S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data

TL;DR

This paper presents a comprehensive stock prediction approach combining technical, fundamental, and sentiment data with advanced machine learning models, achieving notable accuracy in predicting S&P 500 movements.

Contribution

It introduces novel integration of deep language models for sentiment analysis and ensemble modeling across multiple stocks to enhance prediction accuracy.

Findings

66.18% accuracy in S&P 500 directional prediction

62.09% accuracy in individual stock prediction

Effective fusion of diverse data sources and models

Abstract

We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices. A 66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in individual stock directional prediction was achieved by combining different machine learning models such as Random Forest and LSTM together into state-of-the-art ensemble models. The data we use contains weekly historical prices, finance reports, and text information from news items associated with 518 different common stocks issued by current and former S&P 500 large-cap companies, from January 1, 2000 to December 31, 2019. Our study's innovation includes utilizing deep language models to categorize and infer financial news item sentiment; fusing different models containing different combinations of variables and stocks to jointly make predictions; and overcoming the insufficient data problem for machine learning models in time series by using data across different stocks.