Forecasting the Direction of Agricultural Commodity Price Index through ANN, SVM and Decision Tree: Evidence from Raisin

Year 2018, Volume: 18 Issue: 4, 579 - 588, 23.10.2018

Burcu Akın İkbal Ece Dizbay , Şevkinaz Gümüşoğlu Erçin Güdücü

Abstract

To be able to make appropriate actions during buying, selling or holding decisions, economic actors need accurate commodity price forecasts. This study focuses on forecasting raisin price by using predetermined volatile variables. Therefore, we seek for answers of three main questions. Do the social & political issues effect raisin price in countries that have internal disturbance? By using volatile variables, can we represent or predict price index thoroughly? Lastly, which method has the best prediction performance; Artificial Neural Networks (ANN), Decision Tree or Support Vector Machine (SVM)? In accordance with these purposes, ANN, decision tree and SVM methods are implemented for proposed model and their prediction performances are compared. Experimental results showed that accuracy performance of SVM method was found significantly better than ANN method and decision tree.

Keywords

Commodity market, Artificial neural networks, Decision tree, Support vector machines, Social & political issues

YSA, DVM ve Karar Ağacı ile Tarımsal Emtiaların Fiyat Endekslerinin Tahminlenmesi: Kuru Üzüm Örneği

Abstract

tahminlenmesi ekonomik aktörlere doğru alım satım kararları verebilmeleri için fayda sağlamaktadır. Türkiye’deki ticaret borsalarında işlem gören tarımsal ürünlerden biri olan kuru üzüm fiyatlarının oynak değişkenler kullanılarak tahminlenmesinin incelendiği çalışmada üç temel soru üzerinde durulmuştur. İç karışıklığın yüksek olduğu ülkelerde sosyal ve politik olaylar kuru üzüm fiyatlarını etkiler mi? Oynaklığı yüksek olan değişkenler kullanılarak kuru üzüm fiyat endeksleri tahminlenebilir mi? Son olarak, bu tip bir çalışmada Yapay Sinir Ağları (YSA), Karar Ağacı ve Destek Vektör Makineleri (DVM) yöntemlerinden hangisinin tahmin performansı daha yüksektir? Bu amaçla oluşturulan tahmin modeline YSA, KA ve DVM yöntemleri uygulanmış ve yöntemlerin tahmin performansları karşılaştırılmıştır. Uygulama sonuçları, oynak değişkenler ile sosyal ve politik olayların kuru üzüm fiyatlarının tahminlenmesinde kullanılabileceğini ve ilgili modelde DVM yönteminin en yüksek doğruluk oranını verdiğini göstermiştir.

Keywords

Emtia piyasası, Yapay sinir ağları, Karar ağacı, Destek vektör makineleri, Sosyal ve politik olaylar

References

• Ahmed, S. (2008). Aggregate economic variables and stock markets in India. International Research Journal of Finance and Economics, 14, 141-164.
• Azadeh, A., Moghaddam, M., & Khakzad, M. (2012). A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting. Comput. Ind. Eng. 62(2): 421–430.
• Baffes, J., & Haniotis, T. (2016). What explains agricultural price movements? Journal of Agricultural Economics , 67(3), 706-721.
• Chen, A.-S., Leung, M. T., & Daouk, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index . Computers & Operations Research, 30(6), 901-924.
• Chen, Y. C., Rogoff, K., & Rossi, B. (2010). Predicting agri-commodity prices: An asset pricing approach. SSRN.
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
• Dean, J. (2014). Big data, data mining, and machine learning: value creation for business leaders and practitioners. New Jersey: John Wiley & Sons. Computational intelligence methods for rule-based data understanding. Proceedings of the IEEE, 92(5), 771-805.
• Duch, W., Setiono, R., & Zurad, J. (2004). Computational intelligence methods for rule-based data understanding. Proceedings of the IEEE, 92(5), 771805.
• Gargano, A., & Timmermann, A. (2014). Forecasting commodity price indexes using macroeconomic and financial predictors. International Journal of Forecasting, 30(3), 825-843.
• Groen, J., & Pesenti, P. A. (2011). Commodity prices, commodity currencies, and global economic developments . In T. Ito, & A. Rose, Commodity prices and markets, East Asia Seminar on Economics (Vol. 20, pp. 15–42 ). Chicago: University of Chicago.
• Haidar, I., & Wolff, R. (2009). Forecasting crude oil price. Retrieved from http://www.usaee.org/usaee2011/ submissions/ OnlineProceedings/Forecasting%20 Crude%20Oil%20Price%20%28Revisited%29.pdf.
• Hong, H., & Yogo, M. (2012). What does futures market interest tell us about the macroeconomy and asset prices? Journal of Financial Economics, 105(3), 473–490. Hu, J. W.-S., Hu, Y.-C., & Lin, R. R.-W. (2012). Applying neural networks to prices prediction of crude oil futures. Mathematical Problems in Engineering. Jammazi, R., & Aloui, C. (2012). Crude oil price forecasting: experimental evidence from wavelet decomposition and neural network modeling 34: 828–841. Energy Econ., 34(3), 828-841.
• Jha, G. K., & Sinha, K. (2014). Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications, 24(3), 563571.
• Kara, Y., Boyacioglu, M., & Baykan, Ö. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319.
• Kohzadi, N., Bahman, M. S., & K, I. (1996). Comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), -. Larose, D., & Larose, C. (2015). Data mining and predictive analytics. New Jersey: John Wiley & Sons.
• Li, G.-q., Xu, S.-w., & Li, Z.-m. (2010). Short-term price forecasting for agro-products using artificial neural networks. Agric. Sci. Procedia, 1, 278–287.
• Malliaris, A., & Malliaris , M. (2013). Are oil, gold and the euro inter-related? Time series and neural network analysis. Review of Quantitative Finance and Accounting, 40(1), 1-14.
• Mensi, W., Beljid, M., Boubaker, A., & Managi , S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling, 32, 15-22.
• Mitchell, T. (1997). Machine Learning. New York: McGrawHill. Parisi, A. (2008). Forecasting gold price changes: rolling and recursive neural network models. Journal of Multinatl. Financ. Manag, 18(5), 477–487.
• Quinlan, J. (1993). Programs for Machine Learning. California.: Morgan Kaufmann.
• Samanta, S., & Zadeh, A. (2012). Co-movements of oil, gold, the US dollar, and stocks. Modern Economy, 3(1), 111-117.
• Scholkopf, B., & Smola, A. (2001). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press. Shannon, C. (1951). Prediction and entropy of printed English. Bell system technical journal, 30(1), 50-64.
• Sujit, K. S., & Kumar, R. (2011). Study on dynamic relationship among gold price, oil price, exchange rate and stock market returns. International Journal of Applied Business and Economic Research, 9(2), 145-165.
• United States Department of Agriculture. (2017, 10 10). Retrieved from Market and Trade Data: https:// apps.fas.usda.gov/psdonline/app/index.html#/ app/topCountriesByCommodity
• Wang, Y., & Chueh, Y. (2013). Dynamic transmission effects between the interest rate, the US dollar, and gold and crude oil prices. Economic Modelling, 30, 792-798.
• Yakut, E., & Gemici, E. (2017). LR, C5. 0, CART, DVM Yöntemlerini Kullanarak Hisse Senedi Getiri Sınıflandırma Tahmini Yapılması ve Kullanılan Yöntemlerin Karşılaştırılması: Türkiye’de BIST’de Bir Uygulama. Ege Akademik Bakis, 17(4), 461-479.
• Ye, M., Zyren, J., & Shore, J. (2002). Forecasting crude oil spot price using OECD petroleum inventory levels. International Advances in Economic Research, 8(4), 324-333.
• Zou, H., Xia, G., Yang, F., & Wang, H. (2007). An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing, 70(1618), 2913–2923.