Portfolio Management using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based portfolio management system that dynamically allocates asset weights, outperforming traditional methods in risk-adjusted returns through empirical testing in simulated markets.

Contribution

It presents a novel reinforcement learning approach for portfolio management that optimizes asset weight allocations, surpassing benchmark indices in empirical evaluations.

Findings

The proposed method outperforms benchmark indices in risk-adjusted returns.

Reinforcement learning effectively manages asset weights in simulated markets.

The approach demonstrates potential for automated, adaptive portfolio management.

Abstract

Algorithmic trading or Financial robots have been conquering the stock markets with their ability to fathom complex statistical trading strategies. But with the recent development of deep learning technologies, these strategies are becoming impotent. The DQN and A2C models have previously outperformed eminent humans in game-playing and robotics. In our work, we propose a reinforced portfolio manager offering assistance in the allocation of weights to assets. The environment proffers the manager the freedom to go long and even short on the assets. The weight allocation advisements are restricted to the choice of portfolio assets and tested empirically to knock benchmark indices. The manager performs financial transactions in a postulated liquid market without any transaction charges. This work provides the conclusion that the proposed portfolio manager with actions centered on weight allocations can surpass the risk-adjusted returns of conventional portfolio managers.