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Regular version of the site

Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Nitin Harale
Decision Support System for Finance Industry based on Big Data Stream Analytics
Big Data Systems
(Master’s programme)
2017
Finance industry operates in various areas such as risk management, fraud detection, profit analysis, cash flow management, investment analysis and therefore it warrants a system where it could be plausible to have access to real time information from all the aspects. Financial big data streams are huge, complex and unstructured data sets that are quite difficult to manipulate and analyze in a real time. In this paper, machine learning based models are proposed for forecasting real time high frequency movement of stock prices and volatility. ML models, HAR and GARCH models have been tested on the high frequency stock data of company,‘Apple,’ and findings of this research suggest that the applied models give a relatively good prediciton results compared to SVM, Decision Trees, Random Forest. This thesis is primarily aimed at addresing the challenges in analysing high frequency financial data for finding a suitable analytical solution to create reliable decision support system for financial industries. Chosen financial big time series data had high dimensions. ML models are tested and their prediction performance is measured based on the predefined performance criteria to conclude on their suitability.

Key words- High frequency financial data, Machine Learning Methods, Decision support system, etc.

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