Investment strategies based on investors’ mood: Better for crypto
Investment strategies based on investors’ mood: Better for crypto
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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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Agarwal, S., Kumar, S. and Goel, U. (2019). Stock market response to information diffusion through internet sources: A literature review. International Journal of Information Management, 45(6), 118-131.
https://doi.org/10.1016/j.ijinfomgt.2018.11.002 DOI: https://doi.org/10.1016/j.ijinfomgt.2018.11.002
Bayes, T. (1763). An Essay towards solving a Problem in the Doctrine of Chances. Philosophical Transactions of the Royal Society of London, 53, 370-418.
https://doi.org/10.1098/rstl.1763.0053 DOI: https://doi.org/10.1098/rstl.1763.0053
Berument, H., Ceylan, N.B. and Gozpinar, E. (2006). Performance of soccer on the stock market: Evidence from turkey. The Social Science Journal, 43(4), 695-699.
https://doi.org/10.1016/j.soscij.2006.08.021 DOI: https://doi.org/10.1016/j.soscij.2006.08.021
Bouman, S. and Jacobsen, B. (2002). The Halloween indicator, "sell in may y go away": Another puzzle. The American Economic Review, 92(5), 1618-1635.
https://doi.org/10.1257/000282802762024683 DOI: https://doi.org/10.1257/000282802762024683
Carneiro, H.A. and Mylonakis, E. (2009). Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases, 49(10), 1557-1564.
https://doi.org/10.1086/630200 DOI: https://doi.org/10.1086/630200
Chang, S. et al. (2012). Local sports sentiment y returns of locally headquartered stocks: a firm-level analysis. Journal of Empirical Finance, 19(3), 309-318.
https://doi.org/10.1016/j.jempfin.2011.12.005 DOI: https://doi.org/10.1016/j.jempfin.2011.12.005
Choi, H. and Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2-9.
https://doi.org/10.1111/j.1475-4932.2012.00809.x DOI: https://doi.org/10.1111/j.1475-4932.2012.00809.x
Cohen, G. and Kudryavtsev, A. (2012). Investor Rationality and Financial Decisions. Journal of Behavioral Finance, 13(1), 11-16.
https://doi.org/10.1080/15427560.2012.653020 DOI: https://doi.org/10.1080/15427560.2012.653020
Corredor, P., Ferrer, E. y Santamaría, R. (2013). El sentimiento del inversor y las rentabilidades de las acciones. El caso español. Revista Española de Financiación y Contabilidad, 42(158), 211-237.
https://doi.org/10.1080/02102412.2013.10779746 DOI: https://doi.org/10.1080/02102412.2013.10779746
Edmans, A., García, D. and Norli, Ø. (2007). Sports sentiment y stock returns. The Journal of Finance, 62(4), 1967-1998.
https://doi.org/10.1111/j.1540-6261.2007.01262.x DOI: https://doi.org/10.1111/j.1540-6261.2007.01262.x
Gómez, R. (2013). Señales de inversión basadas en un índice de aversión al riesgo. Investigaciones Europeas de Dirección y Economía de la Empresa, 19(3), 147-157.
https://doi.org/10.1016/j.iedee.2012.12.001 DOI: https://doi.org/10.1016/j.iedee.2012.12.001
Gómez, R. y Prado, C. (2014). Sentimiento del inversor, selecciones nacionales de fútbol y su influencia sobre sus índices nacionales. Revista Europea de Dirección y Economía de la Empresa, 23(3), 99-114.
https://doi.org/10.1016/j.redee.2014.02.001 DOI: https://doi.org/10.1016/j.redee.2014.02.001
Hilton, D.J. (2001). The Psychology of Financial Decision-Making: Applications to Trading, Dealing, and Investment Analysis. Journal of Psychology and Financial Markets, 2(1), 37-53.
https://doi.org/10.1207/S15327760JPFM0201_4 DOI: https://doi.org/10.1207/S15327760JPFM0201_4
Hirshleifer, D. and Shumway, T. (2003). Good day sunshine: Stock returns y the weather. The Journal of Finance, 58(3), 1009-1032.
https://doi.org/10.1111/1540-6261.00556 DOI: https://doi.org/10.1111/1540-6261.00556
Jacobsen, B. and Marquering, W. (2008). Is it the weather? Journal of Banking & Finance, 32(4), 526- 540.
https://doi.org/10.1016/j.jbankfin.2007.08.004 DOI: https://doi.org/10.1016/j.jbankfin.2007.08.004
Kaplanski, G. and Levy, H. (2010). Exploitable predictable irrationality: The FIFA world cup effect on the U.S. stock market. Journal of Financial and Quantitative Analysis, 45(2), 535-553.
https://doi.org/10.1017/S0022109010000153 DOI: https://doi.org/10.1017/S0022109010000153
Leshik, E. and Crall, J. (2011). An Introduction to Algorithmic Trading: Basic to Advanced Strategies. New Jersey, USA: Wiley.
https://doi.org/10.1002/9781119206033 DOI: https://doi.org/10.1002/9781119206033
Liu, S. (2015). Investor Sentiment and Stock Market Liquidity. Journal of Behavioral Finance, 16(1), 51-67.
https://doi.org/10.1080/15427560.2015.1000334 DOI: https://doi.org/10.1080/15427560.2015.1000334
Mishra, V. and Smyth, R. (2010). An examination of the impact of India's performance in one-day cricket internationals on the Indian stock market. PacificBasin Finance Journal, 18(3), 319-334.
https://doi.org/10.1016/j.pacfin.2010.02.005 DOI: https://doi.org/10.1016/j.pacfin.2010.02.005
Moat, H.S. et al. (2013). Quantifying Wikipedia usage patterns before stock market moves. Scientific Reports, 3, 1801.
https://doi.org/10.1038/srep01801 DOI: https://doi.org/10.1038/srep01801
Nofsinguer, J.R. (2005). Social Mood and Financial Economics. Journal of Behavioral Finance, 6(3), 144-160.
https://doi.org/10.1207/s15427579jpfm0603_4 DOI: https://doi.org/10.1207/s15427579jpfm0603_4
Rech, J. (2007). Discovering trends in software engineering with google trend. ACM SIGSOFT Software Engineering Notes, 32(2), 1-2.
https://doi.org/10.1145/1234741.1234765 DOI: https://doi.org/10.1145/1234741.1234765
Sharpe, W.F. (1994). The Sharpe ratio properly used it can improve investment management. Journal Portfolio Management, 21(1), 49-58.
https://doi.org/10.3905/jpm.1994.409501 DOI: https://doi.org/10.3905/jpm.1994.409501
Yuan, K., Zheng, L. and Zhu, Q. (2006). Are investors moonstruck? lunar phases y stock returns. Journal of Empirical Finance, 13(1), 1-23.
https://doi.org/10.1016/j.jempfin.2005.06.001 DOI: https://doi.org/10.1016/j.jempfin.2005.06.001
Wu, K. et al. (2013). A Big Data Approach to Analyzing Market Volatility. Algorithmic Finance, 2(3-4), 241-267.
https://doi.org/10.3233/AF-13030 DOI: https://doi.org/10.3233/AF-13030
