【主题】 Deep Learning in Characteristics-Sorted Factor Model
【报告人】冯冠豪（助理教授，香港城市大学）
【时间】12月10日 星期二 15：30-17：00
【地点】 经济学院楼701室
【语言】 英文
【摘要】 To study the characteristics-sorted factor model in asset pricing, we develop a bottom-up approach with state-of-the-art deep learning optimization. With an economic objective to minimize pricing errors, we train a non-reduced-form neural network using firm characteristics [inputs], and generate factors [intermediate features], to fit security returns [outputs]. Sorting securities on firm characteristics provides a nonlinear activation to create long-short portfolio weights, as a hidden layer, from lag characteristics to realized returns. Our model offers an alternative perspective for dimension reduction on firm characteristics [inputs], rather than factors [intermediate features], and allows for both nonlinearity and interactions on inputs. Our empirical findings are twofold. We find robust statistical and economic evidence in out-of-sample portfolios and individual stock returns. To interpret our deep factors, we show highly significant results in factor investing via the squared Sharpe ratio test, as well as improvement in dissecting anomalies.
【报告人】冯冠豪（助理教授，香港城市大学）
【时间】12月10日 星期二 15：30-17：00
【地点】 经济学院楼701室
【语言】 英文
【摘要】 To study the characteristics-sorted factor model in asset pricing, we develop a bottom-up approach with state-of-the-art deep learning optimization. With an economic objective to minimize pricing errors, we train a non-reduced-form neural network using firm characteristics [inputs], and generate factors [intermediate features], to fit security returns [outputs]. Sorting securities on firm characteristics provides a nonlinear activation to create long-short portfolio weights, as a hidden layer, from lag characteristics to realized returns. Our model offers an alternative perspective for dimension reduction on firm characteristics [inputs], rather than factors [intermediate features], and allows for both nonlinearity and interactions on inputs. Our empirical findings are twofold. We find robust statistical and economic evidence in out-of-sample portfolios and individual stock returns. To interpret our deep factors, we show highly significant results in factor investing via the squared Sharpe ratio test, as well as improvement in dissecting anomalies.
