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...
保存先:
| 第一著者: | |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
“Victor Slăvescu” Centre for Financial and Monetary Research
2025-06-01
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| シリーズ: | Financial Studies |
| 主題: | |
| オンライン･アクセス: | http://fs.icfm.ro/Paper03.FS2.2025.pdf |
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