Nonparametric Predictive Regressions for Stock Return Prediction
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- Cheng, T. & Gao, J. & Linton, O., 2019. "Nonparametric Predictive Regressions for Stock Return Prediction," Cambridge Working Papers in Economics 1932, Faculty of Economics, University of Cambridge.
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Cited by:
- Dong, Chaohua & Linton, Oliver & Peng, Bin, 2021. "A weighted sieve estimator for nonparametric time series models with nonstationary variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 909-932.
- Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019. "Machine Learning for Forecasting Excess Stock Returns The Five-Year-View," Graz Economics Papers 2019-06, University of Graz, Department of Economics.
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More about this item
Keywords
kernel estimator; locally stationary process; series estimator; stock return prediction.;All these keywords.
JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2019-04-08 (Econometrics)
- NEP-FOR-2019-04-08 (Forecasting)
- NEP-ORE-2019-04-08 (Operations Research)
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