Before revision:  In order to evaluate QuantNet , we compared its performance in relation to the option of not performing transfer learning, that is, using market-specific old-fashioned machine learning. In summary, our findings suggest that QuantNet performs better than non transfer-based trading strategies, improving Sharpe ratio in 15\% and Calmar ratio in 41\% across 3103 assets in 58 equity markets across the world.
After revision:  In order to evaluate QuantNet , we compared its performance in relation to the option of not performing transfer learning, that is, using market-specific parameters are free to specialize on the problem at hand, whilst market-agnostic parameters are driven to capture signals from all markets. By integrating over idiosyncratic market data we can learn general transferable dynamics, avoiding the problem of overfitting to produce strategies with superior returns. We evaluate QuantNet on historical data across 3103 assets in 58 global equity markets. Against the top performing baseline, QuantNet yielded 51\% higher Sharpe and 69\% Calmar ratios. In addition we show the benefits of our approach over the non-transfer learning variant, with improvements of 15\% and Calmar ratio in 41\% across 3103 assets in 58 equity markets across the world.