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2021, vol. 68, br. 1, str. 203-211
Google trends as predictor of grain prices
(naslov ne postoji na srpskom)
Rey Juan Carlos University, Paseo de los Artilleros, Madrid, Spain

e-adresaraul.gomez.martinez@urjc.es, carmen.delaorden@urjc.es, camilo.prado.roman@urjc.es
Ključne reči: google trends; grains price; algorithmic trading system
(ne postoji na srpskom)
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.
Novododat članak: provera, normiranje i linkovanje referenci u toku.
Agarawal, R. (2004). Forecasting Techniques in Crops. Indian Agricultural Statistics Research Institute, New Delhi
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
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
Carneiro, H.A., & Mylonakis, E. (2009). Google Trends: a web-based tool for realtime surveillance of disease outbreaks. Clinical infectious diseases, 49(10), 1557-1564. https://doi.org/10.1086/630200
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)
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
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)
FAO (2019). Crop monitoring and forecasting. Retrieved from http://www.fao.org/nr/climpag/aw_3_en.asp (January 18, 2020)
Frankel, J.A., & Hardouvelis, G.A. (1985). Commodity prices, money surprises and fed credibility. Journal of Money Credit Banking, 17(4), 425-438
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
Google Ireland Limited. (2020). Google trends: Descubre qué está buscando el mundo. Retrieved from https://trends.google.es/trends/?geo=ES (January 18, 2020)
Gordon, G., & Rouwenhorst, K.G. (2006). Facts and fantasies about commodity futures. Financial Analysis Journal, 62, 47-68
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.jimonfin.2013.06.012
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.jimonfin.2015.06.003
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
IBroker Global Markets SV, SA. (2021). Trading motion: The marketplace for automated trading strategies. Retrieved from https://www.tradingmotion.com/ (January 18, 2020)
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
Kaufman, P. J. (2016). A Guide to Creating A Successful Algorithmic Trading Strategy. Wiley
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
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
Preis, T., Moat, H.S., & Stanley, H.E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3
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
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-financialisation-and-beyond/ (January 18, 2020)

O članku

jezik rada: engleski
vrsta rada: pregledni članak
DOI: 10.5937/ekoPolj2101203G
primljen: 22.03.2020.
prihvaćen: 01.02.2021.
objavljen u SCIndeksu: 09.04.2021.
metod recenzije: dvostruko anoniman
Creative Commons License 4.0

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