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  • 1. Using Sector Valuations to Forecast Market Returns A Contrarian View February 27, 2003 Lewis Kaufman, CFA Cira Qin Justin Robert Shannon Thomas Vidhi Tambiah
  • 2. Table of Contents
    • Overview Using Sector Valuations to Forecast Market Returns
    • Methodology A Contrarian View
    • Regression Results The Model’s Predictive Power
    • Out-of-Sample Limited Data, Promising Results
    • Trading Strategy A Long-Short Approach
    • ARCH Using Conditional Variance
    • Conclusions
  • 3. Overview Using Sector Valuations to Forecast Market Returns
    • Stock market is a discounting mechanism
    • Expectations drive stock prices, change over time
    • Sector valuations reflect these expectations
    • Assume markets driven by fear, greed
    • Use sector valuations to gauge sentiment
    • Build model to forecast returns
    • Key Takeaway: Sector valuations reflect expectations that can be used to forecast market returns
  • 4. Methodology A Contrarian View
    • Establish Framework
      • High P/Es might indicate exuberance, despair depending on sector
      • Take contrarian view: sell greed, buy fear
      • Use P/E spreads to the market to normalize the results
    • Identify Factors, Select Variables
      • Investor sentiment Food Producers
      • Economic expectations Retailers
      • Geopolitical risks Oil and Gas Producers
    • Test Intuition by Predicting t-Stats
      • Food Producers (+), wide spread suggests fear, should be bought
      • Retailers (-), wide spread suggests high consumer confidence, should be sold
      • Oil and Gas Producers (+), wide spread suggests external shock, should be bought
    • Forecast 1-Year Returns for the S&P 500
      • Identify whether sector valuations can forecast returns
  • 5. Methodology A Contrarian View
    • Independent Variable Plot: Food Producers
      • Suggests (+) relationship between spread, future returns
  • 6. Methodology A Contrarian View
    • Independent Variable Plot: Retail
      • Suggests (-) relationship between spread, future returns
  • 7. Methodology A Contrarian View
    • Independent Variable Plot: Energy
      • Suggests (+) relationship between spread, future returns
  • 8. Regression Results The Model’s Predictive Power
    • Regression Output
      • Adjusted R-square of 25.6%
      • Two of three t-stats significant at the 95% level; signs consistent with intuition
      • Low Correlation among independent variables
  • 9.
    • Graphically Appealing
      • Model does credible job of forecasting returns
      • More effective in recent years: access to information, trading volumes, hedge funds
    Regression Results The Model’s Predictive Power
  • 10.
    • Encouraging Scatter Plot
      • Graph suggests linear relationship between forecasted and actual returns.
    Regression Results The Model’s Predictive Power
  • 11.
    • Other Observations
      • Graph suggests linear relationship between forecasted and actual returns
      • Systematic positive bias in-sample, results encouraging out-of-sample
      • Strong predictor of directional change, implications for trading strategies
      • Model more effective in recent years: access to information, hedge funds, volume
      • Considered fitting in-sample data to more recent years and using an earlier period as out-of-sample. Better results for R-square and T-statistics. Dismissed idea because out-of-sample from past periods may not be indicative of success
    Regression Results The Model’s Predictive Power
  • 12. Out-of-Sample Promising Results
    • Limited Data, but Encouraging Results
      • Predicted curve clearly trends with actual returns
      • Promising given limited sample horizon; correctly predicted decline in 2000
      • Model has a positive bias, expect predictability to improve when market rises
  • 13. Trading Strategy A Long-Short Approach
    • Basic Strategy: Long-Short Approach
      • Invest $1 in 1/73, invest $1 in 2/73, invest $1 in 3/73,…
      • Reinvest proceeds from 1/73 on 1/74, reinvest 2/73 on 2/74,…
      • Long-Short investment decision based on model’s predictions
      • Compare against benchmarks, market return and risk-free return
    • Five Strategies
      • Trading Strategy I: Basic Long-Short
      • Trading Strategy II: Long-Short with Risk-free
      • Trading Strategy III: Long-Short with Momentum
      • Trading Strategy IV: Conservative Long-Short with Conditional Variance
      • Trading Strategy V: Long-Short with Conditional Variance
  • 14. ARCH Using Conditional Variance
    • Rationale
      • Needed measure of future volatility to create trading strategy based on volatility prediction
      • ARCH is employed in strategies IV,V
      • We found lags 1,7 and 11 most significant
    • The Results
  • 15. Trading Strategy A Long-Short Approach
    • The Results
      • Out-of-Sample returns all outperform the market, with less volatility
      • Strategy III performs best across whole sample and in-sample.
      • Strategy IV dominates other strategies out-of-sample
      • Trading strategies outperform benchmarks in all data sets
  • 16. Conclusions
    • Sector valuations reflect investor sentiment
    • By taking a contrarian view, we can make abnormal profits
    • Model supports thesis, outperforms both in-sample and out-of-sample
    • Systematic positive bias, though out-of-sample results are encouraging

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