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

被引：0
作者：
de Oliveira, Alexandre Silva [1 ]
Ceretta, Paulo Sergio
Albrecht, Peter [2 ,3 ]
机构：
[1] Fed Univ Pampa, Technol Ctr Alegrete CTA, BR-96400590 Alegrete, Brazil
[2] Fed Univ St Maria, Human Social Sci Ctr CCSH, BR-97105900 Santa Maria, Brazil
[3] Mendel Univ Brno, Fac Business & Econ, Dept Informat, Zemedelska 1, Brno 61300, Czech Republic
来源：
FINANCE RESEARCH LETTERS | 2023年 / 55卷
关键词：
Artificial neural networks; Asymmetric probability; Portfolio management; STOCK-MARKET EFFICIENCY; SELECTION; ASSETS;
D O I：
10.1016/j.frl.2023.103814
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
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 & PRIME;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).
引用
收藏
页数：7
相关论文
共 50 条
  • [41] Performance Comparison of Multiple Linear Regression and Artificial Neural Networks in Predicting Depositor Return of Islamic Bank
    Anwar, Saiful
    Watanabe, Kenji
    E-BUSINESS, MANAGEMENT AND ECONOMICS, 2011, 3 : 9 - +
  • [42] Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods
    Ali, Mohamed A.
    Elsayed, Ashraf
    Elkabani, Islam
    Akrami, Mohammad
    Youssef, M. Elsayed
    Hassan, Gasser E.
    ENERGIES, 2023, 16 (17)
  • [43] Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation
    Whiton, Russ
    Chen, Junshi
    Tufvesson, Fredrik
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (09) : 13883 - 13897
  • [44] Artificial neural networks for the performance prediction of large solar systems
    Kalogirou, S. A.
    Mathioulakis, E.
    Belessiotis, V.
    RENEWABLE ENERGY, 2014, 63 : 90 - 97
  • [45] Performance Evaluation of Artificial Neural Networks Applied in the Classification of Emotions
    Lazaro-Lazaro, Juan-Jose-Ignacio
    Sanchez-DelaCruz, Eddy
    Loeza-Mejia, Cecilia-Irene
    Pozos-Parra, Pilar
    Landero-Hernandez, Luis-Alfonso
    ADVANCES IN COMPUTATIONAL INTELLIGENCE (MICAI 2021), PT I, 2021, 13067 : 228 - 238
  • [46] Improving generalization performance of artificial neural networks with genetic algorithms
    Wu, JS
    Liu, MZ
    2005 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2, 2005, : 288 - 291
  • [47] Earthquake scenarios for building portfolios using artificial neural networks: part I-ground motion modelling
    Kalakonas, Petros
    Silva, Vitor
    BULLETIN OF EARTHQUAKE ENGINEERING, 2024, 22 (07) : 3655 - 3676
  • [48] Earthquake scenarios for building portfolios using artificial neural networks: part II-damage and loss assessment
    Kalakonas, Petros
    Silva, Vitor
    BULLETIN OF EARTHQUAKE ENGINEERING, 2024, 22 (07) : 3627 - 3654
  • [49] Studying the performance of artificial neural networks on problems related to cryptography
    Laskari, E. C.
    Meletiou, G. C.
    Tasoulis, D. K.
    Vrahatis, M. N.
    NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS, 2006, 7 (05) : 937 - 942
  • [50] Investigating the Performance of Artificial Neural Networks in Predicting Affective Responses
    Aydogan, Izzettin
    Tat, Osman
    JOURNAL OF MEASUREMENT AND EVALUATION IN EDUCATION AND PSYCHOLOGY-EPOD, 2025, 16 (01):
12345