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.
@article{arxiv.2407.13427,
title = {DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection},
author = {Donghee Choi and Jinkyu Kim and Mogan Gim and Jinho Lee and Jaewoo Kang},
journal= {arXiv preprint arXiv:2407.13427},
year = {2024}
}