describe the structure of the data. What is the smallest unit of observation?

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Empirical Methods for Finance First Graded Assignment Analysts’ Earnings Forecasts

Companies release their earnings-per-share (EPS) on scheduled announcement dates, typically every quarter. Preceding earnings announcements, financial analysists’ publish their forecasts of companies’ earnings, based on their expectations about companies’ growth and profitability.

In this assignment, you empirically investigate the determinants of analysts’ forecast errors, i.e the difference between forecasted earnings and announced earnings. For this purpose, you are given two datasets, ibes 2019.dta and crsp daily 2019.dta. The variables contained in both datasets are described at the end of this document.

1. BASIC DATA MANIPULATION [25%]

(a) In STATA, open ibes 2019.dta. Briefly describe the structure of the data. What is the smallest unit of observation?

(b) Rename anndats act as date, then merge (m:1) ibes 2019.dta to (using) crsp daily 2019.dta. Keep only the observations that are successfully merged. Compute companies’ market capitalization (mktcap).

(c) Compute the consensus forecast (consensus), defined as the median forecast across analysists, and the standard deviation of the forecasts (dispersion).

Then, compute the following two measures of forecast accuracy:

actual consensus actual

dispersion

• fd =|actual|

(d) Generate a variable coverage equal to the number of analysts pro- viding a forecast for a given earnings announcement.

(e) Drop the following industries from the dataset: international affairs and non-op. establishments (SIC 9000-9999), foreign governments (SIC 8888), utilities (SIC 4000-4999) and agricolture, fishing and hunting (SIC 0000-0999).

Create a dummy variable financials for financial firms (SIC 6000- 6999).

2. SUMMARY STATISTICS AND PLOTS [30%]

(a) How many distinct earnings announcements events are in the data?

(b) Collapse the data at earnings announcement level and keep the mean of fe, fd, coverage, mktcap, financials.

(c) Produce a summary statistics table with the mean, standard devia- tion, min and max of all the variables in the dataset. Include also a correlation matrix between all the variables.

(d) Do a scatter plot of fe against coverage, and of fd against coverage. Label the axes in a meaningful way. Briefly comment on the two charts.

3. OLS: ESTIMATION AND INTERPRETATION OF THE RESULTS [30%]

(a) Run a regression of fe on financials and then of fe on financials and coverage. Interpret the coefficients and the R2 of both regres- sions.

(b) Re-run the above regression, where you additionally control for mktcap. How does the interpretation of the coefficient on coverage change?

(c) Units of measurement.

i. Scale the variable mktcap such that it gives the company market capitalization in billions of dollars. Re-run the last regression. How does the coefficient change? And the t-statistic? Explain.

ii. Suppose that you are allowed to report only two decimals in your tables. Do you see a problem? What would you do?

(d) Under which assumptions can we interpret the coefficient on coverage as measuring the causal effect of analysts’ coverage on forecast accu- racy? What threats to the identification of the causal effect do you see in this case?

4. OLS: ASSUMPTIONS AND MECHANICS [15%]

(a) After running the previous regression of of fe on financials, coverage

and mktcap, compute1

N

uˆi(yˆi − y¯).

i=1

1Hint: after the estimation use predict and predict, residuals to obtain the fitted values and the residuals.

What is the result of the summation? Explain.

(b) [THEORY qUESTION] Consider the following population model yi = β0 + β1Di + β2xi + ε

where Di is a dummy variable = 1 if i is a financial firm. What would be the consequence of estimating the model without Di?

Datasets and Variables Description

The dataset ibes 2019.dta contains the following variables:

• permno: CRSP unique stock level identifier

• analys: analyst identifier

• estimator: analyst’s firm identifier

• value: earnings (EPS) forecast

• actual: realized earnings (EPS)

• anndats: date of forecast announcement

• anndats act: date of actual earnings announcement

The dataset crsp daily 2019.dta contains the following variables:

• permno: CRSP unique stock level identifier

• date: date

• siccd: industry code

• prc: stock price

• shrout: number of shares outstanding (in thousands)

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