Topic: Capital Markets-Financial Engineering & Quantitative Research
Report type: Research Report on Quantitative Stock Selection Models Based on Machine Learning Methods (基于机器学习方法的量化选股模型研究报告)

Machine learning models demonstrate advantages in capturing complex nonlinear interactions among factors, but they also introduce significant risks of overfitting and lack of interpretability (black-box issues). Please prepare a research report on machine learning-based stock selection models, covering at least the following aspects:

1. Overview of machine learning stock selection models:
Compare with traditional linear cross-sectional regression models, and provide an in-depth discussion of how commonly used models such as GBDT or other machine learning approaches capture multi-factor interaction effects and nonlinear return patterns.

2. Model design and construction framework:
Design a machine learning model training framework, detailing key aspects of model construction, including how to design rolling windows to avoid look-ahead bias in time series. Discuss methods to control overfitting, such as early stopping mechanisms and sample weighting schemes (e.g., based on market capitalization or volatility decay).

3. Model interpretability analysis:
For the selected machine learning stock selection model, introduce SHAP or similar feature attribution methods, and explain how SHAP values can be used in cross-sectional settings to identify key factors driving excess returns under current market conditions.

4. Risk analysis of machine learning models:
Based on relevant literature and research reports (with cited references), analyze potential risks of machine learning-based stock selection models, such as model breakdown under structural changes in market conditions.

## Evaluation Criteria for *Research Report on Machine-Learning Stock Selection Models*

This framework evaluates research reports on machine-learning stock selection models. The focus is whether the report clearly compares ML models with traditional linear models, designs a robust training and anti-overfitting framework, provides meaningful interpretability analysis, and adequately identifies model risks and failure scenarios. Total: 100 points.

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### 1. Model Principles and Method Comparison (25 points)

#### (1) Comparison between traditional and ML models (10 points)
- **9–10**: Clearly compares linear cross-sectional regression with GBDT or other ML models in nonlinear fitting, interaction capture, and use cases.
- **6–8**: Basic comparison is provided, but depth is limited.
- **0–5**: Only gives concepts without meaningful comparison.

#### (2) Nonlinearity and interaction effects (8 points)
- **7–8**: Clearly explains why ML models are better suited to capture complex factor relations in stock selection.
- **4–6**: Provides some explanation, but lacks practical linkage.
- **0–3**: Discussion is vague.

#### (3) Model choice rationale (7 points)
- **6–7**: Clearly justifies the choice of GBDT or another model, with strengths and limitations.
- **3–5**: Mentions the model, but without clear rationale.
- **0–2**: Model choice appears arbitrary.

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### 2. Model Design and Training Framework (30 points)

#### (1) Training pipeline and rolling-window design (10 points)
- **9–10**: Presents a complete framework with clear train/validation/test splits and proper rolling-window design that avoids look-ahead bias.
- **6–8**: Framework is broadly complete, but time handling is not rigorous enough.
- **0–5**: Lacks a sound training design.

#### (2) Overfitting control (10 points)
- **9–10**: Systematically explains early stopping, regularization, sample weighting, and related controls, with relevance to stock selection.
- **6–8**: Lists control methods, but mechanism analysis is limited.
- **0–5**: Mentions overfitting risk without concrete solutions.

#### (3) Metrics and validation design (10 points)
- **9–10**: Clearly defines evaluation metrics and backtesting design, balancing predictive power and investment usability.
- **6–8**: Includes metrics, but not comprehensively.
- **0–5**: Lacks a proper evaluation framework.

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### 3. Interpretability Analysis (25 points)

#### (1) SHAP or similar method explanation (8 points)
- **7–8**: Accurately explains the core idea and value of SHAP or similar attribution methods.
- **4–6**: Basically correct, but not deep enough.
- **0–3**: Explanation is vague or incorrect.

#### (2) Cross-sectional factor attribution (10 points)
- **9–10**: Uses SHAP values to identify key drivers of excess returns under the current market regime, with a clear analytical structure.
- **6–8**: Includes attribution analysis, but not systematically.
- **0–5**: Stops at method introduction only.

#### (3) Investment implications of interpretability results (7 points)
- **6–7**: Connects attribution results to factor logic, market style, or portfolio construction.
- **3–5**: Some linkage is made, but depth is limited.
- **0–2**: Lacks investment interpretation.

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### 4. Risk Analysis and Research Quality (20 points)

#### (1) Model failure risk identification (10 points)
- **9–10**: Systematically analyzes risks such as regime shifts, sample drift, style rotation, and liquidity shocks.
- **6–8**: Covers major risks, but mechanism analysis is limited.
- **0–5**: Risk analysis is insufficient.

#### (2) Literature and research support (5 points)
- **5**: Uses sufficient and credible references, properly cited and well integrated into the argument.
- **3–4**: Includes references, but with limited support value.
- **0–2**: Lacks reliable references.

#### (3) Report structure and professionalism (5 points)
- **5**: Clear structure, proper terminology, and professional quantitative research style.
- **3–4**: Basically complete, but average in expression.
- **0–2**: Weak structure and professionalism.