Welcome to the upgraded BRAC University Institutional Repository. We are currently organizing collections after a recent system upgrade. Homepage category counters may temporarily show lower numbers while syncing, but over 27,000 repository items remain safe and accessible. Please use the search bar to find theses, scholarly outputs, and institutional documents.

Advanced portfolio management using markowitz portfolio theory

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorMajumdar, Mahbubul Alam
dc.contributor.authorKundu, Souvik
dc.contributor.authorKhan, Mustaqim
dc.contributor.authorRahman, Faisal
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2020-08-19T15:35:33Z
dc.date.available2020-08-19T15:35:33Z
dc.date.copyright2019
dc.date.issued2019-12
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 30).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.en_US
dc.description.abstractA portfolio is a collection of stocks made from different companies, the number of stocks can range from 10 to 30 depending on expected return by the investors. Portfolio management is finding the right group of stocks to invest in with detailed risk and return assessment. Finding the right combination is easier said than done, we opted to work with S&P 500 data set. We filter the data set picking the top 10 stocks from each sector based on different criteria, using Markowitz portfolio theory, we generate random portfolios and compare between them on the basis of Voaltility and Sharpe ratio, a ratio generated from return and risk. When plotting all the portfolios a curve is generated called the efficient frontier from which we can select an optimum portfolio based on volatility and return. We then compare our generated portfolio which is dynamic based on the requirements by looking at the most recent stock market data and determine the accuracy for future prediction.en_US
dc.description.degreeBachelor of Science in Computer Science
dc.description.statementofresponsibilitySouvik Kundu
dc.description.statementofresponsibilityMustaqim Khan
dc.description.statementofresponsibilityFaisal Rahman
dc.format.extent30 pages
dc.identifier.otherID 19141015
dc.identifier.otherID 19141013
dc.identifier.otherID 19141014
dc.identifier.urihttp://hdl.handle.net/10361/13987
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectPortfolio managementen_US
dc.subjectS&P 500en_US
dc.subjectMarkowitz modelen_US
dc.subjectSharpe ratioen_US
dc.subjectEfficient frontieren_US
dc.subjectVolatilityen_US
dc.subjectExpected returnen_US
dc.titleAdvanced portfolio management using markowitz portfolio theoryen_US
dc.typeThesisen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
19141015,19141013,19141014_CSE.pdf
Size:
543.02 KB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: