  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.
