A model for forex market price prediction: case of Central Bank of Kenya

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dc.contributor.authorMakiya, David Nyangau
dc.date.accessioned2021-08-06T09:27:50Z
dc.date.available2021-08-06T09:27:50Z
dc.date.issued2020
dc.descriptionA Thesis Submitted to the Faculty of Information Technology in partial fulfillment of the requirements for the award of a degree in Masters of Science in Information Technologyen_US
dc.description.abstractForex markets are full of uncertainties. The forces of demand and supply determine the price of the Kenyan shilling in the international market. The rates usually adjust depending on the prevailing status of the economy, politics and influences of the Central Bank of Kenya (CBK) policies. Forex dealers are the dominant operators within the Kenyan forex market with forex bureaus and commercial banks taking the lead amongst them. A bureau would have a different pricing of a currency against the shilling but would nonetheless be within the bid-ask (buy sell) spread of the CBK ratings. Various online forex trading platforms have been implemented to facilitate trade in the Kenyan market. Predicting forex market prices is quite complicated as a process and subjective in nature for forex dealers, economists and business persons. The potential to make loses due to poor speculative guesses is quite high for multinational organizations located in more than one economy. The aim of this study is to develop a model for forex market price prediction in the Kenyan market using the Central Bank of Kenya data. Using the Data-Driven modelling technique, a model for forex market price prediction has been developed based on historical data from the CBK. The dataset is divided into training and testing data by a splitting of 80-20 respectively. The unique behavior of each of the currency data necessitated separate implementation of the currencies on the model for increased accuracy and lower error levels hence efficiency and optimality. The prediction model is achieved by combining time series analytical techniques with resilient backpropagation neural network. Successful predictions are conducted of up-to eight months forward with accuracy levels ranging 88-98% and Sum of squared residual (SSE) of 0.496-2.667, hence showing that combining time series analytics to resilient backpropagation neural networks to create a forex market prediction model with unique implementation of each currency is optimal for forex market price prediction where more data depicts longer period predictions.en_US
dc.identifier.urihttp://hdl.handle.net/11071/12088
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectForex Market Priceen_US
dc.subjectData-Driven Modellingen_US
dc.subjectTime Series Analyticsen_US
dc.subjectResilient Backpropagation Neural Networken_US
dc.titleA model for forex market price prediction: case of Central Bank of Kenyaen_US
dc.typeThesisen_US
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MSIT Theses and Dissertations (2020)