Forecasting exchange rate better with artificial neural network

2007 ◽  
Vol 29 (2) ◽  
pp. 227-236 ◽  
Author(s):  
Chakradhara Panda ◽  
V. Narasimhan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Artificial Neural

scholarly journals Estimation of Direction of Exchange Rate, Gold Price and Stock Market Returns with High Order Markov Chain Models

2016 ◽  
Author(s):  
Süleyman Bilgin Kılıç ◽  
Salih Çam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Transition Probabilities ◽  
Stock Market Returns ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Returns Forecasting ◽  
Artificial Neural Network Algorithm

This study uses a hybrid high order Markov Chains Model to predict direction of exchange rate, gold price and stock market returns with the Artificial Neural Network Algorithm as an estimator of transition probability matrix. Many forecasting techniques are used to examine the direction of returns forecasting in the literature such as Markov Chains Model and Artificial Neural Network Algorithm. In this study, it is aimed to combine these two techniques and to utilize the predict values of the Artificial Neural Network Algorithm for calculate transition probabilities matrix. Calculations show that the hybrid model gives high correct classification probabilities besides of well approximated transition probabilities. Returns series of USD/TRY exchange rate, closing price of Borsa Istanbul Stock Exchange and gold prices cover the period of 01/01/2003 and 31/01/2016. All series are obtained from database of Central Bank of Turkey. As a result, although the transition probabilities almost equal to 0.5 and so estimation of these series are not easy, the transition probabilities and correct classification probabilities gained from the hybrid model provide substantial information related to direction of returns forecasting. Besides, estimated model provide valuable information to individual investors and companies, and could help them to take position against to risks.

Artificial Neural Network Model with PSO as a Learning Method to Predict Movement of the Rupiah Exchange Rate against the US Dollar

2021 ◽  
pp. 81
Author(s):  
Eko Verianto ◽  
Budi Sutedjo Dharma Oetomo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Pso Algorithm ◽  
Learning Method ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
The Us ◽  
Us Dollar ◽  
Artificial Neural

The movement of currency exchange rate can be predicted in the next few days, this is used by economic actors to get profit. Artificial Neural Network with the backpropagation learning method is good enough to use for forecasting time series data, it's just that in its application this method was considered to have shortcomings such as a long training time to achieve convergence. The purpose of this research is to form a Multilayer Perceptron Artificial Neural Network model with the Particle Swarm Optimization (PSO) algorithm as a learning method in the case of currency exchange rate prediction. This research produced a model that can predict the movement of the Rupiah exchange rate against the US Dollar, while the model formed was the MLP-PSO model with an error rate of 5.6168 x 10-8, slightly better than the MLP-BP model with an error rate of 6.4683 x 10-8. These results indicated that the PSO algorithm can be used as a learning algorithm in the Multilayer Perceptron Artificial Neural Network.

FORECASTING DAILY FOREIGN EXCHANGE RATE IN INDIA WITH ARTIFICIAL NEURAL NETWORK

2003 ◽  
Vol 48 (02) ◽  
pp. 181-199 ◽  
Author(s):  
CHAKRADHARA PANDA ◽  
V. NARASIMHAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Random Walk ◽  
Autoregressive Model ◽  
Out Of Sample ◽  
One Step ◽  
Artificial Neural ◽  
The One ◽  
Better Than

This study compares the efficiency of a non-linear model called artificial neural network with linear autoregressive and random walk models in the one-step-ahead prediction of daily Indian rupee/US dollar exchange rate. We find that neural network and linear autoregressive models outperform random walk model in in-sample and out-of-sample forecasts. The in-sample forecasting of neural network is found to be better than that of linear autoregressive model. As far as out-of-sample forecasting is concerned, the results are mixed and we do not find a "winner" model between neural network and linear autoregressive model. However, neural network is able to improve upon the linear autoregressive model in terms of sign predictions. In addition to this, we also find that the number of input nodes has greater impact on neural network's performance than the number of hidden nodes.

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