DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection
<|reference_start|>DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection: 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.<|reference_end|>
@article{choi2024deepclair:,
    title={DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection},
    author={Donghee Choi, Jinkyu Kim, Mogan Gim, Jinho Lee, Jaewoo Kang},
    journal={arXiv preprint arXiv:2407.13427},
    year={2024},
    doi={10.1145/3627673.3680008},
    archivePrefix={arXiv},
    eprint={2407.13427},
    primaryClass={cs.CE cs.AI}
}