & Sons, Ltd.