Title

The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner

Yapay Sinir Ağları İle Kıymetli Maden Fiyatlarının RapidMiner İle Tahmin Edilmesi


Title
( Turkish )
Author(s)
Author(s)
Abstract
In this paper, an Artificial Neural Network study has been implemented to forecast the prediction of precious metals such as gold, silver, platinum and palladium prices by using RapidMiner data mining software. The five performance measures; root mean squared error, absolute error, relative error, Spearman's Rho and Kendall’s Tau are utilized to evaluate artificial neural network model. This study concentrates on data which includes gold, silver, palladium, platinum, Brent Petrol, natural gas prices, 30 years’ bond, 10 years’ bond, 5 years’ bond, S&P 500, Nasdaq, Dow Jones, FTSE100, DAX, CAC40, SMI, NIKKEI, HANH, SENG and Euro/USD within the period of 4th of January 2010 to 14th of December 2015. The prices on the last quarter of 2015 is used for forecasting and validation. The results show that error rates are accurate in order to foresee the market trends.
Bu çalışmada, RapidMiner veri madenciliği yazılımı kullanılarak Yapay Sinir Ağları ile altın, gümüş, platin ve paladyum gibi kıymetli madenlerin fiyatlarının tahmin edilmesi gerçekleştirilmiştir. Yapay sinir ağlarını değerlendirmek için beş performans ölçütü; ortalama karesel hata, mutlak hata, göreceli hata, Spearman Rho ve Kendall Tau kullanılmıştır. Bu çalışma, 4 Ocak 2010 ile 14 Aralık 2015 tarihleri arasındaki altın, gümüş, platin, paladyum, Brent Petrol, doğal gaz, 30 yıllık bono, 10 yıllık bono, 5 yıllık bono, S&P 500, Nasdaq, Dow Jones, FTSE100, DAX, CAC40, SMI, NIKKEI, HANH, SEND ve Avro/Dolar rakamlarını içeren veriler üzerine odaklanmıştır. 2015 yılının son çeyreğindeki veriler tahmin ve doğrulama için kullanılmıştır. Sonuçlar, pazar tahminleri için hata oranlarının kabul edilebilir olduğunu göstermiştir.
Abstract
( Turkish )
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Cite

Çelik, U., Başarır, Ç. (2017), The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner, Alphanumeric Journal, 5(1), 45-54.

References
  • Abramowitz, M. & Stegun, I. A. 1964. Handbook of mathematical functions: with formulas, graphs, and mathematical tables, Courier Corporation.
  • Adrangi, B. & Chatrath, A. 2002. The Dynamics of Palladium and Platinum Prices. Computational Economics, 19, 179-195.
  • Akgiray, V., Booth, G. G., Hatem, J. J. & Mustafa, C. 1991. Conditional Dependence in Precious Metal Prices. Financial Review, 26, 367-386.
  • Arango Thomas, L., Arias, F. & Florez, L. 2012. Determinants of commodity prices. Applied Economics, 44, 135-145.
  • Awokuse, T. O. & Yang, J. 2003. The informational role of commodity prices in formulating monetary policy: a reexamination. Economics Letters, 79, 219-224.
  • Baffes, J. 2007. Oil spills on other commodities. Resources Policy, 32, 126-134.
  • Batten, J. A., Ciner, C. & Lucey, B. M. 2010. The macroeconomic determinants of volatility in precious metals markets. Resources Policy, 35, 65-71.
  • Beahm, D. 2008. Five Fundamentals Will Drive Gold Price Higher in 2008.
  • Benli, Y. K. & Yildiz, A. 2015. 21) ALTIN FİYATININ ZAMAN SERİSİ YÖNTEMLERİ VE YAPAY SİNİR AĞLARI İLE ÖNGÖRÜSÜ. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 42.
  • Caudill, M. 1987. Neural networks primer, part I. AI Expert, 2, 46-52.
  • Chen, M.-H. 2010. Understanding world metals prices—Returns, volatility and diversification. Resources Policy, 35, 127-140.
  • Chen, X. & Fang, Y. 2013. Enterprise systems in financial sector–an application in precious metal trading forecasting. Enterprise Information Systems, 7, 558-568.
  • Hammoudeh, S. & Yuan, Y. 2008. Metal volatility in presence of oil and interest rate shocks. Energy Economics, 30, 606-620.
  • Hyndman, R. J. & Athanasopoulos, G. 2014. Forecasting: principles and practice, OTexts.
  • Hyndman, R. J. & Koehler, A. B. 2006. Another look at measures of forecast accuracy. International journal of forecasting, 22, 679-688.
  • Jain, A. & Ghosh, S. 2013. Dynamics of global oil prices, exchange rate and precious metal prices in India. Resources Policy, 38, 88-93.
  • Kendall, M. G. 1938. A new measure of rank correlation. Biometrika, 30, 81-93.
  • Minsky, M., Minsky, M. L. & Papert, S. 1969. Perceptrons: An Introduction to Computational Geometry.
  • Minsky, M. & Papert, S. 1987. Perceptrons - Expanded Edition: An Introduction to Computational Geometry.
  • Morariu, N., Iancu, E. & Vlad, S. 2009. A neural network model for time series forecasting. Romanian journal of economic forecasting, 12, 213-223.
  • Morris, R. G. 1999. D.O. Hebb: The Organization of Behavior, Wiley: New York; 1949. Brain Res Bull, 50, 437.
  • Palaskas, T. B. 1993. Commodity prices: implications of the co-movement and excess co-movement. Economic Crisis in Developing Countries: New Perspectives on Commodities, Trade and Finance, edited by M. Nissake, and A. Hewitt. New York: printer, 89-103.
  • Pindyck, R. S. & Rotemberg, J. J. 1988. The excess co-movement of commodity prices. National Bureau of Economic Research Cambridge, Mass., USA.
  • Plourde, A. & Watkins, G. C. 1998. Crude oil prices between 1985 and 1994: how volatile in relation to other commodities? Resource and Energy Economics, 20, 245-262.
  • Sauerbeck, A. 1886. Prices of commodities and the precious metals. Journal of the Statistical Society of London, 49, 581-648.
  • Sensoy, A. 2013. Dynamic relationship between precious metals. Resources Policy, 38, 504-511.
  • Soytas, U., Sari, R., Hammoudeh, S. & Hacihasanoglu, E. 2009. World oil prices, precious metal prices and macroeconomy in Turkey. Energy Policy, 37, 5557-5566.
  • Spearman, C. 1904. The proof and measurement of association between two things. The American journal of psychology, 15, 72-101.
  • Trivedi, P. K. 1995. Tests of some hypotheses about the time series behavior of commodity prices. Advances in Econometrics and Quantitative Economics: Essays in Honor of CR Rao, Oxford: Blackwell, 382-412.
  • Werbos, P. 1974. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Harvard University.
  • Werbos, P. J. 1994. The roots of backpropagation: from ordered derivatives to neural networks and political forecasting, Wiley-Interscience.
  • RapidMiner Open Source Predictive Analytics Platform [Online]. Available: https://www.rapidminer.com/ 2016].