The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs

Giacomo di Tollo (), Joseph Andria and Gianni Filograsso
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Giacomo di Tollo: Dipartimento di Diritto, Economia, Management e Metodi Quantitativi, Università degli Studi del Sannio, 82100 Benevento, Italy
Joseph Andria: Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Palermo, 90100 Palermo, Italy
Gianni Filograsso: Dipartimento di Economia, Università Ca’ Foscari di Venezia, 30121 Venezia, Italy

Mathematics, 2023, vol. 11, issue 16, 1-18

Abstract: Cryptocurrencies are nowadays seen as an investment opportunity, since they show some peculiar features, such as high volatility and diversification properties, that are triggering research interest into investigating their differences with traditional assets. In our paper, we address the problem of predictability of cryptocurrency and stock trends by using data from social online communities and platforms to assess their contribution in terms of predictive power. We extend recent developments in the field by exploiting a combination of stochastic neural networks (NNs), an extension of standard NNs, natural language processing (NLP) to extract sentiment from Twitter, and an external evolutionary algorithm for optimal parameter setting to predict the short-term trend direction. Our results point to good and robust accuracy over time and across different market regimes. Furthermore, we propose to exploit recent advances in sentiment analysis to reassess its role in financial forecasting; in this way, we contribute to the empirical literature by showing that predictions based on sentiment analysis are not found to be significantly different from predictions based on historical data. Nonetheless, compared to stock markets, we find that the accuracy of trend predictions with sentiment analysis is on average much higher for cryptocurrencies.

Keywords: cryptocurrencies; social media; sentiment analysis; prediction; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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