DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection

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.

arXiv commentCIKM 2024 Accepted

References in corpus (5)

• Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
• TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
• Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
• Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market
• MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization