Machine Learning for Stock Prediction Based on Fundamental Analysis

TL;DR

This paper explores the use of machine learning algorithms on 22 years of fundamental financial data to improve stock prediction accuracy, outperforming benchmarks and aiding investment decisions.

Contribution

It introduces a comprehensive analysis of FNN, RF, and ANFIS models with feature selection and model aggregation for fundamental stock prediction.

Findings

RF achieved the best prediction accuracy

Feature selection improved FNN and ANFIS performance

Aggregated models outperformed baseline and DJIA index

Abstract

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks historical data. Most of these existing approaches have focused on short term prediction using stocks historical price and technical indicators. In this paper, we prepared 22 years worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for stock prediction based on fundamental analysis. In addition, we applied RF based feature selection and bootstrap aggregation in order to improve model performance and aggregate predictions from different models. Our results show that RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Moreover, the aggregated model outperforms all baseline models as well as the benchmark DJIA index by an acceptable margin for the test period. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision-making regarding stock investment.