DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection

TL;DR

DeepClair combines transformer-based market forecasting with deep reinforcement learning to improve portfolio selection, using a two-step training process and LoRA optimization to adapt to investment scenarios.

Contribution

It introduces a novel framework that integrates market forecasting with portfolio optimization, employing a two-step training process and LoRA for enhanced adaptation.

Findings

Effective market trend prediction using transformer models.

Improved portfolio decisions through integrated forecasting and reinforcement learning.

Enhanced model fine-tuning with Low-Rank Adaptation (LoRA).

Abstract

Utilizing market forecasts is pivotal in optimizing portfolio selection strategies. We introduce DeepClair, a novel framework for portfolio selection. DeepClair leverages a transformer-based time-series forecasting model to predict market trends, facilitating more informed and adaptable portfolio decisions. To integrate the forecasting model into a deep reinforcement learning-driven portfolio selection framework, we introduced a two-step strategy: first, pre-training the time-series model on market data, followed by fine-tuning the portfolio selection architecture using this model. Additionally, we investigated the optimization technique, Low-Rank Adaptation (LoRA), to enhance the pre-trained forecasting model for fine-tuning in investment scenarios. This work bridges market forecasting and portfolio selection, facilitating the advancement of investment strategies.