Investigating the dynamics and uncertainties in portfolio optimization using the Fourier-Millen transform
Muhammad Hilal Alkhudaydi, Aiedh Mrisi Alharthi
TL;DR
This paper explores how mathematical transforms like the Fourier-Mellin can help identify key factors in portfolio optimization, using machine learning and real stock market data.
Contribution
The novel use of medical-inspired transforms and neural networks for portfolio optimization is tested with real financial data.
Findings
Feature-based models using Fourier-Mellin transform and neural networks were tested for portfolio optimization.
Results suggest these methods can identify key factors influencing optimal portfolio composition.
Comparisons with traditional algorithms like vector autoregression were conducted on US stock market data.
Abstract
Many investors and financial managers view portfolio optimisation as a critical step in the management and selection processes. This is due to the fact that a portfolio fundamentally comprises a collection of uncertain securities, such as equities. For this reason, having a solid understanding of the elements responsible for these uncertainties is absolutely necessary. Investors will always look for a portfolio that can handle the required amount of risk while still producing the desired level of expected returns. This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. These models make use of physical analyses, such as the Fourier transform, wavelet transforms and the Fourier–Mellin transform. Motivated by their use in medical analysis and detection, the purpose of this research was to analyse the…
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Taxonomy
TopicsStock Market Forecasting Methods · Market Dynamics and Volatility · Energy Load and Power Forecasting