Abstract
This paper presents a comprehensive study on predicting the cross section of Chinese stock market returns with a large panel of 75 individual firm characteristics. We use not only the traditional Fama-MacBeth regression, but also the “big-data” econometric methods: principal component analysis (PCA), partial least squares (PLS), and forecast combination to extract information from all the 75 firm characteristics. These characteristics are important return predictors, with statistical and economic significance. Furthermore, firm characteristics that are related to trading frictions, momentum, and profitability are the most effective predictors of future stock returns in the Chinese stock market.
| Original language | English |
|---|---|
| Pages (from-to) | 259-283 |
| Number of pages | 25 |
| Journal | Journal of Management Science and Engineering |
| Volume | 3 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2018 |
Keywords
- Chinese stock market
- Firm characteristics
- Machine learning
- Partial least squares
- Return predictability
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