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Timing trades in the market - measuring skill, aggression and return added

Timing trades in the market - measuring skill, aggression and return added. Brian Munro, Dave Bradfield January 2009. Timing trades in the market. Introduction :

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Timing trades in the market - measuring skill, aggression and return added

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  1. Timing trades in the market - measuring skill, aggression and return added Brian Munro, Dave Bradfield January 2009

  2. Timing trades in the market • Introduction: • The Merton – Hendriksson model is a popular model that attempts to capture the manager’s ability to time the market via the change in portfolio beta – which is measurable from portfolio returns. • This timing measurement is on a portfolio level, but the literature has been fairly silent on measuring the success of timing stock trades. • We’ve adopted and extended the ideas of Andrew Lo that attempts to quantify the value add to portfolios by timing stock trades in the market • There is no reason why we can’t apply these techniques on a • Sector level and • Asset allocation level

  3. Timing trades in the market • Contents: • Illustrate the basic concepts • Derive the Stock-timing model ~ Andrew Lo (2007) • Illustrate different Stock-timing scenarios • Results from the General EQ category • Does sector-timing (sector rotation) add value? • Strategic Vs Tactical Asset allocation? • Summary

  4. Illustrate the basic concepts

  5. out out out out in in in in in Illustrating Stock-timing – naïve example: 1 stock Static (Buy & Hold) = 40%

  6. out out out out in in in in in Illustrating Stock-timing – naïve example: 1 stock • How good is the manager at timing trades in the market? (forecasting 1-time period ahead) • Can we quantify the return added by dynamically stock-timing across all stocks in the portfolio? Active trading = 114% Dynamically changing weights Stock-timing • Timing trades in the market (forecasting 1-time period ahead: • Positive correlation > 0 good stock-timing • Negative correlation < 0 poor stock-timing • Uncorrelated (Correlation = 0) no stock-timing Static (Buy & Hold) = 40%

  7. Derive the stock-timing model ~ Lo (2007)

  8. Std deviation (weightsit) captures the dispersion of weights for stock i. (i.e. how actively the manager is changing the weight) correlation (weightsit,returnsit+1) indicating skill in forecasting 1 time-period ahead Std deviation (Returnsit+1) captures the volatility of the returns for stock i. Stock-timing model • More formally …. …Portfolio return at time t+1 is given by … • It can be shown that the expected portfolio return is equal to: • STATIC COMPONENT – e.g. Buy & Hold strategy • DYNAMIC COMPONENT – actively changing stock weights through time DYNAMIC COMPONENT STATIC COMPONENT • Dynamic component captures the extent to which a manager is adding return through stock-timing • Static component is positive if the average weight x average return is positive (for stock i)

  9. Stock-timing model • Modifying equation… DYNAMIC COMPONENT STATIC COMPONENT STATIC COMPONENT Mean Weight Mean Return

  10. Dispersion of Weights Dispersion of Stock Return Stock-timing model • Modifying equation… DYNAMIC COMPONENT STATIC COMPONENT SLOPE STATIC COMPONENT • γi (slope) captures: • Leverage of skill – or how aggressive managers are at changing weights – • Hedge funds can be more aggressive • short a stock (negative weight) • ρ (correlation) captures the skill at timing trades Hedge fund manager Long-only manager Mean Weight Mean Return

  11. Dispersion of Weights Dispersion of Stock Return Stock-timing model • Modifying equation… DYNAMIC COMPONENT STATIC COMPONENT SLOPE STATIC COMPONENT Hedge fund manager Long-only manager Mean Weight • Dynamic ratio (component) is the proportion of return due to stock-timing: • Captures the value add from actively trading stocks through time • Unit-free (daily, weekly, monthly is irrelevant) • Is independent of the benchmark!! Mean Return

  12. Illustrating different Stock-timing scenarios

  13. Portfolio: Constant Weights – no stock-timing • Portfolio return is all static return

  14. Portfolio: Buy & Hold Strategy – Index Tracker • Portfolio return is all static return

  15. Portfolio: Good Stock-timing – positively correlated • Manager was able to perfectly time increasing / decreasing weights (Correlation (Wit,Rit+1) = 1) • Stock-timing added +22% to the portfolio return

  16. Portfolio: Poor Stock Timing – negatively correlated • Manager got the timing perfectly wrong! (Correlation (Wit,Rit+1) = -1) • Stock-timing destroyed -22% of the portfolio return

  17. Results from the General EQ category

  18. Results: General EQ category • Data: • 20 General EQ funds were used in the analysis – complete history • Quarterly unit trust compositions were used from March 2002 to June 2007 – (Profile Media) • Quarterly returns from June 2002 to September 2007 were used • Important to note: • Any trades within the quarter will not be captured • Calculate: • Mean portfolio return split into DYNAMIC and STATIC component

  19. Results: June 2002 to September 2007 • Biggest contributor to overall return is static return – (driven by the market) • 3 managers have successfully added return through the Dynamic component

  20. Results: June 2002 to September 2007 What differentiates managers: STATIC return or DYNAMIC return? Static component: Correlation = 0.41 • What is the relationship (correlation) between overall fund returns and the Static component? • What is the relationship between overall fund returns and the Dynamic component? • Actively trading (stock-timing) differentiates managers Dynamic component: Correlation = 0.79

  21. Stock level: Mean Quarterly Return • Correlations in red are significant at the 5% significant level, • in dark red at 10% • Top 20 stocks that have the highest dynamic component • EXX and BVT have added the most dynamic return • Highlighted stocks suggest significant skill at forecasting return 1-time period ahead

  22. Mean Out-performance – PSG Alphen Growth vs ALSI • It can easily be shown that the expected out-performance (to a benchmark) can be split into: • STATIC component that outperforms the benchmark • DYNAMIC component that outperforms the benchmark STATIC OUTPERFORMANCE DYNAMIC OUTPERFORMANCE

  23. Mean Out-performance – PSG Alphen Growth vs ALSI • It can easily be shown that the expected out-performance (to a benchmark) can be split into: • STATIC component that outperforms the benchmark • DYNAMIC component that outperforms the benchmark STATIC OUTPERFORMANCE DYNAMIC OUTPERFORMANCE • Dynamic component contributes more to the overall out-performance Dynamic out-performance Static out-performance

  24. What about dynamic Sector changes and Asset Allocation changes, Are they adding value?

  25. Sector and Asset Allocation Sector level (sector rotation): • Perform the analysis on a Major sector level (Resources, Financials, Industrials) • Focus on the manager changing the weights • To compare across managers, assume each manager has the same sector return (JSE index return) • Reconstruct fund return (ignoring stock selection within each sector) – not the true return Asset Allocation (strategic vs tactical asset allocation) • Top 10 Pension fund asset allocation weights through time (March 2003 to December 2007) • Again, to compare across managers use market indices to proxy the different asset class returns • Equity – ALSI (J203T) • Bonds – All Bond Index (JAPI05) • Cash – 3-month T-Bill (TBT3) • Property – Real Estate Index (J253T) • International – 60% MSCI (MORGAN), 40% World Bonds (GLOUS) • Reconstruct fund return (ignoring stock selection within each asset class) – not the true return

  26. Sector-timing: Mean Out-performance (ALSI) • Funds that are sector-timing (sector rotation) are differentiating themselves • (Correlation between Mean quarterly return* and Dynamic component = 0.86)

  27. Asset-allocation: Mean Out-performance (Top10 Peer mean) Strategic asset allocation Tactical asset allocation • Fund managers are adding small amounts of value above the peergroup • Static component (strategic asset allocation) is highly correlated with overall return (Correlation = 0.95) • Dynamic component (tactical asset allocation) is less correlated with overall return (Correlation = 0.41)

  28. Summary • Andrew Lo’s Stock-timing model splits the mean return into a STATIC and DYNAMIC component • We’ve extended these ideas to: • Measure the manager’s skill at timing trades in the market (correlation) • Measure the aggression/leverage at which the skill has been applied (slope) • These ideas can be applied on a stock, sector and asset class level. Stock level: • 3 out of 20 managers successfully added return by actively trading • Actively trading stocks has been a differentiating factor amongst peers. Sector level: • Sector rotation has been a differentiating factor with regards to performance Asset Allocation: • Strategic asset allocation has added more value that tactical asset allocation

  29. The End

  30. Mean Return – PSG Alphen Growth

  31. Split: Dynamic & Static component – PSG Alphen Growth Static component Dynamic component

  32. Dynamic ratio - PSG Alphen Growth

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