Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning

TL;DR

This paper evaluates AI-based machine learning methods for stock market prediction, analyzing technical and fundamental approaches, and finds current AI technology is not yet capable of consistently outperforming the market.

Contribution

It compares regression and classification ML algorithms for technical and fundamental analysis in stock prediction, highlighting current AI limitations.

Findings

Median performance of AI models in stock prediction

AI cannot reliably beat the stock market yet

Sentiment analysis impacts stock forecasts

Abstract

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. In the technical analysis, the historical price data is exploited from Yahoo Finance, and in fundamental analysis, public tweets on Twitter associated with the stock market are investigated to assess the impact of sentiments on the stock market's forecast. The results show a median performance, implying that with the current technology of AI, it is too soon to claim AI can beat the stock markets.