Simula@BI seminar: Luigi Gifuni

Simula@BI invites lecturer Luigi Gifuni, University of Strathclyde to give a talk within the field of machine learning.

Abstract

This paper investigates the predictability of monthly real oil prices when daily and weekly text data are combined with oil market fundamentals. The textual analysis encompasses more than 6 million articles featured in The Financial Times, Thomson Reuters and The Independent from 1982M1 to 2021M12. I show that models containing high-frequency financial and commodity variables do not yield significant improvements on the no-change forecast. In contrast, when text data are used along with commodity variables and oil market fundamentals, the preferred models reduce the MSPE by 18%. However, despite this marginal improvement, gains are low. Indeed, the corresponding models with variables observed at homogeneous frequency, generate similar out-of-sample forecasts in terms of accuracy. I thus conclude that variables sampled at different frequencies do not significantly improve the predictability of monthly real oil prices. This is true for point and density forecasts.

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