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This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs because of high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk. This paper was accepted by Kay Giesecke, finance.

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The growth rate of the turbulent mixing zone, which develops from random perturbations under Rayleigh-Taylor instability, has been studied using the 3D version of the hydrodynamical code VULCAN. Previous studies show large differences between the α parameter of different codes. In its Eulerian mode VULCAN/3D employs Van-Leer scheme for the advection of all variables, and can also use interface tracking for multi-phase flows. Simulations using parallel version of VULCAN/3D give α of about 0.06, a value which agrees very well with experiments and some other simulations.