GOOGLE TRENDS AS PREDICTOR OF GRAIN PRICES

Abstract

This paper examines the predictive power of Google trends on the grain’s futures price movement. The aim was to validate if an algorithmic trading system designed was profitable and able of beating the market. In the research was used data from soybean futures and corn futures, both contracts are listed in the Chicago Mercantile Exchange. The results of the research show that its forecasting power is high when predicting soybean futures and corn futures prices. According to the findings, the formulation of such predictive analysis is a good option for individual traders, investors, and commercial firms.

Downloads

Download data is not yet available.

References

1. Agarawal, R. (2004). Forecasting Techniques in Crops. Indian Agricultural Statistics Research Institute, New Delhi.
2. Anzuini, A., Lombardi, M.J., Pagano, P. (2013). The impact of monetary policy shocks on commodity prices. International Journal of Central Banking, 9(3), 125–150.
3. Basso, B., Cammarano, D., & Carfagna, E. (2013). Review of crop yield forecasting methods and early warning systems. In Proceedings of the first meeting of the scientific advisory committee of the global strategy to improve agricultural and rural statistics, FAO Headquarters, Rome, Italy, 18-19.
4. Carneiro, H.A., & Mylonakis, E. (2009). Google Trends: a web-based tool for real-time surveillance of disease outbreaks. Clinical infectious diseases, 49(10), 1557-1564. https://doi.org/10.1086/630200
5. CME Group. (2021). Daily agricultural volume and open interest. Retrieved from https://www.cmegroup.com/market-data/volume-open-interest/agriculturecommodities-volume.html (January 15, 2020).
6. Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
7. CME (2019). CME Group Reaches Second-Highest Monthly Volume Ever, Averaging 23.9 Million Contracts Per Day in May 2019. News release. Retrieved from https://www.cmegroup.com/media-room/press-releases/2019/6/04 (January 10, 2020).
8. FAO (2019). Crop monitoring and forecasting. Retrieved from http://www.fao.org/nr/climpag/aw_3_en.asp (January 18, 2020).
9. Frankel, J.A., & Hardouvelis, G.A. (1985). Commodity prices, money surprises and fed credibility. Journal of Money Credit Banking, 17(4), 425–438.
10. Gilbert, C.L. (2010). How to understand high food prices. Journal of Agricultular Economics, 61(2), 398–425. https://doi.org/10.1111/j.1477-9552.2010.00248.x
11. Google Ireland Limited. (2020). Google trends: Descubre qué está buscando el mundo. Retrieved from https://trends.google.es/trends/?geo=ES (January 18, 2020).
12. Gordon, G., & Rouwenhorst, K.G. (2006). Facts and fantasies about commodity futures. Financial Analysis Journal, 62, 47–68.
13. Gubler, M., Hertweck, M.S., (2013). Commodity price shocks and the business cycle: structural evidence from the US. Journal of International Money and Finance, 37(C), 324–352 https://doi.org/10.1016/j.jimonfn.2013.06.012
14. Hammoudeh, S., Nguyen, D.K., & Sousa, R.M. (2015). US monetary policy and sectoral commodity prices. Journal of International Money and Finance, 57(C), 61–85. https://doi.org/10.1016/j.jimonfn.2015.06.003
15. Hoogenboom, G., White, J. W., & Messina, C. D. (2004). From genome to crop: integration through simulation modeling. Field Crops Research, 90(1), 145-163. https://doi.org/10.1016/j.fcr.2004.07.014
16. IBroker Global Markets SV, SA. (2021). Trading motion: The marketplace for automated trading strategies. Retrieved from https://www.tradingmotion.com/ (January 18, 2020).
17. Jame, Y. W., & Cutforth, H. W. (1996). Crop growth models for decision support systems. Canadian Journal of Plant Science, 76(1), 9-19. https://doi.org/10.4141/cjps96-003
18. Kaufman, P. J. (2016). A Guide to Creating A Successful Algorithmic Trading Strategy. Wiley.
19. Li, Z., & Lu, X. (2012). Cross-correlations between agricultural commodity futures markets in the US and China. Physica A: Statistical Mechanics and Its Applications, 391(15), 3930–3941. https://doi.org/10.1016/j.physa.2012.02.029
20. Martínez, R.G. (2013). Señales de inversión basadas en un índice de aversión al riesgo. Investigaciones Europeas de Dirección y Economía de la Empresa, 19(3), 147-157. https://doi.org/10.1016/j.iedee.2012.12.001
21. Preis, T., Moat, H.S., & Stanley, H.E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3.
22. Rech, J. (2007). Discovering trends in software engineering with Google Trends. ACM SIGSOFT Software Engineering Notes, 32(2), 1-2. https://doi.org/10.1145/1234741.1234765
23. Valiente, D. (2013). Price Formation Commodities Markets: Financialisation and Beyond. CEPS-ECMI Task Force Report. Centre for European Policy Studies. Retrieved from https://www.ceps.eu/ceps-publications/price-formationcommodities-markets-fnancialisation-and-beyond/ (January 18, 2020).
Published
2021-03-25
How to Cite
MARTÍNEZ, Raúl Gómez; ORDEN-CRUZ, Carmen; PRADO-ROMÁN, Camilo. GOOGLE TRENDS AS PREDICTOR OF GRAIN PRICES. Economics of Agriculture, [S.l.], v. 68, n. 1, p. 203-211, mar. 2021. ISSN 2334-8453. Available at: <http://ea.bg.ac.rs/index.php/EA/article/view/1730>. Date accessed: 18 apr. 2021. doi: https://doi.org/10.5937/ekoPolj2101203G.
Section
Original scientific papers