Modeling Long-Term Stock Returns After Earnings Surprises

# How to model the effect of earnings surprises on long-term returns?


# How to model the effect of earnings surprises on long-term returns?












I'm looking into modeling the relationship between EPS announcement surprises with long-term returns (1 quarter to 3 years with intervals). I've based my current methodology off papers looking at the short term effect (example) but I think that the long time horizon will require a more comprehensive solution.

My ultimate goal is to be able to say with some degree of certainty whether or not beating or missing analysts' EPS estimates has a long term effect on the performance of a stock.

I've set up a regression with variables as follows:

I've defined EPS announcement surprises as

$$ \text{SUPRISE}_i=\dfrac{\text{EPS}_{actual,i}-\text{EPS}_{estimate,i}}{\text{EPS}_{actual,i}} $$

to create 2 variables for positive and negative surprises (POSSUPRISE and NEGSUPRISE)

Defined Returns as

$$ \text{RETURN}_t=\ln(\text{price}_{i+t})-\ln(\text{price}_i) $$

where $t$ is the final day of the time period I am analyzing

so my current regression looks like this

$$ \text{RETURN}_t = \beta_0 + \beta_p \text{POSSUPRISE}_{i}+\beta_n \text{NEGSUPRISE}_{i}+\epsilon_t $$

I've also done a regression with indicator variables for beating and missing estimates

I've run this over a sample set of 30 large cap stocks with EPS data from 1999-2009 and the appropriate pricing data and so far have had mixed results, I found some correlation between 2 year returns and large earnings surprises, but before I explore this question further, I want to make sure I'm going about it the right way

My questions are:

- Is a regression of individual instances the best way to analyze this problem? Should I use time series methods like VAR instead?

- What is the best way to incorporate broad market movement into the returns data? Should I just adjust the return variable to account for the return on an index over the time period as well or is there a better solution?

- Am I better off just considering the surprise variable or should I try to control for other variables in the model such as actual EPS, Market Cap, etc?

## Answer by RndmSymbl (score 1, accepted)

https://quant.stackexchange.com/a/15230

Given that other corporate events are reasonably modelled through regression models (compare The Detection of Earnings Manipulation I would try for using an regression approach. I believe a more recent and related paper has been published but I don't seem to find it at this time. Edit: and now I did - Earnings Manipulation and Expected Returns

That said, you may have specific reasons for looking only at market data. If that is the case I would intuitively suggest to try to control for industry sector, firm size, market capitalisation or percent of floating shares. It is reasonable to assume that any of these attributes influence firms management disposition to try to (positively) surprise the market intentionally. Say, the firm is in a niche industry, small with low capitalisation and float; a surprise will not affect the managements personal wealth nearly as much as if firm belongs to the top 10 companies in each classification.