Beat Wash-Sale Tax with Multigraph Convolutional Neural Networks Based Trading Strategy

Abstract

Stock forecasting is a method that uses historical data and mathematical models to predict the future movement of stocks. It gives an indication of how much profit or loss an investment can make. The use of machine learning for stock forecasting has been widely. But many studies do not take into account correlations between stocks and likelihood that frequent trading could trigger the wash-sale tax rule. Higher taxes cost could offset positive profits. In this study, we proposed a framework based on graph convolutional network, extracting the interdependencies of stocks to increase the prediction accuracy to 62%. Also, we included tax in the calculation of overall net income in simulated trading and tried different constraints on trades to see whether our new model can generate profits high enough to cover the required taxes. The results with 795.5% net return for two years validated the effectiveness of our model and trading strategy.