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Machine Learning in Fine Wine Price Prediction*

Published online by Cambridge University Press:  05 August 2015

Michelle Yeo ,
Tristan Fletcher  and
John Shawe-Taylor
Michelle Yeo
Affiliation:
Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom; email: michelleyeo@uchicago.edu
Tristan Fletcher
Affiliation:
Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom; email: t.fletcher@cs.ucl.ac.uk
John Shawe-Taylor
Affiliation:
Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom; email: J.Shawe-Taylor@cs.ucl.ac.uk
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Abstract

Advanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the trivial and simple ARMA and GARCH time series prediction benchmarks. Lastly, an implementation of an algorithm which performs multi-task feature learning with kernels on the wine returns as an extension to our optimal Gaussian process regression model. Using the optimal covariance kernel from Gaussian process regression, we achieve predictive results which are comparable to that of Gaussian process regression. Altogether, our research suggests that there is potential in using advanced machine learning techniques in wine price prediction. (JEL Classifications: C6, G12)

Keywords

ARMAGARCHGaussian process regressionmulti-task learningtime series
Type
Articles
Information
Journal of Wine Economics , Volume 10 , Issue 2 , November 2015 , pp. 151 - 172
Copyright
Copyright © American Association of Wine Economists 2015 

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Footnotes

*

We would like to thank an anonymous referee and the editor Karl Storchmann for their comments and suggestions on the earlier versions of the paper. This work would also not have been possible without the assistance of Invinio Ltd (www.invin.io).

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