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Machine learning for predicting stock return volatility

Filipović, Damir; Khalilzadeh, Amir - 2021

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility …

Persistent link: https://www.econbiz.de/10012800743

Machine learning for US cross-industry return predictability under information uncertainty

Awijen, Haithem; Zaied, Younes Ben; Ben Lahouel, Bechir; … - In: Research in international business and finance 64 (2023), pp. 1-13

Persistent link: https://www.econbiz.de/10014279809

Application of machine learning in algorithmic investment strategies on global stock markets

Grudniewicz, Jan; Ślepaczuk, Robert - In: Research in international business and finance 66 (2023), pp. 1-24

Persistent link: https://www.econbiz.de/10014463386

A cross-sectional machine learning approach for hedge fund return prediction and selection

Wu, Wenbo; Chen, Jiaqi; Yang, Zhibin; Tindall, Michael L. - In: Management science : journal of the Institute for … 67 (2021) 7, pp. 4577-4601

Persistent link: https://www.econbiz.de/10012624639

Improving earnings predictions and abnormal returns with machine learning

Hunt, Joshua O. S.; Myers, James N.; Myers, Linda A. - In: Accounting horizons : a quarterly publication of the … 36 (2022) 1, pp. 131-149

Persistent link: https://www.econbiz.de/10013252475

Forecasting relative returns for S&P 500 stocks using machine learning

Htet Htet Htun; Biehl, Michael; Petkov, Nicolai - In: Financial innovation : FIN 10 (2024), pp. 1-16

Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fuctuate. The random walk hypothesis and efcient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes...

Persistent link: https://www.econbiz.de/10014547210

Forecasting Korean stock returns with machine learning

Noh, Hohsuk; Jang, Hyuna; Yang, Cheol-Won - In: Asia-Pacific journal of financial studies 52 (2023) 2, pp. 193-241

Persistent link: https://www.econbiz.de/10014315552

Machine learning and the cross-section of emerging market stock returns

Hanauer, Matthias Xaver; Kalsbach, Tobias - In: Emerging markets review 55 (2023), pp. 1-38

Persistent link: https://www.econbiz.de/10014481087

Machine learning portfolio allocation

Pinelis, Michael; Ruppert, David - In: The Journal of finance and data science : JFDS 8 (2022), pp. 35-54

We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is...

Persistent link: https://www.econbiz.de/10014433682

Stock market analysis : a review and taxonomy of prediction techniques

Shah, Dev; Isah, Haruna; Zulkernine, Farhana - In: International Journal of Financial Studies : open … 7 (2019) 2/26, pp. 1-22

Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that … stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We …

Persistent link: https://www.econbiz.de/10012038738

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