Economics at your fingertips  

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

Alexandre Silva de Oliveira, Paulo Sergio Ceretta and Peter Albrecht ()

Finance Research Letters, 2023, vol. 55, issue PA

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).

Keywords: Artificial neural networks; Asymmetric probability; Portfolio management (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

Page updated 2024-03-31
Handle: RePEc:eee:finlet:v:55:y:2023:i:pa:s1544612323001873