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Market Efficiency and Nonlinear Analysis of Soybean Futures

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  • Tao Yin

    (School of Economics, Peking University, Beijing 100871, China)

  • Yiming Wang

    (School of Economics, Peking University, Beijing 100871, China)

Abstract

In this paper, the multifractal detrended fluctuation analysis (MF-DFA) method is used to identify the multifractal structure of in the Chicago Board of Trade (CBOT) soybean futures and quantitatively describe the inefficiency and nonlinearity of the market. The data is the daily price of CBOT soybean futures from 3 January 2000 to 20 December 2019, with a total of 5025 trading days. The empirical results also show that the perspective based on MF-DFA can explain the market’s nonlinear, long-range correlation, predictability and other financial anomalies. At the same time, the prediction of price change direction and risk degree of the market are further studied. It is pointed out that multifractal characteristics are generated under the joint action of fat-tail distribution and long-range correlation. Investors can make use of these market characteristics to make arbitrage possible. Finally, based on the empirical results, some policy suggestions are put forward: strengthening rational investment education, strengthening supervision, reducing information asymmetry and other measures to improve market efficiency.

Suggested Citation

  • Tao Yin & Yiming Wang, 2021. "Market Efficiency and Nonlinear Analysis of Soybean Futures," Sustainability, MDPI, vol. 13(2), pages 1-10, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:518-:d:476494
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    1. Tiwari, Aviral Kumar & Aye, Goodness C. & Gupta, Rangan, 2019. "Stock market efficiency analysis using long spans of Data: A multifractal detrended fluctuation approach," Finance Research Letters, Elsevier, vol. 28(C), pages 398-411.
    2. Marco Corazza & A.G. Malliaris & Carla Nardelli, 1997. "Searching for fractal structure in agricultural futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 17(4), pages 433-473, June.
    3. Yun-Jung Lee & Neung-Woo Kim & Ki-Hong Choi & Seong-Min Yoon, 2020. "Analysis of the Informational Efficiency of the EU Carbon Emission Trading Market: Asymmetric MF-DFA Approach," Energies, MDPI, vol. 13(9), pages 1-14, May.
    4. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    5. S. M. Duarte Queiros, 2005. "On non-Gaussianity and dependence in financial time series: a nonextensive approach," Quantitative Finance, Taylor & Francis Journals, vol. 5(5), pages 475-487.
    6. Mensi, Walid & Tiwari, Aviral Kumar & Yoon, Seong-Min, 2017. "Global financial crisis and weak-form efficiency of Islamic sectoral stock markets: An MF-DFA analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 135-146.
    7. Wei-Xing Zhou, 2009. "The components of empirical multifractality in financial returns," Papers 0908.1089, arXiv.org, revised Oct 2009.
    8. Zied Ftiti & Fredj Jawadi & Wael Louhichi & Mohamed Arbi Madani, 2019. "On the relationship between energy returns and trading volume: a multifractal analysis," Applied Economics, Taylor & Francis Journals, vol. 51(29), pages 3122-3136, June.
    9. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    10. Bouoiyour, Jamal & Selmi, Refk & Wohar, Mark E., 2018. "Are Islamic stock markets efficient? A multifractal detrended fluctuation analysis," Finance Research Letters, Elsevier, vol. 26(C), pages 100-105.
    11. Mensi, Walid & Hamdi, Atef & Shahzad, Syed Jawad Hussain & Shafiullah, Muhammad & Al-Yahyaee, Khamis Hamed, 2018. "Modeling cross-correlations and efficiency of Islamic and conventional banks from Saudi Arabia: Evidence from MF-DFA and MF-DXA approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 576-589.
    12. Shahzad, Syed Jawad Hussain & Nor, Safwan Mohd & Mensi, Walid & Kumar, Ronald Ravinesh, 2017. "Examining the efficiency and interdependence of US credit and stock markets through MF-DFA and MF-DXA approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 351-363.
    13. McNown, Robert & Wallace, Myles, 1989. "Co-integration tests for long run equilibrium in the monetary exchange rate model," Economics Letters, Elsevier, vol. 31(3), pages 263-267, December.
    14. Ruipeng Liu & Riza Demirer & Rangan Gupta & Mark Wohar, 2020. "Volatility forecasting with bivariate multifractal models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 155-167, March.
    15. Lee, Minhyuk & Song, Jae Wook & Park, Ji Hwan & Chang, Woojin, 2017. "Asymmetric multi-fractality in the U.S. stock indices using index-based model of A-MFDFA," Chaos, Solitons & Fractals, Elsevier, vol. 97(C), pages 28-38.
    16. Giannellis, Nikolaos & Papadopoulos, Athanasios P., 2009. "Testing for efficiency in selected developing foreign exchange markets: An equilibrium-based approach," Economic Modelling, Elsevier, vol. 26(1), pages 155-166, January.
    17. Wang, Yudong & Liu, Li, 2010. "Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis," Energy Economics, Elsevier, vol. 32(5), pages 987-992, September.
    18. Cao, Guangxi & Cao, Jie & Xu, Longbing, 2013. "Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 797-807.
    19. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    20. Engelen, Steve & Norouzzadeh, Payam & Dullaert, Wout & Rahmani, Bahareh, 2011. "Multifractal features of spot rates in the Liquid Petroleum Gas shipping market," Energy Economics, Elsevier, vol. 33(1), pages 88-98, January.
    21. Wang, Yudong & Wu, Chongfeng, 2012. "Energy prices and exchange rates of the U.S. dollar: Further evidence from linear and nonlinear causality analysis," Economic Modelling, Elsevier, vol. 29(6), pages 2289-2297.
    22. Alaoui, Marwane El & Bouri, Elie & Roubaud, David, 2019. "Bitcoin price–volume: A multifractal cross-correlation approach," Finance Research Letters, Elsevier, vol. 31(C).
    23. Gopikrishnan, P. & Plerou, V. & Gabaix, X. & Amaral, L.A.N. & Stanley, H.E., 2001. "Price fluctuations and market activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 137-143.
    24. Thompson, James R. & Wilson, James R., 2016. "Multifractal detrended fluctuation analysis: Practical applications to financial time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 126(C), pages 63-88.
    25. Chatrath, Arjun & Adrangi, Bahram & Dhanda, Kanwalroop Kathy, 2002. "Are commodity prices chaotic?," Agricultural Economics, Blackwell, vol. 27(2), pages 123-137, August.
    26. Zunino, L. & Tabak, B.M. & Figliola, A. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2008. "A multifractal approach for stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6558-6566.
    27. V. Plerou & P. Gopikrishnan & X. Gabaix & L. A. N. Amaral & H. E. Stanley, 2001. "Price fluctuations, market activity and trading volume," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 262-269.
    28. Rizvi, Syed Aun R. & Dewandaru, Ginanjar & Bacha, Obiyathulla I. & Masih, Mansur, 2014. "An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 86-99.
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    2. Fousekis, Panos & Tzaferi, Dimitra, 2022. "Price multifractality and informational efficiency in the futures markets of the US soybean complex," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 68-84.
    3. Memon, Bilal Ahmed & Yao, Hongxing & Naveed, Hafiz Muhammad, 2022. "Examining the efficiency and herding behavior of commodity markets using multifractal detrended fluctuation analysis. Empirical evidence from energy, agriculture, and metal markets," Resources Policy, Elsevier, vol. 77(C).

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