On the profitability and errors of predicted prices from deep learning via program trading

Researches on using deep learning models to predict prices usually take magnitude-based error measurements (such as R2) to measure the quality of learning models. Whether the forecasted prices for the models with the lowest error measurement can produce the most profit in actual trading is an issue with little research. In this study, we first find the parameter sets of LSTM and TCN models with low magnitude-based error and then use program trading to find out their profitability. The relationships between these profitability and error measurements are analyzed and studied on three commodities: gold, soybean, and crude oil (from GLOBEX). Our findings are: with given parameter sets, if merchandise (gold and soybean) is of low averaged magnitude error, then its profitability is more stable. If it is of a more significant magnitude error (crude oil), then its profitability is unstable. A high positive correlation does not exist between the profitability and error measurement, and TCN outperforms LSTM in almost all our examples. Our research indicates that, in assessing the performance of deep learning, how to use the predicted values in applications and the application results could also be part of the quality measurement for the model assessment in the learning.