Loading the dataset

The data files are downloaded from LSEG terminal and stored locally. Due to large size, the data is split into multiple files. The following code reads the files and combines them into a single data set. The sample period is from 01/01/1994 to 03/31/2006. The data is stored in four separate files, each containing a portion of the data set. The files are named as follows:

# Read the data files stored as xlsx, 
sdc_raw_zero <- read_csv("data/raw/sdc_1994_1996.csv")
sdc_raw_one <- read_csv("data/raw/sdc_1997_1999.csv")
sdc_raw_two <- read_csv("data/raw/sdc_2000_2002.csv")
sdc_raw_three <- read_csv("data/raw/sdc_2003_2006.csv")


# Append these dataset into one dataset

sdc_raw <- bind_rows(sdc_raw_zero, sdc_raw_one, sdc_raw_two, sdc_raw_three)

# Save as csv
write_csv(sdc_raw, "data/raw/sdc_raw_full.csv")

n<-as.character(nrow(sdc_raw))

# Write as gz as a compressed file for Github

gzip("data/raw/sdc_raw_full.csv", destname = "data/raw/sdc_raw_full.csv.gz", overwrite = TRUE)
# Deal status 

# Read the raw data from the csv file

ma_type_raw_zero <- read_csv("data/raw/sdc_ma_type_1994_1996.csv")
ma_type_one <- read_csv("data/raw/sdc_ma_type_1997_1999.csv")
ma_type_two <- read_csv("data/raw/sdc_ma_type_2000_2002.csv")
ma_type_three <- read_csv("data/raw/sdc_ma_type_2003_2006.csv")

# Append these dataset into one dataset

ma_type_raw <- bind_rows(ma_type_raw_zero, ma_type_one, ma_type_two, ma_type_three)



ma_type_raw <- ma_type_raw %>%
  rename_with(~ tolower(.x), everything()) %>%
  rename_with(~ str_replace_all(.x, "\\.", "-"), everything())

ma_type_raw <- ma_type_raw %>%
  rename(
    da="date announced",
    ma_type = "m&a type (code)",
    deal_number = "sdc deal no",
    deal_statis = "deal status") %>% 
  select(-"da")

# Save as csv

write_csv(ma_type_raw, "data/raw/sdc_ma_type_full.csv")

The raw variables name and their definition

# Show the names of the variables in the raw dataset

names(sdc_raw)
##  [1] "Date Announced"                                                      
##  [2] "Date Originally Announced"                                           
##  [3] "Date Effective"                                                      
##  [4] "Implied Deal Value (USD Millions)"                                   
##  [5] "Deal Value (USD Millions)"                                           
##  [6] "Acquiror Macro Industry"                                             
##  [7] "Target Macro Industry"                                               
##  [8] "Acquiror Total Assets Last 12 Months (USD Millions)...8"             
##  [9] "Acquiror 6-digit CUSIP"                                              
## [10] "Acquiror Industry Group"                                             
## [11] "Acquiror Industry Sector"                                            
## [12] "Acquiror Total Assets Last 12 Months (USD Millions)...12"            
## [13] "Percentage of Shares Held by Acquiror 6 Months Prior to Announcement"
## [14] "Percentage of Shares Acquired in Transaction"                        
## [15] "Percentage of Shares Owned after Transaction"                        
## [16] "SDC Deal Type"                                                       
## [17] "Acquiror Primary SIC (Code)"                                         
## [18] "Target Primary SIC (Code)"                                           
## [19] "Target 6-digit CUSIP"                                                
## [20] "Deal Type"                                                           
## [21] "SDC Deal No"

The raw data contains 412655 observations. We now rename the variables for easier access.

DA: Date Announced
DOA: Date Orginally Announced
DE: Date Effective
IDV: Implied Deal Value
DV: Deal Value
ACQ_MACRO_IND: Macro Industry Code of the Acquirer
TAR_MACRO_IND: Macro Industry Code of the Target
TAR_CUSIP: Target's CUSIP
ACQ_TOTALASSET_12: Acquirer's Total Assets (12 months prior to the deal)
ACQ_CUSIP: Acquirer's CUSIP
ACQ_INDUSTRY: Acquirer's Industry Group
ACQ_SECTOR: Acquirer's Sector
ACQ_SIC: Acquirer's Primary SIC Code
TAR_SIC: Target's Primary SIC Code
DEAL_TYPE: Type of Deal
DEAL_NUMBER: SDC Deal Number
ACQ_PERCENTAGE_OWNED_AFTER: Percentage of Shares Owned by Acquirer After Transaction
ACQ_SHARE: Percentage of Shares Acquired in Transaction
ACQ_PERCENTAGE_OWNED_BEFORE: Percentage of Shares Held by Acquirer 6 Months Prior to Announcement

Cleaning variables

# Renaming the variables. First uncapitalize the names, remove the dots, and then rename them, remove space,

sdc <- sdc_raw %>%
  rename_with(~ tolower(.x), everything()) %>%
  rename_with(~ str_replace_all(.x, "\\.", "-"), everything())


# Drop acquiror total assets last 12 months (usd millions)---15 (duplication)

sdc <- sdc %>%
  select(-"acquiror total assets last 12 months (usd millions)---12")

names(sdc)
##  [1] "date announced"                                                      
##  [2] "date originally announced"                                           
##  [3] "date effective"                                                      
##  [4] "implied deal value (usd millions)"                                   
##  [5] "deal value (usd millions)"                                           
##  [6] "acquiror macro industry"                                             
##  [7] "target macro industry"                                               
##  [8] "acquiror total assets last 12 months (usd millions)---8"             
##  [9] "acquiror 6-digit cusip"                                              
## [10] "acquiror industry group"                                             
## [11] "acquiror industry sector"                                            
## [12] "percentage of shares held by acquiror 6 months prior to announcement"
## [13] "percentage of shares acquired in transaction"                        
## [14] "percentage of shares owned after transaction"                        
## [15] "sdc deal type"                                                       
## [16] "acquiror primary sic (code)"                                         
## [17] "target primary sic (code)"                                           
## [18] "target 6-digit cusip"                                                
## [19] "deal type"                                                           
## [20] "sdc deal no"
# renaming variable
sdc <- sdc %>%
  rename(
    da="date announced",
    doa="date originally announced",
    de="date effective",
    idv="implied deal value (usd millions)",
    dv="deal value (usd millions)",
    acq_macro_ind = "acquiror macro industry",
    tar_macro_ind = "target macro industry",
    acq_totalasset= "acquiror total assets last 12 months (usd millions)---8",
    acq_cusip = "acquiror 6-digit cusip",
    tar_cusip = "target 6-digit cusip",
    acq_ind = "acquiror industry group",
    acq_sec = "acquiror industry sector",
    acq_sic = "acquiror primary sic (code)",
    tar_sic = "target primary sic (code)",
    deal_type = "deal type",
    deal_number = "sdc deal no",
    acq_percentage_owned_after = "percentage of shares owned after transaction",
    acq_share ="percentage of shares acquired in transaction",
    acq_percentage_owned_before = "percentage of shares held by acquiror 6 months prior to announcement")

names(sdc)
##  [1] "da"                          "doa"                        
##  [3] "de"                          "idv"                        
##  [5] "dv"                          "acq_macro_ind"              
##  [7] "tar_macro_ind"               "acq_totalasset"             
##  [9] "acq_cusip"                   "acq_ind"                    
## [11] "acq_sec"                     "acq_percentage_owned_before"
## [13] "acq_share"                   "acq_percentage_owned_after" 
## [15] "sdc deal type"               "acq_sic"                    
## [17] "tar_sic"                     "tar_cusip"                  
## [19] "deal_type"                   "deal_number"

Filtering

Deal Type

The data set contains all type of transactions. We only keep mergers and acquisitions. The variable deal_status contains information on whether the transactions is completed or not. We only care about completed transactions. The variable ma_type is an indicator whether the transaction is mergers or acquisitions. We will filter out transactions that are not mergers or acquisitions. We only keep transactions where the acquirer acquires a majority stake of the target. The variable deal_number is the unique identifier for each transaction.

sdc %>%
  count(deal_statis, name = "count") %>%
  arrange(desc(count)) %>%
  kable("html", col.names = c("Deal Number", "Count")) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%", height = "500px")
Deal Number Count
Completed 310398
Pending 41314
Intended 16978
Status Unknown 16033
Seeking Buyer 13251
Withdrawn 11258
Dismissed Rumor 2207
Seeking Buyer Withdrawn 753
Intent Withdrawn 277
Unconditional 176
NA 64
Partially Completed 1
Pending Regulatory 1

We only keep completed transactions:

sdc <- sdc %>%
  filter(deal_statis == "Completed")

n_sdc_completed <- as.character(nrow(sdc))

We have 310398transactions. We now look at the deal type. The variable ma_type contains the type of transaction. We only keep mergers and acquisitions.

DI: Disclosed value; acquiror gains ≥50% or raises stake above 50%, or acquires remaining interest.

UN: Undisclosed value; acquiror gains ≥50% or raises stake above 50%, or acquires remaining interest.

SP: Minority stake acquired (≤49.99% or 50.1–99.9%).

RE: Repurchase program or share repurchase.

ST: Self-tender offer, recapitalization, or exchange offer.
sdc %>%
  count(ma_type, name = "count") %>%
  mutate(sample_percentage = count / sum(count)) %>%
  arrange(desc(count)) %>%
  kable("html", col.names = c("Deal Type", "Count", "%")) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%", height = "500px")
Deal Type Count %
UN 141157 0.4547613
DI 110308 0.3553760
SP 54574 0.1758194
RE 3510 0.0113081
ST 849 0.0027352 
# only keep mergers and acquisitions

sdc <- sdc %>%
  filter(ma_type %in% c("DI", "SP"))

# count

n_sdc_ma <- as.character(nrow(sdc))

sdc %>%
  count(ma_type, name = "count") %>%
  mutate(sample_percentage = count / sum(count)) %>%
  arrange(desc(count)) %>%
  kable("html", col.names = c("Deal Type", "Count", "%")) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%", height = "500px")
Deal Type Count %
DI 110308 0.6690118
SP 54574 0.3309882

Post-Merger Ownership

After dropping, we have 164882 transactions. We now only keep transactions where the acquirer purchases at least 50% of the target’s shares and where the acquirer’s post-merger ownership is at least 90%.

# Showing a distribution of post-merger ownership in before filtering

sdc %>%
  ggplot(aes(x = acq_percentage_owned_after)) +
  geom_histogram(binwidth = 0.05, fill = "blue", color = "black") +
  labs(title = "Distribution of Post-Merger Ownership",
       x = "Post-Merger Ownership",
       y = "Count") +
  theme_minimal()

# only restrict to post-merger ownership greater than 90\%
sdc <- sdc %>%
  filter(acq_percentage_owned_after >= 90)

# count

sdc_post_ownership <- as.character(nrow(sdc))

After the drop, we have 93243 transactions. Here is the new distribution.

# Showing a distribution of post-merger ownership in before filtering

sdc %>%
  ggplot(aes(x = acq_percentage_owned_after)) +
  geom_histogram(binwidth = 0.05, fill = "blue", color = "black") +
  labs(title = "Distribution of Post-Merger Ownership",
       x = "Post-Merger Ownership",
       y = "Count") +
  theme_minimal()

Industry

We use SIC code to exclude banks and utility companies. Currently we have the following SIC codes. Banking SIC codes is between 6020 - 6099. Utilities SIC codes is between 4900-4999.

Acquiror Industry in Sample

sdc %>%
  count(acq_sic, name = "count") %>%
  mutate(sample_percentage = count / sum(count)) %>%
  arrange(desc(count)) %>%
  kable("html", col.names = c("Deal Type", "Count", "%")) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%", height = "500px")
Deal Type Count %
6799 13559 0.1454157
7372 3955 0.0424161
6798 3610 0.0387160
1311 2476 0.0265543
6021 1656 0.0177600
7375 1544 0.0165589
6552 1455 0.0156044
4813 1331 0.0142745
6000 1302 0.0139635
7011 1059 0.0113574
6311 1057 0.0113360
4911 1025 0.0109928
2834 1003 0.0107568
7373 976 0.0104673
1041 958 0.0102742
6282 869 0.0093197
3674 868 0.0093090
6022 751 0.0080542
7389 751 0.0080542
4832 742 0.0079577
4812 654 0.0070139
7379 617 0.0066171
5812 614 0.0065849
8711 587 0.0062954
6211 581 0.0062310
7371 548 0.0058771
6531 530 0.0056841
3714 497 0.0053302
4841 477 0.0051157
8742 473 0.0050728
3841 467 0.0050084
3661 445 0.0047725
7361 443 0.0047510
4953 439 0.0047081
3679 432 0.0046331
5411 425 0.0045580
4833 419 0.0044936
6726 413 0.0044293
3663 405 0.0043435
2836 389 0.0041719
7374 384 0.0041183
7376 362 0.0038823
2731 355 0.0038073
6512 350 0.0037536
2711 336 0.0036035
1522 332 0.0035606
3577 329 0.0035284
6141 315 0.0033783
5511 311 0.0033354
3089 306 0.0032817
8748 305 0.0032710
6035 303 0.0032496
4899 300 0.0032174
2082 299 0.0032067
2721 290 0.0031102
8099 290 0.0031102
3823 287 0.0030780
6411 283 0.0030351
3312 277 0.0029707
7999 275 0.0029493
8731 273 0.0029278
3845 270 0.0028957
8051 267 0.0028635
3842 263 0.0028206
2819 261 0.0027991
5045 254 0.0027241
3669 252 0.0027026
7311 252 0.0027026
7359 250 0.0026812
7812 250 0.0026812
3812 249 0.0026704
4724 244 0.0026168
4412 242 0.0025954
3571 239 0.0025632
5813 238 0.0025525
4941 235 0.0025203
1521 227 0.0024345
3711 227 0.0024345
3241 226 0.0024238
5065 221 0.0023702
1381 219 0.0023487
5122 218 0.0023380
6331 215 0.0023058
3569 212 0.0022736
3829 207 0.0022200
2086 206 0.0022093
8062 204 0.0021878
2821 200 0.0021449
5311 199 0.0021342
3559 194 0.0020806
2099 192 0.0020591
6712 191 0.0020484
2621 188 0.0020162
4731 188 0.0020162
2899 186 0.0019948
1389 184 0.0019733
6722 184 0.0019733
8071 181 0.0019412
3672 175 0.0018768
2911 173 0.0018554
4213 171 0.0018339
5961 169 0.0018125
4922 166 0.0017803
3728 164 0.0017588
5912 163 0.0017481
2084 161 0.0017267
3533 160 0.0017159
6162 158 0.0016945
3651 156 0.0016730
2869 155 0.0016623
8011 155 0.0016623
6324 150 0.0016087
2671 148 0.0015873
1221 145 0.0015551
3572 141 0.0015122
7382 141 0.0015122
2844 137 0.0014693
1099 135 0.0014478
4011 134 0.0014371
4581 134 0.0014371
3585 131 0.0014049
5999 131 0.0014049
3825 129 0.0013835
5141 129 0.0013835
7363 129 0.0013835
1382 128 0.0013728
5047 128 0.0013728
8082 127 0.0013620
3949 126 0.0013513
8741 126 0.0013513
2752 125 0.0013406
3826 125 0.0013406
8093 125 0.0013406
2835 124 0.0013299
7353 123 0.0013191
6289 121 0.0012977
1021 120 0.0012870
4512 118 0.0012655
7312 117 0.0012548
7349 117 0.0012548
2851 116 0.0012441
3589 114 0.0012226
3944 113 0.0012119
5039 113 0.0012119
5063 111 0.0011904
8299 111 0.0011904
2741 109 0.0011690
5013 109 0.0011690
4931 107 0.0011475
5084 107 0.0011475
7381 107 0.0011475
7319 106 0.0011368
3541 105 0.0011261
8721 105 0.0011261
2841 104 0.0011154
3999 104 0.0011154
1499 103 0.0011046
3861 103 0.0011046
2299 102 0.0010939
1731 101 0.0010832
6159 99 0.0010617
8732 98 0.0010510
4923 97 0.0010403
6719 97 0.0010403
6036 96 0.0010296
3357 95 0.0010188
3731 95 0.0010188
4491 94 0.0010081
5051 94 0.0010081
3272 93 0.0009974
2051 92 0.0009867
3069 92 0.0009867
3317 92 0.0009867
2611 91 0.0009759
8743 91 0.0009759
2676 90 0.0009652
5112 90 0.0009652
3724 88 0.0009438
6513 86 0.0009223
2421 85 0.0009116
3443 83 0.0008901
2879 82 0.0008794
3011 82 0.0008794
4924 82 0.0008794
2026 81 0.0008687
1061 80 0.0008580
4212 80 0.0008580
2329 79 0.0008472
3721 78 0.0008365
5149 78 0.0008365
3593 77 0.0008258
4215 77 0.0008258
499A 77 0.0008258
1541 76 0.0008151
7261 76 0.0008151
1531 75 0.0008043
5099 75 0.0008043
6371 75 0.0008043
2813 74 0.0007936
8059 74 0.0007936
2211 73 0.0007829
5031 73 0.0007829
5172 73 0.0007829
2033 72 0.0007722
2759 72 0.0007722
2041 71 0.0007615
2631 71 0.0007615
5093 71 0.0007615
0811 70 0.0007507
2011 70 0.0007507
3523 70 0.0007507
1611 69 0.0007400
1799 69 0.0007400
2013 69 0.0007400
3629 69 0.0007400
3827 69 0.0007400
5044 69 0.0007400
5074 69 0.0007400
6153 69 0.0007400
1081 68 0.0007293
3851 68 0.0007293
6099 68 0.0007293
3442 66 0.0007078
3621 66 0.0007078
2111 65 0.0006971
3491 65 0.0006971
8744 65 0.0006971
3325 64 0.0006864
3531 64 0.0006864
7331 64 0.0006864
1011 63 0.0006757
2064 63 0.0006757
3411 63 0.0006757
3612 63 0.0006757
3699 63 0.0006757
2679 62 0.0006649
2873 62 0.0006649
4111 60 0.0006435
2038 59 0.0006328
4612 59 0.0006328
7997 59 0.0006328
8734 59 0.0006328
1623 58 0.0006220
3535 58 0.0006220
3555 58 0.0006220
3271 57 0.0006113
6321 57 0.0006113
6351 57 0.0006113
6733 57 0.0006113
3492 56 0.0006006
5012 56 0.0006006
2891 55 0.0005899
7841 55 0.0005899
7996 55 0.0005899
0831 54 0.0005791
1629 54 0.0005791
2023 54 0.0005791
4725 54 0.0005791
7336 54 0.0005791
7941 54 0.0005791
8351 54 0.0005791
2066 53 0.0005684
3562 53 0.0005684
5137 53 0.0005684
5731 53 0.0005684
7819 53 0.0005684
7993 53 0.0005684
3511 52 0.0005577
3561 52 0.0005577
4226 52 0.0005577
7991 52 0.0005577
1542 51 0.0005470
2052 51 0.0005470
2085 51 0.0005470
2273 51 0.0005470
3643 51 0.0005470
3652 51 0.0005470
1044 50 0.0005362
2015 50 0.0005362
3429 50 0.0005362
3843 50 0.0005362
5191 50 0.0005362
6794 50 0.0005362
3565 49 0.0005255
3613 49 0.0005255
4141 49 0.0005255
7832 49 0.0005255
7948 49 0.0005255
2331 48 0.0005148
3086 48 0.0005148
3316 48 0.0005148
3334 48 0.0005148
5621 48 0.0005148
8999 48 0.0005148
2754 47 0.0005041
3499 47 0.0005041
3743 47 0.0005041
7922 47 0.0005041
8021 47 0.0005041
2511 46 0.0004933
2833 46 0.0004933
5023 46 0.0004933
5169 46 0.0004933
5734 46 0.0004933
9511 46 0.0004933
1711 45 0.0004826
3273 45 0.0004826
3564 45 0.0004826
5064 45 0.0004826
5531 45 0.0004826
6519 45 0.0004826
2048 44 0.0004719
2092 44 0.0004719
3545 44 0.0004719
3579 44 0.0004719
3751 44 0.0004719
5085 44 0.0004719
7521 44 0.0004719
1094 43 0.0004612
2076 43 0.0004612
3297 43 0.0004612
5211 43 0.0004612
2321 42 0.0004504
3251 42 0.0004504
3448 42 0.0004504
3084 41 0.0004397
3537 41 0.0004397
3578 41 0.0004397
3678 41 0.0004397
3691 41 0.0004397
5712 41 0.0004397
7514 41 0.0004397
2021 40 0.0004290
2087 40 0.0004290
3081 40 0.0004290
3321 40 0.0004290
5984 40 0.0004290
1031 39 0.0004183
2062 39 0.0004183
3441 39 0.0004183
3575 39 0.0004183
5072 39 0.0004183
8361 39 0.0004183
3211 38 0.0004075
3532 38 0.0004075
3631 38 0.0004075
4119 38 0.0004075
3433 37 0.0003968
5171 37 0.0003968
5199 37 0.0003968
5651 37 0.0003968
5735 37 0.0003968
6029 37 0.0003968
7342 37 0.0003968
7538 37 0.0003968
0181 36 0.0003861
2037 36 0.0003861
2079 36 0.0003861
3563 36 0.0003861
3634 36 0.0003861
5942 36 0.0003861
7323 36 0.0003861
2653 35 0.0003754
2842 35 0.0003754
3231 35 0.0003754
3568 35 0.0003754
4131 35 0.0003754
5611 35 0.0003754
8221 35 0.0003754
2024 34 0.0003646
3452 34 0.0003646
3556 34 0.0003646
3822 34 0.0003646
5142 34 0.0003646
5945 34 0.0003646
6361 33 0.0003539
2761 32 0.0003432
3291 32 0.0003432
3552 32 0.0003432
3641 32 0.0003432
5944 32 0.0003432
999E 32 0.0003432
0273 31 0.0003325
2522 31 0.0003325
3255 31 0.0003325
3261 31 0.0003325
3339 31 0.0003325
3844 31 0.0003325
4789 31 0.0003325
5049 31 0.0003325
5331 31 0.0003325
7513 31 0.0003325
0139 30 0.0003217
3315 30 0.0003217
3469 30 0.0003217
3713 30 0.0003217
3911 30 0.0003217
3993 30 0.0003217
4225 30 0.0003217
4959 30 0.0003217
5032 30 0.0003217
5082 30 0.0003217
5661 30 0.0003217
7929 30 0.0003217
8063 30 0.0003217
1422 29 0.0003110
2095 29 0.0003110
2281 29 0.0003110
2431 29 0.0003110
2657 29 0.0003110
3498 29 0.0003110
3646 29 0.0003110
3732 29 0.0003110
5088 29 0.0003110
5094 29 0.0003110
5136 29 0.0003110
5148 29 0.0003110
7322 29 0.0003110
8069 29 0.0003110
999A 29 0.0003110
2035 28 0.0003003
2311 28 0.0003003
2411 28 0.0003003
3351 28 0.0003003
3599 28 0.0003003
3648 28 0.0003003
4222 28 0.0003003
5599 28 0.0003003
2499 27 0.0002896
2515 27 0.0002896
2678 27 0.0002896
2812 27 0.0002896
2865 27 0.0002896
2952 27 0.0002896
3253 27 0.0002896
3423 27 0.0002896
3462 27 0.0002896
3482 27 0.0002896
3675 27 0.0002896
3695 27 0.0002896
4822 27 0.0002896
5033 27 0.0002896
5091 27 0.0002896
5399 27 0.0002896
5541 27 0.0002896
5722 27 0.0002896
7699 27 0.0002896
3149 26 0.0002788
3399 26 0.0002788
3536 26 0.0002788
3554 26 0.0002788
3625 26 0.0002788
3873 26 0.0002788
5941 26 0.0002788
7299 26 0.0002788
7383 26 0.0002788
7549 26 0.0002788
8092 26 0.0002788
2032 25 0.0002681
3444 25 0.0002681
6231 25 0.0002681
7377 25 0.0002681
1481 24 0.0002574
2221 24 0.0002574
3143 24 0.0002574
3421 24 0.0002574
3567 24 0.0002574
3645 24 0.0002574
3677 24 0.0002574
5943 24 0.0002574
2075 23 0.0002467
2599 23 0.0002467
2673 23 0.0002467
3021 23 0.0002467
3398 23 0.0002467
3446 23 0.0002467
3496 23 0.0002467
4513 23 0.0002467
5147 23 0.0002467
7992 23 0.0002467
0241 22 0.0002359
1622 22 0.0002359
2043 22 0.0002359
2253 22 0.0002359
2519 22 0.0002359
3221 22 0.0002359
3229 22 0.0002359
3275 22 0.0002359
3353 22 0.0002359
3356 22 0.0002359
3465 22 0.0002359
3494 22 0.0002359
4499 22 0.0002359
5092 22 0.0002359
5146 22 0.0002359
8713 22 0.0002359
2083 21 0.0002252
2231 21 0.0002252
2892 21 0.0002252
3542 21 0.0002252
3639 21 0.0002252
5043 21 0.0002252
5113 21 0.0002252
5153 21 0.0002252
5192 21 0.0002252
7213 21 0.0002252
7822 21 0.0002252
1442 20 0.0002145
2022 20 0.0002145
2091 20 0.0002145
2434 20 0.0002145
2677 20 0.0002145
3341 20 0.0002145
3534 20 0.0002145
3692 20 0.0002145
3821 20 0.0002145
4142 20 0.0002145
4925 20 0.0002145
8052 20 0.0002145
999B 20 0.0002145
1222 19 0.0002038
1411 19 0.0002038
2992 19 0.0002038
3479 19 0.0002038
3592 19 0.0002038
3761 19 0.0002038
4522 19 0.0002038
4785 19 0.0002038
5143 19 0.0002038
5182 19 0.0002038
5699 19 0.0002038
5962 19 0.0002038
7335 19 0.0002038
7384 19 0.0002038
8049 19 0.0002038
8072 19 0.0002038
8244 19 0.0002038
2339 18 0.0001930
2399 18 0.0001930
2512 18 0.0001930
2771 18 0.0001930
3363 18 0.0001930
3519 18 0.0001930
3548 18 0.0001930
3824 18 0.0001930
4424 18 0.0001930
4481 18 0.0001930
4932 18 0.0001930
7021 18 0.0001930
7218 18 0.0001930
8249 18 0.0001930
1241 17 0.0001823
2435 17 0.0001823
2451 17 0.0001823
3432 17 0.0001823
3544 17 0.0001823
3581 17 0.0001823
3624 17 0.0001823
4449 17 0.0001823
5131 17 0.0001823
5261 17 0.0001823
7352 17 0.0001823
8712 17 0.0001823
2096 16 0.0001716
2335 16 0.0001716
2452 16 0.0001716
2732 16 0.0001716
3111 16 0.0001716
3324 16 0.0001716
3354 16 0.0001716
3431 16 0.0001716
3671 16 0.0001716
3942 16 0.0001716
4493 16 0.0001716
5111 16 0.0001716
5162 16 0.0001716
6163 16 0.0001716
999C 16 0.0001716
2436 15 0.0001609
2514 15 0.0001609
3199 15 0.0001609
3313 15 0.0001609
3524 15 0.0001609
3632 15 0.0001609
3694 15 0.0001609
5551 15 0.0001609
5713 15 0.0001609
6221 15 0.0001609
7378 15 0.0001609
0191 14 0.0001501
0751 14 0.0001501
2269 14 0.0001501
2326 14 0.0001501
2392 14 0.0001501
2893 14 0.0001501
3052 14 0.0001501
3369 14 0.0001501
3633 14 0.0001501
4613 14 0.0001501
5052 14 0.0001501
5139 14 0.0001501
5521 14 0.0001501
5719 14 0.0001501
5995 14 0.0001501
6553 14 0.0001501
7231 14 0.0001501
8243 14 0.0001501
0179 13 0.0001394
1791 13 0.0001394
2097 13 0.0001394
2098 13 0.0001394
2131 13 0.0001394
2241 13 0.0001394
2389 13 0.0001394
2824 13 0.0001394
3053 13 0.0001394
3365 13 0.0001394
3546 13 0.0001394
3715 13 0.0001394
4489 13 0.0001394
5193 13 0.0001394
5251 13 0.0001394
7033 13 0.0001394
7334 13 0.0001394
7629 13 0.0001394
8111 13 0.0001394
0921 12 0.0001287
1479 12 0.0001287
2077 12 0.0001287
2541 12 0.0001287
2655 12 0.0001287
2874 12 0.0001287
3085 12 0.0001287
3088 12 0.0001287
3281 12 0.0001287
3495 12 0.0001287
3594 12 0.0001287
3644 12 0.0001287
3795 12 0.0001287
4939 12 0.0001287
5714 12 0.0001287
5963 12 0.0001287
6399 12 0.0001287
7032 12 0.0001287
0912 11 0.0001180
2034 11 0.0001180
2046 11 0.0001180
2047 11 0.0001180
2061 11 0.0001180
2426 11 0.0001180
2591 11 0.0001180
2822 11 0.0001180
3262 11 0.0001180
3269 11 0.0001180
3292 11 0.0001180
3366 11 0.0001180
3449 11 0.0001180
3471 11 0.0001180
3931 11 0.0001180
4214 11 0.0001180
4482 11 0.0001180
5083 11 0.0001180
5499 11 0.0001180
5992 11 0.0001180
8211 11 0.0001180
8322 11 0.0001180
8611 11 0.0001180
1474 10 0.0001072
1761 10 0.0001072
2251 10 0.0001072
2252 10 0.0001072
2295 10 0.0001072
2337 10 0.0001072
2341 10 0.0001072
2493 10 0.0001072
2796 10 0.0001072
2816 10 0.0001072
3061 10 0.0001072
3451 10 0.0001072
3549 10 0.0001072
3596 10 0.0001072
3676 10 0.0001072
3799 10 0.0001072
4121 10 0.0001072
4151 10 0.0001072
4729 10 0.0001072
4783 10 0.0001072
5075 10 0.0001072
5181 10 0.0001072
5198 10 0.0001072
5641 10 0.0001072
5921 10 0.0001072
619A 10 0.0001072
999D 10 0.0001072
0172 9 0.0000965
0742 9 0.0000965
1459 9 0.0000965
2045 9 0.0000965
2284 9 0.0000965
2297 9 0.0000965
2672 9 0.0000965
2861 9 0.0000965
3083 9 0.0000965
3484 9 0.0000965
3914 9 0.0000965
3995 9 0.0000965
4492 9 0.0000965
5021 9 0.0000965
5159 9 0.0000965
5461 9 0.0000965
6081 9 0.0000965
619B 9 0.0000965
6514 9 0.0000965
7215 9 0.0000965
7515 9 0.0000965
8733 9 0.0000965
0212 8 0.0000858
0711 8 0.0000858
0781 8 0.0000858
1446 8 0.0000858
2063 8 0.0000858
2261 8 0.0000858
2342 8 0.0000858
2387 8 0.0000858
2491 8 0.0000858
2652 8 0.0000858
3082 8 0.0000858
3331 8 0.0000858
3497 8 0.0000858
3553 8 0.0000858
3716 8 0.0000858
3951 8 0.0000858
3965 8 0.0000858
5571 8 0.0000858
5932 8 0.0000858
5946 8 0.0000858
5983 8 0.0000858
7221 8 0.0000858
7623 8 0.0000858
7833 8 0.0000858
7933 8 0.0000858
8042 8 0.0000858
8331 8 0.0000858
9223 8 0.0000858
9621 8 0.0000858
0721 7 0.0000751
0762 7 0.0000751
0782 7 0.0000751
1321 7 0.0000751
1751 7 0.0000751
2067 7 0.0000751
2121 7 0.0000751
2396 7 0.0000751
2439 7 0.0000751
2448 7 0.0000751
2675 7 0.0000751
2782 7 0.0000751
2951 7 0.0000751
3144 7 0.0000751
3274 7 0.0000751
3296 7 0.0000751
3299 7 0.0000751
3355 7 0.0000751
5736 7 0.0000751
6111 7 0.0000751
6515 7 0.0000751
6732 7 0.0000751
7211 7 0.0000751
7291 7 0.0000751
0133 6 0.0000643
0174 6 0.0000643
0271 6 0.0000643
0851 6 0.0000643
2999 6 0.0000643
3087 6 0.0000643
3171 6 0.0000643
3264 6 0.0000643
3412 6 0.0000643
3586 6 0.0000643
3647 6 0.0000643
3996 6 0.0000643
4221 6 0.0000643
5015 6 0.0000643
5048 6 0.0000643
5087 6 0.0000643
5154 6 0.0000643
5194 6 0.0000643
5947 6 0.0000643
7829 6 0.0000643
8422 6 0.0000643
0119 5 0.0000536
0161 5 0.0000536
0171 5 0.0000536
0211 5 0.0000536
0724 5 0.0000536
1429 5 0.0000536
1796 5 0.0000536
2044 5 0.0000536
2322 5 0.0000536
2353 5 0.0000536
2385 5 0.0000536
2449 5 0.0000536
2521 5 0.0000536
2843 5 0.0000536
3151 5 0.0000536
3259 5 0.0000536
3295 5 0.0000536
3493 5 0.0000536
3955 5 0.0000536
3991 5 0.0000536
5451 5 0.0000536
5632 5 0.0000536
6011 5 0.0000536
7536 5 0.0000536
8699 5 0.0000536
0173 4 0.0000429
0219 4 0.0000429
0251 4 0.0000429
0252 4 0.0000429
0254 4 0.0000429
0722 4 0.0000429
1423 4 0.0000429
1742 4 0.0000429
1752 4 0.0000429
1771 4 0.0000429
1781 4 0.0000429
2258 4 0.0000429
2262 4 0.0000429
2296 4 0.0000429
2298 4 0.0000429
2325 4 0.0000429
2391 4 0.0000429
2531 4 0.0000429
2542 4 0.0000429
2875 4 0.0000429
2895 4 0.0000429
3131 4 0.0000429
3364 4 0.0000429
3463 4 0.0000429
3566 4 0.0000429
3582 4 0.0000429
3792 4 0.0000429
3952 4 0.0000429
4231 4 0.0000429
4619 4 0.0000429
4741 4 0.0000429
5144 4 0.0000429
5561 4 0.0000429
6792 4 0.0000429
7216 4 0.0000429
7313 4 0.0000429
7338 4 0.0000429
7519 4 0.0000429
7542 4 0.0000429
8399 4 0.0000429
9532 4 0.0000429
9611 4 0.0000429
9631 4 0.0000429
0131 3 0.0000322
0175 3 0.0000322
0182 3 0.0000322
0259 3 0.0000322
0723 3 0.0000322
0783 3 0.0000322
1475 3 0.0000322
2323 3 0.0000322
2369 3 0.0000322
2381 3 0.0000322
2656 3 0.0000322
2823 3 0.0000322
3161 3 0.0000322
3263 3 0.0000322
3322 3 0.0000322
3466 3 0.0000322
3483 3 0.0000322
3489 3 0.0000322
3764 3 0.0000322
3769 3 0.0000322
4952 3 0.0000322
5421 3 0.0000322
5948 3 0.0000322
5994 3 0.0000322
6061 3 0.0000322
6082 3 0.0000322
7219 3 0.0000322
7539 3 0.0000322
8621 3 0.0000322
8641 3 0.0000322
9199 3 0.0000322
9411 3 0.0000322
0111 2 0.0000214
0272 2 0.0000214
0291 2 0.0000214
0752 2 0.0000214
1231 2 0.0000214
1455 2 0.0000214
1741 2 0.0000214
1794 2 0.0000214
2053 2 0.0000214
2068 2 0.0000214
2074 2 0.0000214
2259 2 0.0000214
2282 2 0.0000214
2361 2 0.0000214
2371 2 0.0000214
2386 2 0.0000214
2394 2 0.0000214
2791 2 0.0000214
3172 2 0.0000214
3547 2 0.0000214
3635 2 0.0000214
4961 2 0.0000214
5014 2 0.0000214
5046 2 0.0000214
5078 2 0.0000214
5441 2 0.0000214
5949 2 0.0000214
6091 2 0.0000214
6541 2 0.0000214
7041 2 0.0000214
7212 2 0.0000214
7251 2 0.0000214
7369 2 0.0000214
7532 2 0.0000214
7534 2 0.0000214
7694 2 0.0000214
8031 2 0.0000214
8651 2 0.0000214
8661 2 0.0000214
9121 2 0.0000214
9512 2 0.0000214
9651 2 0.0000214
9661 2 0.0000214
0116 1 0.0000107
0132 1 0.0000107
0213 1 0.0000107
0214 1 0.0000107
0253 1 0.0000107
0279 1 0.0000107
0913 1 0.0000107
1721 1 0.0000107
1793 1 0.0000107
1795 1 0.0000107
2141 1 0.0000107
2254 1 0.0000107
2257 1 0.0000107
2384 1 0.0000107
2393 1 0.0000107
2674 1 0.0000107
2789 1 0.0000107
3142 1 0.0000107
3425 1 0.0000107
3953 1 0.0000107
3961 1 0.0000107
4013 1 0.0000107
4432 1 0.0000107
4971 1 0.0000107
5145 1 0.0000107
5271 1 0.0000107
5431 1 0.0000107
7217 1 0.0000107
7533 1 0.0000107
7622 1 0.0000107
7692 1 0.0000107
8231 1 0.0000107
8412 1 0.0000107
8631 1 0.0000107
9221 1 0.0000107
9224 1 0.0000107
9431 1 0.0000107
9531 1 0.0000107
9641 1 0.0000107
999G 1 0.0000107

Target Industry in Sample

sdc %>%
  count(tar_sic, name = "count") %>%
  mutate(sample_percentage = count / sum(count)) %>%
  arrange(desc(count)) %>%
  kable("html", col.names = c("Deal Type", "Count", "%")) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%", height = "500px")
Deal Type Count %
7372 4489 0.0481430
6512 4098 0.0439497
1311 2667 0.0286027
7011 2353 0.0252351
7375 2112 0.0226505
6799 1678 0.0179960
4813 1347 0.0144461
6021 1144 0.0122690
7389 1095 0.0117435
6311 1022 0.0109606
6552 1020 0.0109392
7379 975 0.0104565
2834 972 0.0104244
6000 956 0.0102528
4911 947 0.0101563
7373 895 0.0095986
1041 879 0.0094270
4832 848 0.0090945
6022 813 0.0087192
5812 806 0.0086441
3674 772 0.0082794
4812 692 0.0074215
8711 655 0.0070247
6282 629 0.0067458
6211 620 0.0066493
8742 598 0.0064134
4841 590 0.0063276
8748 550 0.0058986
3714 545 0.0058449
6035 540 0.0057913
7371 539 0.0057806
6798 515 0.0055232
5411 503 0.0053945
6531 487 0.0052229
7361 467 0.0050084
3841 464 0.0049762
4953 463 0.0049655
6141 446 0.0047832
3089 445 0.0047725
4833 433 0.0046438
3679 418 0.0044829
5045 404 0.0043328
8731 391 0.0041933
3661 388 0.0041612
6411 383 0.0041075
2731 377 0.0040432
5511 375 0.0040217
3663 367 0.0039360
6513 367 0.0039360
7999 352 0.0037751
3577 351 0.0037644
2721 350 0.0037536
2836 347 0.0037215
7359 334 0.0035820
8099 332 0.0035606
5813 322 0.0034533
2711 320 0.0034319
2821 317 0.0033997
7311 297 0.0031852
5122 296 0.0031745
4412 294 0.0031531
5999 288 0.0030887
8051 288 0.0030887
1521 285 0.0030565
7374 285 0.0030565
4213 283 0.0030351
3312 282 0.0030244
8062 281 0.0030136
4724 272 0.0029171
3845 269 0.0028849
5065 269 0.0028849
7812 264 0.0028313
8011 254 0.0027241
6726 249 0.0026704
3842 244 0.0026168
5311 244 0.0026168
2819 243 0.0026061
2621 241 0.0025846
3669 239 0.0025632
6162 238 0.0025525
2082 234 0.0025096
3559 232 0.0024881
4899 232 0.0024881
2086 228 0.0024452
4922 226 0.0024238
1522 223 0.0023916
2099 220 0.0023594
4941 216 0.0023165
3571 214 0.0022951
2899 213 0.0022844
8741 213 0.0022844
8071 211 0.0022629
3823 209 0.0022415
5047 208 0.0022307
1381 204 0.0021878
1221 203 0.0021771
2844 202 0.0021664
5912 202 0.0021664
3829 200 0.0021449
5961 197 0.0021128
7382 197 0.0021128
2084 190 0.0020377
3241 182 0.0019519
1389 179 0.0019197
3949 178 0.0019090
4225 175 0.0018768
3569 173 0.0018554
1731 172 0.0018446
2869 172 0.0018446
4731 172 0.0018446
3711 171 0.0018339
3812 169 0.0018125
3672 168 0.0018017
5084 164 0.0017588
2752 161 0.0017267
5063 161 0.0017267
4512 156 0.0016730
8732 155 0.0016623
4581 154 0.0016516
2741 153 0.0016409
6331 152 0.0016301
7997 152 0.0016301
8721 151 0.0016194
2051 148 0.0015873
2759 148 0.0015873
7363 147 0.0015765
3651 145 0.0015551
1021 144 0.0015444
1499 142 0.0015229
6036 142 0.0015229
5013 140 0.0015015
2911 139 0.0014907
7353 137 0.0014693
5149 136 0.0014586
2671 135 0.0014478
2851 135 0.0014478
6159 135 0.0014478
6324 135 0.0014478
7381 135 0.0014478
3585 134 0.0014371
2835 133 0.0014264
3728 133 0.0014264
4011 131 0.0014049
7941 131 0.0014049
7349 127 0.0013620
8299 127 0.0013620
1541 124 0.0013299
8082 124 0.0013299
3069 122 0.0013084
3825 121 0.0012977
5141 121 0.0012977
3731 120 0.0012870
4491 120 0.0012870
2299 119 0.0012762
3357 118 0.0012655
3699 118 0.0012655
7261 118 0.0012655
7312 117 0.0012548
3999 116 0.0012441
5734 116 0.0012441
2833 115 0.0012333
3826 114 0.0012226
5093 113 0.0012119
5169 112 0.0012012
3827 109 0.0011690
1629 108 0.0011583
3531 108 0.0011583
3944 108 0.0011583
7832 108 0.0011583
8059 108 0.0011583
8734 108 0.0011583
3533 107 0.0011475
7376 106 0.0011368
1382 105 0.0011261
3272 105 0.0011261
1099 104 0.0011154
1623 103 0.0011046
5099 103 0.0011046
6153 102 0.0010939
2891 101 0.0010832
5051 101 0.0010832
6099 101 0.0010832
2033 100 0.0010725
2329 100 0.0010725
5541 100 0.0010725
3572 99 0.0010617
4212 99 0.0010617
2011 98 0.0010510
5112 98 0.0010510
8093 98 0.0010510
1799 97 0.0010403
2211 97 0.0010403
1061 96 0.0010296
3589 96 0.0010296
3541 95 0.0010188
2841 94 0.0010081
5172 94 0.0010081
3442 93 0.0009974
1711 92 0.0009867
7319 92 0.0009867
3429 91 0.0009759
4111 91 0.0009759
1611 90 0.0009652
2064 90 0.0009652
4923 88 0.0009438
7992 88 0.0009438
7521 87 0.0009330
5012 86 0.0009223
5031 86 0.0009223
7996 86 0.0009223
3443 85 0.0009116
3621 84 0.0009009
3861 84 0.0009009
5085 84 0.0009009
6321 83 0.0008901
2048 82 0.0008794
3325 82 0.0008794
2026 81 0.0008687
4215 81 0.0008687
7991 81 0.0008687
9511 81 0.0008687
0811 80 0.0008580
3625 80 0.0008580
3433 79 0.0008472
3561 79 0.0008472
4924 79 0.0008472
5074 79 0.0008472
6722 79 0.0008472
3499 78 0.0008365
5199 78 0.0008365
8069 78 0.0008365
2421 77 0.0008258
2879 77 0.0008258
499A 77 0.0008258
7514 77 0.0008258
2511 76 0.0008151
4226 76 0.0008151
5039 75 0.0008043
5712 75 0.0008043
6519 75 0.0008043
2679 74 0.0007936
5044 74 0.0007936
5621 74 0.0007936
7948 74 0.0007936
8021 74 0.0007936
2611 73 0.0007829
2754 73 0.0007829
4612 73 0.0007829
7819 73 0.0007829
7993 73 0.0007829
3081 72 0.0007722
3629 72 0.0007722
5072 72 0.0007722
5941 70 0.0007507
7336 70 0.0007507
2013 69 0.0007400
3086 69 0.0007400
3575 69 0.0007400
6712 69 0.0007400
3523 68 0.0007293
3851 66 0.0007078
5182 66 0.0007078
6289 66 0.0007078
3496 65 0.0006971
3724 65 0.0006971
8351 65 0.0006971
3469 64 0.0006864
3511 64 0.0006864
3491 63 0.0006757
3564 63 0.0006757
5211 63 0.0006757
8743 63 0.0006757
2431 62 0.0006649
3444 62 0.0006649
1081 61 0.0006542
3634 61 0.0006542
3743 61 0.0006542
5611 61 0.0006542
8744 61 0.0006542
1011 60 0.0006435
2041 60 0.0006435
3273 60 0.0006435
3317 60 0.0006435
3613 60 0.0006435
3751 60 0.0006435
5735 60 0.0006435
3721 59 0.0006328
8361 59 0.0006328
1031 58 0.0006220
2676 58 0.0006220
5191 58 0.0006220
7331 58 0.0006220
3321 57 0.0006113
3843 57 0.0006113
4931 57 0.0006113
5731 57 0.0006113
2653 56 0.0006006
2873 56 0.0006006
3545 56 0.0006006
3599 56 0.0006006
3844 56 0.0006006
4222 56 0.0006006
4789 56 0.0006006
7922 56 0.0006006
3462 55 0.0005899
3822 55 0.0005899
5023 55 0.0005899
6794 55 0.0005899
2331 54 0.0005791
3532 54 0.0005791
3652 54 0.0005791
5091 54 0.0005791
7841 54 0.0005791
8221 54 0.0005791
0181 53 0.0005684
2052 53 0.0005684
5651 53 0.0005684
6163 53 0.0005684
7699 53 0.0005684
8049 53 0.0005684
1094 52 0.0005577
2024 52 0.0005577
2085 52 0.0005577
3271 52 0.0005577
3555 52 0.0005577
5942 52 0.0005577
7322 52 0.0005577
2038 51 0.0005470
4449 51 0.0005470
4725 51 0.0005470
7822 51 0.0005470
2037 50 0.0005362
2076 50 0.0005362
3011 50 0.0005362
3423 50 0.0005362
3537 50 0.0005362
5136 50 0.0005362
5142 50 0.0005362
5531 50 0.0005362
6351 50 0.0005362
8249 50 0.0005362
1542 49 0.0005255
3315 49 0.0005255
3316 49 0.0005255
3411 49 0.0005255
3479 49 0.0005255
3993 49 0.0005255
5137 49 0.0005255
5261 49 0.0005255
2273 48 0.0005148
2842 48 0.0005148
3229 48 0.0005148
3612 48 0.0005148
5984 48 0.0005148
2631 47 0.0005041
3253 47 0.0005041
3713 47 0.0005041
5082 47 0.0005041
2023 46 0.0004933
2079 46 0.0004933
2281 46 0.0004933
3441 46 0.0004933
3691 46 0.0004933
4499 46 0.0004933
5995 46 0.0004933
6733 46 0.0004933
7538 46 0.0004933
1044 45 0.0004826
3251 45 0.0004826
3399 45 0.0004826
3494 45 0.0004826
5064 45 0.0004826
5192 45 0.0004826
7378 45 0.0004826
7384 45 0.0004826
8243 45 0.0004826
3519 44 0.0004719
3678 44 0.0004719
5661 44 0.0004719
7323 44 0.0004719
0241 43 0.0004612
2087 43 0.0004612
3211 43 0.0004612
3563 43 0.0004612
5088 43 0.0004612
7549 43 0.0004612
2311 42 0.0004504
2499 42 0.0004504
2813 42 0.0004504
3578 42 0.0004504
3821 42 0.0004504
6371 42 0.0004504
8063 42 0.0004504
8713 42 0.0004504
3143 41 0.0004397
3221 41 0.0004397
5032 41 0.0004397
5399 41 0.0004397
7299 41 0.0004397
8052 41 0.0004397
0172 40 0.0004290
3083 40 0.0004290
3556 40 0.0004290
6719 40 0.0004290
7513 40 0.0004290
8999 40 0.0004290
2321 39 0.0004183
3052 39 0.0004183
3356 39 0.0004183
3492 39 0.0004183
3498 39 0.0004183
4925 39 0.0004183
0831 38 0.0004075
2092 38 0.0004075
2392 38 0.0004075
3535 38 0.0004075
3544 38 0.0004075
3675 38 0.0004075
4119 38 0.0004075
4513 38 0.0004075
5722 38 0.0004075
6514 38 0.0004075
8712 38 0.0004075
2091 37 0.0003968
3231 37 0.0003968
3579 37 0.0003968
3643 37 0.0003968
3648 37 0.0003968
5171 37 0.0003968
5499 37 0.0003968
7377 37 0.0003968
2032 36 0.0003861
2096 36 0.0003861
2657 36 0.0003861
3365 36 0.0003861
3452 36 0.0003861
3465 36 0.0003861
3565 36 0.0003861
3593 36 0.0003861
4822 36 0.0003861
6029 36 0.0003861
2865 35 0.0003754
3353 35 0.0003754
3534 35 0.0003754
3536 35 0.0003754
3639 35 0.0003754
3645 35 0.0003754
5043 35 0.0003754
5083 35 0.0003754
5092 35 0.0003754
5943 35 0.0003754
7033 35 0.0003754
7383 35 0.0003754
1422 34 0.0003646
2066 34 0.0003646
2111 34 0.0003646
3084 34 0.0003646
3398 34 0.0003646
3732 34 0.0003646
4613 34 0.0003646
4785 34 0.0003646
5251 34 0.0003646
5945 34 0.0003646
8092 34 0.0003646
3631 33 0.0003539
4481 33 0.0003539
5461 33 0.0003539
5944 33 0.0003539
8331 33 0.0003539
1321 32 0.0003432
1481 32 0.0003432
2022 32 0.0003432
2812 32 0.0003432
2824 32 0.0003432
5094 32 0.0003432
5148 32 0.0003432
0851 31 0.0003325
2015 31 0.0003325
2021 31 0.0003325
2047 31 0.0003325
2221 31 0.0003325
2522 31 0.0003325
3269 31 0.0003325
3339 31 0.0003325
3448 31 0.0003325
3524 31 0.0003325
3542 31 0.0003325
3552 31 0.0003325
3641 31 0.0003325
3873 31 0.0003325
3911 31 0.0003325
4131 31 0.0003325
5049 31 0.0003325
5113 31 0.0003325
5699 31 0.0003325
6553 31 0.0003325
0912 30 0.0003217
2231 30 0.0003217
2411 30 0.0003217
2521 30 0.0003217
2672 30 0.0003217
3965 30 0.0003217
4141 30 0.0003217
4522 30 0.0003217
5143 30 0.0003217
5146 30 0.0003217
5331 30 0.0003217
7335 30 0.0003217
2035 29 0.0003110
2095 29 0.0003110
2992 29 0.0003110
3255 29 0.0003110
3275 29 0.0003110
3334 29 0.0003110
3562 29 0.0003110
5599 29 0.0003110
7352 29 0.0003110
2062 28 0.0003003
2761 28 0.0003003
3351 28 0.0003003
3471 28 0.0003003
3695 28 0.0003003
5962 28 0.0003003
7515 28 0.0003003
7629 28 0.0003003
3354 27 0.0002896
3446 27 0.0002896
3567 27 0.0002896
3646 27 0.0002896
5131 27 0.0002896
5719 27 0.0002896
2678 26 0.0002788
2893 26 0.0002788
3554 26 0.0002788
4482 26 0.0002788
4619 26 0.0002788
5014 26 0.0002788
0273 25 0.0002681
1479 25 0.0002681
2061 25 0.0002681
2435 25 0.0002681
2452 25 0.0002681
2541 25 0.0002681
2673 25 0.0002681
2952 25 0.0002681
3149 25 0.0002681
3341 25 0.0002681
3363 25 0.0002681
3568 25 0.0002681
3581 25 0.0002681
5075 25 0.0002681
5111 25 0.0002681
8111 25 0.0002681
8733 25 0.0002681
0721 24 0.0002574
1442 24 0.0002574
2326 24 0.0002574
2434 24 0.0002574
2493 24 0.0002574
2519 24 0.0002574
2951 24 0.0002574
3085 24 0.0002574
3632 24 0.0002574
3671 24 0.0002574
3824 24 0.0002574
5021 24 0.0002574
5033 24 0.0002574
5947 24 0.0002574
5963 24 0.0002574
6221 24 0.0002574
8322 24 0.0002574
1411 23 0.0002467
1531 23 0.0002467
2241 23 0.0002467
2339 23 0.0002467
2399 23 0.0002467
2515 23 0.0002467
2531 23 0.0002467
2874 23 0.0002467
4729 23 0.0002467
5193 23 0.0002467
5521 23 0.0002467
0751 22 0.0002359
2252 22 0.0002359
2822 22 0.0002359
2892 22 0.0002359
3053 22 0.0002359
3291 22 0.0002359
3355 22 0.0002359
3692 22 0.0002359
4959 22 0.0002359
0179 21 0.0002252
2043 21 0.0002252
2677 21 0.0002252
3021 21 0.0002252
3088 21 0.0002252
3261 21 0.0002252
3281 21 0.0002252
3295 21 0.0002252
3313 21 0.0002252
3594 21 0.0002252
3596 21 0.0002252
3624 21 0.0002252
3677 21 0.0002252
3694 21 0.0002252
3715 21 0.0002252
3799 21 0.0002252
5046 21 0.0002252
5087 21 0.0002252
5921 21 0.0002252
7334 21 0.0002252
7342 21 0.0002252
0782 20 0.0002145
1796 20 0.0002145
2034 20 0.0002145
2131 20 0.0002145
2253 20 0.0002145
2451 20 0.0002145
3262 20 0.0002145
3299 20 0.0002145
3432 20 0.0002145
3548 20 0.0002145
4121 20 0.0002145
4214 20 0.0002145
5198 20 0.0002145
6231 20 0.0002145
6399 20 0.0002145
1241 19 0.0002038
1474 19 0.0002038
2269 19 0.0002038
2426 19 0.0002038
2816 19 0.0002038
3061 19 0.0002038
3931 19 0.0002038
5052 19 0.0002038
8211 19 0.0002038
1751 18 0.0001930
2512 18 0.0001930
2591 18 0.0001930
2823 18 0.0001930
3161 18 0.0001930
3366 18 0.0001930
3431 18 0.0001930
3644 18 0.0001930
5162 18 0.0001930
5551 18 0.0001930
5713 18 0.0001930
7213 18 0.0001930
7829 18 0.0001930
7929 18 0.0001930
8042 18 0.0001930
0191 17 0.0001823
0212 17 0.0001823
0921 17 0.0001823
2341 17 0.0001823
2389 17 0.0001823
2542 17 0.0001823
2652 17 0.0001823
3111 17 0.0001823
3264 17 0.0001823
3549 17 0.0001823
3942 17 0.0001823
4783 17 0.0001823
5147 17 0.0001823
5159 17 0.0001823
8072 17 0.0001823
0133 16 0.0001716
0781 16 0.0001716
2675 16 0.0001716
3199 16 0.0001716
3297 16 0.0001716
3449 16 0.0001716
3546 16 0.0001716
4142 16 0.0001716
4424 16 0.0001716
4489 16 0.0001716
4492 16 0.0001716
4932 16 0.0001716
4939 16 0.0001716
5078 16 0.0001716
5632 16 0.0001716
1622 15 0.0001609
1761 15 0.0001609
2297 15 0.0001609
2335 15 0.0001609
2514 15 0.0001609
2599 15 0.0001609
2732 15 0.0001609
2999 15 0.0001609
3493 15 0.0001609
4493 15 0.0001609
5153 15 0.0001609
5932 15 0.0001609
6361 15 0.0001609
7313 15 0.0001609
7542 15 0.0001609
7933 15 0.0001609
0211 14 0.0001501
1222 14 0.0001501
1429 14 0.0001501
2046 14 0.0001501
3292 14 0.0001501
5139 14 0.0001501
5181 14 0.0001501
619A 14 0.0001501
1791 13 0.0001394
2083 13 0.0001394
2251 13 0.0001394
2284 13 0.0001394
2295 13 0.0001394
2325 13 0.0001394
3592 13 0.0001394
3647 13 0.0001394
3761 13 0.0001394
5992 13 0.0001394
9223 13 0.0001394
0119 12 0.0001287
0711 12 0.0001287
0742 12 0.0001287
1459 12 0.0001287
2053 12 0.0001287
2077 12 0.0001287
2261 12 0.0001287
2448 12 0.0001287
3087 12 0.0001287
3364 12 0.0001287
3369 12 0.0001287
3566 12 0.0001287
3635 12 0.0001287
3914 12 0.0001287
4151 12 0.0001287
4961 12 0.0001287
5421 12 0.0001287
5736 12 0.0001287
6011 12 0.0001287
7032 12 0.0001287
7218 12 0.0001287
0174 11 0.0001180
1721 11 0.0001180
1771 11 0.0001180
2045 11 0.0001180
2121 11 0.0001180
2298 11 0.0001180
2337 11 0.0001180
2436 11 0.0001180
2655 11 0.0001180
2782 11 0.0001180
3082 11 0.0001180
3144 11 0.0001180
3331 11 0.0001180
3412 11 0.0001180
3421 11 0.0001180
3483 11 0.0001180
3792 11 0.0001180
4013 11 0.0001180
5015 11 0.0001180
5048 11 0.0001180
5194 11 0.0001180
5641 11 0.0001180
5983 11 0.0001180
7231 11 0.0001180
7291 11 0.0001180
7519 11 0.0001180
8399 11 0.0001180
0251 10 0.0001072
0722 10 0.0001072
2097 10 0.0001072
2098 10 0.0001072
2322 10 0.0001072
2369 10 0.0001072
2439 10 0.0001072
2491 10 0.0001072
2771 10 0.0001072
2796 10 0.0001072
2843 10 0.0001072
2875 10 0.0001072
2895 10 0.0001072
3172 10 0.0001072
3484 10 0.0001072
3497 10 0.0001072
3795 10 0.0001072
3951 10 0.0001072
3991 10 0.0001072
7532 10 0.0001072
7539 10 0.0001072
8244 10 0.0001072
0182 9 0.0000965
0723 9 0.0000965
1446 9 0.0000965
1794 9 0.0000965
2044 9 0.0000965
2385 9 0.0000965
2656 9 0.0000965
3296 9 0.0000965
3482 9 0.0000965
3676 9 0.0000965
3952 9 0.0000965
4741 9 0.0000965
5145 9 0.0000965
5571 9 0.0000965
6111 9 0.0000965
619B 9 0.0000965
6515 9 0.0000965
7212 9 0.0000965
7215 9 0.0000965
7217 9 0.0000965
9621 9 0.0000965
0139 8 0.0000858
0724 8 0.0000858
0762 8 0.0000858
1423 8 0.0000858
1742 8 0.0000858
2063 8 0.0000858
2075 8 0.0000858
2258 8 0.0000858
2396 8 0.0000858
2674 8 0.0000858
3259 8 0.0000858
3274 8 0.0000858
3463 8 0.0000858
3466 8 0.0000858
3489 8 0.0000858
3586 8 0.0000858
4221 8 0.0000858
4952 8 0.0000858
7216 8 0.0000858
7221 8 0.0000858
7536 8 0.0000858
8412 8 0.0000858
8422 8 0.0000858
999B 8 0.0000858
0291 7 0.0000751
1752 7 0.0000751
1781 7 0.0000751
2257 7 0.0000751
2262 7 0.0000751
2342 7 0.0000751
2391 7 0.0000751
2449 7 0.0000751
2791 7 0.0000751
3171 7 0.0000751
3553 7 0.0000751
3582 7 0.0000751
3716 7 0.0000751
4231 7 0.0000751
4971 7 0.0000751
5431 7 0.0000751
5946 7 0.0000751
5948 7 0.0000751
7021 7 0.0000751
7338 7 0.0000751
7622 7 0.0000751
8611 7 0.0000751
0131 6 0.0000643
2296 6 0.0000643
2353 6 0.0000643
3131 6 0.0000643
3263 6 0.0000643
3324 6 0.0000643
3495 6 0.0000643
3764 6 0.0000643
3953 6 0.0000643
3955 6 0.0000643
3996 6 0.0000643
4173 6 0.0000643
5144 6 0.0000643
5271 6 0.0000643
5949 6 0.0000643
5994 6 0.0000643
6091 6 0.0000643
7211 6 0.0000643
7533 6 0.0000643
7623 6 0.0000643
7692 6 0.0000643
8621 6 0.0000643
0161 5 0.0000536
0171 5 0.0000536
0175 5 0.0000536
0213 5 0.0000536
0214 5 0.0000536
0219 5 0.0000536
0254 5 0.0000536
0259 5 0.0000536
0752 5 0.0000536
0913 5 0.0000536
1455 5 0.0000536
1795 5 0.0000536
2067 5 0.0000536
2282 5 0.0000536
2387 5 0.0000536
2393 5 0.0000536
2517 5 0.0000536
2861 5 0.0000536
3451 5 0.0000536
3769 5 0.0000536
3995 5 0.0000536
5154 5 0.0000536
5231 5 0.0000536
5441 5 0.0000536
5451 5 0.0000536
5561 5 0.0000536
6062 5 0.0000536
6081 5 0.0000536
7534 5 0.0000536
7694 5 0.0000536
7833 5 0.0000536
7911 5 0.0000536
0111 4 0.0000429
0112 4 0.0000429
0132 4 0.0000429
0919 4 0.0000429
2068 4 0.0000429
2141 4 0.0000429
2254 4 0.0000429
2323 4 0.0000429
2361 4 0.0000429
2381 4 0.0000429
2394 4 0.0000429
2789 4 0.0000429
3633 4 0.0000429
3961 4 0.0000429
6541 4 0.0000429
7219 4 0.0000429
9224 4 0.0000429
9411 4 0.0000429
9631 4 0.0000429
9661 4 0.0000429
0252 3 0.0000322
0253 3 0.0000322
1231 3 0.0000322
1743 3 0.0000322
2259 3 0.0000322
2386 3 0.0000322
3322 3 0.0000322
3915 3 0.0000322
5714 3 0.0000322
5989 3 0.0000322
5993 3 0.0000322
6019 3 0.0000322
6792 3 0.0000322
7041 3 0.0000322
8222 3 0.0000322
8231 3 0.0000322
8699 3 0.0000322
9229 3 0.0000322
9711 3 0.0000322
999E 3 0.0000322
0115 2 0.0000214
0173 2 0.0000214
0272 2 0.0000214
0783 2 0.0000214
1741 2 0.0000214
2074 2 0.0000214
2371 2 0.0000214
2395 2 0.0000214
3142 2 0.0000214
3425 2 0.0000214
3543 2 0.0000214
3547 2 0.0000214
4311 2 0.0000214
4432 2 0.0000214
6061 2 0.0000214
6517 2 0.0000214
7369 2 0.0000214
8031 2 0.0000214
8041 2 0.0000214
8641 2 0.0000214
9311 2 0.0000214
999D 2 0.0000214
0116 1 0.0000107
0271 1 0.0000107
0279 1 0.0000107
0761 1 0.0000107
0971 1 0.0000107
1793 1 0.0000107
2384 1 0.0000107
2429 1 0.0000107
2441 1 0.0000107
6082 1 0.0000107
7241 1 0.0000107
7251 1 0.0000107
7537 1 0.0000107
7631 1 0.0000107
7641 1 0.0000107
8043 1 0.0000107
8811 1 0.0000107
9111 1 0.0000107
9121 1 0.0000107
9199 1 0.0000107
9221 1 0.0000107
9531 1 0.0000107
9532 1 0.0000107
9611 1 0.0000107
9641 1 0.0000107
999C 1 0.0000107
999F 1 0.0000107

We now exclude acquirers and targets in banks or utilities company.

sdc$acq_sic <- as.numeric(sdc$acq_sic)
sdc$tar_sic <- as.numeric(sdc$tar_sic)

sdc <- sdc %>%
  filter(!acq_sic %in% c(6020:6099, 4900:4999) |
         !tar_sic %in% c(6020:6099, 4900:4999))

n_sdc_sic <- as.character(nrow(sdc))

After the drop, we have 89331 transactions.

Mergers Announced and Completed within One Year

The first filter is to drop mergers where the time elapsed between the date announced and the date completion is greater than one year. Before the drop, we have 412655 mergers in the data. Variable time_duration_orginal represents the time duration between the date originally announced and the date completion. Variable time_duration represents the time duration between the date announced and the date completion. In short the differences between date orginally announced and date announced can be explained as follows:

- DA: The date the deal was most recently announced publicly
- DOA: The date the deal was first ever announced, even if it was later withdrawn, renegotiated, or restructured.
# create a variable to measure the time duration (in days) between the date announced and the date completion

sdc <- sdc %>%
  mutate(da = as.Date(da, format = "%m/%d/%Y"),
         de = as.Date(de, format = "%m/%d/%Y"),
         doa = as.Date(doa, format = "%m/%d/%Y"),
         time_duration_orginal = as.numeric(difftime(de, doa, units = "days")),
         time_duration = as.numeric(difftime(de, da, units = "days")))

The article uses DA, but for completeness I am reporting the summary statistics for both the time duration.

# Summary statistics for time_duration_orginal and time_duration
# Dropped missing values
sdc %>%
  filter(!is.na(time_duration)) %>%
  summarise(
    mean_time_duration = mean(time_duration, na.rm = TRUE),
    median_time_duration = median(time_duration, na.rm = TRUE),
    sd_time_duration = sd(time_duration, na.rm = TRUE),
    min_time_duration = min(time_duration, na.rm = TRUE),
    max_time_duration = max(time_duration, na.rm = TRUE)
  ) %>%
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>%
  kable("html", col.names = c("Mean Time Duration", "Median Time Duration", "SD Time Duration", "Min Time Duration", "Max Time Duration")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
Mean Time Duration Median Time Duration SD Time Duration Min Time Duration Max Time Duration
56.742 9 145.155 0 7305 
sdc %>%
  filter(!is.na(time_duration_orginal)) %>%
  summarise(
    mean_time_duration = mean(time_duration_orginal, na.rm = TRUE),
    median_time_duration = median(time_duration_orginal, na.rm = TRUE),
    sd_time_duration = sd(time_duration_orginal, na.rm = TRUE),
    min_time_duration = min(time_duration_orginal, na.rm = TRUE),
    max_time_duration = max(time_duration_orginal, na.rm = TRUE)
  ) %>%
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>%
  kable("html", col.names = c("Mean Time Duration Orginal", "Median Time Duration Orginal", "SD Time Duration Orginal", "Min Time Duration Orginal", "Max Time Duration Orginal")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
Mean Time Duration Orginal Median Time Duration Orginal SD Time Duration Orginal Min Time Duration Orginal Max Time Duration Orginal
67.263 14 170.294 -3050 7305

Dropping time duration greater than 365 days

sdc_drop_one <- sdc %>%
  filter(time_duration <= 365)

n_drop_one <- as.character(nrow(sdc_drop_one))

After dropping the time duration greater than 365 days, we have 87664 observations. Drawing a distribution of mergers that are announced and completed within one year. We now show a distribution of mergers in a histogram

sdc_drop_one %>%
  ggplot(aes(x = time_duration)) +
  geom_histogram(binwidth = 10, fill = "blue", color = "black") +
  labs(title = "Distribution of Mergers where Date Announced and Date Completed are within One Year",
       x = "Time Duration (Days)",
       y = "Count") +
  theme_minimal()

sdc_drop_one %>%
  ggplot(aes(x = time_duration_orginal)) +
  geom_histogram(binwidth = 10, fill = "blue", color = "black") +
  labs(title = "Distribution of Mergers where Orginal Date Announced and Date Completed are within One Year",
       x = "Time Duration (Days)",
       y = "Count") +
  theme_minimal()

From the histogram we see that there is spike at 0 suggesting there are multiple mergers that announced and completed in less than a day, which does not make sense. We drop these mergers. For mergers where the time elapsed between the date originally announced and completion is negative, we will investigate further.

sdc_drop_one %>%
  filter(time_duration_orginal<0)%>%
  ggplot(aes(x = time_duration_orginal)) +
  geom_histogram(binwidth = 10, fill = "blue", color = "black") +
  labs(title = "Distribution of Mergers where Orginal Duration between Date Announced and Date Completed are Negative",
       x = "Time Duration (Days)",
       y = "Count") +
  theme_minimal()

We now drop mergers where both of the time duration is negative.

sdc_drop_two <- sdc_drop_one %>%
  filter(time_duration > 0) %>%
  filter(time_duration_orginal > 0)

n_drop_two <- as.character(nrow(sdc_drop_two))

sdc_drop_two %>%
  ggplot(aes(x = time_duration)) +
  geom_histogram(binwidth = 10, fill = "blue", color = "black") +
  labs(title = "Distribution of Time Duration Between Date Announced and Date Completed",
       x = "Time Duration (Days)",
       y = "Count") +
  theme_minimal()

n_drop_two <- as.character(nrow(sdc_drop_two))

Assets to Deal Size

The paper dropped mergers where the acquirer’s total to deal size less than 5 percent or greater than 150 percent. Variable ACQ_ASSET_DV represents the ratio of total assets to deal size for the acquiring firm.

# Drop deal values with values of 0 or missing

sdc_drop_three <- sdc_drop_two %>%
  filter(dv > 0, !is.na(dv))

#creating the variable ACQ_ASSET_DV

sdc_drop_three <- sdc_drop_three %>%
  mutate(acq_asset_dv = acq_totalasset / dv)

# dropping mergers where acq_asset_dv has missing values

sdc_drop_three <- sdc_drop_three %>%
  filter(!is.na(acq_asset_dv))

# looking at the summary statistics of the variable ACQ_ASSET_DV

sdc_drop_three %>%
  summarise(
    mean_acq_asset_dv = mean(acq_asset_dv, na.rm = TRUE),
    median_acq_asset_dv = median(acq_asset_dv, na.rm = TRUE),
    sd_acq_asset_dv = sd(acq_asset_dv, na.rm = TRUE),
    min_acq_asset_dv = min(acq_asset_dv, na.rm = TRUE),
    max_acq_asset_dv = max(acq_asset_dv, na.rm = TRUE)
  ) %>%
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>%
  kable("html", col.names = c("Mean Acquiror Asset to Deal Size", "Median Acquiror Asset to Deal Size", "SD Acquiror Asset to Deal Size", "Min Acquiror Asset to Deal Size", "Max Acquiror Asset to Deal Size")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
Mean Acquiror Asset to Deal Size Median Acquiror Asset to Deal Size SD Acquiror Asset to Deal Size Min Acquiror Asset to Deal Size Max Acquiror Asset to Deal Size
2110.466 9.57 161051.2 0 25245296 
#Dropping deals where acq_asset_dv is less than 0.05 or greater than 1.5

sdc_drop_four <- sdc_drop_three %>%
  filter(acq_asset_dv >= 0.05, acq_asset_dv <= 1.5)
n_drop_four <- as.character(nrow(sdc_drop_four))

#Export the dataset

write_csv(sdc_drop_four, "data/processed/sdc_filtered.csv")

Currently we have 3805 observations. We now look at the distribution of the variable acq_asset_dv to see if there are any outliers.

sdc_drop_four %>%
  ggplot(aes(x = acq_asset_dv)) +
  geom_histogram(binwidth = 0.05, fill = "blue", color = "black") +
  labs(title = "Distribution of Acquiror Asset to Deal Size",
       x = "Acquiror Asset to Deal Size",
       y = "Count") +
  theme_minimal()

Exporting a list of acquiror and target cusips for merging scripts

We now export the filtered data for merging with CRSP and Compustat data.

write_csv(sdc_drop_four, "data/processed/sdc_clean.csv")