Journal
INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT
Volume 26, Issue 1, Pages 32-45Publisher
JOHN WILEY & SONS LTD
DOI: 10.1002/isaf.1442
Keywords
data science; futures trading; neural networks; technical analysis
Categories
Funding
- National Institute for Agriculture (NIFA) Hatch project [OKL0293]
- Oklahoma Agricultural Experiment Station
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Past efforts determining the profitability of technical analysis reached varied conclusions. We test the profitability of a composite prediction that uses buy and sell signals from technical indicators as inputs. Both machine learning methods, like neural networks, and statistical methods, like logistic regression, are used to get predictions. Inputs are signals from trend-following and mean-reversal technical indicators in addition to the variance of prices. Four representative commodities from agricultural, livestock, financial, and foreign exchange futures markets are selected to determine profitability. Special care is taken to avoid data snooping error. Both neural networks and statistical methods did not show consistent profitability.
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