Hedge Fund Return Prediction and Fund Selection: A Machine-Learning Approach
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References listed on IDEAS
- Getmansky, Mila & Lo, Andrew W. & Makarov, Igor, 2004.
"An econometric model of serial correlation and illiquidity in hedge fund returns,"
Journal of Financial Economics, Elsevier, vol. 74(3), pages 529-609, December.
- Getmansky, Mila & Lo, Andrew & Makarov, Igor, 2003. "An Econometric Model of Serial Correlation and Illiquidity In Hedge Fund Returns," Working papers 4288-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
- Mila Getmansky & Andrew W. Lo & Igor Makarov, 2003. "An Econometric Model of Serial Correlation and Illiquidity in Hedge Fund Returns," NBER Working Papers 9571, National Bureau of Economic Research, Inc.
- Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
- Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Agarwal, Vikas & Naik, Narayan Y., 2000. "Multi-Period Performance Persistence Analysis of Hedge Funds," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(3), pages 327-342, September.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
- Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
More about this item
Keywordshedge fund return prediction; gradient boosting; machine learning; deep neural networks; random forest; the lasso; Hedge fund selection;
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