Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-25T08:21:02.623Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected]
Tristan Fletcher
Affiliation:
Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom; email: [email protected]
John Shawe-Taylor
Affiliation:
Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom; email: [email protected]
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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).

References

Argyriou, A., Evgeniou, T., and Pontil, M. (2006). Multi-Task Feature Learning. In Schölkopf, B., Platt, J., and Hofmann, T. (eds.), Advances in Neural Information Processing Systems. Cambridge, MA: Massachusetts Institute of Technology Press.Google Scholar
Argyriou, A., Evgeniou, T., and Pontil, M. (2008). Convex multi-task feature learning. Machine Learning, 73, 243–272.CrossRefGoogle Scholar
Ashenfelter, O. (2010). Predicting the quality and prices of Bordeaux wine. Journal of Wine Economics, 5, 40–52.Google Scholar
Bakker, B., and Heskes, T. (2003). Task clustering and gating for Bayesian multitask learning. Journal of Machine Learning Research, 4, 83–99.Google Scholar
Bishop, C. (2006). Pattern Recognition and Machine Learning. New York: Springer.Google Scholar
Bonilla, E., Chai, K.M., and Williams, C. (2008). Multi-task Gaussian process prediction. In Advances in Neural Information Processing Systems (NIPS), 22.Google Scholar
Burton, B.J., and Jacobsen, J.P. (2001). The rate of return on wine investment. Economic Inquiry, 39, 337–350.Google Scholar
Byron, R.P., and Ashenfelter, O. (1995). Predicting the quality of an unborn grange. The Economic Record, 71, 40–53.CrossRefGoogle Scholar
Cryer, J. D. and Chan, K.S. (2008). Times Series Analysis with Applications in R. Berlin: Springer.Google Scholar
Duvenaud, D. (2014). Automatic model construction with Gaussian processes (Doctoral dissertation, University of Cambridge).Google Scholar
Fogarty, J.J. (2006). The return to Australian fine wine. European Review of Agricultural Economics, 33, 542–561.Google Scholar
Fogarty, J.J., and Jones, C. (2011). Return to wine: A comparison of the hedonic, repeat sales, and hybrid approaches. Australian Economic Papers, 50, 147–156.Google Scholar
Fogarty, J.J., and Sadler, R. (2014). To Save or savor: A review of approaches for measuring wine as an investment. Journal of Wine Economics, 9, 225–248.CrossRefGoogle Scholar
Haeger, J.W., and Storchmann, K. (2006). Prices of American Pinot Noir wines: climate, craftsmanship, critics. Agricultural Economics, 35, 67–78.Google Scholar
Jaeger, E. (1981). To save or savor: The rate of return to storing wine: Comment. Journal of Political Economy, 89, 584–592.Google Scholar
Jones, G.V., and Storchmann, K. (2001). Wine market prices and investment under uncertainty: An econometric model for Bordeaux Crus Classés. Agricultural Economics, 26, 115–133.CrossRefGoogle Scholar
Lawler, G. (2010). Random Walk and the Heat Equation. Student Mathematical Library, Vol. 55, American Mathematical Society, Providence, Rhode Island.Google Scholar
Lázaro-Gredilla, M., and Titsias, M.K. (2011). Variational heteroscedastic Gaussian process regression. In 28th International Conference on Machine Learning (ICML-11). Bellevue, WA: ACM, 841–848.Google Scholar
Lima, T. (2006). Price and Quality in the Californian Wine Industry: An Empirical Investigation. Journal of Wine Economics, 1, 176–190.Google Scholar
Masset, P., and Henderson, C. (2010). Wine as an alternative asset class. Journal of Wine Economics, 5, 87–118.Google Scholar
Masset, P., and Weisskopf, J.-P. (2013). Wine as an alternative asset class. In: Giraud-Heraud, E., and M. Pichery, M.-C. (eds.), Wine Economics: Quantitative Studies and Empirical Applications. New York: Palgrave Macmillan, 173–199.CrossRefGoogle Scholar
Ou, P., and Wang, H. (2011). Modeling and Forecasting Stock Market Volatility by Gaussian Processes based on GARCH, EGARCH and GJR Models. Proceedings of the World Congress on Engineering, Vol 1. (London, July 6–8).Google Scholar
Petelin, D., Sindelar, J., Prikryl, J., and Kocijan, J. (2011). Financial modeling using Gaussian process models. In 2011 IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS) (Vol. 2)., Prague, Czech Republic, 672–677.Google Scholar
Phuong, N.D. and Phuong, T.M. (2008). Collaborative filtering by multi-task learning. In Research, Innovation and Vision for the Future, 2008. RIVF 2008. IEEE International Conference on research, innovation and vision for the future in computing & communication technologies. University of Science, Vietnam National University Ho Chi Minh City, July 13–17, 227–232.Google Scholar
Rasmussen, C.E. and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. Cambridge, MA: Massachusetts Institute of Technology Press.Google Scholar
Ribeiro, B., and Lopes, N. (2011). Deep belief networks for financial prediction. In Lu, B.L., Zhang, L., and Kwok, J. (eds.), Neural Information Processing Proceedings, Part III. Shanghai, China, November 13–17, 766–773.CrossRefGoogle Scholar
Sanning, L.W., Shaffer, S., and Sharratt, J.M. (2008). Bordeaux Wine as a financial investment. Journal of Wine Economics, 3, 51–71.CrossRefGoogle Scholar
Shawe-Taylor, J., and Cristianini, N. (2004). Kernel Methods for Pattern Analysis. New York: Cambridge University Press.Google Scholar
Storchmann, K. (2012). Wine economics. Journal of Wine Economics, 7, 1–33.Google Scholar
Taylor, G.W. and Hinton, G.E. (2009). Factored conditional restricted Boltzmann Machines for modeling motion style. In Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, QC, Canada, June 14–18, 1025–1032.Google Scholar
Theunissen, R. (2015). WineStein: Your Digital Sommelier. http://www.winestein.com/uk/, accessed May 10, 2015.Google Scholar
Wang, Y., and Khardon, R. (2012). Sparse Gaussian processes for multi-task learning. In Proceedings of the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases, Volume Part 1. Bristol, UK: Springer, 711–727.Google Scholar
Wood, D., and Anderson, K. (2006). What determines the future value of an icon wine? Evidence from Australia. Journal of Wine Economics, 1, 141–161.Google Scholar
Yu, K., Tresp, V., and Schwaighofer, A. (2005). Learning Gaussian Processes from Multiple Tasks. In Proceedings of 22nd International Conference on Machine Learning (ICML), Beijing, China, 1012–1019.Google Scholar
Zajc, Z. (2012). Predicting wine quality. http://fastml.com/predicting-wine-quality/, accessed May 10, 2015.Google Scholar