Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction
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- Tingting Cheng & Jiti Gao & Oliver Linton, 2017. "Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction," Monash Econometrics and Business Statistics Working Papers 13/17, Monash University, Department of Econometrics and Business Statistics.
References listed on IDEAS
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Cited by:
- Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Forecasting benchmarks of long-term stock returns via machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 221-240, February.
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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-2004-04-11 (Econometrics)
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