Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/221888
Title: Stock Price Direction Prediction Applying Soft Computing
Researcher: PANCHAL AMITKUMAR MANSUKHBHAI
Guide(s): JAYESH M PATEL
Keywords: Engineering and Technology
University: Gujarat Technological University
Completed Date: 01-11-2018
Abstract: The stock market is a complex, non-linear, non-stationary, chaotic and dynamic system. This research work proves that properly tuned artificial neural network model can have the capability to efficiently solve non-linear time-series such as stock market indices. This research work proposes Generalized Stock Direction Prediction Model (GSDPM) that will be applicable to any stock market index and/or stock securities. The design of the model is simple, which will give an advantage to other researchers for easily developing their own model. The robustness of the GSDPM model s design is confirmed by testing three different network architectures; a Feed Forward Backpropagation Neural Network, Elman Backpropagation Neural Network, and Cascade-Forward Backpropagation Neural Network. The ten reputed benchmark indices are selected as stock database across the globe for analyzed the prediction performance of the GSDPM model. The proposed research work follows Buy/Sell trading strategy instead of Buy-and-Hold trading strategy. The output of the model converted to the percentage prediction and percentage rate of return for the testing dataset. The prediction performance of GSDPM model has compared in terms of the percentage prediction, the percentage rate of return, against respective index rate of return and against randomly generated trading signals. The result obtains from various experiments shows that the GSDPM model has the capability to effectively predict next day direction of the stock time series. newline newline
Pagination: 107
URI: http://hdl.handle.net/10603/221888
Appears in Departments:Computer/IT Engineering

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02_certificates.pdf27.42 kBAdobe PDFView/Open
03_abstract.pdf137.56 kBAdobe PDFView/Open
04_declaration.pdf37.06 kBAdobe PDFView/Open
05_acknowledgements.pdf15.69 kBAdobe PDFView/Open
06_contents.pdf23.43 kBAdobe PDFView/Open
07_list_of_tables.pdf44.8 kBAdobe PDFView/Open
08_list_of_figures.pdf54.35 kBAdobe PDFView/Open
09_abbreviations.pdf16.68 kBAdobe PDFView/Open
10_chapter1.pdf160.23 kBAdobe PDFView/Open
11_chapter2.pdf451.79 kBAdobe PDFView/Open
12_chapter3.pdf130.29 kBAdobe PDFView/Open
13_chapter4.pdf190.71 kBAdobe PDFView/Open
14_chapter5.pdf626.71 kBAdobe PDFView/Open
15_chapter6.pdf298.13 kBAdobe PDFView/Open
16_chapter7.pdf49.49 kBAdobe PDFView/Open


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