Using Sector Valuations to Forecast Market Returns A Contrarian View February 27, 2003

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 1

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 2

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 3

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 4

5. Methodology A Contrarian View • Independent Variable Plot: Food Producers • Suggests (+) relationship between spread, future returns 5

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

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

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 8

9. Regression Results The Model’s Predictive Power • Graphically Appealing • Model does credible job of forecasting returns • More effective in recent years: access to information, trading volumes, hedge funds 9

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

11. Regression Results The Model’s Predictive Power • 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 11

12. Out-of-SamplePromising 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 12

13. Trading StrategyA 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 13

14. ARCHUsing 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 14

15. Trading StrategyA 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 15

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 16