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The Proposed Mathematical Models for Decision-Making and Forecasting on Euro-Yen in Foreign Exchange Market

Author

Listed:
  • Abdorrahman Haeri

    (Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran)

  • Masoud Rabbani

    (Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran)

  • Ali Habibnia

    (M.A. Student, Faculty of Economics, University of Tehran, Tehran, Iran)

Abstract

In this paper two approaches for trading and forecasting on Euro-Yen exchange rates are suggested. In the first approach three decision-making models are developed to maximize profit of trades during a specific period. Traders have three options to perform a trade at each market time that are: (a) Opening a buy trade, (b) Opening a sell trade and (c) Refusal of trading. These options are considered in the models by using related decision variables. Results of these models conform to qualitative contents in literature of foreign exchange market and present trading strategy on the basis of the indicators to maximize profit. The aim of second approach is forecasting the direction of exchange rate (increase or decrease) over a specific period on the basis of values of indicators in previous time period. In this approach two heuristic models are developed to minimize mean of errors of forecasting. Then mean of errors of developed models are compared with four major classification algorithms. Results show that the proposed model has higher accuracy in forecasting.

Suggested Citation

  • Abdorrahman Haeri & Masoud Rabbani & Ali Habibnia, 2011. "The Proposed Mathematical Models for Decision-Making and Forecasting on Euro-Yen in Foreign Exchange Market," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 16(3), pages 67-91, fall.
    • Handle: RePEc:eut:journl:v:16:y:2011:i:3:p:67
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    References listed on IDEAS

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    1. Harald Hau & Hélène Rey, 2006. "Exchange Rates, Equity Prices, and Capital Flows," The Review of Financial Studies, Society for Financial Studies, vol. 19(1), pages 273-317.
    2. Terence Tai-Leung Chong & Wing-Kam Ng, 2008. "Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30," Applied Economics Letters, Taylor & Francis Journals, vol. 15(14), pages 1111-1114.
    3. Panda, Chakradhara & Narasimhan, V., 2007. "Forecasting exchange rate better with artificial neural network," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 227-236.
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    Cited by:

    1. Rahimi, Fatemeh & Mousavian Anaraki, Seyed Alireza, 2020. "Proposing an Innovative Model Based on the Sierpinski Triangle for Forecasting EUR/USD Direction Changes," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 15(4), pages 423-444, October.

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