1. Earnings Surprises and Signal Analysis
matt mcConnell
David Nabwangu
Eskil Sylwan
Johnson Yeh

2. Agenda Background Hypothesis Methodology Data Fitting
Explanatory Variables
Regression Results
Conclusion

3. Market Reaction to News
We expect stock price to react to news like this:
In a More Realistic World
In an Ideal World
Reality
Delay
Overshoot
Settling Time
Announcement Date

4. Step Response of a Second-Order System
Parameter
Effect
Initial Level
Level before reaction begins
Offset
Beginning of reaction
Magnitude
Size of reaction ωn
Shape of curve ωd ζ + -

5. Hypothesis
Abnormal returns after earnings surprises follow a curved pattern which can be modeled using the step response of a second-order system
6 Curve parameters are predictable using information about the company
* Results could be applicable to any news item – earnings are measurable

6. Methodology Identify earnings surprises (Factset)
600 events, 100 companies
Retrieve price and other data series (Datastream)
Calculate abnormal returns in ±30 day window
Fit a curve to each event
Least squares method with solver
6 parameters for each event
Regress 6 parameters on several explanatory variables

7. Fitting the Data Average Correlation = 80%
In some instances the data fit very well
In some instances fit not good
Correlation = 91.6%
Average Correlation = 80%
Correlation = 70%

8. Explanatory Variables
6 explanatory variables for curve parameters
Quarterly Earnings Surprise %
Positive influence on magnitude, Zeta
1-Year Price Growth
Negative Impact on Offset, Positive Impact on Magnitude, and Zeta
Quarterly Earnings Surprise $
Positive Impact on Magnitude, Zeta
Price to Earnings Ratio
Positive Impact on Offset, Negative Impact on Zeta
Beta
Positive Impact on wm, wd, & Magnitude
10-Day Abnormal Return
Negative Impact on Magnitude

9. Regression Results

10. Conclusion Holds promise: Some predictive power Paths forward:
Better fitting method
Least squares method more applicable to linear
Improve predictive regressions
More predictor variables
Non-linear predictor variables
Test predictability over time
Larger data set
Create and test trading strategies