Forecasting model of Shanghai and CRB commodity indexes

Göleç, Adem; Murat, Atilim; Tokat, Ekin; Türkşen, I.B.

doi:10.1016/j.eswa.2012.02.077

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Thereby, using models that include macroeconomic and financial variables, empirical research has been undertaken to explore the predictability of these assets via the application of various approaches. Göleç et al, (2012) employed classical least square estimation, SVR, Adaptive Neuro-Fuzzy Inference Systems (ANFIS), GENFIS3:Mamdani, Fuzzy System Modeling approach based on Improved Fuzzy Functions (FSMIFF) and Fuzzy Functions with Standard Fuzzy C-Means techniques to predict the Commodity Research Bureau (CRB) index using Shanghai and CRB index values. After determining the relationship between these indexes by the Granger causality test and VECM, predicitons were obtained.…”

Section: Literature Reviewmentioning

confidence: 99%

Maki̇ne Öğrenmesi̇nde Regresyon Modelleri̇ni̇n Tahmi̇n Performanslarinin Karşilaştirilmasi: Türki̇ye Ürün İhti̇sas Borsasi Buğday Endeksi̇ Üzeri̇ne Bi̇r Uygulama

BURHAN

2023

Nişantaşı Üniversitesi Sosyal Bilimler Dergisi

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Agricultural commodity prices have significant impacts on economies by leading to changes and regulations in both fiscal and monetary policies. These also have effects on household economies and consumer purchasing power particularly in developing countries. Thereby, instability and variability in these prices constitute adverse effects on these economies. On the other hand, assets of the commodity markets become popular just as bonds and stocks. Because of this growing interest, needs for managing risks, stable prices and lowering transaction costs has led to establishment of the commodity exchanges. In this context, Turkey put the licensed warehousing system into operation by founding the Turkish Mercantile Exchange (TMEX) to operate trades of Electronic Warehouse Receipts (EWRs). In this study, a model including US Dollar-Turkish Lira exchange rate (USD/TRY), Brent crude-oil prices, overnight interest rate and a daily dataset for the 01/04/2021-20/02/2023 period were used to assess several machine learning regression methods in predicting the TMEX Wheat Index (TMXWHT). As verified by comparisons with actual values and considering performance evaluation criteria, all methods yielded successful outcomes, furthermore, tree-based methods revealed better overall performance.

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Section: Literature Reviewmentioning

confidence: 99%