Saffron future contract yield prediction using a modified quadratic model

Document Type : Research Paper


Department of Accounting, South Tehran Azad University, Tehran, Iran


The main purpose of this study is to predict the future yield of saffron contracts using a modified quadratic model, which is a library documentary research from the aspect of data collection, and from the aspect of results, it is applied and quantitative research. The time period of the quantitative part is a 5-year period from 2019/03/20 to 2023/03/20 in the form of daily frequency of the Ministry of Jihad, Agriculture and Customs of Iran from the website of the Iran Commodity Exchange, which was collected and the modified second-order model in terms of complexity, from The type of nonlinear polynomial problems that the proposed methods are modelled by coding in Matlab software environment with normal data. Overall, the results indicate that the neural network model has a higher reliance on power compared to the adjusted quadratic model in predicting the saffron contract yield, and the calculation results show that price fluctuations, cash price, transaction volume, and liquidity are the most important in order They have the contractual yield of saffron.


[1] A.M. Ahmadvand and F. Javadi Rizi, Presenting a model for predicting the price of gold coins in Iran using neural networks, genetic phase algorithm, Master’s thesis, Computer Engineering-Artificial Intelligence and Robotics, Faculty of Electricity, Computer and Mechanics, University of Eyvanekey, 2018.
[2] H. Basirzadeh, M. Siraj and S.A. Mohammadi Yousefnejad, Modified quadratic programming method for solving non-linear programming problems, Master’s thesis, operations research, Shahid Chamran University of Ahvaz, Faculty of Mathematical and Computer Sciences, 2016.
[3] D.G. Black, Success and Failure of Futures Contracts: Theory and Empirical Evidence, Monograph Series in Finance and Economics, Graduate School of Business, New York University, 1986.
[4] N. Bollen, T. Smith and R. Whaley, Optimal contract design: For whom?, J. Futures Markets 23 (2003), 719–750.
[5] Sh. Brown, P. Laux and B. Schachter, On the existence of an optimal tick size, Rev. Futures Markets 10 (1991), 50–72.
[6] W. Brorsen and N.F. Fofana, Success and failure of agricultural futures contracts, J. Agribus. 19 (2001), 129–145.
[7] J.H.E. Christensen, F.X. Diebold and D.G. Rudebusch, The affine arbitrage-free class of Nelson–Siegel term structure models, J. Econ. 164 (2011), no. 1, 4–20.
[8] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons, New York London Sydney Toronto, 1973.
[9] R.W. Gray, Why does futures trading succeed or fail: an analysis of selected commodities, Futures Trad. Seminar. 3 (1966) 115–137.
[10] L. Harris, Optimal dynamic order submission strategies in some stylized trading problems, Financ. Markets Inst. Instr. 7 (1998), no. 2.
[11] S.A. Hosseini Yekani, Optimal design of agricultural products futures contracts in Iran, PhD thesis, Shiraz University, 2008.
[12] C. Kalayci, O. Polat and M.A. Akbay, An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization, Swarm Evolut. Comput. 54 (2020), 100662.
[13] A.K. Karagozoglu and T.F. Martell, Changing the size of a futures contract: liquidity and microstructure effects, Financ. Rev. 34 (1999), 75–94.
[14] F. Longin, Optimal margin level in futures markets: extreme price movements, J. Futures Markets 19 (1999), 127–152.
[15] M.T.G. Meulenberg and J.M.E. Pennings, A marketing approach to commodity futures exchanges: A case study of the Dutch hog industry, J. Agricul. Econ. 53 (2002), 51–64.
[16] J.M.E. Pennings and T.M. Egelkraut, Research in agricultural futures markets: integrating the finance and marketing approach, Agrarwirtschaft. 52 (2003), 300–308.
[17] J.M.E. Pennings and R.M. Leuthold, Commodity futures contract viability: a multidisciplinary approach, NCR-134 Proc., 2001, pp. 273–288.
[18] M.J. Powers, Effects of contract provisions on the success of a futures contract, J. Farm Econ. 49 (1967), 833–843.
[19] Sh. Shams, M. Yahyazadefar and B. Ataei, A comparative study between the combined genetic algorithm-artificial neural network model and the modified quadratic discriminant function model to detect stock price manipulation in companies listed on the Tehran stock exchange, School of Economic and Administrative Sciences, Department of Business Management, Master’s Thesis, Department of Management Business (financial orientation), 2015.
[20] E. Tashjian, Optimal futures contract design, Quart. Rev. Econ. Finance 35 (1995), 153–162.
[21] H. Zhao, Z. Chen, Z. Zhan and S. Kwong, Multiple populations co-evolutionary particle swarm optimization for multi-objective cardinality constrained portfolio optimization problem, Neurocomputing 430 (2021), 58–70.
Volume 15, Issue 1
January 2024
Pages 263-276
  • Receive Date: 25 November 2022
  • Revise Date: 18 January 2023
  • Accept Date: 06 February 2023