Neural Networks for Technical Forecasting of Foreign Exchange Rates

2002 ◽  
pp. 189-204
Author(s):  
Jing Tao Yao ◽  
Chew Lim Tan
Keyword(s):  
Neural Networks ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Time Series Data ◽  
Series Data ◽  
Technical Indicators ◽  
Currency Exchange ◽  
The Neural Network ◽  
Out Of Sample ◽  
Japanese Yen

This chapter describes the application of neural networks in foreign exchange rate forecasting between American dollar and five other major currencies: Japanese yen, Deutsch mark, British pound, Swiss franc and Australian dollar. Technical indicators and time series data are fed to neural networks to mine, or discover, the underlying “rules” of the movement in currency exchange rates. The results presented in this chapter show that without the use of extensive market data or knowledge, useful prediction can be made and significant paper profit can be achieved for out-of-sample data with simple technical indicators. The neural-network-based forecasting is also shown to compare favorably with the traditional statistical approach.

Time Series Data Analysis by Ultra-High Frequency Trigonometric Higher Order Neural Networks

2021 ◽  
pp. 218-261
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exchange Rates ◽  
High Frequency ◽  
Time Series Data ◽  
Higher Order ◽  
Series Data ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks ◽  
Time Series Data Analysis

This chapter develops a new nonlinear model, ultra high frequency trigonometric higher order neural networks (UTHONN) for time series data analysis. UTHONN includes three models: UCSHONN (ultra high frequency sine and cosine higher order neural networks) models, UCCHONN (ultra high frequency cosine and cosine higher order neural networks) models, and USSHONN (ultra high frequency sine and sine higher order neural networks) models. Results show that UTHONN models are 3 to 12% better than equilibrium real exchange rates (ERER) model, and 4–9% better than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models. This study also uses UTHONN models to simulate foreign exchange rates and consumer price index with error approaching 10-6.

scholarly journals Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Narayanan Manikandan ◽  
Srinivasan Subha
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Exchange Rates ◽  
Stock Prices ◽  
Time Series Data ◽  
Financial Analysis ◽  
Parallel Implementation ◽  
Series Data ◽  
Artificial Neural

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

scholarly journals Pengaruh Produk Domestik Bruto, Ekspor Dan Kurs Terhadap Cadangan Devisa Di Indonesia

2020 ◽  
Vol 4 (2) ◽  
pp. 301-313
Author(s):  
Fuji Astuty
Keyword(s):  
Gross Domestic Product ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Time Series Data ◽  
Domestic Product ◽  
Series Data ◽  
Foreign Exchange Reserves ◽  
Federal Reserve Bank ◽  
Reserve Bank ◽  
Analysis Technique

This study aims to analyze the effect of gross domestic product, exports and exchange rate on foreign exchange reserves in Indonesia. This research is in the form of quantitative based on quantitative data and is associative to see the relationship between variables or more. The data used is time series data from 2001 to 2018 using Eviews 9.0. And sourced from Bank Indonesia, the Central Bureau of statistics and the Federal Reserve Bank of St. Louis. This research uses data analysis technique is multiple linear analysis. The results showed that the variables of gross domestic product, exports and exchange rates have a positive and significant effect on Indonesia’s foreign exchange reserve. The R-square value in this study is 95.36, indicating that 95,36% of the variation in foreign exchange reserves can be explained by the gross domestic product, exchange rates and exports, while the remaining 4.64% is explained by other variables outside of this research model

Ultra High Frequency Trigonometric Higher Order Neural Networks for Time Series Data Analysis

2009 ◽  
pp. 133-163 ◽  
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Data Analysis ◽  
Exchange Rates ◽  
Time Series Data ◽  
Higher Order ◽  
Series Data ◽  
Higher Order Neural Networks ◽  
Time Series Data Analysis

This chapter develops a new nonlinear model, Ultra high frequency Trigonometric Higher Order Neural Networks (UTHONN), for time series data analysis. Results show that UTHONN models are 3 to 12% better than Equilibrium Real Exchange Rates (ERER) model, and 4 – 9% better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models. This study also uses UTHONN models to simulate foreign exchange rates and consumer price index with error approaching 0.0000%.

scholarly journals Foreign Trade-Foreign Exchange Nexus in Nigeria: A Vector Error Correction Modelling Approach

2016 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Olushina Olawale Awe ◽  
Damola M. Akinlana ◽  
Sherifat Omolola Adesunkanmi
Keyword(s):  
Exchange Rates ◽  
Foreign Trade ◽  
Foreign Exchange ◽  
Time Series Data ◽  
Series Data ◽  
Vector Error Correction ◽  
Long Run ◽  
Integration Test ◽  
Annual Time Series ◽  
Trade And Exchange

This study investigates trade foreign exchange nexus in Nigeria. This study is also done with a view to detecting the kind of relationship that exists between the two and also to investigate their co-integration. Annual time series data for the period 1996 – 2010 was used for the study. The Vector Correction Model (VECM) approach was employed to determine both the short and long run relationships. Results showed that the series becomes stationary after second difference. The co – integration test reveals five co – integrating vectors in the model, implying that the variables have the same stochastic drift. The study concludes that a long-term relationship exists between foreign trade and exchange rates implying that foreign trade flows have a strong link with exchange rates in Nigeria.

SHORT-TERM TREND PREDICTION OF FOREIGN EXCHANGE RATES WITH A NEURAL-NETWORK BASED ENSEMBLE OF FINANCIAL TECHNICAL INDICATORS

2013 ◽  
Vol 22 (03) ◽  
pp. 1350016 ◽  
Author(s):  
THEODOROS ZAFEIRIOU ◽  
DIMITRIS KALLES
Keyword(s):  
Neural Network ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Short Term ◽  
Trend Prediction ◽  
Technical Indicators ◽  
The Neural Network ◽  
Exchange Markets ◽  
Short Term Forecasting ◽  
One Year

This paper is about designing, developing and training a neural network for short-term forecasting of buy-sell trends in foreign exchange markets. We use a set of established financial technical indicators as inputs to the neural network and we develop the architecture to predict a trend and then train the network based on the accuracy of the prediction. We perform extensive real time testing with the closing prices (one per minute) of the USD/EUR exchange rates for a one-year period. The overall approach delivers a system that predicts trends substantially better than individual technical indicators.

CURRENCY EXCHANGE RATE PREDICTION TECHNIQUE BY FUZZY INFERENCING ON THE CHAOTIC NATURE OF TIME SERIES DATA

1996 ◽  
Vol 04 (05) ◽  
pp. 431-448 ◽  
Author(s):  
SABYASACHI GHOSHRAY
Keyword(s):  
Time Series ◽  
Dynamic System ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Time Series Data ◽  
Point Of View ◽  
Series Data ◽  
Dimensional Euclidean Space ◽  
Reconstruction Method ◽  
Foreign Exchange Rates

Predicting foreign exchange rates and stock market indices have been a well researched topic in the field of financial engineering. However, most methods suffer from serious drawback due to the inherent uncertainty in the data acquisition process. Here, we have analyzed the very nature of the time series data from a pure dynamic system point of view and explored the deterministic chaotic characteristic in it. In this research, the concept of chaos has been analyzed thoroughly and the relationships among chaos, stability and order have been explained with respect to the concept of time. A method of predicting time series data based on deterministic dynamically system has been presented in this monograph. The present research revolves around the concepts of embedding and fuzzy reconstruction. In this regard, the necessary and sufficient condition for this reconstruction of the state space of the dynamic system in a multi-dimensional Euclidean space has been substantiated in accordance to Theory of embedding. Finally, a fuzzy reconstruction method based on fuzzy multiple regression analysis method has been used to predict the foreign exchange rates with accuracy.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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