HFMRD: Hedge Funds Misreported Returns Detector

A framework for detecting misreported returns in hedge funds.

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Updated 25 Aug 2019

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HFMRD: Hedge Funds Misreported Returns Detector

This script represents a full-featured framework for detecting misreported returns in hedge funds through the following tests:

They are all based on the normality assumption of returns.

Requirements

The minimum Matlab version required is R2014a. In addition, the following products and toolboxes must be installed in order to properly execute the script:

  • Financial Toolbox
  • Image Processing Toolbox
  • Mapping Toolbox
  • Statistics and Machine Learning Toolbox

Usage

  1. Create a properly structured database (see the paragraph below).
  2. Edit the run.m script following your needs.
  3. Execute the run.m script.

The Test Results plot created by the plot_results function is interactive and based on a singleton pattern. Detailed plots concerning a specific test for a specific hedge fund can be displayed by clicking on the corresponding table cell.

Dataset

Datasets must be built following the structure of the default one included in every release of the framework (Datasets/Example.xlsx). The latter, based on the US financial sector, defines the following entities:

Benchmark (BM) & Risk-Free Rate (RF)

The benchmark is represented by the market proxy defined in Fama & French (1993): the value-weighted returns of all the US CRSP firms listed on AMEX, NASDAQ or NYSE that have a CRSP share code of 10 or 11 at the beginning of month t, good shares and price data at the beginning of t, and good return data for t. The 1M treasury bill rate is taken as the risk-free rate.

Hedge Funds (3):

  • The Growth Fund of America - Class A (AGTHX)
  • The Gateway Fund - Class A (GATEX)
  • The Fairfield Sentry Fund of Bernard Madoff (SENTRY)

Style Factors (18):

  • MRKEXC: the excess return on the market, automatically calculated as benchmark minus risk-free rate.
  • Fama & French 5 Factors from the French Data Library (10)
    • CMA: conservative minus aggressive, the average return on two conservative investment portfolios minus the average return on two aggressive investment portfolios.
    • HML: high minus slow, the average return on two value portfolios minus the average return on two growth portfolios.
    • MF: the momentum factor, the average return on two high prior return portfolios minus the average return on two low prior return portfolios.
    • RMW: robust minus weak, the average return on two robust operating profitability portfolios minus the average return on two weak operating profitability portfolios.
    • SMB: small minus big, the average return on nine small stock portfolios minus the average return on nine big stock portfolios.
    • The squared values of the above factors, proposed by Bollen & Pool to capture nonlinearities in exposure generated by dynamic trading and/or derivatives.
  • Fung & Hsieh Trend-following Factors from the Hsieh Website (7)
    • PTFSBD: the returns of a portfolio of options on bonds, based on a primitive trend-following strategy.
    • PTFSFX: the returns of a portfolio of options on foreign currencies, based on a primitive trend-following strategy.
    • PTFSCO: the returns of a portfolio of options on commodities, based on a primitive trend-following strategy.
    • PTFSIR: the returns of a portfolio of options on short−term interest rates, based on a primitive trend-following strategy.
    • PTFSST: the returns of a portfolio of options on stock indices, based on a primitive trend-following strategy.
    • TBR10Y: the 10Y treasury bond rate.
    • CRESPR: the change in the credit spread (the BAA corporate bond rate minus the 10Y treasury bond rate).

Notes

  • Financial time series must contain a benchmark index, the risk-free rate, the returns of at least 3 hedge funds and at least 3 style factors. They must have a monthly frequency and contain enough observations to run consistent calculations (the minimum required amount is 126, which translates into half of a business year). They must have been previously validated and preprocessed by removing rows with NaNs or filling the gaps through interpolation.

  • Groups are optional. If the sheet is omitted, all the hedge funds in the dataset are all assigned to the same style; otherwise a maximum of 10 groups can be defined. Groups are based on indices and every hedge fund must be assigned to a specific group through an integer value. For example, the following groups definition:

    Hedge Funds in the Returns Sheet: A, B, C, D, E, F, G, H
    Group Indices: 1, 2, 2, 1, 1, 2, 3, 3

    produces the following outcome:

    Group 1 contains A, D and E
    Group 2 contains B, C and F
    Group 3 contains G and H

Screenshots

Interactive Test Results

Low Correlation Test

Digits Conformity Test

Data Quality Test

Cite As

Tommaso Belluzzo (2023). HFMRD: Hedge Funds Misreported Returns Detector (https://github.com/TommasoBelluzzo/HFMRD), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2018a
Compatible with R2014b to R2022b
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes
1.3.0

GitHub Version

1.2.0

Minor fixes and improvements.

1.1.9

Minor fixes and improvements.

1.1.8

Changed image.

1.1.7

Minor fixes and improvements.

1.1.6

Minor fixes and improvements.

1.1.5

Minor fixes and improvements.

1.1.4

Updated details concerning compatibility & requirements.

1.1.3

Updated details concerning compatibility & requirements.

1.1.2

Minor fixes and improvements.

1.1.1

Project website.

1.1.0

Target release.

1.0.9

Improved tags.

1.0.8

Improved tags.

1.0.7

Improved tags.

1.0.6

Improved description.

1.0.5

Improved description.

1.0.4

Improved description.

1.0.3

Added screenshot.

1.0.2

Minor fixes and improvements.

1.0.1

Added details concerning compatibility & requirements.

1.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.