Empirical asset pricing via Machine Learning in the Korean market

Caveats:

• Cleaning up and moving the code into .py files
• Some notebooks are very messy and outdated; they should be revised

Methodology

• This project is a replication of Gu, Kelly, and Xiu, "Empirical Asset Pricing via Machine Learning." Review of Financial Studies, 2020 using data from thr Korean stock market, both KOSPI and KOSDAQ.
• I expanded the neural net models suggested in the paper into models with deeper structure, but the number of factors I gathered here is less than the paper, possibly incurring smaller $R^2$ and more volatile results from the paper's result.

File explanation

1. Marketdata_crawler: Currently dismissed the crawler
2. The factors that I used initially are" Beta, SMB, HML, Market portfolio, Moving Average, Momentum, PER
3. ML_pricing: machine learning pricing models. OLS, ElasticNet, PCR, PLS, RandomForest, GBR
4. NN_pricing: Neural net settings of pricing models
5. NN_pricing_changed_setting: I tested several settings of neural nets by changing the optimizers and training methods
6. FF3 test: statistics related to the pricing models, also generate Decile portfolios.

Limitations of the research

• Data availability. There is a survivorship bias in the data since the only data available through Korea Exchange is for the securites that are currently traded in the market.
• Lack of factors and data that almost 90% of the data used in the Gu's paper was not available within my reachouts.

Revision

• 2022.05.23:
  • revised the code for ML_pricing
  • make ML_pricing as .py file