DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection
This work addresses portfolio optimization for investors by combining forecasting and selection, but it appears incremental as it builds on existing methods like transformers and reinforcement learning.
The paper tackles portfolio selection by introducing DeepClair, a framework that uses a transformer-based model to forecast market trends and integrates it with deep reinforcement learning, resulting in improved investment strategies.
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.