What Drives the Value of Analysts' What Drives the Value of Analysts' - PowerPoint PPT Presentation

What Drives the Value of Analysts' What Drives the Value of Analysts' Recommendations: Cash Flow Recommendations: Cash Flow Recommendations: Cash Flow Recommendations: Cash Flow Estimates or Discount Rate Estimates? Estimates or Discount Rate Estimates? Ambrus Kecskés (Virginia Tech) Roni Michaely (Cornell and IDC) Kent Womack (Dartmouth) Roni Michaely, March 2010 1

Background Background  Security analysts provide investment advice  Reports  Earnings estimates  Earnings estimates  Stock recommendations  Upgrades and downgrades when their valuation is different than that of the market  Empirically: Price impact of recommendation changes  On average changes in recommendations have a significant price  On average, changes in recommendations have a significant price impact  Not all information is impounded in prices immediately  E.g., Womack (1996), Barber et. al. (2001) Roni Michaely, March 2010 2

The Framework The Framework  The basic valuation framework C C   t t  P t  (1 r ) t C  P  r g g  Valuation (of analysts and market) can diverge b/c of:  Different assessments of cash flows and/or  Different assessments of cash flows and/or  Different assessments of discount rate Roni Michaely, March 2010 3

The Framework The Framework  When an analyst changes her recommendation and at the same time changes her (short‐term) earnings estimate  We refer to these as Earnings ‐ Based Recommendations  Recommendations that are not accompanied by a change in estimated earnings are (implicitly or explicitly) based in estimated earnings are (implicitly or explicitly) based on changes in estimated discount rate and/or changes in long‐term earnings growth rate  We refer to these as Discount Rate ‐ Based Recommendations  Equivalently: Non‐earnings based recommendations Roni Michaely, March 2010 4

Why might earnings Why might earnings ‐ based recommendations have based recommendations have diff different information content than different diff t i f t information content than i f ti ti t t t th t th discount discount rate rate ‐ based recommendations? based recommendations?  Hard information  Soft information  Earnings are the most  Discount rates and changes followed statistics in company p y in growth rates are hardly g o t ates a e a d y reporting ever mentioned explicitly  Always the focus of analysts'  No company guidance for reports more than 2‐3 years out  Verifiable  Verifiable  Not verifiable N ifi bl  The accuracy of earnings  Hard to estimate, hard to estimates are easily verifiable verify ex post  Short forecast horizon  Noisy estimates  Noisy estimates  Earnings are reported  Long forecast horizon frequently (quarterly)  Easier to estimate short‐term than long‐term factors than long term factors Roni Michaely March 2010 5

Earnings Earnings ‐ based recommendations vs. based recommendations vs. di discount rate ‐ based recommendations discount rate di t t t t b b based recommendations d d d ti d ti  Earnings‐based recommendations  Easier to estimate, less noisy  Less possibilities for incentive biases  Less possibilities for cognitive biases  Discount rate‐based recommendations  Longer forecast horizon: More subject to congnitive baises (e.g. Ganzach and Krantz, 1991)  Not verifiable: Easier to be biased‐‐whether heuristics  Not verifiable: Easier to be biased whether heuristics or conflict of interests, (e.g., Daniel, Hirshleifer and Subrahmanyam, 1998; Gervais and Odean 2001) Roni Michaely, March 2010 6

The Hypothesis The Hypothesis yp yp  Earnings‐based recommendations are more i f informative than discount rate‐based i h di b d recommendations Roni Michaely, March 2010 7

Related Literature Related Literature  Value of recommendations  Stickel (1995), Womack (1996), Barber et al. (2001)  Biases in recommendations  Lin & McNichols (1998), Michaely & Womack (1999)  What makes recommendations more valuable h k d l bl  Firm characteristics: Jegadeesh et al. (2004)  Recommendation characteristics: Loh and Stulz  R d ti h t i ti L h d St l (2009)  Cash flow vs. discount rate information  Cash flow vs. discount rate information  Cohen, Polk, Vuolteenaho (2003), Campbell, Polk, Vuolteenaho (2009) Roni Michaely, March 2010 8

Testable Implications: Testable Implications: p Initial Market Reaction Initial Market Reaction  An upgrade with earnings increased (earnings‐ A d ith i i d ( i based rec) should be viewed more positively than an upgrade without an earnings increase an upgrade without an earnings increase (discount rate‐based rec)  A downgrade with earnings decreased (earnings‐  A downgrade with earnings decreased (earnings based rec) should be viewed more negatively than a downgrade without an earnings decrease (discount rate‐based rec) Roni Michaely, March 2010 9

Testable Implications: The Drift Testable Implications: The Drift  A priori, it is not clear whether the drift after earnings‐based recommendation changes should be bigger or smaller than after recommendation changes should be bigger or smaller than after discount rate‐based recommendation changes.  The market appears to undervalue information about intangibles versus tangibles (e g Lev and Sougiannis (1996) Daniel and versus tangibles (e.g., Lev and Sougiannis (1996), Daniel and Titman (2006)  The drift after earnings‐based recommendation changes should be smaller  Pre io s st dies on recommendations (as other corporate  Previous studies on recommendations (as other corporate events) document a drift in the same direction as the initial return.  Since earnings based recommendation changes appear to be more  Since earnings‐based recommendation changes appear to be more informative as evidenced by their bigger initial price reaction, the drift could be bigger Roni Michaely, March 2010 10

Plan for the Remainder of Plan for the Remainder of P P Presentation Presentation i i  Data  Univariate results  Multivariate results  What if the analysts opinion did not change but the Wh if h l i i did h b h market’s expectations changed?  The role of Growth rate  The role of Growth rate  Large (and innovative) changes in earnings and recommendaiotns  Robustness  Trading strategy  Conclusion C l Roni Michaely, March 2010 11

Data and Sample Data and Sample  123,250 recommendation changes (firm‐date observations)  Between 1994 and 2007  Between 1994 and 2007  7,040 unique firms  3,517 unique trading dates  Daily trading data from CRSP  Recommendations and earnings from I/B/E/S (analyst‐ firm‐date observations) firm‐date observations)  Annual accounting data from Compustat  Quarterly institutional ownership from Thomson's 13‐F Q y p filings  Analyst rankings from Institutional Investor magazine  Random sample of 150 analyst reports Roni Michaely, March 2010 12

Recommendation Change Categories Recommendation Change Categories  Recommendation changes  Categories and earnings estimate and earnings estimate  Upgrades with  Upgrades with changes on the same day  Earnings increased (tried 1‐month long  Earnings not changed window as well) d ll)  Earnings decreased  Downgrades with  Definition of earnings  Earnings increased estimate change estimate change  Earnings not changed  At least one of FY1 and FY2  Earnings decreased increases and neither decreases decreases  At least one of FY1 and FY2 decreases and neither increases increases Roni Michaely, March 2010 13

E E Excess Returns for Event ‐ time Analysis Excess Returns for Event R t R t f f E E t t ti time Analysis ti A A l l i i  Daniel, Grinblatt, Titman, and Wermers (1997) Daniel, Grinblatt, Titman, and Wermers (1997) excess of characteristics returns (matched on size quintiles, book‐to‐market quintiles, and momentum quintiles) Roni Michaely, March 2010 14

[T1] Percent of observations in [T1] Percent of observations in [T1] Percent of observations in [T1] Percent of observations in each recommendation change each recommendation change category category All upgrades (56,341 observations) 100.00 Upgrades with earnings increased pg g 32.49 Upgrades with no earnings change 53.46 Upgrades with earnings decreased 14.04 All downgrades (66,909 observations) 100.00 Downgrades with earnings increased Downgrades with earnings increased 10 34 10.34 Downgrades with no earnings change 53.57 Downgrades with earnings decreased 36.09 Roni Michaely, March 2010 15

[T1] Summary statistics for variable means [T1] Summary statistics for variable means across all recommendation change categories across all recommendation change categories ll ll d d i i h h i i Characteristic Range g 76 th to 82 nd percentile Market cap 35 th to 44 th percentile Book‐to‐market p 70 th to 71 st percentile Turnover 73 rd to 75 th percentile Institutional ownership Institutional ownership 73 to 75 percentile Analyst coverage 14 to 16 analysts 37 th to 41 st percentile Return volatility Return volatility 37 to 41 percentile Prestigious/not brokers 30% to 34% of rec chgs Star/not analysts Star/not analysts 11% to 12% of rec chgs 11% to 12% of rec chgs Roni Michaely, March 2010 16