Skip to main content Accessibility help

Machine Learning in Fine Wine Price Prediction*

  • Michelle Yeo (a1), Tristan Fletcher (a2) and John Shawe-Taylor (a3)


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)



Hide All

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 (



Hide All
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.
Argyriou, A., Evgeniou, T., and Pontil, M. (2008). Convex multi-task feature learning. Machine Learning, 73, 243272.
Ashenfelter, O. (2010). Predicting the quality and prices of Bordeaux wine. Journal of Wine Economics, 5, 4052.
Bakker, B., and Heskes, T. (2003). Task clustering and gating for Bayesian multitask learning. Journal of Machine Learning Research, 4, 8399.
Bishop, C. (2006). Pattern Recognition and Machine Learning. New York: Springer.
Bonilla, E., Chai, K.M., and Williams, C. (2008). Multi-task Gaussian process prediction. In Advances in Neural Information Processing Systems (NIPS), 22.
Burton, B.J., and Jacobsen, J.P. (2001). The rate of return on wine investment. Economic Inquiry, 39, 337350.
Byron, R.P., and Ashenfelter, O. (1995). Predicting the quality of an unborn grange. The Economic Record, 71, 4053.
Cryer, J. D. and Chan, K.S. (2008). Times Series Analysis with Applications in R. Berlin: Springer.
Duvenaud, D. (2014). Automatic model construction with Gaussian processes (Doctoral dissertation, University of Cambridge).
Fogarty, J.J. (2006). The return to Australian fine wine. European Review of Agricultural Economics, 33, 542561.
Fogarty, J.J., and Jones, C. (2011). Return to wine: A comparison of the hedonic, repeat sales, and hybrid approaches. Australian Economic Papers, 50, 147156.
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, 225248.
Haeger, J.W., and Storchmann, K. (2006). Prices of American Pinot Noir wines: climate, craftsmanship, critics. Agricultural Economics, 35, 6778.
Jaeger, E. (1981). To save or savor: The rate of return to storing wine: Comment. Journal of Political Economy, 89, 584592.
Jones, G.V., and Storchmann, K. (2001). Wine market prices and investment under uncertainty: An econometric model for Bordeaux Crus Classés. Agricultural Economics, 26, 115133.
Lawler, G. (2010). Random Walk and the Heat Equation. Student Mathematical Library, Vol. 55, American Mathematical Society, Providence, Rhode Island.
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, 841848.
Lima, T. (2006). Price and Quality in the Californian Wine Industry: An Empirical Investigation. Journal of Wine Economics, 1, 176190.
Masset, P., and Henderson, C. (2010). Wine as an alternative asset class. Journal of Wine Economics, 5, 87118.
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, 173199.
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).
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, 672677.
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, 227232.
Rasmussen, C.E. and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. Cambridge, MA: Massachusetts Institute of Technology Press.
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, 766773.
Sanning, L.W., Shaffer, S., and Sharratt, J.M. (2008). Bordeaux Wine as a financial investment. Journal of Wine Economics, 3, 5171.
Shawe-Taylor, J., and Cristianini, N. (2004). Kernel Methods for Pattern Analysis. New York: Cambridge University Press.
Storchmann, K. (2012). Wine economics. Journal of Wine Economics, 7, 133.
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, 10251032.
Theunissen, R. (2015). WineStein: Your Digital Sommelier., accessed May 10, 2015.
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, 711727.
Wood, D., and Anderson, K. (2006). What determines the future value of an icon wine? Evidence from Australia. Journal of Wine Economics, 1, 141161.
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, 10121019.
Zajc, Z. (2012). Predicting wine quality., accessed May 10, 2015.



Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed