Currency Exchange Rate Prediction Research Articles

Prediction of Currency Exchange Rate Based on Transformers

Prediction of Currency Exchange Rate Based on Transformers

Comparative Analysis of ML Models for Currency Exchange Rate Prediction

The primary aim of this research is to improve the prediction of the exchange rate between the United States Dollar (USD) and the Indian Rupee (INR), which is an area that has received little attention in the field of financial forecasting. In contrast to widespread methodologies that consolidate results over several currency pair, this study specifically concentrates on the USD to INR pair, recognising the distinctive economic and political dynamics between the United States and India. This study aims to do a comparative analysis of four various machine learning models, namely RNN, ARIMA, LSTM, and Random Forest, in order to determine the best effective tool for predicting exchange rates in a certain context. This research employs a holistic methodology, including a wide range of variables like trade balances, interest rates, and geopolitical events, so providing a multifaceted forecasting approach. The growing economic interdependence between the United States and India highlights the practical importance of precise predictions for many stakeholders, such as traders, investors, and politicians. Furthermore, the study examines the concept of dynamic model updating, a novel attribute that augments flexibility within the ever-changing financial industry. The primary objective of this work is to address a significant need in current academic research by developing a reliable and practical instrument for accurately forecasting the exchange rate between the United States Dollar (USD) and the Indian Rupee (INR). This research is notable for its specific concentration, use of comparative methods, and its potential to make substantial advancements in both academic and practical domains pertaining to currency exchange rate forecasting.

Convolutional Neural Network – Based Algorithm for Currency Exchange Rate Prediction

The foreign exchange market is one of the complex monetary markets in the world. Each day trillions of dollars are traded in the FOREX market by banks, retail traders, corporations, and individuals. It is very challenging to predict the price in advance due to the complex, volatile and high fluctuation. Investors and traders are constantly searching for innovative ways to outperform the market and increase their profits. As an outcome, forecasting models are continually being developed by scholars around the globe to accurately predict the characteristics of this nascent market. This study intends to apply the Random Forest (RF) approach to Convolutional Neural Networks, which involves two key steps. The first step is starting with feature selection using Convolutional neural network.The attention layer is then employed to assign weight.The random forest strategy is designed in the second stage to generate high-quality feature subsets. Thus the better result generated by CNN-RF model. Actually, this strategy combines the advantages of two different strategies to produce an outcome that is more consistent with what exchange market decision-makers anticipate happening in the exchange market.The main currency pairs considered in this study's proposed model for predicting exchange rates five and ten minutes in advance are the British Pound Sterling (GBP) against the US Dollar (USD), the Australian Dollar (AUD) against the US Dollar (USD), and the European Euro (EUR) against the Canadian Dollar (CAD) are also used to evaluate the performance of the proposed model. In compared to the other three models (Multi-Layer Perceptron, Autoregressive Integrated Moving Average, and Recurrent Neural Network), CNN-RF yields better results. This conclusion has been backed by a large body of empirical research, which also suggested that this methodology be regularly used due to its high efficacy.

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

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.

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 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).

Performance analysis of a higher order neural network with an improved shuffled frog leaping algorithm for currency exchange rate prediction

Accurate and unbiased prediction of future currency exchange rate is always a potential field of research in domain of financial time series analysis. In this paper, an attempt is urged to examine the predictability of a higher order neural network called Pi-Sigma network for forecasting the highly non linear and dynamic currency exchange rates. An Improved Shuffled Frog Leaping (ISFL) algorithm is set forth to estimate the unrevealed parameters of the network. The network is also examined with few other meta-heuristic learning techniques and compared with other state of art models. Empirically the model validation is realized over three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. Practical analysis of results suggests that the Pi-Sigma network learned with ISFL is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

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.

An optimized SVM-k-NN currency exchange forecasting model for Indian currency market

This paper considers the prediction of currency exchange rate, volatility, and momentum prediction by exploring the capabilities of Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). In this work, the parameters such as penalty C and kernel $$\gamma$$ of SVM have been tuned with few optimization techniques such as random search, grid search, genetic algorithm, particle swarm optimization, ant colony optimization, firefly optimization, and BAT optimization algorithm. The final prediction has been obtained using k-NN by searching the neighborhood elements for either profit or loss. The performance of the proposed system has been tested with the Indian rupees with dollar (USD), British Pound (GBP), and Euro (EUR) for daily, weekly, and monthly in advance for prediction of currency exchange rate, volatility, and momentum in the currency market. The model BAT-SVM-k-NN has been found with the best forecasting ability based on performance measures such as mean absolute percentage error, root mean square error, mean squared forecast error, root mean squared forecast error, and mean absolute forecast error in comparison with other optimization techniques mentioned above.

An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

Optimization of neural network for nonlinear discrete time system using modified quaternion firefly algorithm: case study of Indian currency exchange rate prediction

Success of neural networks depends on an important parameter, initialization of weights and bias connections. This paper proposes modified quaternion firefly algorithm (MQFA) for initial optimal weight and bias connection to neural networks. The proposed modified quaternion firefly method is based on updating population, moving fireflies and best solution in quaternion space. The combination of modified quaternion firefly and neural network is developed with the scope of creating an improved balance between premature convergence and stagnation. The performance of the proposed method is tested on two nonlinear discrete time systems, Box–Jenkins time series data and exchange rate prediction of Indian currency. Results of the MQFA with back-propagation neural network (MQFA-BPNN) compared with existing differential evolution-based neural network and opposite differential evolution-based neural network. Results obtain using MQFA-BPNN envisage that this method is effective and provides better identification accuracy. Computational complexity of MQFA-BPNN is deliberated, and validation of proposed method is tested by statistical methods.

Does Artificial Neural Network Forecast Better for Excessively Volatile Currency Pairs?

This study predicts the exchange rates for three currency pairs (USD-INR, GBP-INR, and EUR-INR). We have used multi-layer perceptron (MLP) neural network architecture based on feed-forward with back-propagation learning method. The sample of the study covers daily data for the period from January 2009 to January 2016. The findings of the study confirm that the neural network predicts better for more volatile currency pairs (GBP-INR and EUR-INR) as compared to a less volatile currency pair (USD-INR). The study further observes that the optimal forecast horizon for the neural network model should be equal to the optimal lag length used in the construction of the model. This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays a crucial role in the macro-economy of a country. Hence, prediction of currency exchange rate becomes imperative for various stakeholders such as government, the central bank, and investors to maximize the returns and minimize the risk in their decision-making.

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 an EWMA combining technique to the prediction of currency exchange rates

Financial forecasting is an important and challenging task for both academic researchers and business practitioners. The recent trend to improve the prediction accuracy is to combine individual forecasts using a simple average or weighted average where the weight reflects the inverse of the prediction error. In the existing combining methods, however, the errors between actual and predicted values are equally reflected in the weights regardless of the time order in a forecasting horizon. In this paper, we propose a new approach where the forecasting results of Generalized AutoRegressive Conditional Heteroskedastic (GARCH), neural network, and random walk models are combined based on a weight that reflects the inverse of the exponentially weighted moving average of the Mean Absolute Percentage Error (MAPE) of each individual prediction model. The results of an empirical study indicate that the proposed method has a better accuracy than the GARCH, neural network, and random walk models, and also combining methods based on using the MAPE for the weight.

Statistical fuzzy interval neural networks for currency exchange rate time series prediction

In this paper, the statistical fuzzy interval neural network with statistical interval input and output values is proposed to perform statistical fuzzy knowledge discovery and the currency exchange rate prediction. Time series data sets are grouped into time series data granules with statistical intervals. The statistical interval data sets including week-based averages, maximum errors of estimate and standard deviations are used to train the fuzzy interval neural network to discover fuzzy IF-THEN rules. The output of the fuzzy interval neural network is an interval value with certain percent confidence. Simulations are completed in terms of the exchange rates between US Dollar and other three currencies (Japanese Yen, British Pound and Hong Kong Dollar). The simulation results show that the fuzzy interval neural network can provide more tolerant prediction results.

Conditional density and value-at-risk prediction of Asian currency exchange rates

We first demonstrate the simultaneous need for both more general GARCH structures and non-normal innovation distributions for modelling the returns on certain return series such as the highly volatile exchange rates on East Asian currencies against the US dollar. This is accomplished not only via in-sample goodness-of-fit criteria, but also in terms of the precision of Value-at-Risk calculations made on out-of-sample density predictions. Second, a forecasting strategy using weighted maximum likelihood estimation is proposed. We show that it gives rise to considerably improved forecast performance over longer horizons. Copyright © 2000 John Wiley & Sons, Ltd.

Currency Exchange Rate Forecasting with Neural Networks

This paper presents the prediction of foreign currency exchange rates using artificial neural networks. Since neural networks can generalize from past experience, they represent a significant advancement over traditional trading systems, which require a knowledgeable expert to define trading rules to represent market dynamics. It is practically impossible to expect that one expert can devise trading rules that account for, and accurately reflect, volatile and rapidly changing market conditions. With neural networks, a trader may use the predictive information alone or with other available analytical tools to fit the trading style, risk propensity, and capitalization. Numerous factors affect the foreign exchange market, as they will be described in this paper. The neural network will help minimize these factors by simply giving an estimated exchange rate for a future day (given its previous knowledge gained from extensive training). Because the field of financial forecasting is too large, the scope in this paper is narrowed to the foreign exchange market, specifically the value of the Japanese Yen against the United States Dollar, two of the most important currencies in the foreign exchange market.

Forecasting time series with genetic fuzzy predictor ensemble

This paper proposes a genetic fuzzy predictor ensemble (GFPE) for the accurate prediction of the future in the chaotic or nonstationary time series. Each fuzzy predictor in the GFPE is built from two design stages, where each stage is performed by different genetic algorithms (GA). The first stage generates a fuzzy rule base that covers as many of training examples as possible. The second stage builds fine-tuned membership functions that make the prediction error as small as possible. These two design stages are repeated independently upon the different partition combinations of input-output variables. The prediction error will be reduced further by invoking the GFPE that combines multiple fuzzy predictors by an equal prediction error weighting method. Applications to both the Mackey-Glass chaotic time series and the nonstationary foreign currency exchange rate prediction problem are presented. The prediction accuracy of the proposed method is compared with that of other fuzzy and neural network predictors in terms of the root mean squared error (RMSE).

Reconfigurable combined forecasts in a non‐stationary inflationary environment

AbstractThis paper presents a composite method for non‐stationary economic forecasts, incorporating reconfigurable exponential trend extraction and linear combinations of parabolic and spectral estimators for short‐term prediction. The method automatically identifies the points of time series misalignment induced by sharp environmental changes. An application to the problem of hard currency exchange rate prediction in Russia is presented.

Prediction of future currency exchange rates from current currency futures prices: The case of GM and JY

Journal of Futures MarketsVolume 14, Issue 1 p. 25-36 Article Prediction of future currency exchange rates from current currency futures prices: The case of GM and JY George Y. Jabbour, George Y. Jabbour George M. Jabbour is an Associate Professor in the Department of Finance at George Washington University.Search for more papers by this author George Y. Jabbour, George Y. Jabbour George M. Jabbour is an Associate Professor in the Department of Finance at George Washington University.Search for more papers by this author First published: February 1994 https://doi.org/10.1002/fut.3990140104Citations: 5 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume14, Issue1February 1994Pages 25-36 RelatedInformation