🚀 Alpha Go Everywhere: Machine Learning and International Stock Returns

This is the README file for the project Alpha Go Everywhere: Machine Learning and International Stock Returns (SSRN link) accepted by Review of Asset Pricing Studies. It provides an overview of the project structure and instructions on how to use and contribute to the codebase.

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📑 Table of Contents

• Project Structure
• Usage
• Data
• Computing Environment

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🏗️ Project Structure

The project is organized as follows (key scripts highlighted):

• ❗️ Rank_Norm.py: Rank-normalize the data, like GKX's paper.
• 📂 Load_Data.py: Necessary functions for loading or preprocessing data
• ⚙️ SetUp.py: Variable definitions
• 🛠️ Local{US}_Factor{GapQ}.py: Create Local{US} factor{GapQ}
• 🔗 Merge_Factor+GapQ.py: Merge US factors, US gaps, and local factors
• 🌐 International_Pool.py: Integrate all standardized market data into one dataset
• 🤖 ML{NN}_Market.py: Train various ML{NN} models for each market
• 🗽 ML{NN}_Market_USmodel.py: Predict international markets using the USA model (no further training)
• 🚀 ML{NN}_Market_Enhanced.py: Train enhanced market-specific models with USA factors, gaps, and local features

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🚀 Usage

To use the project, follow these steps:

1. Run Rank_Norm.py to rank-normalize the predictors (as in GKX’s paper).
2. Run Local{US}_Factor{GapQ}.py to create Local{US} factor{GapQ}.
3. Run Merge_Factor+GapQ.py to merge US factors, gaps, and local factors.
4. Run International_Pool.py to integrate all standardized market data into one international dataset.
5. Run ML{NN}_Market.py to train ML models for each market.
6. Run ML{NN}_Market_USmodel.py to predict international markets using the USA model.
7. Run ML{NN}_Market_Enhanced.py to train enhanced models with additional features.

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🗄️ Data

• US data from CRSP
• China data from CSMAR
• Other markets data from DataStream

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💻 Computing Environment

To run the reproducibility checks, the following environment and packages might be required:

• Hardware

  • Nvidia A100 GPU (40 GB)
  • AMD EPYC 7713 64-Core @ 1.80 GHz (128 cores)
  • 1.0 TB RAM
  • Ubuntu 20.04.4 LTS

• Software

  • 🐍 Python 3.8.18
  • 🔥 PyTorch 2.0.1+cu117
  • 📊 numpy 1.22.3
  • 📑 pandas 2.0.3
  • 📈 scikit-learn 1.3.0
  • 📊 matplotlib 3.7.2