Asset Allocation: From Markowitz to Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2208.07158v1
• Date: Thu, 14 Jul 2022 14:44:04 GMT
• Title: Asset Allocation: From Markowitz to Deep Reinforcement Learning
• Authors: Ricard Durall
• Abstract summary: Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets. We conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques.
• Score: 2.0305676256390934
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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. For reproducibility, we provide the code implementation code in this repository.

Related papers

• Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent [44.99833362998488]
  We develop a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy.
  arXiv  Detail & Related papers  (2024-07-19T17:40:39Z)
• Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization [49.396692286192206]
  We study the use of deep reinforcement learning for responsible portfolio optimization by incorporating ESG states and objectives. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation.
  arXiv  Detail & Related papers  (2024-03-25T12:04:03Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• Deep Reinforcement Learning for Robust Goal-Based Wealth Management [0.0]
  Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. reinforcement learning is a machine learning technique appropriate for sequential decision-making. In this paper, a novel approach for robust goal-based wealth management based on deep reinforcement learning is proposed.
  arXiv  Detail & Related papers  (2023-07-25T13:51:12Z)
• E2EAI: End-to-End Deep Learning Framework for Active Investing [123.52358449455231]
  We propose an E2E that covers almost the entire process of factor investing through factor selection, factor combination, stock selection, and portfolio construction. Experiments on real stock market data demonstrate the effectiveness of our end-to-end deep leaning framework in active investing.
  arXiv  Detail & Related papers  (2023-05-25T10:27:07Z)
• Factor Investing with a Deep Multi-Factor Model [123.52358449455231]
  We develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights. Tests on real-world stock market data demonstrate the effectiveness of our deep multi-factor model.
  arXiv  Detail & Related papers  (2022-10-22T14:47:11Z)
• MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization [17.759687104376855]
  We propose a novel two-stage-based approach for portfolio management. In the first stage, incorporates an imitation learning into the reinforcement learning framework. In the second stage, learns a meta-policy to recognize the market conditions and decide on the most proper learned policy to follow.
  arXiv  Detail & Related papers  (2022-09-01T07:58:06Z)
• Learning Risk Preferences from Investment Portfolios Using Inverse Optimization [25.19470942583387]
  This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization. We demonstrate our methods on real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings.
  arXiv  Detail & Related papers  (2020-10-04T21:29:29Z)
• Time your hedge with Deep Reinforcement Learning [0.0]
  Deep Reinforcement Learning (DRL) can tackle this challenge by creating a dynamic dependency between market information and hedging strategies allocation decisions. We present a realistic and augmented DRL framework that: (i) uses additional contextual information to decide an action, (ii) has a one period lag between observations and actions to account for one day lag turnover of common asset managers to rebalance their hedge, (iii) is fully tested in terms of stability and robustness thanks to a repetitive train test method called anchored walk forward training, similar in spirit to k fold cross validation for time series and (iv) allows managing leverage of our hedging
  arXiv  Detail & Related papers  (2020-09-16T06:43:41Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.