Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module

Abstract: Dynamic Portfolio optimization is the process of distribution and rebalancing of a fund into different financial assets such as stocks, cryptocurrencies, etc, in consecutive trading periods to maximize accumulated profits or minimize risks over a time horizon. This field saw huge developments in recent years, because of the increased computational power and increased research in sequential decision making through control theory. Recently Reinforcement Learning(RL) has been an important tool in the development of sequential and dynamic portfolio optimization theory. In this paper, we design a Deep Reinforcement Learning(DRL) framework as an autonomous portfolio optimization agent consisting of a Latent Feature State Space(LFSS) Module for filtering and feature extraction of financial data which is used as a state space for deep RL model. We develop an extensive RL agent with high efficiency and performance advantages over several benchmarks and model-free RL agents used in prior work. The noisy and non-stationary behaviour of daily asset prices in the financial market is addressed through Kalman Filter. Autoencoders, ZoomSVD, and restricted Boltzmann machines were the models used and compared in the module to extract relevant time series features as state space. We simulate weekly data, with practical constraints and transaction costs, on a portfolio of S&P 500 stocks. We introduce a new benchmark based on technical indicator Kd-Index and Mean-Variance Model as compared to equal weighted portfolio used in most of the prior work. The study confirms that the proposed RL portfolio agent with state space function in the form of LFSS module gives robust results with an attractive performance profile over baseline RL agents and given benchmarks.

• The paper introduces a novel DRL framework that integrates a Latent Feature State Space module to enhance state representation for portfolio optimization.
• It combines Kalman filtering with autoencoder-based techniques to reduce market noise and extract robust latent features.
• Performance improvements are evidenced by higher Sharpe and Sortino ratios compared to traditional portfolio strategies.

Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module

This paper introduces a novel framework that utilizes Deep Reinforcement Learning (DRL) for dynamic portfolio optimization, focusing on the deployment of a Latent Feature State Space (LFSS) Module. The primary objective is to address the inherent challenges posed by the dynamic and noisy nature of financial markets and to improve upon existing model-free RL agents.

Overview of the Approach

Dynamic Portfolio Optimization and DRL

Portfolio optimization involves dynamically reallocating funds among various financial assets to maximize returns or minimize risks. Traditional models based on Modern Portfolio Theory (MPT) have limitations in real-time applications due to their static nature. The introduction of DRL provides a means of navigating continuous action spaces, with neural networks approximating value functions over these spaces, facilitating real-time portfolio adjustments.

Latent Feature State Space (LFSS) Module

The LFSS module serves as a pivotal component, enhancing state representation within the deep RL agent. It comprises two main units:

1. Filtering Unit: Employs a Kalman Filter to reduce noise in asset price data, providing a more stable estimate of true asset values.
2. Latent Feature Extractor Unit: Utilizes models like Autoencoders, ZoomSVD, and Restricted Boltzmann Machines (RBM) to extract compressive representations of filtered price data, thus facilitating lower-dimensional feature spaces accurately reflecting market conditions.

RL Architecture and Implementation

Key aspects of the DRL architecture include:

• State Space: The state space is enriched using LFSS, providing a more refined input to the RL model, featuring both filtered price data and reduced dimensionality features.
• Policy Network: Utilizes a CNN architecture to handle high-dimensional inputs, improved by Identical Independent Evaluators (IIE) and Portfolio-Vector Memory (PVM) for efficient learning.
• Training Methodology: Deep Deterministic Policy Gradients (DDPG) are employed, benefiting from continuous action representation which aligns with trading environments.

Benchmarking and Evaluation

The effectiveness of the proposed framework is evaluated against several benchmarks, including traditional methods like equal-weighted portfolios and more advanced strategies utilizing mean-variance models. The LFSS-augmented RL agent demonstrates superior performance in terms of portfolio value and risk-adjusted returns, as measured by metrics such as the Sharpe and Sortino ratios.

Implications and Future Directions

Practical Implications

The integration of sophisticated non-linear filtering and feature extraction techniques into RL frameworks shows potential for significantly improving real-world portfolio management strategies. The methodology offers robustness in environments characterized by high asset price volatility and non-stationarity, common in financial markets.

Theoretical Implications

From a theoretical perspective, the LFSS module exemplifies how deep learning paradigms like autoencoders and variational approaches like RBM can be applied within financial contexts. The paper suggests future exploration into deeper network architectures, more sophisticated filtering methods, and diverse market environments as data inputs for customized RL agent training.

Speculations on Future Developments

Potential future developments include enhancing feature extractors with adversarial robustness to better handle outliers and integrating hybrid models that combine supervised learning insights with reinforcement strategies. Further exploration into the utilization of alternative RL algorithms such as PPO within the context of LFSS modules also represents a promising area for development.

Conclusion

The proposed LFSS module within a DRL framework marks a significant progression in the field of portfolio optimization. By incorporating advanced filtering and feature extraction methods into the state space design for DRL agents, the paper paves the way for more robust, accurate, and adaptive financial decision-making tools that cater to the needs of dynamic market conditions.