Investment strategies based on investors’ mood: Better for crypto

Investment strategies based on investors’ mood: Better for crypto


Raúl Gómez Martínez
María Luisa Medrano García
Jaime Veiga Mateos

Objective.Analyze the utility of an algorithmic trading system based on artificial intelligence models that uses Google Trends as predictor of dozens of financial terms, to predict the evolution of S&P 500 index and Bitcoin. Methodology. A trading algorithmic system has been developed that opens a weekly long or short position in S&P 500 and Bitcoin, following the signals issued by an artificial intelligence model that uses Google Tends as predictor for next week market trend. The artificial intelligence models were trained using weekly data from 2013 to 2018 and have been tested in a prospective way from February 2018 to December 2021. Results. Google Trends is a good predictor for global investors’ mood. The artificial intelligence algorithmic trading systems tested in a prospective way has been profitable. Trading strategies based on investors’ mood have been more accurate and profitable for Bitcoin (beating the evolution of the cryptocurrency) than for S&P 500 (not beating the index). Conclusions. This evidence opens a new field for the investigation of trading systems based on big data instead of Chartism. Although there are many trading systems based on Chartism, there are no artificial intelligence trading syste

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Biografía del autor/a / Ver

Raúl Gómez Martínez, Universidad Rey Juan Carlos de Madrid

Doctor en Economía y Finanzas de la Empresa. Universidad Rey Juan Carlos de Madrid, Madrid, España.

María Luisa Medrano García, Universidad Rey Juan Carlos de Madrid

Doctor en Economía y Finanzas de la Empresa. Universidad Rey Juan Carlos de Madrid, Madrid, España.

Jaime Veiga Mateos, Universidad de Santiago de Compostela

Ingeniero químico. Universidad de Santiago de Compostela, La Coruña, España


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