Sarfaraz, Leyla and Afsar, Amir (2005): بررسي عوامل موثر بر قيمت طلا و ارايه مدل پيش بيني قيمت آن به كمك شبكه هاي عصبي فازي. Published in: Tarbiat Modaress Economic Reasearch Journal No. 16 (2007)
Preview |
PDF
MPRA_paper_2855.pdf Download (194kB) | Preview |
Abstract
Throughout the history man has considered gold as a precious metal and its forcast has always been important. Traditional methods of forcast, e.g.Regresion, ARIMA, Exponential Smoothing, Moving Average, and methods of this kind have been applied. Only recently Artificial Intelligence, Neural Networks and Fuzzy Logic have been proposed as forcast models. In this paper after considering gold role in the international finance, its Demand and supply, and the relationship between gold and Dollar, factors affecting the gold price fluctuations are considered; then a Neuro-Fuzzy approach based on the Takagy-Sogno Moel is employed to forcast gold price. The results obtained by this method are compared with Regression Analysis, which show that a Neuro-Fuzzy yields a better and more promissing forcast.
Item Type: | MPRA Paper |
---|---|
Original Title: | بررسي عوامل موثر بر قيمت طلا و ارايه مدل پيش بيني قيمت آن به كمك شبكه هاي عصبي فازي |
English Title: | A study on the factors affecting gold price and a neuro-fuzzy model of forcast |
Language: | Persian |
Keywords: | Neural Networks; Fuzzy Logic; Neuro-Fuzzy; Artificial Intelligence; gold price |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General |
Item ID: | 2855 |
Depositing User: | leyla sarfaraz |
Date Deposited: | 21 Apr 2007 |
Last Modified: | 28 Sep 2019 02:57 |
References: | 1] بانك مركزي جمهوري اسلامي ايران، گزارش اقتصادي و ترازنامه سال 1379 ] 2] بانك مركزي جمهوري اسلامي ايران، گزارش اقتصادي و ترازنامه سال 1358 ] .1363- 3] بانك مركزي جمهوري اسلامي ايران، گزارش اقتصادي و ترازنامه سالهاي 65 ] 4] بوژ و كاريند، ژ . كاميين آ، دلوبرا، "سيماي اقتصادي جهان " ترجمه سيد حسين مطيعي ] . لنگرودي، صفحه 113 ، انتشارات استان قدس رضوي، مشهد 1370 5] سرفراز،ليلا ( 1374 )."بررسي ارتباط طلا، دلار و حق برداشت مخصوص در منظري تاريخي " ] . مجله دانشكده علوم اداري و اقتصادي دانشگاه فردوسي مشهد. شماره 3 6] سرفراز،ليلا. ( 1376 ) پيدايش سازمانهاي پولي و مالي بين المللي در ماليه بين الملل . انتشارات ] نويد. [7] Apple yard & Field (1995). "International Economics, Payments, Exchange Rates, Macroeconomic Policy, University of North Carolina at chapel Hill, USA. [8] Bogner, Stephan. (2003). "Gold vs. US Dollar". http://216.239.37.104/translate_c?hl=en&sl=de&u=http://www.goldseiten.de/ansichten/bonger- 03.htm&... [9] Carbaugb, R. J. (1992). "International Economies", Wadsworth Publishing Company. [10] Dhar,V.& chou,D.(2001). A comparison of nonlinear methods for predicting earnings surprises and returns. IEEE Transactions on Neural networks, 12(4),907-921. ١٨ [11] Hamilton, Adam. (2003), "Gold in Euros and Yen". http://www.zeall/c.com/2003/goldfx.htm. [12] Jang, J. R. and Sun, C. (1995). Nero Fuzzy Modelling and Control, Proc. of the IEEE, P.P: 378-405. [13] Kuo, R.J., Chen, C.H. and Hwang, Y.C. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network, Fuzzy Sets and Systems, 118 . [14] Le Cun, Y. (1985). Une procedure d'apprentissage pour reseau a seuil assymetrique. Cognitive, 85, 599-604. [15] Lee, C.C.(1990). Fuzzy logic in control systems: fuzzy logic controller, Part I, IEEE Trans. Systems Man Cybernet. 20 (2) , 404 - 418. [16] Lippmann, R.P. (1987). An introduction to computing with neural nets, IEEE Mag. 3 (4), P.P: 4-22. [17] Morrison, Kevin. (2003), "Gold bulls wait for further dollar slide". http://www.siliconinvestor.com/stocktallc/msg.gsp?msgid=19529668. [18] Parker, D.B. (1985). Learning logic: Casting the cortex of the human brain in silicon. Technical Report TR-47. Cambridge, MA: Center for Computational Research in Economics and Management, MIT. [19] Qi,M.(2001). Predicting US reccessions with leading indicators via neural network models. International Journal of Forecastinig,17(3),383-401. [20] Tong, R. M. (1997). A control engineering review of fuzzy systems, Automatic, 13(6): P.P: 559-569. [21] Wang, L. X., Mendel J. M. (1992). Fuzzy basis functions, universal approximation and orthogonal least-squares learning, IEEE Transaction on Neural Networks, 807-814. [22] Werbos, P.J. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Cambridge, MA: Harvard University, Ph.D. dissertation. [23] Wong, B.K., Jiang, L. and Lam, J. (2000). A bibliography of neural network business application research: 1994-1998. Computers and Operations Research, 27(11), 1045-1076. [24] World Gold Council, Gold as a Reserve asset. 2003, http://www.gold.org/value/reserve-asset/gold-as/background.html. [25] Zadeh, L.A. (1973). Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Systems Man Cybernet. SMC-3 (1), P.P: 28-44. [26] Zimmermann, M. J. (1996). Fuzzy Set Teory and its application, Kluwer Academic Publishers, Boston |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/2855 |