Mining for Oil Forecasts
Nida Cakir Melek (),
Charles Calomiris and
Harry Mamaysky ()
No RWP 20-20, Research Working Paper from Federal Reserve Bank of Kansas City
In this paper, we study the usefulness of a large number of traditional determinants and novel text-based variables for in-sample and out-of-sample forecasting of oil spot and futures returns, energy company stock returns, oil price volatility, oil production, and oil inventories. After carefully controlling for small-sample biases, we find compelling evidence of in-sample predictability. Our text measures hold their own against traditional variables for oil forecasting. However, none of this translates to out-of-sample predictability until we data mine our set of predictive variables. Our study highlights that it is difficult to forecast oil market outcomes robustly.
Keywords: Asset Pricing; Commodity Markets; Energy Forecasting; Model Validation (search for similar items in EconPapers)
JEL-codes: C52 G14 G17 G18 Q47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cwa, nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:fip:fedkrw:89532
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in Research Working Paper from Federal Reserve Bank of Kansas City Contact information at EDIRC.
Bibliographic data for series maintained by ().