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arXiv · 2407.13427

DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection

Donghee Choi·Jinkyu Kim·Mogan Gim·Jinho Lee·Jaewoo Kang
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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.

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Donghee Choi, Jinkyu Kim, Mogan Gim, Jinho Lee, Jaewoo Kang. 2024-07-18. DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection. https://doi.org/10.1145/3627673.3680008

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