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<p>This paper explores the impact of Environmental, Social, and Governance (ESG) related news sentiment and investors' sentiment (IS) on forecasting stock prices by applying machine learning (ML). X (formerly Twitter) data is used to analyze IS using Natural Language Processing (NLP). While ESG sentiment is sourced from the Amenity Analytics dataset, which uses several news sources from LexisNexis to understand the ESG-related sentiment, and both these sentiments are used to forecast stock prices. Several ML models, such as Long Short-Term Memory (LSTM), Random Forest (RF), and Bayesian Ridge Regression (BRR), were employed to predict stock prices for all thirty Dow Jones Industrial Average (DJIA) constituent companies, the top ten companies of S&amp;P 500 ESG Index, and the DJIA index price as a proxy for the US stock market, for the period between January 2018 and December 2020. The results validate the success of the proposed framework. They suggest that adding ESG news sentiment and investors' sentiment to historical stock prices improves the forecast accuracy as measured by MAPE by as much as 1516bps over the base of a 10-day moving average model. Based on the results from the model, we can categorize the stocks into separate groups driven by the predisposition of investors toward the company and ESG news.</p>

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Do stock markets care about ESG and sentiments? Impact of ESG and investors' sentiment on share price prediction using machine learning

  • 期刊论文

  • 发表日期：

    2025年1月21日

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  • Sougata Banerjee,
  • Divya Aggarwal,
  • Pooja Sengupta

摘要

This paper explores the impact of Environmental, Social, and Governance (ESG) related news sentiment and investors' sentiment (IS) on forecasting stock prices by applying machine learning (ML). X (formerly Twitter) data is used to analyze IS using Natural Language Processing (NLP). While ESG sentiment is sourced from the Amenity Analytics dataset, which uses several news sources from LexisNexis to understand the ESG-related sentiment, and both these sentiments are used to forecast stock prices. Several ML models, such as Long Short-Term Memory (LSTM), Random Forest (RF), and Bayesian Ridge Regression (BRR), were employed to predict stock prices for all thirty Dow Jones Industrial Average (DJIA) constituent companies, the top ten companies of S&P 500 ESG Index, and the DJIA index price as a proxy for the US stock market, for the period between January 2018 and December 2020. The results validate the success of the proposed framework. They suggest that adding ESG news sentiment and investors' sentiment to historical stock prices improves the forecast accuracy as measured by MAPE by as much as 1516bps over the base of a 10-day moving average model. Based on the results from the model, we can categorize the stocks into separate groups driven by the predisposition of investors toward the company and ESG news.