Machine learning and asset allocation
研究了投资者如何利用大量结构化与非结构化数据（如价格股息比、宏观经济序列和美联储会议文本）进行资产配置，通过引入简洁性偏好参数来预测权益溢价，对金融从业者和研究者有参考价值。
Abstract Investors have access to a large array of structured and unstructured data. We consider how these data can be incorporated into financial decisions through the lens of the canonical asset allocation decision. We characterize investor preference for simplicity in models of the data used in the asset allocation decision. The simplicity parameters then guide asset allocation along with the usual risk aversion parameter. We use three distinct and diverse macroeconomic data sets to implement the model to forecast equity returns (the equity risk premium). The data sets we use are (a) price‐dividend ratios, (b) an array of macroeconomic series, and (c) text data from the Federal Reserve's Federal Open Market Committee (FOMC) meetings.