Reference no: EM132543061

25936 Funds Management - University of Technology Sydney

Overview: The aim of the assignment in Funds Management (25936) is to:

a. make sure that you don't get out of this course without knowing how to estimate fund performance based on return-based models (RBM) and holdings-based models (HBM);

b. make sure that you are comfortable downloading and organising the basic data from: (a) CRSP Mutual Funds Survivorship Bias Free Database (MFDB), (b) CRSP Security Prices and (c) Compustat;

c. get you looking at real data before you actually have to produce a paper if you would like to investigate empirically your research proposal in Funds Management;

d. lower the bar to doing empirical work by giving you a starter series of Python codes that can be modified, augmented, and expanded in the future to suit your needs.

The assignment will consequently require a bit of coding and data analysis, but will provide you with very valuable training on how to use the most advanced and up-to-date methods of estimating fund performance.

Assignment: You have been asked to evaluate the empirical performance of ten mutual fund portfolios using:

1. the holdings-based performance measure proposed by Grinblatt and Titman (1993) - hereafter GT1993.1 After computing the GT1993 measure, draw your conclusions on the performance estimates

2. the Characteristic Selectivity (CS) measure proposed by Daniel, Grinblatt, Titman, and Wermers (1997) - hereafter DGTW1997.

3. the fund performance based on the four-factor risk-adjusted model of Carhart (1997). To this end, you can use an estimation window of 36 months, with a minimum of 30 months of valid portfolio returns in the estimation window.

After computing all these performance measures, you have been asked to:

i. Compare the estimated risk-adjusted performance measure of the Carhart (1997) model with the CS performance measure of DGTW (1997). Please explain any difference in performance between these two measures and highlight possible limitations of these approaches, if any.

ii. Relate the outcomes of your performance estimation with the fee-setting policies of the ten funds considered, and ultimately draw conclusions on whether investors are paying a fair price.

iii. Prepare a report containing the details of the steps followed when downloading the data, matching different datasets, and constructing the performance measures. Your report does not need professional editing but it should still look and feel professional. In addition, your report should be accompanied by the Python (or Matlab) codes with appropriate commentary to allow your colleagues to easily understand and validate the logic and accuracy of your calculations.

Data sample: You are required to download data for a selection of ten fund portfolios from the CRSP MFDB. Please collate the data in the following text files, for replicability:

a. FUND_DATA_1: this text file should contain the portfolio holdings of your ten mutual fund portfolios identified by their unique portfolio identifier (Portno) including their market value and number of share of the securities held, the CRSP Permno identifying each security holding (i.e., CRSP's Permanent Stock Issue Identifier), security CUSIP, the official ticker of each security holding, and the portfolio weight of each security (i.e., security's percentage of the total net assets). The sample period of your data extraction should be from 1 Jan 2010 to 31 Dec 2019.

b. FUND_DATA_2: this text file should contain monthly information on total net assets, after-fee returns, and total operating expenses of the 10 mutual fund portfolios. Since these characteristics can only be extracted by a fund's Fundno, you need to use the file Fund-Portfolio Map in CRSP MFDB to match the 10 mutual fund Portno in FUND_DATA_1 with their associated Fundno in FUND_DATA_2. The sample period of your data extraction should be from 1 Jan 2010 to 31 Dec 2019;

To facilitate your task, you can use the Python code DGTW_returns.py available in the folder of Lecture 4 on UTS Online. This code first downloads data from Compustat and CRSP, and then constructs the size, book-to-market, and momentum controlled value-weighted characteristics-based benchmark returns as proposed by DGTW1997. Please make sure to restrict the sample period to the interval from 1 Jan 2010 to 31 Dec 2019 when using this code.

APA referencing

Attachment:- Funds Management.rar