Detecting market pattern changes: A machine learning approach
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DOI: 10.1016/j.frl.2021.102621
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- Jushan Bai & Pierre Perron, 1998.
"Estimating and Testing Linear Models with Multiple Structural Changes,"
Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
- Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Universite de Montreal, Departement de sciences economiques.
- Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Andrew Ang & Allan Timmermann, 2012.
"Regime Changes and Financial Markets,"
Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
- Andrew Ang & Allan Timmermann, 2011. "Regime Changes and Financial Markets," NBER Working Papers 17182, National Bureau of Economic Research, Inc.
- Timmermann, Allan & Ang, Andrew, 2011. "Regime Changes and Financial Markets," CEPR Discussion Papers 8480, C.E.P.R. Discussion Papers.
- Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
- 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.
- Roubaud, David & Arouri, Mohamed, 2018.
"Oil prices, exchange rates and stock markets under uncertainty and regime-switching,"
Finance Research Letters, Elsevier, vol. 27(C), pages 28-33.
- David Roubaud & Mohamed Arouri, 2018. "Oil prices, exchange rates and stock markets under uncertainty and regime-switching," Post-Print hal-02059403, HAL.
- 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 T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- Bruce E. Hansen, 2001. "The New Econometrics of Structural Change: Dating Breaks in U.S. Labour Productivity," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 117-128, Fall.
- Bracke, Philippe & Datta, Anupam & Jung, Carsten & Sen, Shayak, 2019. "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers 816, Bank of England.
- Christopher A. Sims & Tao Zha, 2006.
"Were There Regime Switches in U.S. Monetary Policy?,"
American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
- Christopher A. Sims & Tao Zha, 2004. "Were there regime switches in U.S. monetary policy?," FRB Atlanta Working Paper 2004-14, Federal Reserve Bank of Atlanta.
- Christopher A. Sims & Tao Zha, 2005. "Were There Regime Switches in U.S. Monetary Policy?," Working Papers 92, Princeton University, Department of Economics, Center for Economic Policy Studies..
- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
- Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
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- Oliveira, Alexandre Silva de & Ceretta, Paulo Sergio & Albrecht, Peter, 2023. "Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios," Finance Research Letters, Elsevier, vol. 55(PA).
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More about this item
Keywords
Machine learning application; US economy; Structural change;All these keywords.
JEL classification:
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
- C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
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