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Providing a model for predicting futures contract of gold coin price by using models based on Z-numbers

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Abstract

In this article, firstly the factors influencing the prices of cash market transactions on the basis of gold coin (Bahar Azadi coin) prices and futures contract trading on the Iran Mercantile Exchange are examined during a full year. Then, based on these factors, two new models for predicting the price of the futures contract of gold coin have been presented. These patterns are based on the general linear regression model in a vague and Z-based environment. To this end, regression estimation by the neural network with Z-number-based coefficients and D distance-based optimization technique and the Z-numbering method are used. We also compare some of the proposed methods in terms of efficiency with our previous method (which is the only method available to estimate regression coefficients). We show that the proposed method in this paper has less accuracy and less computational cost. It shows that the new proposed method has better accuracy and less computational cost. Finally, in two practical examples the price of forthcoming coins is anticipated.

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References

  1. Allahviranloo, T.: Advanced Uncertainty and Linear Equations, Uncertain Information and Linear Systems. Studies in Systems, Decision and Control, vol. 254, pp. 211–254. Springer, Cham (2020)

    Google Scholar 

  2. Alive, R.A., Alizadeh, A.V., Huseynov, O.H.: The arithmetic of discrete Z-numbers. Inf. Sci. 290, 134–155 (2015)

    MathSciNet  MATH  Google Scholar 

  3. Alive, R.A., Huseynov, O.H., Alive, R.R., Alizadeh, A.V.: The Arithmetic of Z-Numbers. Theory and Applications. World Scientific, Singapore (2015)

    Google Scholar 

  4. Alive, R.A., Huseynov, O.H., Serdaroglu, R.: Ranking of Z-numbers, and its application in decision making‎. Int. J. Inf. Technol. Decis. Mak. 15, 1503–1519 (2016)

    Google Scholar 

  5. Bakar, A.S.A., Gegov, A.: Multi-layer decision methodology for ranking Z-numbers. Int. J. Comput. Intell. Syst. 8, 395–406 (2015)

    Google Scholar 

  6. Bardossy, A.: Note on fuzzy regression. Fuzzy Sets Syst. 37, 65–75 (1990)

    MathSciNet  MATH  Google Scholar 

  7. Bardossy, A., Bogardi, I., Duckstein, L.: Fuzzy regression in hydrology. Water Resour. Res. 26, 1497–1508 (1990)

    Google Scholar 

  8. Blose, L.E.: Gold prices, cost of carry, and expected inflation. J. Econ. Bus. 62(1), 35–47 (2010)

    Google Scholar 

  9. Cheng, C.B., Lee, E.S.: Fuzzy regression with radial basis function network. Fuzzy Sets Syst. 119(2), 291–301 (2001)

    MathSciNet  Google Scholar 

  10. Coppi, R.: Management of uncertainty in statistical reasoning: the case of regression analysis. Int. J. Approx. Reason. 47(3), 284–305 (2008)

    MathSciNet  MATH  Google Scholar 

  11. Diamond, P.: Fuzzy least squares. Inf. Sci. 46, 141–157 (1988)

    MathSciNet  MATH  Google Scholar 

  12. Ezadi, S., Allahviranloo, T.: Numerical solution of linear regression based on Z-numbers by improved neural network. Intell. Autom. Soft Comput. 24(1), 1–11 (2017)

    Google Scholar 

  13. Ezadi, S., Allahviranloo, T.: New multi-layer method for Z-number ranking using hyperbolic tangent function and convex combination. Intell. Autom. Soft Comput. 24(1), 1–7 (2017)

    Google Scholar 

  14. Ezadi, S., Allahviranloo, T.: Two new methods for ranking of Z-numbers based on sigmoid function and sign method. Int. J. Intell. Syst. 33(7), 1476–1487 (2018)

    Google Scholar 

  15. Kang, B., Wei, D., Li, Y., Deng, Y.: Decision making using Z-numbers under uncertain environment. J. Comput. Inf. Syst. 7, 2807–2814 (2012)

    Google Scholar 

  16. Hojati, M., Bector, C.R., Smimou, K.A.: Simple method of fuzzy linear regression. Eur. J. Oper. Res. 166, 172–184 (2005)

    MathSciNet  MATH  Google Scholar 

  17. Kang, B., Wei, D., Li, Y., Deng, Y.: A method of converting Z-number to classical fuzzy number. J. Inf. Comput. Sci. 3, 703–709 (2012)

    Google Scholar 

  18. Kao, C., Chyu, C.L.: Least-squares estimates in fuzzy regression analysis. Eur. J. Oper. Res. York 148, 426–435 (2003)

    MathSciNet  MATH  Google Scholar 

  19. Liu, C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45(3), 503–528 (1989)

    MathSciNet  MATH  Google Scholar 

  20. Modarres, M., Nasrabadi, E., Nasrabadi, M.M.: Fuzzy linear regression models with least square errors. Appl. Math. Comput. 163, 977–989 (2005)

    MathSciNet  MATH  Google Scholar 

  21. Mohamad, D., Shaharani, S.A., Kamis, N.H.: A Z-number based decision making procedure with ranking fuzzy numbers method. AIP Conf. Proc. 1635, 160–166 (2014)

    Google Scholar 

  22. Mosleh, M., Allahviranloo, T., Otadi, M.: Evaluation of fully fuzzy regression models by fuzzy neural network. Neural Comput. Appl. 21, 105–112 (2012)

    Google Scholar 

  23. Mosleh, M., Otadi, M., Abbasbandy, S.: Fuzzy polynomial regression with fuzzy neural networks. Appl. Math. Model. 35, 5400–5412 (2011)

    MathSciNet  MATH  Google Scholar 

  24. Mohammadi, J., Taheri, S.M.: Pedomodels fitting with fuzzy least squares regression. Iran. J. Fuzzy Syst. 1(2), 45–61 (2004)

    MathSciNet  MATH  Google Scholar 

  25. MelekAcarBoyacioglu, A., Avci, D.: An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul Stock Exchange. Expert Syst. Appl. 37, 7908–7912 (2010)

    Google Scholar 

  26. Mashhadizadeh, M., Dastgir, M., Salahshour, S.: Economic appraisal of investment projects in solar energy under uncertainty via fuzzy real option approach (case study: a 2-MW photovoltaic plant in south of Isfahan, Iran). Adv. Math. Finance Appl. 3(4), 29–51 (2018)

    Google Scholar 

  27. Nouriani, H., Ezzati, R.: The subject of the article is said: application of Simpson quadrature rule and iterative method for solving nonlinear fuzzy delay integral equations. Fuzzy Sets Syst. (2020) (in press)

  28. Pousti, F., Sadeghiani, J.: An econometrics method for estimating gold coin futures prices. Manag. Sci. Lett. 1, 621–630 (2011)

    Google Scholar 

  29. Rohaninasab, N., Maleknejad, K., Ezzati, R.: Numerical solution of high-order Volterra-Fredholm integro-differential equations by using Legendre collocation method. Appl. Math. Comput. 328(c), 171–188 (2018)

    MathSciNet  MATH  Google Scholar 

  30. Rahaman, M., Mondal, S.P., Shaikh, A.A., Ahmadian, A., Senu, N., Salahshour, S.: Arbitrary-order economic production quantity model with and without deterioration: generalized point of view. Adv. Differ. Equ. 16, 1–30 (2020)

    MathSciNet  Google Scholar 

  31. Peters, G.: Fuzzy linear regression with fuzzy intervals. Fuzzy Sets Syst. 63, 45–55 (1994)

    MathSciNet  Google Scholar 

  32. Rosenblatt, F.: Principles of Neuro dynamics. Perceptrons and the Theory of Brain Mechanisms, pp. 242–248. Spartan Books, Washington, DC (1962)

    MATH  Google Scholar 

  33. Savic, D.A., Pedrycz, W.: Evaluation of fuzzy linear regression models. Fuzzy Sets Syst. 39, 51–63 (1991)

    MathSciNet  MATH  Google Scholar 

  34. Shirian, J., ÖzgurKis-i, B.: Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Comput. Geosci. 37, 1692–1701 (2011)

    Google Scholar 

  35. Shafiee, S., Topal, E.: An overview of global gold market and gold price forecasting. Resour. Policy 35(3), 178–189 (2010)

    Google Scholar 

  36. Salahshour, S., Allahviranloo, T., Abbasbandy, S.: Solving fuzzy fractional differential equations by fuzzy Laplace transforms. Commun. Nonlinear Sci. Numer. Simul. 17(3), 1372–1381 (2012)

    MathSciNet  MATH  Google Scholar 

  37. Sarkar, B., Mondal, S.P., Hur, S., Ahmadian, A., Salahshour, S., Guchhait, R., Waqas Iqbal, M.: An optimization technique for national income determination model with stability analysis of differential equation in discrete and continuous process under the uncertain environment. RAIRO Oper. Res. 53, 1649–1674 (2019)

    MathSciNet  MATH  Google Scholar 

  38. Shaikh, A.H., Zahid, I.: Using neural networks for forecasting volatility of S&P 500 Index futures prices. J. Bus. Res. 57(10), 1116–1125 (2004)

    Google Scholar 

  39. Tanaka, H., Havashi, I., Watada, J.: Possibilistic linear regression analysis for fuzzy data. Eur. J. Oper. Res. 40, 389–396 (1989)

    MathSciNet  MATH  Google Scholar 

  40. Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Trans. Syst. Man Cybern. 12(6), 903–907 (1982)

    MATH  Google Scholar 

  41. Tabrizi, H.A., Panahian, H.: Stock price prediction by artificial neural networks: a study of Tehran’s Stock Exchange (T.S.E), pp. 1–7. Hand Research Foundation (2013). http://www.handresearch.org/

  42. Tully, E., Lucey, B.M.: A power GARCH examination of the gold market. Res. Int. Bus. Finance 21(2), 316–325 (2007)

    Google Scholar 

  43. Yager, R.R.: On Z-valuations using Zadeh’s Z-numbers. Int. J. Intell. Syst. 27, 259–278 (2012)

    Google Scholar 

  44. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    MATH  Google Scholar 

  45. Zadeh, L.A.: A note on Z-numbers. Inf. Sci. 181, 2923–2932 (2011)

    MATH  Google Scholar 

  46. Zhang, Y.J., Wei, Y.M.: The crude oil market and the gold market: evidence for cointegration, causality and price discovery. Resour. Policy 35(3), 168–177 (2010)

    Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Nina Daryakenari & Mostafa Nouri

  2. Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey

    Tofigh Allahviranloo

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  1. Nina Daryakenari
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  2. Tofigh Allahviranloo
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  3. Mostafa Nouri
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Correspondence to Tofigh Allahviranloo.

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Daryakenari, N., Allahviranloo, T. & Nouri, M. Providing a model for predicting futures contract of gold coin price by using models based on Z-numbers. Math Sci 15, 215–228 (2021). https://doi.org/10.1007/s40096-020-00347-4

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  • DOI: https://doi.org/10.1007/s40096-020-00347-4

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