Efficient online portfolio simulation using dynamic moving average model and benchmark index
Source of Publication
International Journal of Modeling Simulation and Scientific Computing
Online portfolio selection and simulation are some of the most important problems in several research communities, including finance, engineering, statistics, artificial intelligence, machine learning, etc. The primary aim of online portfolio selection is to determine portfolio weights in every investment period (i.e., daily, weekly, monthly, etc.) to maximize the investor’s final wealth after the end of investment period (e.g., 1 year or longer). In this paper, we present an efficient online portfolio selection strategy that makes use of market indices and benchmark indices to take advantage of the mean reversal phenomena at minimal risks. Based on empirical studies conducted on recent historical datasets for the period 2000 to 2015 on four different stock markets (i.e., NYSE, S&P500, DJIA, and TSX), the proposed strategy has been shown to outperform both Anticor and OLMAR — the two most prominent portfolio selection strategies in contemporary literature.
World Scientific Publishing
Business | Computer Sciences
Online portfolio selection, Online portfolio optimization, Risk management, Adaptive portfolio allocation, Dynamic portfolio allocation, Risk-adverse portfolio allocation
Nazir, Amril, "Efficient online portfolio simulation using dynamic moving average model and benchmark index" (2021). All Works. 4617.
Indexed in Scopus