Abstract

Not all errors from models predicting asset returns are equal in terms of impact on the

efficiency of the algorithm: a small error could trigger poor investment decisions while

a significant error has no financial consequences. This economic asymmetry, critical for

assessing the performance of algorithms, can usefully be replicated within the machine

learning algorithms itself through the loss function to improve its prediction capability. .

In this article: (a) we analyze symmetric and asymmetric loss functions for deep learning

algorithms. We develop custom loss functions that mimic the asymmetry in economic

consequences of prediction errors. (b) We compare the efficiency of these custom loss

functions with MSE and the linear-exponential loss “LinEx”. (c) We present an efficient

custom loss function that significantly improves the prediction of asset returns with improved

risk-return metrics (like Sharpe ratio twice better), and which we confirm to be

robust.

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