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Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios

Author

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  • Oliveira, Alexandre Silva de
  • Ceretta, Paulo Sergio
  • Albrecht, Peter

Abstract

This work aims to compare the performance of the traditional portfolios of the S&P500, Markowitz, and Sharpe with the multifractal trend fluctuation portfolios (MF-DFA) and portfolios of artificial neural networks with Student's asymmetric probability classification (ANN-t). In this study, we use daily data for S&P500 stocks between January 18, 2018, and July 12, 2022, where we backtest return and risk metrics such as annual volatility, Value at Risk, Sharpe Ratio, Sortino Ratio, Beta, and Jensen´s Alpha. For both return and risk, we obtain the results confirming that the ANN-t technique might indicate better investment entries, which contradicts the Efficient Market Hypothesis (EMH).

Suggested Citation

  • Oliveira, Alexandre Silva de & Ceretta, Paulo Sergio & Albrecht, Peter, 2023. "Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios," Finance Research Letters, Elsevier, vol. 55(PA).
    • Handle: RePEc:eee:finlet:v:55:y:2023:i:pa:s1544612323001873
    DOI: 10.1016/j.frl.2023.103814
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    as
    1. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Ko, Hee-Un & Yoon, Seong-Min & Kang, Sang Hoon, 2020. "Why cryptocurrency markets are inefficient: The impact of liquidity and volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    3. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, May.
    4. Puertas, Antonio M. & Clara-Rahola, Joaquim & Sánchez-Granero, Miguel A. & de las Nieves, F. Javier & Trinidad-Segovia, Juan E., 2023. "A new look at financial markets efficiency from linear response theory," Finance Research Letters, Elsevier, vol. 51(C).
    5. David Peón & Manel Antelo & Anxo Calvo, 2019. "A guide on empirical tests of the EMH," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 18(2), pages 268-295, March.
    6. Maciel, Leandro, 2021. "A new approach to portfolio management in the Brazilian equity market: Does assets efficiency level improve performance?," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 38-56.
    7. Arshad, Shaista & Rizvi, Syed Aun R. & Ghani, Gairuzazmi Mat & Duasa, Jarita, 2016. "Investigating stock market efficiency: A look at OIC member countries," Research in International Business and Finance, Elsevier, vol. 36(C), pages 402-413.
    8. repec:eme:mfppss:03074350010767034 is not listed on IDEAS
    9. David Peón & Manel Antelo & Anxo Calvo, 2019. "A guide on empirical tests of the EMH," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 18(2), pages 268-295, March.
    10. Albulescu, Claudiu Tiberiu, 2021. "COVID-19 and the United States financial markets’ volatility," Finance Research Letters, Elsevier, vol. 38(C).
    11. Shamima Ahmed & Muneer Alshater & Anis El Ammari & Helmi Hammami, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Post-Print hal-03697290, HAL.
    12. Li, Danyang & Shi, Yukun & Xu, Liao & Xu, Yahua & Zhao, Yang, 2022. "Dynamic asymmetric dependence and portfolio management in cryptocurrency markets," Finance Research Letters, Elsevier, vol. 48(C).
    13. Choi, Gahyun & Park, Kwangyeol & Yi, Eojin & Ahn, Kwangwon, 2023. "Price fairness: Clean energy stocks and the overall market," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    14. Tiwari, Aviral Kumar & Aye, Goodness C. & Gupta, Rangan, 2019. "Stock market efficiency analysis using long spans of Data: A multifractal detrended fluctuation approach," Finance Research Letters, Elsevier, vol. 28(C), pages 398-411.
    15. Mustafa, Andy Ali & Lin, Ching-Yang & Kakinaka, Makoto, 2022. "Detecting market pattern changes: A machine learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
    16. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    17. Kappou, Konstantina & Brooks, Chris & Ward, Charles, 2010. "The S&P500 index effect reconsidered: Evidence from overnight and intraday stock price performance and volume," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 116-126, January.
    18. Tania Morris & Jules Comeau, 2020. "Portfolio creation using artificial neural networks and classification probabilities: a Canadian study," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 133-163, June.
    19. Ali, Sajid & Shahzad, Syed Jawad Hussain & Raza, Naveed & Al-Yahyaee, Khamis Hamed, 2018. "Stock market efficiency: A comparative analysis of Islamic and conventional stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 139-153.
    20. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    21. 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.
    22. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    23. Sant’Anna, Leonardo R. & Filomena, Tiago P. & Caldeira, João F., 2017. "Index tracking and enhanced indexing using cointegration and correlation with endogenous portfolio selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 146-157.
    24. A. L. Soyster, 1973. "Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming," Operations Research, INFORMS, vol. 21(5), pages 1154-1157, October.
    25. Stoyanov, Stoyan V. & Rachev, Svetlozar T. & Racheva-Iotova, Boryana & Fabozzi, Frank J., 2011. "Fat-tailed models for risk estimation," Working Paper Series in Economics 30, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    26. Zhang, Yongjie & Chu, Gang & Shen, Dehua, 2021. "The role of investor attention in predicting stock prices: The long short-term memory networks perspective," Finance Research Letters, Elsevier, vol. 38(C).
    27. Jang, Bong-Gyu & Park, Seyoung, 2016. "Ambiguity and optimal portfolio choice with Value-at-Risk constraint," Finance Research Letters, Elsevier, vol. 18(C), pages 158-176.
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