@inproceedings{d8b9276175f54e88b723b4d083113657,
title = "Machine Learning in Empirical Asset Pricing Models",
abstract = "Although machine learning has achieved great success in computer science, its performance in the canonical problem of asset pricing in finance is yet to be fully investigated. To compare machine learning techniques and traditional models, we use 8 macroeconomic predictors and 102 firm characteristics to predict stock returns in a monthly basis. It is shown that the neural network outperforms others: Specifically, when building bottom-up portfolios based on the predicted stock-level returns for both buy-and-hold and long-short strategies, XGBoost and neural networks produce portfolios with the highest Sharpe ratios. Limitations and challenges in using machine learning techniques in empirical asset pricing models are also discussed. ",
keywords = "Empirical asset pricing models, Fama-MacBeth regression, XGBoost, elastic net, machine learning, neural network, random forest, regression tree, return prediction",
author = "Teng, {Huei Wen} and Li, {Yu Hsien} and Chang, {Shang Wen}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 ; Conference date: 03-12-2020 Through 05-12-2020",
year = "2020",
month = dec,
doi = "10.1109/ICPAI51961.2020.00030",
language = "English",
series = "Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "123--129",
booktitle = "Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020",
address = "美國",
}