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.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: semi-strong efficiency, agricultural commodities
• Subjects: G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture > Q11 - Aggregate Supply and Demand Analysis ; Prices; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture > Q14 - Agricultural Finance

References

Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO.