Paper: arXiv 2406.18394

Abstract

The complexity of financial data, characterized by its variability and low signal-to-noise ratio, necessitates advanced methods in quantitative investment that prioritize both performance and interpretability.Transitioning from early manual extraction to genetic programming, the most advanced approach in the alpha factor mining domain currently employs reinforcement learning to mine a set of combination factors with fixed weights. However, the performance of resultant alpha factors exhibits inconsistency, and the inflexibility of fixed factor weights proves insufficient in adapting to the dynamic nature of financial markets. To address this issue, this paper proposes a two-stage formulaic alpha generating framework AlphaForge, for alpha factor mining and factor combination. This framework employs a generative-predictive neural network to generate factors, leveraging the robust spatial exploration capabilities inherent in deep learning while concurrently preserving diversity. The combination model within the framework incorporates the temporal performance of factors for selection and dynamically adjusts the weights assigned to each component alpha factor. Experiments conducted on real-world datasets demonstrate that our proposed model outperforms contemporary benchmarks in formulaic alpha factor mining. Furthermore, our model exhibits a notable enhancement in portfolio returns within the realm of quantitative investment and real money investment.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces advanced neural network architectures (generative-predictive models) and dynamic weighting schemes with some mathematical notation, but the core challenge is implementation-heavy. The rigorous validation via real-world datasets, portfolio simulations, and financial metrics like IC/ICIR positions it for practical application.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>Alpha Factor Mining & Combination"] --> B["Input: Financial Market Data"]
  B --> C["Stage 1: Factor Mining<br/>Generative-Predictive Neural Network"]
  C --> D["Stage 2: Dynamic Factor Combination<br/>Time-Aware Weight Adjustment"]
  D --> E["Output: Adaptive Formulaic Alphas"]
  E --> F["Outcomes<br/>Higher Returns & Better Adaptability"]

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2 of 62 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.