Forecasting Currency Exchange Rates via Feedforward Backpropagation Neural Network

Joseph C Chen,Naga Hrushikesh R Narala

doi:10.13189/ujme.2017.050302

Joseph C Chen, Naga Hrushikesh R Narala

Open Access

https://doi.org/10.13189/ujme.2017.050302

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Abstract

The latest globalization trends resulted in increasingly interdependent economies of nations and multinational firms. This may leave companies operating internationally at the mercy of the volatility in currency exchange rates. Forecasting these exchange rates became very important in international trade and commerce, as it involves key decisions of foreign investment, forward contracts and expanding business to new horizons. This research paper describes a Feedforward Backpropagation Neural Network (FBNN) model and its application to currency exchange rate forecasting. A study of FBNN model is conducted for forecasting exchange rates between Indian rupee and US dollar, based on previous data of inflation, real interest rates, gross domestic product (GDP), current account balances, government budget balances and debts of both countries. The weights used in neural networks were optimized using gradient descent and backpropagation method. Models with different hidden neuron layers were developed by comparing the actual exchange rates with forecasted monthly exchange rates from January 2001 to December 2014. The most effective model was then used to simulate exchange rates for the year 2015. The FBNN model with ten neurons in the hidden layer has the least Mean average percentage error (MAPE) value of 1.32% and is considered to be most impressive model.

Highlights

A currency exchange rate is essentially what its name implies: it is the rate at which one would need to exchange one’s currency for the currency of other country
For any investment that is crossing borders, the risk and returns are dependent on the exchange rates between countries
The fluctuations pose a significant risk and uncertainty to every sector involved in business [1]. It may be for an investment or a trade or a business, having knowledge of exchange rate is important to minimize risks and maximize profits

Summary

Introduction

A currency exchange rate is essentially what its name implies: it is the rate at which one would need to exchange one’s currency for the currency of other country. For any investment that is crossing borders, the risk and returns are dependent on the exchange rates between countries. The fluctuations pose a significant risk and uncertainty to every sector involved in business [1]. It may be for an investment or a trade or a business, having knowledge of exchange rate is important to minimize risks and maximize profits. Exchange rates are dependent on various economic factors. Flow patterns for majority of these factors follow non-linearity. Artificial neural networks are a natural choice for a forecast involving non-linear trends. Landavazo and Fogell [2] suggest changing all weights in the network simultaneously to reduce network operation time

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