Paper: arXiv 2508.19609

Authors: Zhuohang Zhu, Haodong Chen, Qiang Qu, Vera Chung

Abstract

Financial time-series forecasting is critical for maintaining economic stability, guiding informed policymaking, and promoting sustainable investment practices. However, it remains challenging due to various underlying pattern shifts. These shifts arise primarily from three sources: temporal non-stationarity (distribution changes over time), multi-domain diversity (distinct patterns across financial domains such as stocks, commodities, and futures), and varying temporal resolutions (patterns differing across per-second, hourly, daily, or weekly indicators). While recent deep learning methods attempt to address these complexities, they frequently suffer from overfitting and typically require extensive domain-specific fine-tuning. To overcome these limitations, we introduce FinCast, the first foundation model specifically designed for financial time-series forecasting, trained on large-scale financial datasets. Remarkably, FinCast exhibits robust zero-shot performance, effectively capturing diverse patterns without domain-specific fine-tuning. Comprehensive empirical and qualitative evaluations demonstrate that FinCast surpasses existing state-of-the-art methods, highlighting its strong generalization capabilities.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper proposes novel mathematical components (Point-Quantile Loss, frequency embeddings) and uses advanced architectures (Mixture-of-Experts transformers), indicating high math complexity. It also presents a large-scale foundation model trained on 20B+ time points with zero-shot benchmarks across multiple domains, demonstrating strong empirical implementation and evaluation rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop a Foundation Model<br>for Financial Time-Series Forecasting"] --> B["Data: Large-Scale Financial Datasets<br>(Stocks, Commodities, Futures)"]
  B --> C{"Methodology: FinCast Training"}
  C --> D["Computational Process:<br>Deep Learning Foundation Model"]
  D --> E["Key Findings:<br>Robust Zero-Shot Performance"]
  E --> F["Outcomes:<br>Superior Generalization<br>No Domain Fine-Tuning Needed"]
  F --> G["Impact:<br>Surpasses SOTA Methods"]

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1 of 49 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.