The Residual Income Model

1. Stock Screening on the Canadian Market: The Residual Income Model Thesis Presentation By: Tom Nicholson Advisors: Ken MacAulay and Kirk Collins

2. “To Determine the effectiveness of an operationalized residual income model as a screener for over(under)valued stocks on the Canadian market.” • ie: Is the residual income model and effective way of finding a firm’s intrinsic value? The Objective

3. Academic: The first time that the residual income model will be tested using analyst forecasts on the Canadian Market • Further Canadian residual income model research • Foreign application of American Research • Practical: Provides investors with a portfolio creation strategy • Buy undervalued stocks • Short overvalued stocks The Purpose

4. A firm’s intrinsic value can be estimated as its current Book Value, plus the present value of future cash flows. • Residual income valuation captures these cash flows using “residual income” • So what is “Residual Income?” • Net Income less a charge for common shareholders’ opportunity cost in generating net income The Fundamentals

5. Which leads us to this general model: • This should encompass all future cash flows • Clean Surplus Relationship: The Fundamentals

6. To make this model practicable, Frankel and Lee (1998) modify it to estimate intrinsic value for one to three years ahead, based on analyst EPS forecasts. • Notice that now, residual income is represented by return on equity in surplus of cost of equity. Making the Model

7. Frankel and Lee (1998) create FROE and future book value estimations based on a few pieces of information: Information Needed: Analyst EPS forecast Dividend Payout Ratio Cost of Equity Capital 5 Year Growth Rate • Where: Making the Model

8. Wharton Research Data Services: I/B/E/S • Historical Analyst EPS Forecasts • 5-Year Long-term growth rates • Same database used by Frankel and Lee (1998) • Company I/S & B/S • Dividend Payout Ratio (EPS & DPS) • Time t book value Collecting The Data

9. Small Sample Size: • 266 firms originally chosen to analyze (based on size) • Only 87 of these firms fit all necessary criteria • Much smaller than American sample • Why so few? • Model rejects firms with the following characteristics: • Lack of analyst EPS forecasts (majority of nullifications) • Negative book values (ROE cannot be interpreted) • Extremely low book values or earnings (unreliable results) • Unreasonable “k” estimations (>100% were eliminated) Issues with the data

10. The initial intent: • As many time periods as possible (5-10) • Chosen time period • 2004 – 2007: Bubble. The Time Period

11. Data Description

12. Running the model: • The key time restraint • Proxies • Use collected data to estimate intrinsic values for companies in times t+1, t+2, t+3 (2005, 2006, 2007) • Compare these intrinsic values (V) to price at initial time (P0) • Creates “V/P” ratios for 2005 (V1/P), 2006 (V2/P) and 2007 (V3/P). Creating Portfolios

13. Low V/P Firms High V/P Firms UNDERVALUED OVERVALUED LONG SHORT Creating Portfolios

14. To test the performance of the model, firms are broken into quintiles based on their V/P ratio. • The highest quintile (Q5) contains firms with high V/P firms • Theoretically, the most undervalued • The lowest quintile (Q1) contains firms with low V/P firms • Theoretically, the most overvalued How to Test?

15. Frankel and Lee (1998), showed a significant difference between the buy and hold returns seen in Q5 (high V/P) and Q1 (low V/P) Previous Results & Expectations

16. Frankel and Lee (1998) • Create and operationalized model to calculate V1, V2 and V3 • Run model on American markets from 1979-1991 What does this get the investor?

17. Characteristics of V/P • Monotonic increases as time period increases Results

18. Comparison of Quintiles: Results

19. If the investor had followed the model, and created short and long portfolios based on its results, they would have seen the following returns: How would the investor have done?

20. Why did the short portfolio perform so poorly!? • What was in the short portfolio? Short Portfolio Performance… • Would have an investor have invested in the short portfolio?

21. Market Capitalization Difference (5%, two-tailed) • Are analysts biased towards firms with larger market caps? …probably not. Potential Explanations • A more likely explanation: • Bigger firm More analysts Better estimates

22. Sample Size • Difficult to find reliable results with only 87 observations • Data was re-run with portfolios created by thirds. • Time period chosen • Huge bubble not predicted by analysts • Hurts short portfolio • Proxy for risk? • Model needs regression against and leverage and financial distress Potential Explanations

23. Insufficient analyst data in Canada • Due to fewer analyst reports, trends may not be captured • The model worked with realized figures (Liu, 2000) Potential Explanations

24. Larger Sample • More diverse economic conditions • More firms per three-year period • Tailored cost of equity • CAPM • APT • Risk analysis • Do results simply show riskier firms? Further Research

25. Questions? Thank You!