Showing 1 - 9 of 9
Automated machine learning extends the search space to include hyperparameters and algorithm selection. We apply automated machine learning (AutoML) to cross sectional stock return prediction with factors. We formulate factor dimension reduction and hyperparameter tuning in conventional ML...
Persistent link: https://www.econbiz.de/10014346975
Automated machine learning extends the search space to include hyperparameters and algorithm selection. We apply automated machine learning (AutoML) to cross sectional stock return prediction with factors. We formulate factor dimension reduction and hyperparameter tuning in conventional ML...
Persistent link: https://www.econbiz.de/10014353489
We propose a unsupervised learning approach to construct latent factor model for cross sectional asset returns where firm characteristics instrument for the dynamic factor exposures. Firm characteristics are clustered with consideration to their prior economic content. Our method can also be...
Persistent link: https://www.econbiz.de/10014256230
We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional...
Persistent link: https://www.econbiz.de/10012847704
Persistent link: https://www.econbiz.de/10012305708
This paper extends the machine learning methods developed in Han et al. (2019) for forecasting cross-sectional stock returns to a time-series context. The methods use the elastic net to refine the simple combination return forecast from Rapach et al. (2010). In a time-series application focused...
Persistent link: https://www.econbiz.de/10012865775
We use machine learning tools to analyze industry return predictability based on theinformation in lagged industry returns from across the entire economy. Controlling forpost-selection inference and multiple testing, we nd significant in-sample evidence ofindustry return predictability. Lagged...
Persistent link: https://www.econbiz.de/10012900047
Persistent link: https://www.econbiz.de/10013260016
In this paper, we explore what factors drive expected corporate bond returns all over the world. With a novel dataset, and utilizing machine learning models, we find there is strong predictability of corporate bond returns in international markets. However, the documented factors that drive...
Persistent link: https://www.econbiz.de/10013405279