mlsanityData to 14 Oct 2026

DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection

arXiv 2407.13427 ↗First posted 18 Jul 2024cs.CE cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 53 references that are in the map. First posted on arXiv 18 Jul 2024.

Builds on

24 of 53 references.

References · 24

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12 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025DeepAries: Adaptive Rebalancing Interval Selection for Enhanced Portfolio Selectioncited by 0
  2. 2026Bayesian Robust Financial Trading with Adversarial Synthetic Market Datacited by 0

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