  Statistical arbitrage exploits temporal price differences between similar
assets. We develop a unifying conceptual framework for statistical arbitrage
and a novel data driven solution. First, we construct arbitrage portfolios of
similar assets as residual portfolios from conditional latent asset pricing
factors. Second, we extract their time series signals with a powerful
machine-learning time-series solution, a convolutional transformer. Lastly, we
use these signals to form an optimal trading policy, that maximizes
risk-adjusted returns under constraints. Our comprehensive empirical study on
daily US equities shows a high compensation for arbitrageurs to enforce the law
of one price. Our arbitrage strategies obtain consistently high out-of-sample
mean returns and Sharpe ratios, and substantially outperform all benchmark
approaches.
