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Independent Study Project A Market-Neutral Strategy Lewis Kaufman, CFA Fuqua School of Business, ‘03 lewis.kaufman@alumni.duke.edu Faculty Advisor: Campbell R. Harvey November 12, 2014. Agenda. Performance, 1979-2001 Strategy Data Collection Factor Analysis Risk Optimization

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Independent Study Project

A Market-Neutral Strategy

Lewis Kaufman, CFA

Fuqua School of Business, ‘03

lewis.kaufman@alumni.duke.edu

Faculty Advisor: Campbell R. Harvey

November 12, 2014

1

agenda
Agenda
  • Performance, 1979-2001
  • Strategy
  • Data Collection
  • Factor Analysis
  • Risk Optimization
  • Analysis of Results
  • Further Study
  • Conclusions

2

performance 1979 2001
Performance, 1979-2001

Hedge, In-Sample Hedge, Out-of-Sample S&P 500

3

strategy
Strategy
  • Use intuition about drivers of stock price performance to develop a quantitative, market-neutral strategy
  • Focus on the S&P 500 where liquidity is greatest and transaction costs are minimized
  • Employ risk-optimization techniques to maximize returns for a pre-specified level of risk
  • Explore other potential enhancements to the base strategy

4

data collection
Data Collection
  • Identify appropriate time horizon
    • Obtain fundamental data for each stock in the S&P 500 dating back to 1979
    • Sample period includes several different market environments
    • Test out of sample (1996-2001) to verify that the strategy is robust
    • Focus on quarterly intervals
  • Limited access to data
    • No access to expectational data (i.e. no EPS estimates)
    • All fundamental factors are therefore on a trailing basis
    • Companies with negative book value or net income excluded
    • EPS, NOPAT annualized each quarter

5

factor analysis
Factor Analysis
  • Identify key factors at the outset
    • Important not to mine data for best result – use intuition
    • Key factors EPS, P/B, P/S, ROE, ROIC
  • Create three fractile portfolios for each factor
    • Perform sort each quarter based on each factor
    • Group stocks into top, middle, bottom portfolios
    • Track performance of each fractile for coming quarter
    • Rebalance each quarter
  • Evaluate each pre-specified factor both in, out of sample
    • For each portfolios, perform diagnostics for all three fractiles
    • Select top three factors based on returns, risk

6

risk optimization
Risk Optimization
  • Risk optimization performed under supervision of Professor Campbell R. Harvey
  • Optimal portfolios weights determined
  • Scoring system established for individual security selection
  • Additional information available upon request

7

analysis of results
Analysis of Results
  • In-sample results: 1979-1995
    • 28.6% compounded annual return, 11.5% for S&P 500
    • 0.04 quarterly beta, implying negligible systematic risk
    • 6.0% quarterly standard deviation of returns, 7.0% for S&P 500
    • Outperformed S&P 500 16 of 17 years
    • Outperformed 53% of time during up quarters for S&P 500
    • Outperformed 100% of time during down quarters for S&P 500
  • Out-of-sample results: 1996-2001
    • 27.2% compounded annual return, 10.1% for S&P 500
    • -0.41 quarterly beta, implies negative correlation with market
    • 10.3% quarterly standard deviation of returns, 7.6% for S&P 500
    • Outperformed S&P 500 4 of 6 years
    • Outperformed 21% of time during up quarters for S&P 500
    • Outperformed 90% of time during down quarters for S&P 500
  • Out-of-sample analysis: A robust strategy
    • Positive absolute returns in all but one year despite a difficult market environment
    • -22.5% in 1999: peak of the bubble, fundamentals irrelevant
    • 2000 and 2001 were fantastic years: 65.6% and 78.9%, respectively
    • 1999 would not have put us out of business; 2000, 2001 more than compensate

8

further study
Further Study
  • Incorporate expectational data
    • Inherent limitations to relying on past accounting data
    • Does not capture market expectations
  • Use other indicators to enhance strategy
    • Incorporate macro factors such as interest rates, dollar
  • Consider using leverage
    • All returns assume no leverage
  • Develop security weighting system
    • Examine value-weighted returns

9