Presented at EDAMBA summer school, Soréze (France) 23 July – 27 July 2009

Data Sourcing, Statistical Processing and Time Series Analysis

An Example from Research into Hedge Fund Investments

Presenter: University: Supervisor: Research Title: Contact: Florian Boehlandt University of Stellenbosch – Business School Prof Eon Smit Prof Niel Krige

Pricing hedge funds a.k.a.

The sustainability of parametric and semi parametric pricing models as estimators of hedge fund performance

[email protected]

‘In the business world, the rearview mirror is always clearer than the windshield’

- Warren Buffett -

Research Purpose

1. Developing accurate parametric pricing models for hedge funds and fund of hedge funds 2. Accounting for the special statistical properties of alternative investment funds 3. Providing practitioners and statisticians with a framework to assess, categorize and predict hedge fund investments

Research Approach

Research Philosophy Logical-positivistic, deductive research: Postulation of hypotheses that are tested via standard statistical procedures Research Approach Empirical analysis: Interpreting the quality of pricing models on the basis of historical data Data Sourcing External secondary data: Historic time series adjusted for data-bias effects

Data Sourcing

Data Sources Hedge Fund Databases Financial Databases Risk Simulation Monte Carlo (Solver) Confidence (RiskSim) CISDM/MAR DATA POOL

DATA POOL

Data Treatment

Data Treatment Risk Simulation Statistical Processing Excel / VBA Statistica EViews FACTOR ANALYSIS STATISTICAL CLUSTERING MODEL BUILDING

Data Import

Excel Pivot table report Access Database

Information • Code • Fund (Name) • Main Strategy Performance • MM_DD_YYYY (Date) • Yield • Ptype (ROI or AUM) System Information • Leverage (Yes/No)

Database Management

• • • • • • • Avoiding duplicate entries Cross-referencing data from various sources Combining and aggregating different databases Efficient storage due to relational data management Queries allow for retrieval/display of specific data Linked-in with Microsoft VBA and Excel (data displayable as Pivot table reports) Searching for specific entries via SQL

Survivorship Self Selection Database Instant History Look-ahead

Data Bias

Inclusion of graveyard funds Multiple databases Rolling-window observation / Incubation period

Statistical tests for TSA

• • • • • Regression Statistics (Alpha, Average Error term, Information 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)

Prediction Models

AR ARMA ARIMA GLS Univariate Multivariate PCA Prediction Models Polynomial Fitting Constrained Simulation Taylor Series Lagrange Higher Co Moments KKT Conditional

Literature Review

• • • Hedge Fund Linear Pricing Models – Sharpe 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)

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