Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/998
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dc.contributor.advisorMajumder, Prasenjit
dc.contributor.authorSoni, Maulik
dc.date.accessioned2022-05-06T05:33:26Z
dc.date.available2023-02-19T05:33:26Z
dc.date.issued2021
dc.identifier.citationSoni, Maulik (2021). Algorithmic Trading using Machine Learning. Dhirubhai Ambani Institute of Information and Communication Technology. vii, 35 p. (Acc.No: T00938)
dc.identifier.urihttp://drsr.daiict.ac.in//handle/123456789/998
dc.description.abstractThis works aims to understand the different factors which influence the stock market and further use those factors to forecast its future values. The Nifty50 and NiftyBANK under National Stock Exchange India(NSE India) are considered the target indexes in this research. The parameters which affects the Stock Market are diverse in nature which mainly categorise in two types: Trend in historical stock prices and events happening around the world available in unstructured format. The later part generally involves like social blogs, news etc, which can change the perspective of investors towards the Stock Market. Additionally, we have explore two different entities, Gold and Rupee-Dollar exchange, and tried to Anderstand how its impact the stock market. In addition to this, the Retrospective Analysis between the Stock Market and Nifty50 carried out which inspect the particular events responsible for changes happened in the Stock movement. As Machine Learning proves very successful in solving complex problems in past, we also have leveraged the advantage of popular algorithms/models to forecast the market.
dc.publisherDhirubhai Ambani Institute of Information and Communication Technology
dc.subjectTime Series Analysis
dc.subjectStock Market Prediction
dc.subjectNeural Networks
dc.subjectFinancial Sentiment
dc.classification.ddc510 SON
dc.titleAlgorithmic Trading using Machine Learning
dc.typeDissertation
dc.degreeM. Tech
dc.student.id201911009
dc.accession.numberT00938
Appears in Collections:M Tech Dissertations

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