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Management Forecasts, Disclosure Quality, and Market Efficiency

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  1. Management Forecasts, Disclosure Quality, and Market Efficiency Jeffrey Ng, İrem Tuna, and Rodrigo Verdi

  2. What do we do? • We examine short-term and long-term market reaction to management forecasts • We test the effect of disclosure quality on the long-term market reaction

  3. Motivation • There is substantial evidence of market underreaction to news events e.g., • PEAD (Bernard and Thomas, 1989, 1990) • Conservatism theory (Barberis et al., 1998) • Rational structural uncertainty (Brav and Heaton, 2002) • Is there a post-management-forecast drift? • Is underreaction to earnings news a function of perceived credibility of the news (e.g., stronger underreaction to good news)?

  4. Motivation • Management forecasts constitute an interesting setting to study reaction to news because of credibility concerns (voluntary disclosure) • Stronger short-term market reaction for bad news forecasts despite no difference in bias (Rogers and Stocken, 2005) • Management forecasts allow us to examine whether disclosure quality affects the market reaction to news

  5. Overview of Results Figure 1a – Annual earnings forecasts

  6. Anilowski, Feng, and Skinner (JAE 2007)

  7. Kato, Skinner, and Kunimura (WP 2007)

  8. Hypothesis I • PEAD literature provides evidence of underreaction to earnings surprises • Both conservatism and rational structural uncertainty can explain an underreaction phenomenon • We hypothesize that credibility concerns related to management forecasts can exacerbate underreaction to earnings news H1: Future stock returns are positively associated with management forecast news

  9. Hypothesis II • Larger stock price reaction to bad news forecasts than to good news forecasts (e.g., Hutton et al., 2003) • Typical explanation is that bad news forecasts are more credible than good news forecasts • No difference in the forecast bias between bad news and good news forecasts (Rogers and Stocken, 2005) H2: The magnitude of the future returns is larger for firms forecasting good news

  10. Hypothesis III • Better disclosure may help investors understand the future cash flow implications of an information signal • Prior literature provides evidence that better disclosure mitigates the magnitude of market inefficiency • PEAD (Francis et al., 2005; Kimbrough, 2005) • Accruals anomaly (Richardson et al., 2005; Levi, 2007) • H3: The magnitude of the hedge portfolio returns from the PMFD trading strategy is lower for firms whose forecasts are of higher quality.

  11. Sample Description • Follow Anilowski et al. (2007): • 17,184 (14,890) forecasts of annual (quarterly) EPS • 6,369 (5,859) annual (quarterly) forecasts that do not overlap with earnings announcements • Ordinary shares listed on NYSE / AMEX / NASDAQ from 1996 to 2005

  12. Key Variables • Surprise= (Manag. Forec – Analyst Forec) / Price • Abnormal returns: • Size-adjusted buy-hold returns • Size-BM adjusted buy-hold returns • Factor alphas (3-factor, 4-factor, 5-factor) • Short-term: 3-day around the forecast • Long-term: 12-month subsequent to the forecast month

  13. Short- and long-term reaction Return = β0+ β1 Good News + β2 Surprise + β3 Surprise x Good News + ∑ βj Surprise x Controls + ∑ βm Controls + ε • Return is either: - AbRet3d (3-day return around forecast) - AbRet (future 12-month return) • Fama-MacBeth regressions with Newey-West corrected standard errors

  14. Table 3 – FRC Regressions

  15. Hedge Portfolio Analyses • We sort the observations into quintiles based on the previous year’s distribution of forecast surprises • Hedge portfolio strategy: • Buy shares of firms in Q5 (extreme good news) • Short shares of firms in Q1 (extreme bad news) • Abnormal returns: • Size-adjusted and size-BM-adjusted buy-hold returns • 3-factor, 4-factor, and 5-factor alphas

  16. Hedge Portfolio – Annual Forecasts

  17. Short- and Long-term returns

  18. Mean Returns by Quarter

  19. Median Returns by Quarter

  20. Factor Alphas – Annual Forecasts

  21. Conservatism vs. Structural Uncertainty • Analyst forecast dispersion • Proxy for precision of pre-forecast signals • Lower dispersion, greater conservatism • Intraday return volatility • Proxy for the degree of uncertainty generated by the disclosure signal • Higher volatility, more structural uncertainty

  22. Research Design AbRet = β0+ β1 QSurprise + β2 QSurprise * Dispersion + β3 QSurprise * Intraday Vol + β4 Dispersion + β5 Intraday Vol + ∑ βj Risk Controls • Prediction: β2 < 0 and β3 > 0 • AbRet (future 12-month size-adjusted return) • Fama-MacBeth regressions with Newey-West corrected standard errors • QSurprise is a quintile variable re-scaled from 0 to 1. • Dispersion and IntradayVol are dummy variables based on median.

  23. Table 6 – Theories

  24. Disclosure Quality (H3) • Accuracy = -1*|Earnt-1 – Man Forecastt-1| / Price • Precision: Point versus range forecasts • Horizon: # of days between forecast and Fiscal-year end

  25. Research Design AbRet = β0+ β1 QSurprise + ∑ βi QSurprise * Disclosure Quality + ∑ βj Disclosure Quality + ∑ βm Risk Controls • Prediction: • βj < 0 for Accuracy • βj < 0 for Precision • βj > 0 for Horizon • QSurprise is a quintile variable re-scaled from 0 to 1. • Disclosure Quality are dummy variables based on median.

  26. Table 7 – Disclosure Quality

  27. PMFD vs. PEAD

  28. Summary of Findings • The short-term reaction larger for bad news • The future long-term reaction is larger for good news • A hedge portfolio earns annual abnormal returns above 25% • Both analyst forecast dispersion and intraday volatility explain the cross-sectional variation in the PMFD • Hedge returns smaller for firms with higher prior forecast accuracy

  29. Implications • Significant underreaction to management forecasts. • Results consistent with behavioral theories of conservatism (Barberis et al., 1998) and rational structural uncertainty (Brav and Heaton, 2002) • Disclosure quality appears to reduce the market underreaction to management forecasts • An unresolved question is why the effect is not arbitraged away – As Merton (1987) states: “…an anomaly must uncovered and learned before one can arbitrage…even when learned, there are limits to arbitrage…”