MFIN6210 Week 5 (Lab Sessions, in regular weekly classes, Monday, Thursday)

Objective: To test whether US acquiring firms with more anti-takeover provisions (ATPs) make poorer investment decisions.

Make sure you bring your notebook/touchscreen/computer.

Lab session: Data analysis I (manipulation and basic STATA commands). See lab folder on Moodle for additional readings and data. The Computer Lab classes in Weeks 4 and 5 are in your regular classes using “myAccess”,, which gives you access to both MS Excel and STATA-SE 14 on your own laptop/computer. It also gives you access at home, as well as at UNSW.

Useful  readings:

· Masulis, R., Wang, C. and F. Xie, ‘Agency problems at dual-­‐class companies’, Journal of Finance 62, 2008, 1851-1889. (See Moodle, Lab sessions and group project)


1. Open the dataset (lab_dataset.xls) in Microsoft excel (available from Moodle). The spreadsheet contains data on US acquiring firms, comprising of:  (1)  acquiring  firm   cumulative   abnormal   returns;   (2)   accounting and industry data; and (3) governance, deal and other  financial characteristics. We will follow closely the test performed by Masulis et al. (2008) in Table 6 (Panel A and B), but will substitute our proxies for governance (APTs) for their excess control rights variable (Ratio).

2. I have created a worksheet in the spreadsheet  called ‘my work’. Using the  raw data from the dataset, create variables in ‘my work’ that are required to perform the  replication of the Masulis  et al., (2008) test. You can use  the  @if command to create dummy variables for some  of  the  deal  characteristics (e.g., public, private, subsidiary, cash, stock,  diversifying). Note that you also have industry  average  variables,  so  you  could  also create   industry-­‐adjusted   variables   for    alternative    dependent variables using value and performance metrics (i.e., Q, ROA,  etc).  You should be aware of the implicit assumptions you are  making  when using value  metrics for  a  sample  of  acquiring  firms  (hint  –  Q  will  be measured for the year prior to takeover).

3. Create dummy variables for high/low ATP firms for the GIM index and the BCF  index.  Use  cut-­‐offs  of  GIM=>10,  <10  and  BCF=>3,  <3  for  high/low portfolios. Use these cut-­‐ offs to perform a univariate analysis of whether high/low ATP firms (using GIM, BCF, CBOARD) have significantly different deal and firm characteristics.

4. Create a new spreadsheet (CSV, comma delimited) for input to STATA. Your spreadsheet should contain the cumulative abnormal returns (CAR) for all acquiring firms plus variables (governance,  deal  and  financial characteristics, including newly created variables) that may explain the cross-­‐sectional differences in CARs and other value metrics. Note that you will also have to create a CAR dummy (=1, if CAR<0, 0 otherwise) and interactive dummies for public status and method-of-payment). You can  create the variables in excel or use the generate command in STATA. Open STATA. Use the file-­‐import command  to  read  the  spreadsheet  (text; csv) into STATA.

5. Calculate summary statistics for the variables in your worksheet (mean, median, minimum, maximum, standard deviation). What do you infer from  the descriptive statistics?

6. Estimate pairwise correlation coefficients for all variables in your spreadsheet. On examination of the pair-­‐wise correlations, which variables, if any, are likely to pose multicollinearity problems (denote which are significant at the 5% level)?

7. Estimate standard OLS regressions (statistics-­‐linear models-­‐linear regression) to test whether CARs can be explained by governance, deal, acquiring firm characteristics. I suggest you estimate several models, each containing a governance variable (i.e., GIM, GIM-dummy, BCF, BCF- dummy, CBOARD). Re-estimate using the CAR dummy variable. Re- estimate for different sample specifications: (1) completed deals;  (2) TV>$1m.

8. Perform standard OLS regression diagnostics (i.e., heteroskedasticity, multicollinearity) to establish if your regressions are well-­‐specified. Is there any year or industry-­‐fixed effects in your regressions? (Hint: include year and/or industry dummies). Are the standard errors robust to  possible clustering by acquiring firms? (Hint: use robust cluster options for acquirer identity code (npermno)).

9. Perform additional  robustness tests:  (1) outliers  (use the winsor command   to test if outliers impact on your  results).  {winsor  car,  gen(carwin)  p(0.01)}.

10. Replicate the model in Masulis et al. (2008) Table 6 (Panel B). You will need to generate a new dependent variable to denote failed and successful deals (call it ‘compm’ to denote completed as defined in Masulis et al (2008).

11. Create a STATA do file to automate  the above. You can augment  your do  file from week 6. This do file will be useful for your project.


TobinQ : (AE-AN+AK*AR) / AE


Lev (AF + AL) / (AE-AN+AK*AR)

Inda_q TobinQ – BN

ssc install winsor