Machine Learning for Stock Prediction Based on Fundamental Analysis
Study on machine learning algorithms for long-term stock prediction using fundamental analysis, comparing FNN, RF, and ANFIS.
What it examines
This paper investigates the use of machine learning for long-term stock prediction based on fundamental analysis. It compares the performance of Feed-forward Neural Network (FNN), Random Forest (RF), and Adaptive Neural Fuzzy Inference System (ANFIS) using 22 years of quarterly financial data from S&P 100 stocks.
What it concludes
The research demonstrates that machine learning models can effectively predict long-term stock performance using fundamental analysis. Future research could explore more data, additional algorithms, and incorporate technical and sentiment analysis to further enhance prediction accuracy.
Evidence objects
The research demonstrates that machine learning models can effectively predict long-term stock performance using fundamental analysis. Future research could explore more data, additional algorithms, and incorporate technical and sentiment analysis to further enhance prediction accuracy.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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 h… ▽ More 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. △ Less
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