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UNSW-FINS3640-S2,2010-Week 4
 

UNSW-FINS3640-S2,2010-Week 4

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    UNSW-FINS3640-S2,2010-Week 4 UNSW-FINS3640-S2,2010-Week 4 Presentation Transcript

    • School of Banking and Finance www.banking.unsw.edu.au
    • FINS3640 - Investment Management Modeling
      Week 4 - 11 Aug 2010
      Introduction to using Stata
      for Financial Modeling
    • Revision
      Stata commands
      Regression and t-test interpretation
      R2
      P-value
      Hypothesis testing
      Null hypothesis H0
      Conditional testing
      Binary
      Visual plot analysis
      Price observation, holding period return, dividend and stock split adjustments
      WRDS
    • Multivariate Models
    • Capital Asset Pricing Model
      Market Premium
    • Fama-French Three-Factor Model
      Size Premium
      Value Premium
    • Four-Factor Model
      Momentum
      Momentum-Driven
      Portfolio Rebalancing
    • Five-Factor Model
      Asset Growth
    • Generic Multivariate Model
      This is what you need to build
    • How? Some hints:
      Read contemporary research (see suggested reading list)
      Retrieve & process data (in-class demonstration)
      Establish hypotheses (should be from a quantitative course)
      Test the hypotheses
      Correlation
      R2 & adjusted R2
      P-value
      t-value
      Other econometrics indicators (chi2, autocorrelation…)
      Build and optimise the model(s)
    • Modeling
    • Data Management
      Wharton WRDS
      French Data
      Stock selection
      Monthly
      5-30 years
    • Weighting
    • Risk-free Rate
      CAPM assumption: single risk-free rate
      Is this practically true?
    • Statistical Testing
      Multivariate regression
      t-test
      Visual analysis
    • Multicollinearity
      Variables are almost linearly dependent
      Large standard errors lead to insignificant t-values
      Little explanatory power
      Test Collinearity
      Correlation matrix
      Rotation of variables: R2 does not change much when one variable is dropped
      Remedy
      Drop collinear variable(s)
    • Outliers & Influential Points
      Extreme values
      Market crashes e.g. October 1987, PG 2010
      Market capitalisation of top firms from highly concentrated indices e.g. BHP from ASX
      Skews the model
      'Black swans'
      Identify: unusual observations on visual display
      Remedies
      Time series method: log normal
      Exclusion of influential points
    • Andersen T G, Bollerslev T, Diebold F X, Wu G, 2006, 'Realized Beta: Persistence and Predictability', Econometric Analysis of Financial and Economic Time Series, Advances in Econometrics, Volume 20, 1-39, Elsevier, 2006
    • Binary Variables
      Grouping
      by
    • Heteroskedasticity
      White 1980
      Run regression
      reg y x
      The Het test right after running regression
      hettest
      Save the residual
      predict res, r
      Plot the residuals
      plot res x
      Bibliograph
      http://www.polsci.wvu.edu/duval/ps602/Notes/STATA/heteroskedasticity.htm
      http://web.missouri.edu/~kolenikovs/stata/Duke/class3.html
      http://www.stata.com/support/faqs/stat/panel.html
    • Autocorrelation
      Autocorrelation
      ac
      Partial autocorrelation
      pac
      Q-statistics
      Correlogram
      corrgram
    • ARIMA
      Autoregressive integrated moving average
      arima
      predict
    • Reading
    • Reading (1)
      Required Reading
      Reeves J. J. 2008
      Recommended Reading
      Multivariate Models
      Louise Swift, Sally Piff, Quantitative Methods for Business, Management and Finance, 2nd edition
      John Y. Campbell, Yeung Lewis Chan, Luis M. Viceira, 2001, 'A multivariate model of strategic asset allocation'
      Alvin C. Rencher, 2002, Methods of multivariate analysis
    • Reading (2)
      Fama French
      Fama, Eugene F.; French, Kenneth R. (1993). "Common Risk Factors in the Returns on Stocks and Bonds". Journal of Financial Economics 33 (1): 3–56
      Fama, Eugene F.; French, Kenneth R. (1992). "The Cross-Section of Expected Stock Returns". Journal of Finance 47 (2): 427–465
    • Reading (3)
      Momentum
      Barberis, N., A. Shleifer, and R. Vishny. “A Model of Investor Sentiment.” Journal of Financial Economics, 49, 1998.
      Crombez, J. "Momentum, Rational Agents and Efficient Markets." The Journal of Psychology and Financial Markets, 2, 2001.
      Daniel, K., D. Hirschleifer, and A. Subrahmanyam. “A Theory of Overconfidence, Self-Attribution, and Security Market Under and Over-reactions.” Journal of Finance, 53, 1998.
      Jegadeesh, N., and S. Titman. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance, 48, 1993.
      Jegadeesh, N., and S. Titman. “Profitability of Momentum Strategies: An Evaluation of Alternative Explanations.” NBER Working paper #7159, 1999.
    • Reading (4)
      Asset Growth
      Cooper, Gulen & Schill, 2009, 'The asset growth effect in stock returns'
      Business Cycle
      DeStefano, Michael, 'Stock Returns and the Business Cycle'. Financial Review, Vol. 39, No. 4, November 2004
      Corporate Governance
      Shane A. Johnson, Ted Moorman, and Sorin Sorescu, 2005, 'Governance, Stock Returns, and Market Efficiency'
    • Reference
      Reeves J. J. 2008
      Andersen et al 2006
      Heng et al 2010
      http://www.polsci.wvu.edu/duval/ps791c/Notes/Stata/arimaident.html