Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/146811
Title: Benchmarking Strategies for Asset Allocation
Author: Durall López, Ricard
Tutor: Morales Moreno, Carolina Natividad
Keywords: asset allocation
deep reinforcement learning
benchmarking
Issue Date: Jul-2022
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon. Unfortunately, there is no simple formula that can find the right allocation for every individual. As a result, investors may use different asset allocations’ strategy to try to fulfil their financial objectives. In this work, we conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques. In particular, we focus on traditional approaches based on Modern Portfolio Theory, and on machine-learning approaches based on deep reinforcement learning. We assess the model’s performance under different market tendency, i.e., both bullish and bearish markets, as well as different time frequencies of reallocation., i.e., on a daily and weekly basis.
Language: English
URI: http://hdl.handle.net/10609/146811
Appears in Collections:Trabajos finales de carrera, trabajos de investigación, etc.

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