Paper: arXiv 2402.07080

Abstract

The formulaic alphas are mathematical formulas that transform raw stock data into indicated signals. In the industry, a collection of formulaic alphas is combined to enhance modeling accuracy. Existing alpha mining only employs the neural network agent, unable to utilize the structural information of the solution space. Moreover, they didn’t consider the correlation between alphas in the collection, which limits the synergistic performance. To address these problems, we propose a novel alpha mining framework, which formulates the alpha mining problems as a reward-dense Markov Decision Process (MDP) and solves the MDP by the risk-seeking Monte Carlo Tree Search (MCTS). The MCTS-based agent fully exploits the structural information of discrete solution space and the risk-seeking policy explicitly optimizes the best-case performance rather than average outcomes. Comprehensive experiments are conducted to demonstrate the efficiency of our framework. Our method outperforms all state-of-the-art benchmarks on two real-world stock sets under various metrics. Backtest experiments show that our alphas achieve the most profitable results under a realistic trading setting.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced concepts like MCTS, reward-dense MDPs, and risk-seeking policy optimization, indicating high math complexity, while demonstrating empirical rigor through comprehensive experiments on real-world datasets, backtests, and ablation studies.

Research Flowchart

  flowchart TD
  A["Research Goal: Discover formulaic alphas<br>that generate high returns<br>via Risk-Seeking MCTS"] --> B["Data Input:<br>Raw Stock Data<br>Preprocessing"]
  B --> C["Core Methodology:<br>Reward-Dense MDP Formulation"]
  C --> D["MCTS Agent:<br>Risk-Seeking Policy<br>Optimizes Best-Case Scenarios"]
  D --> E["Alpha Generation:<br>Formulaic Alphas<br>discovered by MCTS"]
  E --> F["Experiments & Backtest:<br>Performance Evaluation"]
  F --> G["Key Findings:<br>Outperforms SOTA benchmarks<br>Most profitable in trading settings"]

Cited References (36)

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  3. Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning — → 2306.12964
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  7. AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment (2021)
  8. AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment (2020) — link
and 28 more
  1. Stock Alpha Mining Based On Genetic Algorithm (2019) — link
  2. Revisiting Stock Alpha Mining Based On Genetic Algorithm (2019) — link
  3. Factorvae: A probabilistic dynamic factor model based on variational autoencoder for predicting cross-sectional stock returns (2022)
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  9. Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction — → 2309.00073
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3 of 36 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.