FORECASTING STOCK MARKET LIQUIDITY WITH MACHINE LEARNING: AN EMPIRICAL EVALUATION IN THE GERMAN MARKET
The study benchmarks four machine-learning algorithms— Random Forest, XGBoost, CatBoost and Long Short-Term Memory (LSTM) networks—for forecasting stock market liquidity in Germany’s DAX equity market. Using data from January 2006 to May 2025, a Liquidity Score is constructed as a turnover-t...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
“Victor Slăvescu” Centre for Financial and Monetary Research
2025-06-01
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| Series: | Financial Studies |
| Subjects: | |
| Online Access: | http://fs.icfm.ro/Paper03.FS2.2025.pdf |
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