Loading the dataset

The current SDC data set is stored locally. The current dataset contains the following variables:

# Loading df

df <- read_excel("SDC_Export.xlsx")

nrow_df <- as.character(nrow(df))

names(df)

##  [1] "Deal Number"                       "Date Announced"                   
##  [3] "Date Originally Announced"         "Date Effective"                   
##  [5] "Target Short Name"                 "Target 6-digit CUSIP"             
##  [7] "Target PermID"                     "Target Primary Ticker Symbol"     
##  [9] "Acquiror Short Name"               "Acquiror 6-digit CUSIP"           
## [11] "Acquiror PermID"                   "Acquiror Primary Ticker Symbol"   
## [13] "Deal Value (USD Millions)"         "Implied Deal Value (USD Millions)"
## [15] "Consideration Structure"           "Termination Reason"

# uncapitalize all column names, remove all parentheses, and replace space with underscore 

names(df) <- tolower(names(df))
names(df) <- gsub("\\(.*\\)", "", names(df))
names(df) <- gsub(" ", "_", names(df))

names(df)

##  [1] "deal_number"                    "date_announced"                
##  [3] "date_originally_announced"      "date_effective"                
##  [5] "target_short_name"              "target_6-digit_cusip"          
##  [7] "target_permid"                  "target_primary_ticker_symbol"  
##  [9] "acquiror_short_name"            "acquiror_6-digit_cusip"        
## [11] "acquiror_permid"                "acquiror_primary_ticker_symbol"
## [13] "deal_value_"                    "implied_deal_value_"           
## [15] "consideration_structure"        "termination_reason"

Raw Data

n_raw <- as.character(nrow(df))

The raw data contains 11417 M &A transactions from 2010 to 2022. We are only interested in transactions where the acquirer is a public firm. We define public firms as those who have a 6-digit CUSIP and a primary ticker symbol.

# Only keeping public acquirers

df1 <- df %>% filter(!is.na(`acquiror_6-digit_cusip`) & !is.na(acquiror_primary_ticker_symbol))

nrow_df1 <- as.character(nrow(df1))

Currently, there are 6149 M &A transactions where the acquirers is a public firm.

Summary Statistics

In this section, I will provide a summary statistics of the current data set after excluding non-public acquirers.

Deal Size

# summary statistics of deal size and implied deal size

df1 %>% 
  summarise(
    mean_deal_value = mean(deal_value_, na.rm = TRUE), 
    median_deal_value = median(deal_value_, na.rm = TRUE), 
    sd_deal_value = sd(deal_value_, na.rm = TRUE), 
    min_deal_value = min(deal_value_, na.rm = TRUE), 
    max_deal_value = max(deal_value_, na.rm = TRUE)
  ) %>% 
  kable("html", col.names = c("Mean Deal Value", "Median Deal Value", "SD Deal Value", "Min Deal Value", "Max Deal Value")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Mean Deal Value	Median Deal Value	SD Deal Value	Min Deal Value	Max Deal Value
2062.08	201.87	7682.918	0.01	148705.3

Deal Size by Termination Reason

# summary statistics of deal size and implied deal size by termination reason; 3 digits; order by deal size

df1 %>% 
  group_by(termination_reason) %>% 
  summarise(
    mean_deal_value = mean(deal_value_, na.rm = TRUE), 
    median_deal_value = median(deal_value_, na.rm = TRUE), 
    sd_deal_value = sd(deal_value_, na.rm = TRUE), 
    min_deal_value = min(deal_value_, na.rm = TRUE), 
    max_deal_value = max(deal_value_, na.rm = TRUE)
  ) %>% 
  arrange(desc(mean_deal_value)) %>% 
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>% 
  kable("html", col.names = c("Termination Reason", "Mean Deal Value", "Median Deal Value", "SD Deal Value", "Min Deal Value", "Max Deal Value")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Termination Reason	Mean Deal Value	Median Deal Value	SD Deal Value	Min Deal Value	Max Deal Value
Competing Bid Is Successful;Acquiror Pursues Other Strategic Options	64839.370	64839.370	NA	64839.37	64839.37
Shareholder Or Board Approval;Acquiror Pursues Other Strategic Options	21534.300	21534.300	NA	21534.30	21534.30
Regulation	17002.477	1577.825	37051.297	1.08	145785.26
Unable To Engage Target In Meaningful Discussions;Unsolicited Offer is Withdrawn	14797.560	14797.560	20605.403	227.34	29367.78
Unable To Engage Target In Meaningful Discussions	5663.055	333.730	18828.287	2.52	148705.26
Shareholder Or Board Approval	4317.869	296.370	13849.480	0.73	77567.80
Litigation;Economic And Or Market Conditions	3952.410	3952.410	NA	3952.41	3952.41
Litigation;Unable To Engage Target In Meaningful Discussions	3786.240	3786.240	NA	3786.24	3786.24
Acquiror Pursues Other Strategic Options	3769.275	400.150	8407.796	0.50	43002.86
Acquiror Pursues Other Strategic Options;Regulation	3708.720	3708.720	NA	3708.72	3708.72
Competing Bid Is Successful	2744.726	430.850	7030.540	2.60	47814.30
Economic And Or Market Conditions	2577.168	122.180	6092.692	0.79	34874.90
Other	2571.070	171.380	7345.200	0.59	36905.12
Not Disclosed	1826.928	194.945	5706.196	0.26	46150.10
Unsolicited Offer is Withdrawn;Competing Bid Is Successful	1779.540	1779.540	NA	1779.54	1779.54
NA	1778.002	190.160	6216.903	0.01	101490.57
Unsolicited Offer is Withdrawn	1455.538	281.485	3126.071	0.10	18606.18
Terminated Based On The MnA LT Criteria	1290.389	343.700	2264.695	10.27	8906.12
Litigation	1205.875	1205.875	1693.500	8.39	2403.36
Economic And Or Market Conditions;Shareholder Or Board Approval	1202.890	1202.890	NA	1202.89	1202.89
Defensive Technique Is Successful;Competing Bid Is Successful	1167.540	1167.540	NA	1167.54	1167.54
Lapsed Offer	982.383	81.360	2178.436	0.25	10417.12
Financing	643.676	57.135	1157.121	2.94	3363.50
Shareholder Or Board Approval;Regulation	289.950	289.950	NA	289.95	289.95
Regulation;Unable To Engage Target In Meaningful Discussions	248.360	248.360	NA	248.36	248.36
Defensive Technique Is Successful	186.930	147.810	186.912	8.31	457.31
New Program Is Authorized	123.815	123.815	26.255	105.25	142.38
Other;Lapsed Offer	43.240	43.240	NA	43.24	43.24
Unsolicited Offer is Withdrawn;Economic And Or Market Conditions	11.600	11.600	NA	11.60	11.60
Acquiror Pursues Other Strategic Options;Other	9.550	9.550	NA	9.55	9.55
Other;Economic And Or Market Conditions	2.850	2.850	NA	2.85	2.85
Unable To Engage Target In Meaningful Discussions;Lapsed Offer	2.570	2.570	NA	2.57	2.57
Shareholder Or Board Approval;Financing	0.340	0.340	NA	0.34	0.34
Economic And Or Market Conditions;Unable To Engage Target In Meaningful Discussions	NaN	NA	NA	Inf	-Inf
Other;Unable To Engage Target In Meaningful Discussions	NaN	NA	NA	Inf	-Inf

Cleaning

df1 <- df1 %>%
  mutate(termination_group = case_when(
    grepl("Competing Bid|Strategic Options|Unable To Engage|Unsolicited Offer|Defensive Technique|Lapsed Offer|New Program", termination_reason, ignore.case = TRUE) ~ "Strategic and Competitive Factors",
    grepl("Regulation|Litigation|MnA LT Criteria", termination_reason, ignore.case = TRUE) ~ "Regulatory and Legal Factors",
    grepl("Financing|Economic And Or Market Conditions", termination_reason, ignore.case = TRUE) ~ "Financial and Economic Factors",
    grepl("Shareholder Or Board Approval|Not Disclosed|Other|NA", termination_reason, ignore.case = TRUE) ~ "Governance and Approval Factors",
    TRUE ~ "Other"
  ))

df1 %>% 
  group_by(termination_group) %>% 
  summarise(
    mean_deal_value = mean(deal_value_, na.rm = TRUE), 
    median_deal_value = median(deal_value_, na.rm = TRUE), 
    sd_deal_value = sd(deal_value_, na.rm = TRUE), 
    min_deal_value = min(deal_value_, na.rm = TRUE), 
    max_deal_value = max(deal_value_, na.rm = TRUE)
  ) %>% 
  arrange(desc(mean_deal_value)) %>% 
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>% 
  kable("html", col.names = c("Termination Group", "Mean Deal Value", "Median Deal Value", "SD Deal Value", "Min Deal Value", "Max Deal Value")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Termination Group	Mean Deal Value	Median Deal Value	SD Deal Value	Min Deal Value	Max Deal Value
Regulatory and Legal Factors	10350.667	597.170	29060.550	1.08	145785.3
Strategic and Competitive Factors	3338.561	287.830	11032.599	0.10	148705.3
Governance and Approval Factors	2656.010	233.765	8836.697	0.26	77567.8
Financial and Economic Factors	2222.436	117.780	5584.931	0.34	34874.9
Other	1778.002	190.160	6216.903	0.01	101490.6

Deal Size by Consideration Structure

df1 <- df1 %>%
  mutate(consideration_group = case_when(
    consideration_structure %in% c("Cash Only") ~ "Cash Only",
    consideration_structure %in% c("Stock Only") ~ "Stock Only",
    consideration_structure %in% c("Cash and Stock Combination",
                                   "Choice between Cash or Stock or Combination of Both") ~ "Cash and Stock Mix",
    TRUE ~ "Other Non-standard Types"
  ))

# summary statistics of deal size and implied deal size by consideration structure; 3 digits; order by deal size

df1 %>% 
  group_by(consideration_group) %>% 
  summarise(
    mean_deal_value = mean(deal_value_, na.rm = TRUE), 
    median_deal_value = median(deal_value_, na.rm = TRUE), 
    sd_deal_value = sd(deal_value_, na.rm = TRUE), 
    min_deal_value = min(deal_value_, na.rm = TRUE), 
    max_deal_value = max(deal_value_, na.rm = TRUE)
  ) %>% 
  arrange(desc(mean_deal_value)) %>% 
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>% 
  kable("html", col.names = c("Consideration Structure", "Mean Deal Value", "Median Deal Value", "SD Deal Value", "Min Deal Value", "Max Deal Value")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Consideration Structure	Mean Deal Value	Median Deal Value	SD Deal Value	Min Deal Value	Max Deal Value
Cash and Stock Mix	3888.612	363.135	11728.673	0.10	148705.26
Other Non-standard Types	2374.470	205.250	10544.840	0.49	142419.45
Cash Only	1777.005	346.450	4592.875	0.05	75621.31
Stock Only	1250.607	89.155	5975.210	0.01	145785.26

Deal terminated due to regulation

Occurences over time

# Plot of deal terminated due to regulation over time; x axis in year, not decimal places

df1 %>% 
  filter(termination_group != "Other") %>%
  mutate(year = lubridate::year(date_announced)) %>% 
  group_by(year, termination_group) %>% 
  summarise(count = n(), .groups = 'drop') %>% 
  ggplot(aes(x = year, y = count, color = termination_group)) + 
  geom_line() + 
  geom_point() + 
  labs(title = "Deal Terminations by Group Over Time", x = "Year", y = "Count", color = "Termination Group") + 
  theme_minimal() +
  ylim(0, 50) + 
  scale_x_continuous(breaks = seq(2010, 2022, by = 1))

Deal size

# Remove missing deal value 
df1 <- df1 %>% filter(!is.na(deal_value_))

# create a group of deal sizes: If deal value is between 0 and 20, them group as small, 20 and 100 as medium, and large is over 100
df1 <- df1 %>%
  mutate(deal_size = case_when(
    deal_value_ < 20 ~ "Small",
    deal_value_ >= 20 & deal_value_ < 100 ~ "Medium",
    deal_value_ >= 100 ~ "Large"
  ))

df1 %>% 
  group_by(deal_size) %>%
  summarise(
    n = n()
  ) %>%
  mutate(percentage = n / sum(n) * 100) %>%
  ggplot(aes(x = deal_size, y = percentage)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = round(percentage, 2)), vjust = -0.5) +
  labs(title = "Deal Size Distribution", x = "Deal Size", y = "Percentage") +
  theme_minimal() + 
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  scale_x_discrete(limits = c("Small", "Medium", "Large")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Termination reasons and consideration structure

# summary statistics of deal size and implied deal size by termination reason and consideration structure; 3 digits

df1 %>% 
  group_by(termination_group, consideration_group) %>% 
  summarise(
    mean_deal_value = mean(deal_value_, na.rm = TRUE), 
    median_deal_value = median(deal_value_, na.rm = TRUE), 
    sd_deal_value = sd(deal_value_, na.rm = TRUE), 
    min_deal_value = min(deal_value_, na.rm = TRUE), 
    max_deal_value = max(deal_value_, na.rm = TRUE)
  ) %>%  
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>% 
  kable("html", col.names = c("Termination Reason", "Consideration Structure", "Mean Deal Value", "Median Deal Value", "SD Deal Value", "Min Deal Value", "Max Deal Value")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Termination Reason	Consideration Structure	Mean Deal Value	Median Deal Value	SD Deal Value	Min Deal Value	Max Deal Value
Financial and Economic Factors	Cash Only	2263.019	859.860	2987.269	26.06	8760.75
Financial and Economic Factors	Cash and Stock Mix	3582.683	803.140	9168.977	2.89	34874.90
Financial and Economic Factors	Other Non-standard Types	908.215	195.080	1550.574	15.08	3227.62
Financial and Economic Factors	Stock Only	1760.690	47.470	4676.271	0.34	19775.98
Governance and Approval Factors	Cash Only	1010.389	260.365	1776.448	4.76	9576.05
Governance and Approval Factors	Cash and Stock Mix	6847.385	600.045	15350.575	2.46	77567.80
Governance and Approval Factors	Other Non-standard Types	2734.451	240.370	7971.816	1.10	36905.12
Governance and Approval Factors	Stock Only	1962.677	84.160	7463.009	0.26	66140.09
Other	Cash Only	1734.033	336.155	4479.860	0.05	75621.31
Other	Cash and Stock Mix	3175.997	338.130	9471.630	0.57	101490.57
Other	Other Non-standard Types	1521.233	181.970	5994.720	0.49	79376.82
Other	Stock Only	1090.786	86.630	4904.867	0.01	86831.16
Regulatory and Legal Factors	Cash Only	3261.242	1176.110	4949.273	11.62	19730.62
Regulatory and Legal Factors	Cash and Stock Mix	22958.604	6208.940	39069.494	252.58	117067.48
Regulatory and Legal Factors	Other Non-standard Types	31309.477	190.320	57319.122	13.91	142419.45
Regulatory and Legal Factors	Stock Only	6788.437	335.180	25991.899	1.08	145785.26
Strategic and Competitive Factors	Cash Only	2315.480	418.720	6397.078	0.50	64839.37
Strategic and Competitive Factors	Cash and Stock Mix	7810.591	887.305	19991.449	0.10	148705.26
Strategic and Competitive Factors	Other Non-standard Types	3612.850	395.220	9245.533	2.60	43999.48
Strategic and Competitive Factors	Stock Only	1212.239	104.225	3344.428	0.25	23444.82

Deal size by termination reason

df1 %>% 
  group_by(deal_size, termination_group) %>% 
  summarise(
    mean_deal_value = mean(deal_value_, na.rm = TRUE), 
    median_deal_value = median(deal_value_, na.rm = TRUE), 
    sd_deal_value = sd(deal_value_, na.rm = TRUE), 
    min_deal_value = min(deal_value_, na.rm = TRUE), 
    max_deal_value = max(deal_value_, na.rm = TRUE)
  ) %>% 
  arrange(desc(mean_deal_value)) %>% 
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>% 
  kable("html", col.names = c("Deal Size", "Termination Reason", "Mean Deal Value", "Median Deal Value", "SD Deal Value", "Min Deal Value", "Max Deal Value")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Deal Size	Termination Reason	Mean Deal Value	Median Deal Value	SD Deal Value	Min Deal Value	Max Deal Value
Large	Regulatory and Legal Factors	13021.531	1361.765	32115.898	111.49	145785.26
Large	Strategic and Competitive Factors	4831.355	1136.820	13029.250	100.00	148705.26
Large	Governance and Approval Factors	4365.755	705.080	11033.453	113.01	77567.80
Large	Financial and Economic Factors	4148.953	1202.890	7160.655	114.65	34874.90
Large	Other	2933.469	679.950	7805.825	100.00	101490.57
Medium	Financial and Economic Factors	62.614	67.020	25.287	24.84	99.19
Medium	Strategic and Competitive Factors	55.751	52.085	24.917	20.07	99.08
Medium	Other	53.420	50.190	23.115	20.00	99.97
Medium	Governance and Approval Factors	47.496	39.120	24.385	20.00	99.62
Medium	Regulatory and Legal Factors	47.347	47.235	18.601	21.79	75.87
Small	Other	8.777	8.220	6.047	0.01	19.96
Small	Governance and Approval Factors	7.911	6.600	5.833	0.26	19.18
Small	Regulatory and Legal Factors	7.313	8.390	4.479	1.08	13.91
Small	Financial and Economic Factors	7.308	7.310	5.014	0.34	16.11
Small	Strategic and Competitive Factors	7.180	5.100	5.442	0.10	19.66

Time duration

From date announced to date effective

# Create a new column for time duration in days
df1 <- df1 %>% 
  mutate(
    date_announced = as.Date(date_announced),
    date_effective = as.Date(date_effective),
    time_duration = as.numeric(difftime(date_effective, date_announced, units = "days"))
  )

# summary statistics of time duration by deal size; 3 digits; order by deal size

df1 %>% 
  group_by(deal_size) %>% 
  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)
  ) %>% 
  arrange(desc(mean_time_duration)) %>% 
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>% 
  kable("html", col.names = c("Deal Size", "Mean Time Duration", "Median Time Duration", "SD Time Duration", "Min Time Duration", "Max Time Duration")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Deal Size	Mean Time Duration	Median Time Duration	SD Time Duration	Max Time Duration
Large	162.071	128.0	133.146	1616
Small	139.775	96.5	205.100	3706
Medium	132.123	112.0	119.639	2503

Average duration for each year grouped by deal size

# Plot of average time duration for each year; x axis in year, not decimal places

df1 %>% 
  mutate(year = year(date_announced)) %>% 
  group_by(year, deal_size) %>% 
  summarise(mean_time_duration = mean(time_duration, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, y = mean_time_duration, color=deal_size)) + 
  geom_line() + 
  geom_point() + 
  labs(title = "Average Time Duration for Each Year", x = "Year", y = "Average Time Duration (days)") + 
  theme_minimal() + 
  scale_x_continuous(breaks = seq(2010, 2022, by = 1))

Average duration for each year grouped by consideration structure

# Plot of average time duration for each year; x axis in year, not decimal places

df1 %>% 
  mutate(year = year(date_announced)) %>% 
  group_by(year, consideration_group) %>% 
  summarise(mean_time_duration = mean(time_duration, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, y = mean_time_duration, color=consideration_group)) + 
  geom_line() + 
  geom_point() + 
  labs(title = "Average Time Duration for Each Year", x = "Year", y = "Average Time Duration (days)") + 
  theme_minimal() + 
  scale_x_continuous(breaks = seq(2010, 2022, by = 1))

Politics

# create a category for political factors, call it party. 2008-2016 and 2020-2022, the president was a Democrat. From 2002 to 2009, and 2016-2020, the president was a Republican.

df1 <- df1 %>% 
  mutate(party = case_when(
    year(date_announced) >= 2008 & year(date_announced) <= 2016 ~ "Democrat",
    year(date_announced) >= 2020 & year(date_announced) <= 2022 ~ "Democrat",
    year(date_announced) >= 2002 & year(date_announced) <= 2009 ~ "Republican",
    year(date_announced) >= 2016 & year(date_announced) <= 2020 ~ "Republican",
    TRUE ~ NA_character_
  ))

# summary statistics of deal size and implied deal size by party; 3 digits; order by deal size

df1 %>% 
  group_by(party) %>% 
  summarise(
    mean_deal_value = mean(deal_value_, na.rm = TRUE), 
    median_deal_value = median(deal_value_, na.rm = TRUE), 
    sd_deal_value = sd(deal_value_, na.rm = TRUE), 
    min_deal_value = min(deal_value_, na.rm = TRUE), 
    max_deal_value = max(deal_value_, na.rm = TRUE)
  ) %>% 
  arrange(desc(mean_deal_value)) %>% 
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>% 
  kable("html", col.names = c("Party", "Mean Deal Value", "Median Deal Value", "SD Deal Value", "Min Deal Value", "Max Deal Value")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Party	Mean Deal Value	Median Deal Value	SD Deal Value	Min Deal Value	Max Deal Value
Republican	2586.982	226.65	9781.730	0.01	148705.3
Democrat	1906.374	195.70	6933.232	0.01	145785.3

Party and Deal termination

# summary statistics of deal size and implied deal size by party and termination reason; 3 digits; order by deal size

df1 %>% 
  group_by(party, termination_group) %>% 
  summarise(
    mean_deal_value = mean(deal_value_, na.rm = TRUE), 
    median_deal_value = median(deal_value_, na.rm = TRUE), 
    sd_deal_value = sd(deal_value_, na.rm = TRUE), 
    min_deal_value = min(deal_value_, na.rm = TRUE), 
    max_deal_value = max(deal_value_, na.rm = TRUE)
  ) %>% 
  mutate(across(where(is.numeric), ~ round(.x, 3))) %>% 
  kable("html", col.names = c("Party", "Termination Reason", "Mean Deal Value", "Median Deal Value", "SD Deal Value", "Min Deal Value", "Max Deal Value")) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Party	Termination Reason	Mean Deal Value	Median Deal Value	SD Deal Value	Min Deal Value	Max Deal Value
Democrat	Financial and Economic Factors	1504.280	114.650	3118.704	0.79	16232.92
Democrat	Governance and Approval Factors	2687.654	251.235	8660.860	0.26	77567.80
Democrat	Other	1655.325	181.580	5811.745	0.01	101490.57
Democrat	Regulatory and Legal Factors	10713.829	827.535	27598.051	1.08	145785.26
Democrat	Strategic and Competitive Factors	2667.626	297.185	6784.221	0.10	49383.20
Republican	Financial and Economic Factors	4472.659	143.650	9838.247	0.34	34874.90
Republican	Governance and Approval Factors	2527.094	164.415	9604.826	0.92	66140.09
Republican	Other	2198.212	223.220	7427.444	0.01	86831.16
Republican	Regulatory and Legal Factors	9154.369	576.640	34353.424	13.91	142419.45
Republican	Strategic and Competitive Factors	5019.289	258.290	17599.536	0.50	148705.26

Analysis

Deal Size

# create a dummy variable for termination reason == regulation 

df1 <- df1 %>% 
  mutate(termination_regulation = ifelse(termination_group == "Regulatory and Legal Factors", 1, 0))

# runninng a regression of time duration on termination reason == regulation, deal size, and consideration structure

model1 <- lm(termination_regulation ~ deal_value_, data = df1)
summary(model1)

## 
## Call:
## lm(formula = termination_regulation ~ deal_value_, data = df1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.31199 -0.01234 -0.01065 -0.01034  0.98973 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.026e-02  1.723e-03   5.955 2.78e-09 ***
## deal_value_ 2.029e-06  2.167e-07   9.364  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1183 on 5051 degrees of freedom
## Multiple R-squared:  0.01706,    Adjusted R-squared:  0.01687 
## F-statistic: 87.69 on 1 and 5051 DF,  p-value: < 2.2e-16

model2 <-lm(termination_regulation ~ deal_size, data = df1)

summary(model2)

## 
## Call:
## lm(formula = termination_regulation ~ deal_size, data = df1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.01883 -0.01883 -0.01883 -0.01083  0.99475 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.018831   0.002148   8.766  < 2e-16 ***
## deal_sizeMedium -0.013577   0.004131  -3.287  0.00102 ** 
## deal_sizeSmall  -0.008001   0.004660  -1.717  0.08608 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1192 on 5050 degrees of freedom
## Multiple R-squared:  0.002315,   Adjusted R-squared:  0.00192 
## F-statistic:  5.86 on 2 and 5050 DF,  p-value: 0.002871

model3 <-lm(termination_regulation ~ consideration_group, data = df1)
summary(model3)

## 
## Call:
## lm(formula = termination_regulation ~ consideration_group, data = df1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.01935 -0.01559 -0.01451 -0.01451  0.98915 
## 
## Coefficients:
##                                             Estimate Std. Error t value
## (Intercept)                                 0.010850   0.003589   3.023
## consideration_groupCash Only                0.003661   0.004676   0.783
## consideration_groupOther Non-standard Types 0.008505   0.007670   1.109
## consideration_groupStock Only               0.004745   0.004452   1.066
##                                             Pr(>|t|)   
## (Intercept)                                  0.00251 **
## consideration_groupCash Only                 0.43370   
## consideration_groupOther Non-standard Types  0.26755   
## consideration_groupStock Only                0.28661   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1194 on 5049 degrees of freedom
## Multiple R-squared:  0.0003403,  Adjusted R-squared:  -0.0002536 
## F-statistic: 0.573 on 3 and 5049 DF,  p-value: 0.6327

model4 <-lm(termination_regulation ~ deal_value_ + consideration_group, data = df1)
summary(model4)

## 
## Call:
## lm(formula = termination_regulation ~ deal_value_ + consideration_group, 
##     data = df1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.31422 -0.01418 -0.01300 -0.01088  0.99677 
## 
## Coefficients:
##                                              Estimate Std. Error t value
## (Intercept)                                 2.704e-03  3.657e-03   0.739
## deal_value_                                 2.095e-06  2.185e-07   9.586
## consideration_groupCash Only                8.085e-03  4.658e-03   1.736
## consideration_groupOther Non-standard Types 1.168e-02  7.609e-03   1.535
## consideration_groupStock Only               1.027e-02  4.450e-03   2.308
##                                             Pr(>|t|)    
## (Intercept)                                   0.4597    
## deal_value_                                   <2e-16 ***
## consideration_groupCash Only                  0.0827 .  
## consideration_groupOther Non-standard Types   0.1249    
## consideration_groupStock Only                 0.0210 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1183 on 5048 degrees of freedom
## Multiple R-squared:  0.01821,    Adjusted R-squared:  0.01743 
## F-statistic: 23.41 on 4 and 5048 DF,  p-value: < 2.2e-16

model5 <-lm(termination_regulation ~ deal_value_ + consideration_group + party, data = df1)
summary(model5)

## 
## Call:
## lm(formula = termination_regulation ~ deal_value_ + consideration_group + 
##     party, data = df1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.31370 -0.01414 -0.01325 -0.01107  0.99697 
## 
## Coefficients:
##                                               Estimate Std. Error t value
## (Intercept)                                  2.948e-03  3.756e-03   0.785
## deal_value_                                  2.097e-06  2.187e-07   9.589
## consideration_groupCash Only                 8.091e-03  4.658e-03   1.737
## consideration_groupOther Non-standard Types  1.161e-02  7.614e-03   1.524
## consideration_groupStock Only                1.030e-02  4.452e-03   2.314
## partyRepublican                             -1.131e-03  3.970e-03  -0.285
##                                             Pr(>|t|)    
## (Intercept)                                   0.4326    
## deal_value_                                   <2e-16 ***
## consideration_groupCash Only                  0.0824 .  
## consideration_groupOther Non-standard Types   0.1275    
## consideration_groupStock Only                 0.0207 *  
## partyRepublican                               0.7756    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1183 on 5047 degrees of freedom
## Multiple R-squared:  0.01823,    Adjusted R-squared:  0.01726 
## F-statistic: 18.74 on 5 and 5047 DF,  p-value: < 2.2e-16

Quantifying Anticipation of Regulatory Scrutiny

Kiet Le

2024-08-17

Loading the dataset

Raw Data

Summary Statistics

Deal Size

Deal Size by Termination Reason

Cleaning

Deal Size by Consideration Structure

Deal terminated due to regulation

Occurences over time

Deal size

Termination reasons and consideration structure

Deal size by termination reason

Time duration

From date announced to date effective

Average duration for each year grouped by deal size

Average duration for each year grouped by consideration structure

Politics

Party and Deal termination

Analysis

Deal Size