DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection

• URL: http://arxiv.org/abs/2407.13427v3
• Date: Fri, 16 Aug 2024 06:54:26 GMT
• Title: DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection
• Authors: Donghee Choi, Jinkyu Kim, Mogan Gim, Jinho Lee, Jaewoo Kang,
• Abstract summary: We introduce DeepClair, a novel framework for portfolio selection. DeepClair leverages a transformer-based time-series forecasting model to predict market trends.
• Score: 29.43115584494825
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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