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Machine Learning for Semi-Strong Efficiency Test of Inter-Market Wheat Futures

Phélippé-Guinvarc'h, Martial and Cordier, Jean (2015): Machine Learning for Semi-Strong Efficiency Test of Inter-Market Wheat Futures.

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Abstract

This paper proposes an original work on world wheat futures market efficiency test to conclude on the semi-strong inefficiency of wheat futures. Our model uses american and european data together to estimate pair trading arbitrage returns on the wheat futures market. Some variables like transportation and balance sheet of USDA are significative in CART regression. Then, pair trading arbitrage is predictible with public information and we deduce of the semi-strong inefficiency of inter-market wheat futures.

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