What predicts returns on assets with "hard-to-value" fundamentals, such as Bitcoin and stocks in new industries? We propose an equilibrium model that shows how rational learning enables return predictability through technical analysis. We document that ratios of prices to their moving averages...

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...

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...

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...

Bid and ask sizes at the top of the order book provide information on short-term price moves. Drawing from classical descriptions of the order book in terms of queues and order-arrival rates (Smith et al (2003)), we consider a diffusion model for the evolution of the best bid/ask queues. We...