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Use of Econometric Predictors and Artificial Neural Networks for the Construction of Stock Market Investment Bots

Author

Listed:
  • Ciniro A. L. Nametala

    (Universidade de São Paulo
    Instituto Federal de Minas Gerais, Campus Bambuí)

  • Jonas Villela de Souza

    (Universidade de São Paulo)

  • Alexandre Pimenta

    (Instituto Federal de Minas Gerais)

  • Eduardo Gontijo Carrano

    (Universidade Federal de Minas Gerais)

Abstract

The gradual replacement of human operators by investor bots in financial markets has changed the way assets are traded on stock markets. The use of strategies based on artificial intelligence has became a hot trend, especially due to improvements in computer processing capacity. Recent works showed that the adoption of strategies based on classical predictors has decreased considerably over the past years. In this sense, hybrid approaches, which combine artificial intelligence and classical predictors, have risen as an interesting research subject. This manuscript introduces an investor bot that combines predictions made by two classes of artificial neural networks and three classes of econometric predictors. Such a combination are accomplished by ensembles, which are continuously re-optimized with the intention of identifying profitable opportunities. Data from the Brazilian stock market, with daily granularity, was used. Models generated from such data were applied in a period of economic and political crisis, which leaded to the fall of the Brazilian international investment rating. Superior results were obtained against the benchmarks, despite the brokerage costs and high-price volatility in such a period.

Suggested Citation

  • Ciniro A. L. Nametala & Jonas Villela de Souza & Alexandre Pimenta & Eduardo Gontijo Carrano, 2023. "Use of Econometric Predictors and Artificial Neural Networks for the Construction of Stock Market Investment Bots," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 743-773, February.
    • Handle: RePEc:kap:compec:v:61:y:2023:i:2:d:10.1007_s10614-021-10228-0
    DOI: 10.1007/s10614-021-10228-0
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    as
    1. Leandro Maciel, 2020. "Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model," Empirical Economics, Springer, vol. 58(4), pages 1513-1540, April.
    2. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    3. Roll, Richard & Ross, Stephen A, 1980. "An Empirical Investigation of the Arbitrage Pricing Theory," Journal of Finance, American Finance Association, vol. 35(5), pages 1073-1103, December.
    4. Chamon, Marcos & Garcia, Márcio, 2016. "Capital controls in Brazil: Effective?," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 163-187.
    5. Hadhri, Sinda & Ftiti, Zied, 2019. "Asset allocation and investment opportunities in emerging stock markets: Evidence from return asymmetry-based analysis," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 187-200.
    6. Zied Ftiti and Fredj Jawadi, 2019. "On the Oil Price Uncertainty," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    7. Hillier, David & Loncan, Tiago, 2019. "Political uncertainty and Stock returns: Evidence from the Brazilian Political Crisis," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 1-12.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Daniel Kahneman, 2003. "Maps of Bounded Rationality: Psychology for Behavioral Economics," American Economic Review, American Economic Association, vol. 93(5), pages 1449-1475, December.
    10. Malhado, Acácia C.M. & Araujo, Lindemberg M. & Ladle, Richard J., 2013. "Missed opportunities: sustainable mobility and the 2014 FIFA World Cup in Brazil," Journal of Transport Geography, Elsevier, vol. 31(C), pages 207-208.
    11. Suellen Teixeira Zavadzki de Pauli & Mariana Kleina & Wagner Hugo Bonat, 2020. "Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction," Annals of Data Science, Springer, vol. 7(4), pages 613-628, December.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    13. Piccoli, Pedro & Chaudhury, Mo & Souza, Alceu, 2017. "How do stocks react to extreme market events? Evidence from Brazil," Research in International Business and Finance, Elsevier, vol. 42(C), pages 275-284.
    14. Weber, Martin & Camerer, Colin F., 1998. "The disposition effect in securities trading: an experimental analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 167-184, January.
    15. Zied Ftiti & Fredj Jawadi, 2019. "Forecasting Inflation Uncertainty in the United States and Euro Area," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 455-476, June.
    16. Shin, Yongcheol & Schmidt, Peter, 1992. "The KPSS stationarity test as a unit root test," Economics Letters, Elsevier, vol. 38(4), pages 387-392, April.
    17. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    18. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    19. G. Barbato & E. M. Barini & G. Genta & R. Levi, 2011. "Features and performance of some outlier detection methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2133-2149.
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