Forecasting Spot Oil Price Using Google Probabilities

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Krzysztof Drachal ;
Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, PMLR 58:31-40, 2017.

Abstract

In this paper DMA (Dynamic Averaging Model) is expanded by adding certain probabilities based on Google Trends. Such a method is applied to forecasting spot oil prices (WTI). In particular it is checked whether a dynamic model including stock prices in developed markets, stock prices in China, stock prices volatility, exchange rates, global economic activity, interest rates, production, consumption, import and level of inventories as independent variables might be improved by including a certain measure of Google searches. Monthly data between 2004 and 2015 were analysed. It was found that such a modification leads to slightly better forecast. However, the weight ascribed to Google searches should be quite small. Except that it was found that even unmodified DMA produced better forecast than that based on futures contracts or naive forecast.

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