data sourcing, statistical processing and time series analysis
DESCRIPTION
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 . ‘In the business world, the rearview mirror is always clearer than the windshield’ - PowerPoint PPT PresentationTRANSCRIPT
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
Presenter: Florian BoehlandtUniversity: University of Stellenbosch – Business SchoolSupervisor: Prof Eon Smit
Prof Niel KrigeResearch Title: A Risk-Return Assessment of Fund of Hedge
Funds in Comparison to Single Hedge Funds – An Empirical Analysis
Contact: [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
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 Data
Data Sources
Hedge Fund Databases
CISDM/MAR
Financial Databases Risk Simulation
Monte Carlo (Solver)
Confidence (RiskSim)
Data Sourcing
DATA POOL
FACTOR ANALYSIS
Data Treatment
Risk Simulation Statistical Processing
Excel / VBA
Statistica
EViews
Data Treatment
DATA POOL
MODEL BUILDING
STATISTICAL CLUSTERING
STATISTICAL SIGNIFICANCE
Data Processing (1/2)
Data Import •Extract relevant data from Access (SQL)•Import data as Pivot table report
Data Treatment •Test for serial correlation /databias•Calculate adjusted excess returns
Data Analysis •Select funds with consistent data series •Determine statistical model
Data Processing (2/2)
Weighting •Estimate weighted average parameters•Construct style indices
Comparative Analysis •Calculate within-group variation•Calculate between-group variation
Data Output •Tabular display of aggregate results•Construction of line - bar charts
Data Import
•Code•Fund (Name)•Main Strategy
Information
•MM_DD_YYYY (Date)•Yield•Ptype (ROI or AUM)
Performance
•Leverage (Yes/No)System
Information
Access Database Excel Pivot table report
Access Database Management
1. Introduce Autonumber as primary keys2. Define foreign keys for data queries3. Define table relationships (one-to-many)4. Build junction tables (many-to-many)5. Write SQL queries to display relevant data6. Integrate SQL in VBA code
Why Access?
• 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
Data Validity
• Consistency of performance history across different database providers
• Degree of history-backfilling bias• Exclusion of defaulted funds/non-reporting
funds from databases (survivorship bias)• Extent of infrequent or inconsistent pricing of
assets (managerial bias)
Data Bias
Survivorship
Self-Selection
Database
Instant History
Look-ahead
Inclusion of graveyard funds
Multiple databases
Rolling-window observation / Incubation period
Hedge Fund Categories (TASS)
Categories
DirectionalDedicated
Short
Bias
Global Macro
Emerging Markets
Global Macro
Long /
Short Equity
Managed Futures Fund of Hedge Funds Market Neutral
Equity Market
Neutral
Event Driven
Event Driven
Convertible Arbitrage
Fixed Income
Arbitrage
Statistical tests
• Regression 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)
t – test (betweenstrategies)
UnbalancedANOVA (withinand betweentreatments)
t – test (leveragevs. no leverage)
t – test forequal means
t – test forequal means
t – test forequal means
Comparative Analysis
Strategy 1Leverage
Strategy 1No
Leverage
t – test forequal means
Strategy 2Leverage
Strategy 2No
Leverage
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 strategy
• A limited number of principal components explains a significant proportion of cross-sectional return variation
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)
Prediction Models
Prediction Models
AR
ARMA
ARIMA
GLS
Univariate
Multivariate
Conditional
PCA Polynomial Fitting
Taylor Series
Higher Co-Moments
Constrained
Lagrange
KKT
Simulation
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