1. 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. 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. 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. 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 sentimentFood Producers –Economic expectationsRetailers –Geopolitical risksOil 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. 5 Methodology A Contrarian View Independent Variable Plot: Food Producers –Suggests (+) relationship between spread, future returns

6. 6 Methodology A Contrarian View Independent Variable Plot: Retail –Suggests (-) relationship between spread, future returns

7. 7 Methodology A Contrarian View Independent Variable Plot: Energy –Suggests (+) relationship between spread, future returns

8. 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. 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. 10 Encouraging Scatter Plot –Graph suggests linear relationship between forecasted and actual returns. Regression Results The Model’s Predictive Power

11. 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. 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. 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. 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. 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. 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