Earnings Announcements and Price Behavior Sam Lim

A Little Background  Information Content of Earnings Announcements  Beaver 1968  Landsman and L. Maydew 2002  Abnormal volatility  Volatility increases around quarterly earnings announcements  Kinney et al 2002  “Surprise” materiality in returns  Most surprises and returns are of same sign, but 43 to 45% of firms’ surprises associated with returns of opposite sign  S-shaped surprise return relation  Use HAR-RV as suggested

Summary of last time  HAR-RV   Earnings surprise factor (percentage)  ( EPS actual - EPS estimate ) / EPS actual * 100  Split-sign regression  Statistically significant positive findings  Surprise correlated with increase in volatility  Not too surprising.

(continued)  Standardize returns, as suggested by Dr. Tauchen  Return divided by square root of RV  Alison Keane finds weekly RV works relatively better than daily or monthly RV, so I follow suit  Mostly same result–surprise often correlated with overnight returns, but not intraday returns. Previously, had a problem— surprise sometimes correlated with intraday returns. Turns out F-statistic is very low in those cases, so cannot reject null hypothesis that surprise does not determine direction of intraday returns.  Price corrections happen fairly quickly, before market open

Not all firms of same interest  E.g. Goldman Sachs  GS  32 positive surprises, 3 negative surprises, 1 hits estimate (no surprise). Not very interesting.  Positive surprises positively correlated with volatility at.1% level, positively correlated with overnight returns at 1% level, no correlation with intraday returns.  McDonald’s  14 positive surprises, 10 negative surprises, 19 hits estimate

McDonald’s  4/9/1997 to 1/7/2009  14 positive, 10 negative, 19 no surprise  Did not account for quarters when firm just hits estimate (SURPRISE=0), so generate dummy variable for earnings release with no surprise.  Generate dummy variables for positive and negative days as well, for comparison purposes.  May be better to do anyway?

MCD  HAR-RV regression, omit RV 1, RV 5, RV 22 for simplicity.  All significant at.1% level  Nice results? Fits with theory that negative news has more impact on the market than positive news.  No surprise days also increase volatility!  Why?  Analyst estimates discounted?  Dispersion of estimates?  Hopefully not, but could also be release of other news on same day. RV t PositiveNegativeNo surprise Coefficient

Another look – Merck, UPS, Pepsi  MRK - 17 positive surprises, 7 negative, 19 no surprises  Positive significant at 5% level, negative and no surprise significant at 1% level  UPS - 19 positive, 5 negative, 9 no surprises  All significant at.1% level  PEP – 24 positive, 8 negative, 11 no surprises  Positive and no surprise significant at.1%, negative at 1%  Though positive coefficient is larger… RV t PositiveNegativeNo surprise Coefficient RV t PositiveNegativeNo surprise Coefficient RV t PositiveNegativeNo surprise Coefficient

Accounting for dispersion?  Account for dispersion in analyst estimates, as suggested by Dr. Bollerslev  The less consensus among analysts, the less information the market has (mean estimate has less informative value)  Interaction term created with standard deviation of analyst estimates  No surprise days (using McDonald’s)  No surprise significant at 5%, dispersion at.1%, interaction at 10% RV t No surpriseDispersionInteraction Coefficient

Dispersion, continued  Makes more sense using absolute value of surprise, but this begs the question of whether I should use the surprise value, or the dummy values.  All statistically significant at.1% level. RV t |SURPRISE|DispersionInteraction Coefficient

Another issue: Sub-sampling

Walmart: Importance of sampling rate  28 positive, 6 negative, 10 no surprise  Sampled at 15 minutes Positive significant at 5%, no surprise significant at 10%  Sampled at 10 minutes Negative significant at 10%  Problematic? RV t PositiveNegativeNo surprise Coefficient1.03Not significant1.24 RV t PositiveNegativeNo surprise CoefficientNot significant1.66Not significant

Further work  Have a list of different S&P 100 firms, is there some systematic pattern to results? Industry?  When looking at returns, picture further complicated.  Do announcements of one firm affect another firm’s stock behavior?  Jumps?  Last time, concluded jumps do not occur in higher frequencies on earnings release dates. Perhaps this is not the case?  IBM – 27.9% jumps (BNS test at 5% level) on earnings release dates, 16.3% other days.. Similar numbers for Intel.