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Data vs. information: Using clustering techniques to enhance stock returns forecasting

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  • Vásquez Sáenz, Javier
  • Quiroga, Facundo Manuel
  • Bariviera, Aurelio F.

Abstract

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.

Suggested Citation

  • Vásquez Sáenz, Javier & Quiroga, Facundo Manuel & Bariviera, Aurelio F., 2023. "Data vs. information: Using clustering techniques to enhance stock returns forecasting," International Review of Financial Analysis, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:finana:v:88:y:2023:i:c:s1057521923001734
    DOI: 10.1016/j.irfa.2023.102657
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    References listed on IDEAS

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    8. Basalto, N. & Bellotti, R. & De Carlo, F. & Facchi, P. & Pascazio, S., 2005. "Clustering stock market companies via chaotic map synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(1), pages 196-206.
    9. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
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