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# Earnings Surprises and Signal Analysis - PowerPoint PPT Presentation

Earnings Surprises and Signal Analysis. matt mcConnell David Nabwangu Eskil Sylwan Johnson Yeh. Agenda. Background Hypothesis Methodology Data Fitting Explanatory Variables Regression Results Conclusion. Market Reaction to News. In an Ideal World. Reality. In a More Realistic World.

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### Earnings Surprises and Signal Analysis

matt mcConnell

David Nabwangu

Eskil Sylwan

Johnson Yeh

• Background

• Hypothesis

• Methodology

• Data Fitting

• Explanatory Variables

• Regression Results

• Conclusion

In an Ideal World

Reality

In a More Realistic World

We expect stock price to react to news like this:

Delay

Overshoot

Settling Time

AnnouncementDate

• 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

• 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

• In some instances the data fit very well

• In some instances fit not good

Correlation = 91.6%

Average Correlation = 80%

Correlation = 70%

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

• 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