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Tracking Error of Passive Equity Funds. Data Analysis Using Morningstar Financial Data

Leone, Stefano (2021) Tracking Error of Passive Equity Funds. Data Analysis Using Morningstar Financial Data. Masters thesis, Dublin, National College of Ireland.

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This data analytics project examines the financial data publicly available on Morningstar for index mutual funds and Exchange-Traded Funds (ETFs), aiming to identify the key aspects that make them successful in replicating the return of the indices they track. The gap between fund’s and index’s returns is called “tracking error”, a metric frequently investigated in the past using small samples of funds along with their indices, and that often resulted in contradictory conclusions regarding the impact of several fund aspects such as management fees, exit fees, number of portfolio securities, asset size, and fund age.

The innovative elements of this analysis regard the larger financial data – retrieved using the Morningstar Rest API – and the Extract, Transform, Load (ETL) process designed to scrape the funds information along with their prices and the prices related to their indices, which allow to calculate the tracking error metric that is eventually used by machine learning models for the identification of its driving factors.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
H Social Sciences > HG Finance > Investment > Investment Companies. Investment Trusts. Mutual Funds.
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 07 Dec 2021 16:27
Last Modified: 07 Dec 2021 16:27

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