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Fibonacci retracement pattern recognition for forecasting GBP/USD foreign exchange market

Master of Science in Engineering Mathematics

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Bibliographic Details
Main Author: Mohd Fauzi, Ramli
Other Authors: Ahmad Kadri, Junoh, Dr.
Format: Dissertation
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2017
Subjects:
Fibonacci numbers
Elliott wave principle
Foreign exchange
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77633
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spelling my.unimap-776332023-01-10T04:58:14Z Fibonacci retracement pattern recognition for forecasting GBP/USD foreign exchange market Mohd Fauzi, Ramli Ahmad Kadri, Junoh, Dr. Fibonacci numbers Elliott wave principle Foreign exchange Master of Science in Engineering Mathematics This study presents an approach to the Fibonacci retracement implicates a forecast of future movements in foreign exchange rates (forex) of the previous movement inductive analysis. The forex market is one of the utmost intricate markets through the characteristics of high volatility, nonlinearity and irregularity. Meantime, these characteristics also make it very difficult to forecast forex. The problem are contain pattern recognition, classification and forecasting. The research objectives are to recognize the pattern using the Elliott wave pattern, to compare accuracy patterns classification between K - Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) and to forecast short term forex market using Fibonacci retracement method. The results show two different type of trend patterns which are uptrend and downtrend. KNearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) algorithm are the general pattern recognition method for nonlinearly feature mining from high dimensional input Elliott wave patterns. Results show that LDA is better than KNN in terms of classification accuracy data which are 99.43%. Technical analysis by using Fibonacci retracements for forecasting will be through after the trends of pattern were recognise. The market trend upward or downward will have a retracement wave before the next impulse wave approaches new region. Fibonacci price retracements are determined from a previous low to high swing to identify potential support levels as the market pulls back from a high. Retracements are also run from a previous high to low swing using the same ratios, looking for probable resistance levels as the market reverse from a low. After a significant price movement up or down, the new support and resistance levels are often at or near these retracement lines. Among of three levels of Fibonacci retracement which are 38.2%, 50.0% and 61.8% results, the 38.2% shows the best forecasting for Great Britain Pound pair to US Dollar currency as major pair by using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (r) as the statistical measurements which are 0.001884, 0.000019 and 0.992253 for uptrend and 0.001685, 0.000019 and 0.998806 for downtrend. As conclusion, 38.2% is the best Fibonacci retracement level to forecast forex market for uptrend and downtrend. 2017 2023-01-10T04:58:14Z 2023-01-10T04:58:14Z Dissertation http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77633 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) Institute of Engineering Mathematics
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Fibonacci numbers
Elliott wave principle
Foreign exchange
spellingShingle Fibonacci numbers
Elliott wave principle
Foreign exchange
Mohd Fauzi, Ramli
Fibonacci retracement pattern recognition for forecasting GBP/USD foreign exchange market
description Master of Science in Engineering Mathematics
author2 Ahmad Kadri, Junoh, Dr.
author_facet Ahmad Kadri, Junoh, Dr.
Mohd Fauzi, Ramli
format Dissertation
author Mohd Fauzi, Ramli
author_sort Mohd Fauzi, Ramli
title Fibonacci retracement pattern recognition for forecasting GBP/USD foreign exchange market
title_short Fibonacci retracement pattern recognition for forecasting GBP/USD foreign exchange market
title_full Fibonacci retracement pattern recognition for forecasting GBP/USD foreign exchange market
title_fullStr Fibonacci retracement pattern recognition for forecasting GBP/USD foreign exchange market
title_full_unstemmed Fibonacci retracement pattern recognition for forecasting GBP/USD foreign exchange market
title_sort fibonacci retracement pattern recognition for forecasting gbp/usd foreign exchange market
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2017
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77633
_version_ 1772813078959751168
score 13.232683

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