Presented at EDAMBA summer school Soreze France 23 July 27 July 2009 An Example from Research into Hedge Fund Investments Presenter Florian Boehlandt University University of ID: 495806
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Slide1
Data Sourcing, Statistical Processing and Time Series Analysis
Presented at EDAMBA summer school, Soreze (France) 23 July – 27 July 2009
An
Example from Research into Hedge Fund Investments Slide2
Presenter:
Florian
Boehlandt
University:
University of
Stellenbosch – Business School
Supervisor:
Prof Eon
Smit
Prof
Niel
Krige
Research
Title:
A Risk-Return Assessment of Fund of Hedge Funds in Comparison to Single Hedge Funds
– An Empirical Analysis
Contact:
14959747@sun.ac.zaSlide3
‘In the business world, the rearview mirror is always clearer than the windshield’
- Warren Buffett -Slide4
Research Purpose
Developing accurate parametric pricing models for hedge funds and fund of hedge fundsAccounting for the special statistical properties of alternative investment fundsProviding practitioners and statisticians with a framework to assess, categorize and predict hedge fund investmentsSlide5
Research Approach
Positivistic, deductive research:
Postulation of hypotheses that are tested via standard statistical procedures
Research Philosophy
Empirical analysis:
Interpreting the quality of pricing models on the basis of historical data
Research Approach
External secondary data:
Historic time series adjusted for data-bias effects
Primary DataSlide6
Data Sourcing
DATA POOLSlide7
FACTOR
ANALYSIS
Data Treatment
DATA POOL
MODEL
BUILDING
STATISTICAL CLUSTERINGSlide8
STATISTICAL SIGNIFICANCESlide9
Data Processing (1/2)Slide10
Data Processing (2/2)Slide11
Data Import
Access Database
Excel Pivot table reportSlide12
Access Database Management
Introduce Autonumber as primary keysDefine foreign keys for data queries
Define table relationships (one-to-many)Build junction tables (many-to-many)Write SQL queries to display relevant data
Integrate SQL in VBA codeSlide13
Why Access?
Avoiding duplicate entriesCross-referencing data from various sourcesCombining and aggregating different databasesEfficient storage due to relational data management
Queries allow for retrieval/display of specific dataLinked-in with Microsoft VBA and Excel (data displayable as Pivot table reports)Searching for specific entries via SQLSlide14
Data Validity
Consistency of performance history across different database providersDegree of history-backfilling biasExclusion of defaulted funds/non-reporting funds from databases (survivorship bias)
Extent of infrequent or inconsistent pricing of assets (managerial bias)Slide15
Data Bias
Survivorship
Self-Selection
Database
Instant History
Look-ahead
Inclusion of graveyard funds
Multiple databases
Rolling-window observation / Incubation periodSlide16
Hedge Fund Categories
(TASS)Slide17
Statistical tests
Regression AlphaAverage Error termInformation Ratio
Normality (Chi-squared, Jarque Bera)
Goodness of fit, phase-locking and
collinearity
(
Akaike
Information Criterion,
Hannan
-Schwartz)
Serial Correlation (Durbin-Watson, Portmanteau)
Non-
stationarity (unit root)Slide18
t – test (between
strategies)
Unbalanced
ANOVA (within
and between
treatments)
t – test (leverage
vs. no leverage)
t – test for
equal means
t – test for
equal means
t – test for
equal means
Comparative Analysis
Strategy 1
Leverage
Strategy 1
No Leverage
t – test for
equal means
Strategy 2
Leverage
Strategy 2
No LeverageSlide19
Empirical Findings
The accuracy of pricing models could be significantly improved when accounting for special statistical properties of hedge funds (Non-normality, non-linearity)Hedge fund performance can be attributed to location choice as well as trading strategyA limited number of principal components explains a significant proportion of cross-sectional return variationSlide20
Literature Review
Hedge Fund Linear Pricing ModelsSharpe Factor Model (Sharpe, 1992)Constrained Regression (Otten, 2000)Fama-French Factor Model (
Fama, 1992)Factor Component Analysis (Fung, 1997)Simulation of Trading component (lookback straddle)Slide21
Prediction
ModelsSlide22
Sources
Fama
, E.F. & French, K.R. 1992. The Cross-Section of Expected Stock Returns.
Journal of Finance
,
47
(2), June, 427-465. [Online] Available:
http://links.jstor.org/sici?sici=0022-1082%28199206%2947%3A2%3C427%3ATCOESR%3E2.0.CO%3B2-N
Fung, W. & Hsieh, D.A. 1997. Empirical characteristics of dynamic trading strategies: the case of hedge funds.
Review of Financial Studies
,
10
(2), Summer, 275-302. [Online] Available:
http://faculty.fuqua.duke.edu/~dah7/rfs1997.pdf
Otten
, R. &
Bams
, D. 2000.
Statistical Tests for Return-Based Style Analysis
. Paper delivered at EFMA 2001 Lugano Meetings, July. [Online] Available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=277688
Sharpe, W.F. 1992. Asset allocation: management style and performance measurement.
Journal of Portfolio Management, Winter, 7-19. [Online] Available:
www.uic.edu/classes/fin/fin512/Articles/sharpe.pdf