Forecasting Crude Oil Market Crashes Using Machine Learning Technologies
Yulian Zhang () and
Shigeyuki Hamori ()
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Yulian Zhang: Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Energies, 2020, vol. 13, issue 10, 1-14
To the best of our knowledge, this study provides new insight into the forecasting of crude oil futures price crashes in America, employing a moving window. One is the fixed-length window and the other is the expanding-length window, which has never been reported in the past. We aimed to investigate if there is any difference when historical data are discarded. As the explanatory variables, we adapted 13 variables to obtain two datasets, 16 explanatory variables for Dataset1 and 121 explanatory variables for Dataset2. We try to observe results from the different-sized sets of explanatory variables. Specifically, we leverage the merits of a series of machine learning techniques, which include random forests, logistic regression, support vector machines, and extreme gradient boosting (XGBoost). Finally, we employ the evaluation metrics that are broadly used to assess the discriminatory power of imbalanced datasets. Our results indicate that we should occasionally discard distant historical data, and that XGBoost outperforms the other employed approaches, achieving a detection rate as high as 86% using the fixed-length moving window for Dataset2.
Keywords: oil futures prices crashes; foresting; random forests; logistical regression; support vector machines; extreme gradient boosting; moving window (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:10:p:2440-:d:357298
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