The investigation focused on the prediction of stock prices amidst significant macroeconomic and geopolitical volatility, particularly targeting North-American and European banks in 2022 - a year marked by intense economic shocks, including inflation, geopolitical tensions, and supply chain disruptions. A multidimensional approach was employed, integrating advanced Artificial Intelligence (AI) techniques such as Recurrent Neural Networks (RNNs) and sentiment analysis, utilizing a comprehensive dataset that includes traditional financial metrics and sentiment-driven data from social media, specifically Twitter (recently renamed X). By employing LSTM and FinBERT models, the study revolved around several key analyses: assessing the impact of different market conditions across the US and EU; exploring the potential benefits of data aggregation from multiple banks within these markets; examining the influence of varying historical data spans on model performance; and integrating sentiment analysis to capture the nuanced influence of public sentiment on stock movements. The findings indicate that market-specific dynamics significantly affect the predictive models, with higher inter-bank correlation observed in the US compared to a more fragmented European market. Additionally, models incorporating recent data streams and public sentiments tend to outperform those relying on traditional, longer historical data.

Investigating Stock Prediction Using LSTM Networks and Sentiment Analysis of Tweets Under High Uncertainty: A Case Study of North American and European Banks

Bacco L.;Vollero L.;Papi M.;Merone M.
2024-01-01

Abstract

The investigation focused on the prediction of stock prices amidst significant macroeconomic and geopolitical volatility, particularly targeting North-American and European banks in 2022 - a year marked by intense economic shocks, including inflation, geopolitical tensions, and supply chain disruptions. A multidimensional approach was employed, integrating advanced Artificial Intelligence (AI) techniques such as Recurrent Neural Networks (RNNs) and sentiment analysis, utilizing a comprehensive dataset that includes traditional financial metrics and sentiment-driven data from social media, specifically Twitter (recently renamed X). By employing LSTM and FinBERT models, the study revolved around several key analyses: assessing the impact of different market conditions across the US and EU; exploring the potential benefits of data aggregation from multiple banks within these markets; examining the influence of varying historical data spans on model performance; and integrating sentiment analysis to capture the nuanced influence of public sentiment on stock movements. The findings indicate that market-specific dynamics significantly affect the predictive models, with higher inter-bank correlation observed in the US compared to a more fragmented European market. Additionally, models incorporating recent data streams and public sentiments tend to outperform those relying on traditional, longer historical data.
2024
Sentiment analysis, Predictive models, Data models, Social networking (online), Feature extraction
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/81224
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