Machine Learning Based Explainable Financial Forecasting

The non-linear nature of the stock market prices and trends make it one of the most highly researched areas in the financial domain. People invest in the stock market based on multiple prediction techniques, classified into two main categories: classic methods like fundamental and technical analysis and AI-based prediction models. Both these techniques have their benefits and shortcomings. While the classical methods provide high interpretability, they may not be able to predict the complex trends of the stock market. AI-based models like random forests and neural networks can predict the trends with higher accuracy but provide little to no interpretability for their predictions, making them an uncertain tool for investment advice. In this paper, we use explainable artificial intelligence XAI to predict stock market trends and explain the predictions using two of the most prominent XAI tools, LIME and SHAP. The proof of concept and the experimental results are presented which show the promising application of machine learning in financial forecasting.