Foreign Exchange Rate Prediction Research Articles

The Relationship between Foreign Exchange rate prediction using Artificial Intelligence and Audit Effort

The Relationship between Foreign Exchange rate prediction using Artificial Intelligence and Audit Effort

Enhancement Support Vector Regression Using Black Widow Optimization for Predicting Foreign Exchange Rate

Prediction of foreign exchange rates is one of the time series problems that have fluctuating value movements. There are several algorithms that can make predictive models for this problem, one of which is Support Vector Regression (SVR). In this study, foreign exchange rate predictions were made using Hybrid SVR and Black Widow Optimization (BWO). This is done with the aim of improving the performance of the SVR in order to produce a better predictive model for the EUR/USD foreign exchange rate data in 2020. The results of the proposed algorithm get better performance in terms of R2, MSE, RMSE, MAE, and MAPE compared to SVR.

Enhancement Support Vector Regression Using Black Widow Optimization for Predicting Foreign Exchange Rate

Prediction of foreign exchange rates is one of the time series problems that have fluctuating value movements. There are several algorithms that can make predictive models for this problem, one of which is Support Vector Regression (SVR). In this study, foreign exchange rate predictions were made using Hybrid SVR and Black Widow Optimization (BWO). This is done with the aim of improving the performance of the SVR in order to produce a better predictive model for the EUR/USD foreign exchange rate data in 2020. The results of the proposed algorithm get better performance in terms of R2, MSE, RMSE, MAE, and MAPE compared to SVR.

SUDF-RS: A new foreign exchange rate prediction method considering the complementarity of supervised and unsupervised deep representation features

The supervised and unsupervised deep representation features have been adopted and verified their effective roles separately in the foreign exchange rate prediction (FERP). However, the complementarity of these two types of features for FERP remained unexplored. To address this problem, we proposed a novel method for one day ahead FERP, which improves Random Subspace by simultaneously considering both Supervised and Unsupervised Deep representation Features, namely, SUDF-RS. Feature extraction and model construction are two important stages in the SUDF-RS method. Firstly, the supervised and unsupervised deep representation features are extracted by the long short-term memory networks and deep belief networks respectively. Secondly, an improved RS method, which incorporates random forest-based feature weighting mechanism is developed to generate high-quality feature subsets. Thirdly, each feature subset is used to train the corresponding base learner and the final results are obtained by averaging the results of each base learner. Experiments on three exchange rate datasets, namely EUR/USD, GBP/USD and USD/JPY validate that the proposed SUDF-RS significantly outperforms the benchmark methods in terms of both MAPE and RMSE.

WITHDRAWN: Multi currency exchange rate prediction using convolutional neural network

Abstract Financial market is non-linear and chaotic in nature. So, Accurate prediction of foreign exchange rate is very difficult & a challenging task. Hence, many neural network techniques are used for forecasting various country’s currency exchange rates with different parameters. This article proposed convolutional neural networks for foreign currency exchange rate prediction. In this article we would like to propose a model which could develop a multivariate exchange rates information and put these features for better use. The performance of the proposed system has been tested with Australian Dollar against Us Dollar (AUD/USD), European Euro against Us Dollar (EUR/USD), European Euro against Canadian Dollar (EUR/CAD), British pound against Us Dollar (GBP/USD), Us Dollar against Canadian Dollar (USD/CAD), Us Dollar against Japanese Yen (USD/JPY) and used to predict one day and five days exchange rate in advance. Adaptive learning rate method (ADAM) optimization technique is used here which generates optimal weight for the proposed Model. The proposed model has been found with the best prediction result after measuring the performance of R2, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error and CEV in comparison with ARIMA, Linear Regression and Multi-layer perceptron techniques.

Forecasting Malaysian Exchange Rate using Artificial Neural Network

In todays fast paced global economy, the accuracy in forecasting the foreign exchange rate or predicting the trend is a critical key for any future business to come. The use of computational intelligence based techniques for forecasting has been proved to be successful for quite some time. This study presents a computational advance for forecasting the Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar. A neural network based model has been used in forecasting the days ahead of exchange rate. The aims of this research are to make a prediction of Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar using artificial neural network and determine practicality of the model. The Alyuda NeuroIntelligence software was utilized to analyze and to predict the data. After the data has been processed and the structural network compared to each other, the network of 2-4-1 has been chosen by outperforming other networks. This network selection criteria are based on Akaike Information Criterion (AIC) value which shows the lowest of them all. The training algorithm that applied is Quasi-Netwon based on the lowest recorded absolute training error. Hence, it is believed that experimental results demonstrate that Artificial Neural Network based model can closely predict the future exchange rate.

Short-Term Prediction of Foreign Exchange Rates by Collective Knowledge of Counterparty Banks

Short-Term Prediction of Foreign Exchange Rates by Collective Knowledge of Counterparty Banks

Forecasting Foreign Currency Exchange Price using Long Short-Term Memory with K-Nearest Neighbor Method

With the growing population in the world, economic stability varies day by day. In case of India all banking transaction rules and regulations are taken by Reserve bank of India (RBI) whereas for other countries it is different. Therefore numerous academicians have projected their research on forecasting the currency exchange rate for diverse countryside. Foreign currency exchange rate prediction is a very pivotal task for international market. Hence researchers have explored different methods for predicting foreign currency exchange rate. In this work, we have taken Indian rupees (INR) with two different country’s data set such as Japanese yen (JPY) andChinese Yuan (CNY)for daily, weekly and monthlyprediction beforehand. We implemented a hybrid model oflong short-term memory (LSTM) with K-nearest neighbour (KNN) which gives better opening price prediction accuracy on our dataset. The accuracy of the prediction results are measured by the help of performance standards such as mean absolute percentage error (MAPE) and root mean square error (RMSE).

Foreign currency exchange rate prediction using neuro-fuzzy systems

The complex nature of the foreign exchange (FOREX) market along with the increased interest towards the currency exchange market has prompted extensive research from various academic disciplines. With the inclusion of more in-depth analysis and forecasting methods, traders will be able to make an informed decision when trading. Therefore, an approach incorporating the use of historical data along with computational intelligence for analysis and forecasting is proposed in this paper. Firstly, the Gaussian Mixture Model method is applied for data partitioning on historical observations. While the antecedent part of the neuro-fuzzy system of AnYa type is initialised by the partitioning result, the consequent part is trained using the fuzzily weighted RLS algorithm based on the same data. Numerical examples based on the real currency exchange data demonstrated that the proposed approach trained with historical data produce promising results when used to forecast the future foreign exchange rates over a long-term period. Although implemented in an offline environment, it could potentially be utilised in real-time application in the future.

Foreign Exchange Rate Forecasting using Levenberg- Marquardt Learning Algorithm

Background/Objectives: Foreign currency Exchange (FOREX) plays a vital role for currency trading in the international market. Accurate prediction of foreign currency exchange rate is a challenging task. The paper investigates the FOREX prediction using feed forward neural network. Methods/Statistical analysis: This paper employs artificial neural network to forecast foreign currency exchange rate in India during 2010-2015.The exchange rates considered between Indian Rupee and four major currencies Euro, Japanese Yen, Pound Sterling and US Dollar. The network developed consists of an input layer, hidden layer and output layer. The neural network was trained with Levenberg-Marquardt (LM) learning algorithm. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Forecasting Error (FE) are used as indicators for the performance of the networks. Findings: Simulation results are presented to show the performance of the proposed system. The paper also aims to suggest about network topology that must be chosen in order to fit time series kind of complicated data to a neural network model. The proposed technique gives the evidence that there is possibility of extracting information hidden in the foreign exchange rate and predicting into the future. Applications/Improvements: Finally, this paper presents the best network topology for FOREX prediction by comparing the effectiveness of various hidden layer performance algorithm using MATLAB neural network software as a tool.

Application of the Nash Nonlinear Grey Bernoulli Model for Forecasting Foreign Exchange Rate of Taiwan’s Top Two Trading Partners

The precise prediction of foreign exchange rate is very important for international traders and investors. This study adopts nonlinear grey Bernoulli model (NGBM) and Nash NGBM (NNGBM) to predict the currency exchange rate of Taiwan’s two top trading partners, America and China. The simulation results show that Taiwan’s currency will appreciate against USD and CNY from the fourth quarter of 2015 to the second quarter of 2016. The conclusions can act as reference for international traders and investors.

Chemical reaction optimisation: a hybrid technique applied to functional link artificial neural networks with least mean square learning for foreign exchange rates forecasting

Forecasting foreign exchange rates has long been an important issue in international finance. Most of the standard econometric methods are unable to produce significant superior forecasts because of its built-in complexity and practical applications. Taking into consideration the worldwide financial capital market, the foreign exchange (FOREX) market has a very crucial role to play. Due to the globalisation of fiscal investment, the investors are interested to learn the co-movement of foreign exchange, so as to make their investments safe and earn profits in return. In this work, an improved ANN model is being proposed that hybridises chemical reaction optimisation with functional link artificial neural network for prediction of foreign exchange (FOREX) rate. Experimental result shows that the proposed model with least mean square (LMS) training outperforms other methods, which ultimately indicates that the proposed model can be an effective way to improve forecasting accuracy achieved by other counterparts.

Forecasting exchange rate using deep belief networks and conjugate gradient method

Forecasting exchange rates is an important financial problem. In this paper, an improved deep belief network (DBN) is proposed for forecasting exchange rates. By using continuous restricted Boltzmann machines (CRBMs) to construct a DBN, we update the classical DBN to model continuous data. The structure of DBN is optimally determined through experiments for application in exchange rates forecasting. Also, conjugate gradient method is applied to accelerate the learning for DBN. In the experiments, three exchange rate series are tested and six evaluation criteria are adopted to evaluate the performance of the proposed method. Comparison with typical forecasting methods such as feed forward neural network (FFNN) shows that the proposed method is applicable to the prediction of foreign exchange rate and works better than traditional methods.

Exchange Rate Forecasting using ARIMA, Neural Network and Fuzzy Neuron

Prediction of Exchange rates has been a challenging task for traders and practitioners in modern financial markets. Statistical and econometric models are extensively used in the analysis and prediction of foreign exchange rates. This paper investigates the behavior of daily exchange rates of the Indian Rupee (INR) against the United States Dollar (USD), British Pound (GBP), Euro (EUR) and Japanese Yen (JPY). This paper attempts to examine the performance of ARIMA, Neural Network and Fuzzy neuron models in forecasting the currencies traded in Indian foreign exchange markets. Daily RBI reference exchange rates from January 2010-April 2015 were used for the analysis.

A continuous time Bayesian network classifier for intraday FX prediction

Prediction of foreign exchange (FX) rates is addressed as a binary classification problem in which a continuous time Bayesian network classifier (CTBNC) is developed and used to solve it. An exact algorithm for inference on CTBNC is introduced. The performance of an instance of these classifiers is analysed and compared to that of dynamic Bayesian network by using real tick by tick FX rates. Performance analysis and comparison, based on different metrics such as accuracy, precision, recall and Brier score, evince a predictive power of these models for FX rates at high frequencies. The achieved results also show that the proposed CTBNC is more effective and more efficient than dynamic Bayesian network classifier. In particular, it allows to perform high frequency prediction of FX rates in cases where dynamic Bayesian networks-based models are computationally intractable.

Research on Automated Forex Trading System Based on BP Neural Network

This paper reports on an expert advisor for forex trading based on Back Propagation Neural Network (BPNN) in MetaTrader4 platform. A single hidden layer feedforward network was established for foreign exchange rate prediction. Trading rules based on the prediction results was designed and realized. Finally, we optimized the parameters according to the profitability performed on EUR/USD, GBP/USD currency pairs separately. The optimized results are able to achieve good results in the training series. In the test series, the strategies are consistently profitable for at least the first twenty days. It is concluded that the BPNN based model do have the ability to make profits from the experimental currency pairs for the period investigated.

Research on Application of Improved Gray System Theory into Foreign Exchange Rate Prediction

Research on Application of Improved Gray System Theory into Foreign Exchange Rate Prediction

Intelligent Computing and Foreign Exchange Rate Prediction: What We Know and We Don��t

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LINEAR RELATIONSHIP BETWEEN THE AUD/USD EXCHANGE RATE AND THE RESPECTIVE STOCK MARKET INDICES: A COMPUTATIONAL FINANCE PERSPECTIVE

SUMMARYIn the recent era, computational intelligence techniques have found an increased popularity in addressing varied financial issues, including foreign exchange rate prediction. This article, through an intelligent system research framework, relates the Australian dollar (AUD)/US dollar (USD) exchange rate to the Australian and the US stock market indices. Information for exchange rate, All Ordinaries Index (AOI) and Dow Jones Industrial Average (DJI) for the trading days over the period January 1991–May 2011 is considered in this research. Utilizing a set of statistical and computational intelligence techniques, the research establishes that the AUD/USD exchange rate is best estimated by a linear forecast model compared with the nonlinear and ensemble‐based intelligent system models. This research further highlights that, among the competing linear models, the model with both the stock market indices and historical exchange rate values as the predictors is the best forecaster. Parameters of the linear model are deduced through a Monte Carlo stochastic approach. Relative importance of the predictors is also studied, and the influence of historical exchange rates, the immediate impact of AOI and the lagged effect of DJI are noted. Copyright © 2012 John Wiley & Sons, Ltd.

Computational Models of Financial Price Prediction: A Survey of Neural Networks, Kernel Machines and Evolutionary Computation Approaches

A review of the representative models of machine learning research applied to the foreign exchange rate and stock price prediction problem is conducted. The article is organized as follows: The first section provides a context on the definitions and importance of foreign exchange rate and stock markets. The second section reviews machine learning models for financial prediction focusing on neural networks, SVM and evolutionary methods. Lastly, the third section draws some conclusions.