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

• URL: http://arxiv.org/abs/2108.10826v1
• Date: Tue, 24 Aug 2021 16:18:52 GMT
• Title: S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data
• Authors: Shan Zhong and David B. Hitchcock
• Abstract summary: We summarized both common and novel predictive models used for stock price prediction. We 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.
• Score: 5.420890357732937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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