Expert system for portfolio management based on technical indicators using ANN

dc.contributor.advisorPal, A.K.
dc.contributor.authorPandey, Ankita
dc.date.accessioned2019-01-05T05:55:45Z
dc.date.available2019-01-05T05:55:45Z
dc.date.issued2018-06
dc.description.abstractPresent work entitled “EXPERT SYSTEM FOR PORTFOLIO MANAGEMENT BASED ON TECHNICAL INDICATORS USING ANN” treated the issue of developing an ANN model which generates future values and portfolio system based on technical indicators. Artificial neural networks are very good in the stock market prediction as they non linear and complex models. In this work, we have used the artificial neural network to predict the stock prices and on the bases of their prediction we generate a portfolio system. The presented study comprises five chapters. The first chapter is aimed to fulfil the basic need of introducing artificial neural network, time series, portfolio and the use of neural network in portfolio management. Chapter two accomplished majority of passed research work related with present work. Chapter three covers the material and methods used for the present study. We have presented the neural network for the prediction and simulated it in MATLAB software. Chapter four describes the result and discussion of the encountered taken in chapter three. The work has been summarized in chapter five. Along with the finding of the study, the literature used in the course of the study has been referred under the section of literature cited. The related study may be helpful for the investors of stock market to make wise decisions so that they can have maximum profit.en_US
dc.identifier.urihttp://krishikosh.egranth.ac.in/handle/1/5810089039
dc.keywordsneural networksen_US
dc.language.isoenen_US
dc.pages84en_US
dc.publisherG.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)en_US
dc.research.problemExpert Systemen_US
dc.subMathematicsen_US
dc.subjectnullen_US
dc.themeNeural Networksen_US
dc.these.typeM.Scen_US
dc.titleExpert system for portfolio management based on technical indicators using ANNen_US
dc.typeThesisen_US
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