Automated stock picking using random forests
Author
Abstract
Suggested Citation
DOI: 10.1016/j.jempfin.2023.05.001
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Fuente,Angel de la, 2000. "Mathematical Methods and Models for Economists," Cambridge Books, Cambridge University Press, number 9780521585293, September.
- Ivo Welch & Amit Goyal, 2008.
"A Comprehensive Look at The Empirical Performance of Equity Premium Prediction,"
The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
- Amit Goyal & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," Yale School of Management Working Papers amz2412, Yale School of Management, revised 01 Jan 2006.
- Amit Goyal & Ivo Welch & Athanasse Zafirov, 2021. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction II," Swiss Finance Institute Research Paper Series 21-85, Swiss Finance Institute.
- Amit Goval & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," NBER Working Papers 10483, National Bureau of Economic Research, Inc.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- Martin Lettau & Markus Pelger & Stijn Van Nieuwerburgh, 2020.
"Factors That Fit the Time Series and Cross-Section of Stock Returns,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2274-2325.
- Lettau, Martin & Pelger, Markus, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," CEPR Discussion Papers 13049, C.E.P.R. Discussion Papers.
- Martin Lettau & Markus Pelger, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," NBER Working Papers 24858, National Bureau of Economic Research, Inc.
- Ravi Jagannathan & Tongshu Ma, 2003.
"Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps,"
Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
- Ravi Jagannathan & Tongshu Ma, 2002. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," NBER Working Papers 8922, National Bureau of Economic Research, Inc.
- Malcolm Baker & Jeffrey Wurgler, 2006.
"Investor Sentiment and the Cross‐Section of Stock Returns,"
Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
- Malcolm Baker & Jeffrey Wurgler, 2004. "Investor Sentiment and the Cross-Section of Stock Returns," NBER Working Papers 10449, National Bureau of Economic Research, Inc.
- Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
- Gah-Yi Ban & Noureddine El Karoui & Andrew E. B. Lim, 2018. "Machine Learning and Portfolio Optimization," Management Science, INFORMS, vol. 64(3), pages 1136-1154, March.
- Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
- Martin Lettau & Markus Pelger, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," Review of Finance, European Finance Association, vol. 33(5), pages 2274-2325.
- Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
- Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000.
"Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation,"
Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
- Andrew Lo & Harry Mamaysky & Jiang Wang, 1999. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Computing in Economics and Finance 1999 402, Society for Computational Economics.
- Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," NBER Working Papers 7613, National Bureau of Economic Research, Inc.
- Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
- MacKinnon, James G. & White, Halbert, 1985.
"Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties,"
Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
- James G. MacKinnon & Halbert White, 1983. "Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties," Working Paper 537, Economics Department, Queen's University.
- repec:bla:jfinan:v:58:y:2003:i:4:p:1651-1684 is not listed on IDEAS
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
- Shi, Qi, 2023. "The RP-PCA factors and stock return predictability: An aligned approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
- van Binsbergen, Jules H. & Boons, Martijn & Opp, Christian C. & Tamoni, Andrea, 2023. "Dynamic asset (mis)pricing: Build-up versus resolution anomalies," Journal of Financial Economics, Elsevier, vol. 147(2), pages 406-431.
- Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
- Ni, Xuanming & Zheng, Tiantian & Zhao, Huimin & Zhu, Shushang, 2023. "High-dimensional portfolio optimization based on tree-structured factor model," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
- Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
- Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
- Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
- Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
- Thomas Conlon & John Cotter & Iason Kynigakis, 2021.
"Machine Learning and Factor-Based Portfolio Optimization,"
Papers
2107.13866, arXiv.org.
- Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
- Andrea Rigamonti, 2024. "Can machine learning make technical analysis work?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 38(3), pages 399-412, September.
- Bagnara, Matteo, 2024. "The economic value of cross-predictability: A performance-based measure," SAFE Working Paper Series 424, Leibniz Institute for Financial Research SAFE.
- De Nard, Gianluca & Zhao, Zhao, 2023. "Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 23-35.
- Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023.
"Machine-learning the skill of mutual fund managers,"
Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
- Ron Kaniel & Zihan Lin & Markus Pelger & Stijn Van Nieuwerburgh, 2022. "Machine-Learning the Skill of Mutual Fund Managers," NBER Working Papers 29723, National Bureau of Economic Research, Inc.
- Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-Learning the Skill of Mutual Fund Managers," CEPR Discussion Papers 18129, C.E.P.R. Discussion Papers.
- Zhaobo Zhu & Licheng Sun, 2024. "When Buffett Meets Bollinger: An Integrated Approach to Fundamental and Technical Analysis," Post-Print hal-04703041, HAL.
- Son, Bumho & Lee, Jaewook, 2022. "Graph-based multi-factor asset pricing model," Finance Research Letters, Elsevier, vol. 44(C).
- Simon Hediger & Jeffrey Näf & Marc S. Paolella & Paweł Polak, 2023. "Heterogeneous tail generalized common factor modeling," Digital Finance, Springer, vol. 5(2), pages 389-420, June.
- Francisco Peñaranda & Enrique Sentana, 2024.
"Portfolio management with big data,"
Working Papers
wp2024_2411, CEMFI.
- Penaranda, Francisco & Sentana, Enrique, 2024. "Portfolio management with big data," CEPR Discussion Papers 19314, C.E.P.R. Discussion Papers.
- Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
- Andrew Y. Chen & Jack McCoy, 2022. "Missing Values Handling for Machine Learning Portfolios," Papers 2207.13071, arXiv.org, revised Jan 2024.
More about this item
Keywords
Stock picking; Machine learning; Random forest; Portfolio optimization;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:empfin:v:72:y:2023:i:c:p:532-556. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jempfin .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.