论文标题
通过集合高斯流程回归的经验资产定价
Empirical Asset Pricing via Ensemble Gaussian Process Regression
论文作者
论文摘要
我们介绍了一种基于高斯工艺回归（GPR）的合奏学习方法，以预测给定股票水平和宏观经济信息的条件预期股票回报。我们的合奏学习方法大大降低了GPR推论中固有的计算复杂性，并将其赋予一般的在线学习任务。我们对1962年至2016年的美国股票进行大量横截面进行了经验分析。我们发现，我们的方法在统计和经济上以样本外$ r $ r $ r $平方的比率和夏普的预测组合占主导地位。利用GPR的贝叶斯性质，我们就预期股票回报的预测不确定性分布引入了均值变化的最佳投资组合。它吸引了不确定性反对投资者，并显着主导了相等和价值加权的预测分数投资组合，这表现优于标准普尔500指数。
We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and lends itself to general online learning tasks. We conduct an empirical analysis on a large cross-section of US stocks from 1962 to 2016. We find that our method dominates existing machine learning models statistically and economically in terms of out-of-sample $R$-squared and Sharpe ratio of prediction-sorted portfolios. Exploiting the Bayesian nature of GPR, we introduce the mean-variance optimal portfolio with respect to the prediction uncertainty distribution of the expected stock returns. It appeals to an uncertainty averse investor and significantly dominates the equal- and value-weighted prediction-sorted portfolios, which outperform the S&P 500.
