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Artificial Neural Network Based Decisive Prediction Models on High Frequency Financial Data

BROWSE_DETAIL_CREATION_DATE: 23-03-2017

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: EngD

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: Economic growth, development, planning, Management. Industrial management, Finance management. Business finance.,

BROWSE_DETAIL_CREATORS: Karaçor, Adil Gürsel (Author),

BROWSE_DETAIL_CONTRIBUTERS: Erkan, Turan Erman (Advisor),

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Predictability, Artificial Neural Network, quantitative analysis, real-time trading robot, foreign exchange currency


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High frequency financial data are somewhat hard to model or predict, if not totally impossible, as stochastic processes and many other random factors are involved. In this thesis; a novel Artificial Intelligence model is designed and developed for financial time series prediction and decision making. Possibility to enhance prediction accuracy for foreign exchange rates is investigated in two ways: first applying an outside the box approach by bringing about methodology and techniques to facilitate the use of predictive models in engineering design to model price graphs by exploiting their visual properties together with principles of chaos theory, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs makes use of density regions along with high and low values describing the shape just as in Machine Vision. Mainly Artificial Neural Networks are used in modeling. However, other state-of-the-art methods; Extreme Gradient Boosting and Support Vector Machine are too used for comparison. The designed system is also software coded as a real-time trading robot. Comparable prediction results and profits are achieved in tests and simulations.


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