Forecasting Spot Oil Price Using Google Probabilities

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v58-drachal17a, title = {Forecasting Spot Oil Price Using Google Probabilities}, author = {Drachal, Krzysztof}, booktitle = {Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers}, pages = {31--40}, year = {2017}, editor = {Guy, Tatiana V. and Kárný, Miroslav and Rios-Insua, David and Wolpert, David H.}, volume = {58}, series = {Proceedings of Machine Learning Research}, month = {09 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v58/drachal17a/drachal17a.pdf}, url = {https://proceedings.mlr.press/v58/drachal17a.html}, 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.} }
Endnote
%0 Conference Paper %T Forecasting Spot Oil Price Using Google Probabilities %A Krzysztof Drachal %B Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers %C Proceedings of Machine Learning Research %D 2017 %E Tatiana V. Guy %E Miroslav Kárný %E David Rios-Insua %E David H. Wolpert %F pmlr-v58-drachal17a %I PMLR %P 31--40 %U https://proceedings.mlr.press/v58/drachal17a.html %V 58 %X 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.
APA
Drachal, K.. (2017). Forecasting Spot Oil Price Using Google Probabilities. Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, in Proceedings of Machine Learning Research 58:31-40 Available from https://proceedings.mlr.press/v58/drachal17a.html.

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