Layer-wise Relevance Propagation (LRP) Based Technical and Macroeconomic Indicator Impact Analysis for an Explainable Deep Learning Model to Predict an Increase and Decrease in KOSPI

Jae-Eung Lee, Ji-Hyeong Han

http://doi.org/10.5626/JOK.2021.48.12.1289

Most of the research on stock prediction using artificial intelligence has focused on improving the accuracy. However, reliability, transparency, and equity of decision-making should be secured in the field of finance. This study proposes a layer-wise relevance propagation (LRP) approach to create an explainable stock prediction deep learning model, which is trained using macroeconomic and technical indicators as the input features. Also, the definition of the problem is simplified by prediction of an increase or decrease in the KOSPI closing price from the previous day instead of prediction of the KOSPI value itself. To show how the proposed method works, experiments are conducted. The results show that the model trained with data by the selected features via LRP is more accurate than the vanilla model. Moreover, we show that LRP results are meaningful by analyzing the tendency of the positive effect of each feature for the prediction results.