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Predicttion of Brent Oil Spot Prices Using Country Based Inventory and Trading Data
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Usta, İsmail Batur; Ağralı, Semra
Crude oil price forecasting has been the focus of numerous authorities, yet the task still persists on being a challenging one. The extremely volatile nature of oil market and high number of active players in it makes establishing a solid forecasting model that is constantly relevant to time very difficult. Recent advancements on data technologies, mainly ever-increasing computing power and trending big data technologies allowed new approaches to be born. From online learners to natural language processing, advanced data analytics models were employed with the help of easily accessible and diverse data. This project is an attempt on making use of such available data in order to forecast Brent oil spot price. By using monthly country by country inventory, trading and economic data, strong drivers of crude price was explored. The data used in this project comes from various sources and in multiple formats, with the final merged data frame has over 17000 observations and contains information on 86 countries. To enhance prediction power, a specialized learner is fit on each country individually and then the predictions are accumulated and filtered before outputting a single prediction. Compared to a single predictor, this approach enhanced the predictive power of the algorithm by adapting to dynamics of each country.
The Effect of Exchange Rate Volatility on Export and Import of Turkey on Sectoral Basis
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Ulutürk Tekten, Yağmur; Karamollaoğlu, Nazlı
In this study, the effects of Exchange rate volatility on export and import of Turkey is analysed by employing monthly trade data for the period from January 2004 to November 2015. The study is extended to cover both sectoral and country specific export and import volumes. The major aim of this study is to show how fluctuations in foreign exchange rate change the volume of exports and imports among various sectors in Turkey. In this paper export and import volume equation is formulated using sectoral data in which explanatory variables are derived from the volatility of each country’s nominal exchange rate against the TRY, bilateral real effective exchange rates for each country that Turkey has foreign trade relationship. The dependent or target variable is the percentage change in the trade size in USD amount both for export and import. In this analysis, 6 different regression algorithms are utilized to explain the effect of exchange rate volatility on industrial activities for exportand import in Turkey. The impact of features on the target feature is analyzed using linear, ridge, lasso, random forest, decision ree and gradient boosting regression algorithms. According to results of these 6 algorithms, for Turkey, the volatility of exchangerate has significant impact on some sectors and on broad product group categories in both export and import up to 26%. The sectors that most exposed to Exchange rate volatilities are seen in ‘Giyim Eşyası’ in export and ‘Binek otomobilleri’ in import. For export, ‘Rusya Federasyonu’, and for import ‘İtalya’ is the most sensitive countries against exchange rate volatility in Turkey.

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