1. Empirical testing of the CAPM on the JSE Mike Ward, Chris Muller Gordon Institute of Business Science University of Pretoria NERSA Conference August 2012

2. An economic return on the RAB? Regulatory Asset Base Shareholder Capital Debt Capital The cost of equity “The CAPM” Re = Rf + β.MRP The cost of debt

3. The Capital Asset Pricing Model Beta = 1.0 Return Rf = 7% MarketRiskPremium = 5% High beta shares are more risky, so give better returns 0.8 Rf = 11% Risk (beta)

4. Betting Against Beta, Andrea Frazzini and Lasse H. Pedersen, Oct 2011 Prior Research Data: All US Shares 1928 - 2009

5. Betting Against Beta, Andrea Frazzini and Lasse H. Pedersen, Oct 2011 Data: 18 International Markets 1984 - 2009

6. Fama and French (2004) estimated betas for every share on the NYSE, AMEX and NASDAQ from 1923 – 2003 using 2-5 years prior data and compared with their return over the next 12 months:

7. The largest 600 US shares over the period 1963 – 2006 placed into 10 portfolios ito beta.

8. Prior research on the JSE Strugnell, Gilbert & Kruger (2011) IAJ – “Beta has no predictive power for returns on the JSE” – Data from 1994 – 2007 – Included too many small shares van Rensburg & Robertson (2003) IAJ – “If anything, beta is inversely related to returns!” – Data from 1990 – 2000 – Included too many small shares

9. Rational for research The CAPM is a pillar of financial theory: – taught on all finance courses – found in all finance text books – used regularly in the financial services industry – Markowitz, Miller & Sharpe shared a Nobel prize We have 25 years of JSE data – 1985 to 2011 We can improve on the methodology

10. Methodology Select the largest 160 companies in Dec 1984 Estimate betas using prior years return data – OLS beta 60 monthly data points – Dimson Multiple regression (+1,0,-1,-2,-3,-4) Rank betas Construct 5 equal weighted portfolios of 32 shares Measure portfolio return over the next 3 months Repeat for next quarter

11. 99% of JSE’s market capitalisation

13. Presentation of findings We track the daily value of each portfolio (quintile) We re-balance each portfolio quarterly – We retain the value of the portfolio – Equally weight – We ignore transaction costs We graph the results We benchmark against the ALSI total return index We plot a price relative versus the J203

14. Results

15. OLS Betas - monthly

16. OLS Betas - weekly

17. Dimson Betas - monthly

18. Dimson Betas - weekly

19. Volatility - Daily

20. Summary of Results Annualised returns for equal weighted portfolio quintiles over the period 31Dec1986 - 31Dec2011 Risk Measure Number of Obs ALSI Index R203 Highest Beta Quintile Quintile 2 Quintile 3 Quintile 4 Lowest Beta Quintile OLS Monthly Beta6015.7%7.7%12.1%18.1%21.6%20.4% OLS Weekly Beta10415.7%4.6%15.4%20.4%19.9%19.3% Dimson Monthly Beta6015.7%7.9%16.3%19.3%19.0%17.4% Dimson Weekly Beta10415.7%5.7%15.3%19.0%19.5%20.8% Volatility Daily6015.7%9.7%13.5%17.7%20.8%18.2% Average annualised Return15.7%7.1%14.5%18.9%20.2%19.2%

22. Conclusion: High risk (beta) = Low return Ben Graham once argued that: "Beta is a more or less useful measure of past price fluctuations of common stocks. What bothers me is that authorities now equate the beta idea with the concept of risk.

23. Questions… For those interested: The full paper will be published in the forthcoming: – Investment Analyst Journal – http://www.iassa.co.za/journals/ http://www.iassa.co.za/journals/