An Evaluation of Equity Premium Prediction Using Multiple Kernel Learning with Financial Features

This paper introduces and extensively explores a forecasting procedure based on multivariate dynamic kernels to re-examine—under a non-linear, kernel methods framework—the experimental tests reported by Welch and Goyal (Rev Financ Stud 21(4):1455–1508, 2008 ) showing that several variables proposed...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Neural processing letters Ročník 52; číslo 1; s. 117 - 134
Hlavní autoři:	Arratia, Argimiro, Belanche, Lluís A., Fábregues, Luis
Médium:	Journal Article Publikace
Jazyk:	angličtina
Vydáno:	New York Springer US 01.08.2020 Springer Nature B.V
Témata:	62 Statistics 62P Applications Artificial Intelligence Classificació AMS Complex Systems Computational Intelligence Computer Science Econometric models Estadística matemàtica Finance Finances Financial time series Forecasting Kernel functions Kernel functions for time series Learning Matemàtiques i estadística Mathematical models Models economètrics Multiple kernel learning Ratios Resampling Securities markets Support vector classification Support vector regression Time series Valuation Variables Àrees temàtiques de la UPC Multiple kernel learning Support vector classification Support vector regression Financial time series Kernel functions for time series
ISSN:	1370-4621, 1573-773X, 1573-773X
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	This paper introduces and extensively explores a forecasting procedure based on multivariate dynamic kernels to re-examine—under a non-linear, kernel methods framework—the experimental tests reported by Welch and Goyal (Rev Financ Stud 21(4):1455–1508, 2008 ) showing that several variables proposed in the finance literature are of no use as exogenous information to predict the equity premium under linear regressions. For this new approach to equity premium forecasting, kernel functions for time series are used with multiple kernel learning (MKL) in order to represent the relative importance of each of the variables. We find that, in general, the predictive capabilities of the MKL models do not improve consistently with the use of some or all of the variables, nor does the predictability by single kernels, as determined by different resampling procedures that we implement and compare. This fact tends to corroborate the instability already observed by Welch and Goyal for the predictive power of exogenous variables, now in a non-linear modelling framework.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1370-4621 1573-773X 1573-773X
DOI:	10.1007/s11063-018-09971-7