1 00:00:00,160 --> 00:00:03,840 Welcome back to Data Driven, the podcast where we explore the big 2 00:00:03,840 --> 00:00:07,200 ideas in data science, AI, and all things data 3 00:00:07,200 --> 00:00:10,800 engineering. Today, we're diving into the complex world of 4 00:00:10,800 --> 00:00:14,179 mergers and acquisitions where data meets corporate strategy 5 00:00:14,465 --> 00:00:18,145 and not always in the friendliest way. With us are 2 top tier 6 00:00:18,145 --> 00:00:21,685 experts who know this landscape inside and out, Baruch Lev, 7 00:00:21,744 --> 00:00:25,505 professor emeritus from NYU, and Phong Gu, professor of 8 00:00:25,505 --> 00:00:29,320 accounting at the University of Buffalo. We're going to unpack why 9 00:00:29,320 --> 00:00:33,000 up to 75% of mergers fail and how to spot the ones 10 00:00:33,000 --> 00:00:36,840 that succeed. Buckle up. It's data driven insight at its 11 00:00:36,840 --> 00:00:37,340 finest. 12 00:00:41,239 --> 00:00:44,725 Hello, and welcome to Data Driven, the podcast where we explore the 13 00:00:44,725 --> 00:00:48,405 emerging fields of data science, artificial intelligence, and, of course, data 14 00:00:48,405 --> 00:00:52,245 engineering. Today, we're gonna talk about a branch of, I 15 00:00:52,245 --> 00:00:56,024 guess, applied analytics, where we analyze how 16 00:00:56,970 --> 00:01:00,809 mergers and acquisition data, goes through. And with us, we 17 00:01:00,809 --> 00:01:04,489 have 2 esteemed guests today. It's not every day we have 2 guests. 18 00:01:04,650 --> 00:01:08,490 So I'm gonna read the bio of 1, and Andy will 19 00:01:08,490 --> 00:01:12,275 read the bio of the other guest. With us today is Baruch 20 00:01:12,275 --> 00:01:16,115 Lev, a professor emeritus at NYU Stern 21 00:01:16,115 --> 00:01:19,955 School of Business, where he taught and conducted research on mergers and 22 00:01:19,955 --> 00:01:23,729 acquisitions for decades. He worked formally at 23 00:01:23,729 --> 00:01:27,570 UC Berkeley and University of Chicago. His work has been widely cited 24 00:01:27,570 --> 00:01:29,990 in academic and professional circles, 25 00:01:31,330 --> 00:01:34,770 and with over 63,000 Google Google Scholar 26 00:01:34,770 --> 00:01:38,305 citations. He's a leading authority in corporate finance and valuation. 27 00:01:39,645 --> 00:01:43,185 And also with us is Feng Gu. He's a professor 28 00:01:43,325 --> 00:01:47,104 of accounting at the University of Buffalo and has extensive 29 00:01:47,244 --> 00:01:50,604 experience in analyzing the financial aspects of corporate 30 00:01:50,604 --> 00:01:54,299 acquisitions. His research focuses on the economic 31 00:01:54,439 --> 00:01:57,880 consequences of corporate decisions and has been 32 00:01:57,880 --> 00:02:01,640 published in top tier academic journals. Welcome 33 00:02:01,640 --> 00:02:05,159 gents. Thank you. Thank you for having 34 00:02:05,159 --> 00:02:08,324 us. Yeah. Thank you for the invitation. 35 00:02:09,345 --> 00:02:12,865 Yeah. No problem. We're we're always great to to to have you here. And 36 00:02:12,865 --> 00:02:15,665 and part of our listeners are wondering, hey, I thought this was a data science 37 00:02:15,665 --> 00:02:19,425 podcast. And and I would say that if you are 38 00:02:19,425 --> 00:02:23,129 having an IT career, not just a data career or any career, you are 39 00:02:23,129 --> 00:02:26,909 gonna be impacted at some point along, by a merger and or acquisition. 40 00:02:27,530 --> 00:02:31,310 Sure. And I don't have a lot I don't know about you, Andy. 41 00:02:31,689 --> 00:02:35,505 I don't have a lot of fond memories of them all working out. It's 42 00:02:35,505 --> 00:02:39,185 always been a change, and, you know, change change is always, 43 00:02:39,425 --> 00:02:42,965 brings challenges. Yes. And I'm sure these gentlemen, 44 00:02:43,665 --> 00:02:46,965 study those challenges and have a lot to share with our audience, 45 00:02:47,745 --> 00:02:51,350 and us. You work for a large company in 46 00:02:51,350 --> 00:02:54,810 IT. I own a small boutique consulting 47 00:02:54,870 --> 00:02:58,310 company that that provides data engineering and and and 48 00:02:58,310 --> 00:03:01,830 similar services. So I'm excited to learn what you got going 49 00:03:01,830 --> 00:03:05,655 on. In case someone wants to acquire, my 50 00:03:05,655 --> 00:03:09,495 company. And I'm sure you're keeping an eye on this, Frank, 51 00:03:09,495 --> 00:03:13,255 in case someone wants to merge with yours. Well 52 00:03:13,334 --> 00:03:16,989 and, again, I wanna be clear. The current company I work 53 00:03:16,989 --> 00:03:20,430 for, I joined post IBM acquisition. Right? So all of these horror 54 00:03:20,430 --> 00:03:24,030 stories are actually the worst merger I was ever privy 55 00:03:24,030 --> 00:03:27,870 to was, as an employee, was, well, I guess I can say 56 00:03:27,870 --> 00:03:30,450 it now, the Bankers Trust Deutsche Bank acquisition, 57 00:03:31,325 --> 00:03:33,745 which, Deutsche Bank being a German company, 58 00:03:35,004 --> 00:03:38,525 when they passed out and and Bankers Trust was an American company. When they passed 59 00:03:38,525 --> 00:03:41,885 out the cards announcing the merger or celebrating the merger, the 60 00:03:42,444 --> 00:03:45,584 I speak German, so the English sides called it a merger. 61 00:03:46,140 --> 00:03:49,580 The German side used the word Uber Nemen, which means 62 00:03:49,580 --> 00:03:53,420 takeover. That's yeah. I know just enough Latin to 63 00:03:53,420 --> 00:03:56,940 pick that up. Which was, which I thought was 64 00:03:56,940 --> 00:04:00,765 interesting because that's basically what it was. So to when I talk about my 65 00:04:00,765 --> 00:04:03,565 merger horror stories, I'm not talking about where I am now. This is 20 years 66 00:04:03,565 --> 00:04:07,325 back. And, the other thing as a 67 00:04:07,325 --> 00:04:11,084 customer, when the the the companies 68 00:04:11,084 --> 00:04:14,460 I use have merged, I've not really been a happy customer. I think Sirius 69 00:04:14,460 --> 00:04:17,760 XM, XM Radio was a much better 70 00:04:19,019 --> 00:04:22,860 satellite radio provider than Sirius XM is. And that's just my 71 00:04:22,860 --> 00:04:26,620 opinion. That's not the opinion of anyone else. My wife seems to 72 00:04:26,620 --> 00:04:29,985 enjoy it, but it is what it is. So what really excited me about this 73 00:04:30,065 --> 00:04:33,425 so before our listeners start, like, what the heck are we gonna talk about? These 74 00:04:33,425 --> 00:04:36,465 guys are gonna bring data to the table, and that's why I'm excited to have 75 00:04:36,465 --> 00:04:39,825 them there. So I'm gonna get off my soapbox because people don't wanna hear us 76 00:04:39,825 --> 00:04:41,365 banging on. They wanna hear you guys. 77 00:04:43,919 --> 00:04:47,680 So starting with the data side, we 78 00:04:47,680 --> 00:04:51,440 have probably the largest sample of 79 00:04:51,440 --> 00:04:54,740 mergers and acquisitions ever assembled. 80 00:04:55,474 --> 00:04:59,074 We have a sample of 40,000 mergers and 81 00:04:59,074 --> 00:05:00,455 acquisitions worldwide, 82 00:05:02,675 --> 00:05:06,514 spending over the last 40 years. And 83 00:05:06,514 --> 00:05:10,220 on this huge sample, we 84 00:05:10,220 --> 00:05:14,000 have developed a quite sophisticated 85 00:05:14,860 --> 00:05:18,320 statistical model, multivariate 86 00:05:19,020 --> 00:05:21,840 statistical model with 43 variables 87 00:05:23,354 --> 00:05:25,455 to identify statistically, 88 00:05:27,914 --> 00:05:30,735 the attributes, 89 00:05:31,514 --> 00:05:34,895 the factors that contribute to success 90 00:05:35,995 --> 00:05:38,095 and failure of companies. 91 00:05:40,380 --> 00:05:43,600 Excuse me, of mergers and acquisitions. So, basically, 92 00:05:45,100 --> 00:05:48,860 the entire work that we did, which is summarized in the book, of 93 00:05:48,860 --> 00:05:52,240 course, is heavily data 94 00:05:52,380 --> 00:05:55,775 driven. It's also supported by 95 00:05:56,315 --> 00:05:59,695 other study, which are always 96 00:06:00,075 --> 00:06:03,215 data, driven large sample studies 97 00:06:03,915 --> 00:06:07,295 of specific issues of mergers and acquisitions 98 00:06:07,755 --> 00:06:10,410 that, we didn't examine. 99 00:06:11,590 --> 00:06:15,050 So, we combine all of this 100 00:06:15,510 --> 00:06:19,030 to a set of of 101 00:06:19,030 --> 00:06:22,515 observations and recommendations of 102 00:06:22,995 --> 00:06:26,615 why 70 to 75% 103 00:06:27,315 --> 00:06:29,895 of all mergers fail 104 00:06:30,755 --> 00:06:34,435 fail to achieve sales growth, fail 105 00:06:34,435 --> 00:06:38,180 to achieve synergies in in cost 106 00:06:38,180 --> 00:06:41,800 of sales efficiencies, failed to maintain 107 00:06:42,180 --> 00:06:44,920 the share price of the buying, 108 00:06:45,540 --> 00:06:49,220 companies. It's an amazing number that 109 00:06:49,220 --> 00:06:52,920 surprises most most people who 110 00:06:53,565 --> 00:06:57,324 see it. That that is a large 111 00:06:57,324 --> 00:07:00,685 number, and I'm kinda shocked to learn that. I would have 112 00:07:00,685 --> 00:07:04,205 thought that, you know, it would have been on the positive side of 113 00:07:04,205 --> 00:07:07,759 that that 5050 mark that, that the 114 00:07:07,759 --> 00:07:11,599 mergers and acquisitions succeeded, and there were benefits enjoyed 115 00:07:11,599 --> 00:07:15,199 by all. But it sounds like what you're saying is no about 3 116 00:07:15,199 --> 00:07:19,039 quarters of those fail on some or, you know, some or maybe 117 00:07:19,039 --> 00:07:22,845 all, of those desired outcomes. Yeah. I'm 118 00:07:22,845 --> 00:07:26,385 actually not surprised. I had heard that statistic before and kind of based on 119 00:07:26,445 --> 00:07:29,985 based on my anecdotal kind of personal experience, I think that that sounds reasonable. 120 00:07:30,685 --> 00:07:34,205 But the question I have is if if it if the situation is so 121 00:07:34,205 --> 00:07:37,840 bad, a lot of questions, How do they how do these companies convince their 122 00:07:37,840 --> 00:07:40,320 respective boards to take the buyout? Is it just a, 123 00:07:42,080 --> 00:07:43,780 how did how do they pull that off? 124 00:07:47,520 --> 00:07:51,065 The way to an acquisition is 125 00:07:51,065 --> 00:07:54,445 usually a failure of the acquiring 126 00:07:54,665 --> 00:07:58,265 company. Sales slow 127 00:07:58,265 --> 00:08:01,705 down, earnings turn to 128 00:08:01,705 --> 00:08:05,165 losses, market share is lost, 129 00:08:06,380 --> 00:08:09,440 and everything gets excited, 130 00:08:09,820 --> 00:08:13,440 particularly investors who are, of course, losing money 131 00:08:13,980 --> 00:08:17,740 and influential investors who have a a 132 00:08:17,740 --> 00:08:21,535 big say on company. Directors 133 00:08:22,395 --> 00:08:25,755 are are looking, and the 134 00:08:25,755 --> 00:08:29,115 call gets out of we have to 135 00:08:29,115 --> 00:08:32,900 do something big. And, usually, the 136 00:08:32,900 --> 00:08:36,360 something big is a big acquisition. 137 00:08:38,740 --> 00:08:42,039 And that's how that's how that's the usual 138 00:08:42,260 --> 00:08:45,400 way of getting, to this. 139 00:08:46,245 --> 00:08:49,225 Managers, are optimists. 140 00:08:50,005 --> 00:08:53,705 Lots of psychological studies have shown that 141 00:08:53,765 --> 00:08:57,065 managers are much above average 142 00:08:57,365 --> 00:09:00,345 optimists. Some of them are overoptimists. 143 00:09:02,300 --> 00:09:05,839 They may be they may be aware that many 144 00:09:06,060 --> 00:09:09,820 most, m and a, fail, but they 145 00:09:09,820 --> 00:09:13,360 are convinced that they will make it. 146 00:09:14,155 --> 00:09:17,675 And they are convincing their board of directors and 147 00:09:17,675 --> 00:09:21,295 sometimes even shareholders to, support it. 148 00:09:22,155 --> 00:09:25,995 Yeah. So the persuasion and 149 00:09:25,995 --> 00:09:29,820 the pressure to acquire also come from 150 00:09:29,880 --> 00:09:32,540 frequently, investment bankers, 151 00:09:33,240 --> 00:09:37,000 financial analysts, and consultants. These people, of course, 152 00:09:37,000 --> 00:09:40,220 say, you know, have obtained financial benefits 153 00:09:40,440 --> 00:09:44,154 from, completed deals. They always pressure 154 00:09:44,154 --> 00:09:47,915 the acquiring company to by pointing out, hey. This is a good 155 00:09:47,915 --> 00:09:51,355 deal for you, and we can help you, you know, go through 156 00:09:51,355 --> 00:09:55,035 this and clear all the hurdles and everything will work 157 00:09:55,035 --> 00:09:58,740 out fine. And, so this is really the 158 00:09:58,740 --> 00:10:02,580 best decision for you to make. They're really 159 00:10:02,580 --> 00:10:06,260 play these consultants and investment bankers really play a very 160 00:10:06,260 --> 00:10:09,860 important role in convincing, both sides of the 161 00:10:09,860 --> 00:10:12,840 acquisition to complete the deal as soon as possible. 162 00:10:13,355 --> 00:10:17,195 Gotcha. That sounds like sorry. Go ahead. I 163 00:10:17,195 --> 00:10:19,935 just want to say in conclusion, you know, 164 00:10:20,715 --> 00:10:24,315 some, m and a proposals are being 165 00:10:24,315 --> 00:10:28,000 rejected. Not everything is accepted. Just 166 00:10:28,060 --> 00:10:31,520 recently, an Israeli company 167 00:10:31,740 --> 00:10:34,880 got, an acquisition 168 00:10:35,980 --> 00:10:39,665 proposal from no less than Google for 169 00:10:39,885 --> 00:10:43,725 $23,000,000,000. Goodness. After 170 00:10:43,725 --> 00:10:47,404 after consideration, they, they rejected it. So 171 00:10:47,404 --> 00:10:51,105 not everything is accepted. But 172 00:10:51,404 --> 00:10:54,880 many, many acquisition strongly 173 00:10:54,880 --> 00:10:58,660 supported by the CEO are indeed 174 00:10:58,720 --> 00:11:02,320 accepted. Well, it 175 00:11:02,320 --> 00:11:05,839 sounds like there's financial incentive, for the 176 00:11:05,839 --> 00:11:09,615 people around the process for the process to 177 00:11:09,615 --> 00:11:12,975 conclude? Because I imagine they don't get paid unless the 178 00:11:12,975 --> 00:11:16,735 acquisition goes through. Correct? Yes. And there are 179 00:11:16,735 --> 00:11:20,435 also there are also quite large, incentives 180 00:11:20,735 --> 00:11:24,410 for managers for concluding the deal. 181 00:11:24,870 --> 00:11:27,029 A recent study showed that, 182 00:11:28,790 --> 00:11:32,410 many managers get, acquisition bonuses 183 00:11:32,949 --> 00:11:34,009 between $5,15,000,000. 184 00:11:37,014 --> 00:11:40,654 Got it. And that's for concluding the deal, not 185 00:11:40,654 --> 00:11:44,295 for succeeding, but for just 186 00:11:44,295 --> 00:11:47,195 concluding the deal. Wow. And, 187 00:11:47,975 --> 00:11:51,514 we have we have in the book, we show statistics, 188 00:11:52,200 --> 00:11:55,960 which I've never seen anywhere else, that, 189 00:11:56,520 --> 00:11:57,900 serial acquirers, 190 00:12:00,680 --> 00:12:04,460 their tenure is 4 to 5 years 191 00:12:04,520 --> 00:12:08,024 longer than CEOs that 192 00:12:08,024 --> 00:12:11,485 don't acquire or acquire just few companies. 193 00:12:12,584 --> 00:12:16,125 My guess is that, directors are 194 00:12:16,985 --> 00:12:20,365 somehow satisfied with very active CEO 195 00:12:20,505 --> 00:12:24,080 who try to change the course of the company, 196 00:12:24,700 --> 00:12:28,460 let them acquire our companies, and then they give them, 197 00:12:28,780 --> 00:12:31,040 more time to to somehow 198 00:12:32,140 --> 00:12:35,900 somehow, complete the complete the deal and complete 199 00:12:35,900 --> 00:12:39,195 the integration. But I was, 200 00:12:39,575 --> 00:12:43,415 someone someone just recently asked me, what surprised you most? One 201 00:12:43,415 --> 00:12:47,175 of the things that surprised me most in researching the 202 00:12:47,175 --> 00:12:50,795 book was this 4, 5 year, 203 00:12:51,015 --> 00:12:54,200 10 year edge of serial 204 00:12:54,420 --> 00:12:57,959 acquirer CEOs, irrespective 205 00:12:59,220 --> 00:13:01,399 of the success of the mergers. 206 00:13:03,459 --> 00:13:07,300 Yeah. And this difference of CEO tenure by 4 207 00:13:07,300 --> 00:13:10,685 to 5 years is obtained after we have 208 00:13:10,685 --> 00:13:14,525 controlled for other contributors to CEO tenure, like 209 00:13:14,525 --> 00:13:18,285 corporate performance and other important factors. So in 210 00:13:18,285 --> 00:13:22,070 other words, our conclusion basically says with everything else equal, 211 00:13:22,070 --> 00:13:25,830 if you make a series of acquisitions, your 212 00:13:25,830 --> 00:13:29,350 CEO tenure is going to be extended by 4 to 5 213 00:13:29,350 --> 00:13:33,029 years on average, which is really a 214 00:13:33,029 --> 00:13:36,005 long, long extension. Acquisitions are almost, 215 00:13:37,685 --> 00:13:40,345 tenure insurers or CEOs. 216 00:13:41,845 --> 00:13:45,285 So it sounds like the, the, 217 00:13:45,765 --> 00:13:47,464 incentives are a little bit lopsided. 218 00:13:49,490 --> 00:13:53,190 Yeah. Definitely are from all sides. As Frank mentioned, 219 00:13:53,410 --> 00:13:56,930 the, commission hungry, investment bankers, and 220 00:13:56,930 --> 00:14:00,230 consultants benefit from the deal. 221 00:14:02,005 --> 00:14:05,685 CEOs benefit from, the deal. The only 222 00:14:05,685 --> 00:14:09,365 one who paid the price are the shareholders. And and 223 00:14:09,365 --> 00:14:12,905 many times, employees are being fired. 224 00:14:13,445 --> 00:14:14,905 Customers, suppliers, 225 00:14:17,610 --> 00:14:21,230 suffer. Mergers have an 226 00:14:21,290 --> 00:14:24,910 overall effect on the entire economy 227 00:14:25,450 --> 00:14:29,290 on the Which I think this. Yeah. Which I think, like, begs the question, 228 00:14:29,290 --> 00:14:31,070 like, if you play this out long enough, 229 00:14:33,075 --> 00:14:36,915 more people lose than win. And, like, what's the effect of this in 230 00:14:36,915 --> 00:14:40,435 the global economy? Because a lot of during times of 231 00:14:40,435 --> 00:14:44,195 uncertainty, a lot of the star performers leave because they're not sure 232 00:14:44,195 --> 00:14:46,935 what's gonna happen to them. Yeah. Right? Because usually 233 00:14:48,410 --> 00:14:51,870 usually, the the acquiring company tends to keep more of their people. 234 00:14:54,250 --> 00:14:57,770 What and and and I think that's probably a different game if, you know, if 235 00:14:57,770 --> 00:15:01,050 a if an £800 gorilla buys a small start up. I think that's one type 236 00:15:01,050 --> 00:15:04,565 of dynamic. But if you have kind of these 2 industry 237 00:15:04,565 --> 00:15:08,404 titans that buy each other, right, something more 238 00:15:08,404 --> 00:15:11,385 akin to Deutsche Bank and Bankers Trust, right, 239 00:15:12,245 --> 00:15:15,385 there's probably a lot of because they see each one of them sees 240 00:15:16,170 --> 00:15:19,930 sees each other well, one side sees itself as a peer and the other 241 00:15:19,930 --> 00:15:23,610 sees it as superior itself as superior. And that's gotta lead 242 00:15:23,610 --> 00:15:26,670 to all kinds of weird personal interdynamics. 243 00:15:27,610 --> 00:15:30,970 Yeah. You're perfectly right. I 244 00:15:30,970 --> 00:15:34,265 mean, acquisition of large 245 00:15:34,485 --> 00:15:38,025 targets relative to the size of the acquiring 246 00:15:38,085 --> 00:15:41,925 company are almost, a recipe for 247 00:15:41,925 --> 00:15:45,625 failure. We analyze in the book the examples, 248 00:15:46,165 --> 00:15:49,910 several years ago of Sprint acquiring 249 00:15:50,130 --> 00:15:53,490 Nextel. That's the 3rd and the 250 00:15:53,490 --> 00:15:57,110 5th, at the at the time. The 3rd and the 5th, 251 00:15:57,889 --> 00:16:01,605 wireless operators. This was they 252 00:16:01,605 --> 00:16:05,445 were about the same size. Sprint was a little 253 00:16:05,445 --> 00:16:09,225 larger. This was an unmitigated, 254 00:16:10,805 --> 00:16:14,550 disaster, the whole thing. They 255 00:16:14,550 --> 00:16:18,170 they completely failed in in, 256 00:16:18,710 --> 00:16:20,170 merging the employees. 257 00:16:22,470 --> 00:16:26,230 They even they even kept the separate headquarters of 258 00:16:26,230 --> 00:16:29,725 the 2 companies and the separate operating 259 00:16:29,865 --> 00:16:33,705 systems. Customers will ask, do you want to 260 00:16:33,705 --> 00:16:37,145 join the operating system of Nextel or 261 00:16:37,145 --> 00:16:40,445 Sprint? I mean, huge churn, 262 00:16:40,585 --> 00:16:44,290 huge desertion of customers, 263 00:16:45,230 --> 00:16:47,970 and then the whole thing, collapsed. 264 00:16:48,750 --> 00:16:51,810 Yeah. Acquisition of large 265 00:16:52,350 --> 00:16:55,790 targets are very, very difficult to 266 00:16:55,790 --> 00:16:59,495 integrate. And you indicated most of the reasons, with your 267 00:16:59,495 --> 00:17:02,714 example of Deutsche Bank. Right. Right. And I'm a former 268 00:17:03,095 --> 00:17:06,855 Nextel customer. Same. And I was not 269 00:17:08,135 --> 00:17:11,740 I think I think the Sprint acquisition could have been worse. But if that's your 270 00:17:11,740 --> 00:17:15,500 metric, it could have been worse as from a customer's point of view. Yeah. 271 00:17:15,500 --> 00:17:18,460 I I suppose based on the numbers you're telling me, it could have been worse. 272 00:17:18,460 --> 00:17:21,919 It sounds like a pretty good pretty soft pretty safe outcome. 273 00:17:22,220 --> 00:17:25,980 I'm doing the low bar symbol. If you're watching the video, you could see that. 274 00:17:25,980 --> 00:17:29,654 But I'm the bar is down here for could have been worse. 275 00:17:32,195 --> 00:17:36,034 Frank, you asked about how this type of deals may 276 00:17:36,034 --> 00:17:39,715 affect employees of the target versus the acquiring company. 277 00:17:39,715 --> 00:17:43,289 I I think it's a great question. In the research for this 278 00:17:43,289 --> 00:17:46,970 book, we spent a lot of time looking into how 279 00:17:46,970 --> 00:17:49,870 acquisition deals may affect, employees. 280 00:17:50,490 --> 00:17:54,169 And we did look at, the reaction from the target 281 00:17:54,169 --> 00:17:57,555 company's employees, and we find that as soon as the news of 282 00:17:57,555 --> 00:18:01,315 mergers acquisition comes out, a growing number 283 00:18:01,315 --> 00:18:05,075 of target company's employees decide to leave the company. And 284 00:18:05,075 --> 00:18:08,515 this happened even before the merger, gets 285 00:18:08,515 --> 00:18:12,120 completed. So they learn from their experience 286 00:18:12,180 --> 00:18:15,780 or maybe from your experience involved in this 2 large bank merger 287 00:18:15,780 --> 00:18:19,620 that the target employees always get, you 288 00:18:19,620 --> 00:18:22,995 know, relatively unfair share in the post 289 00:18:22,995 --> 00:18:26,595 acquisition termination, for the purpose of 290 00:18:26,595 --> 00:18:30,295 creating synergies, cost savings, and so on. So 291 00:18:30,675 --> 00:18:34,115 on average, mergers acquisitions have not been 292 00:18:34,115 --> 00:18:37,809 friendly to employees. We're documenting one chapter of our 293 00:18:37,809 --> 00:18:41,490 books, the loss of job positions on 294 00:18:41,490 --> 00:18:43,350 average is about 5 to 7% 295 00:18:45,250 --> 00:18:48,770 of the combined entities workforce, which is a 296 00:18:48,770 --> 00:18:52,445 significant number. Yeah. You know, it sounds a little low 297 00:18:52,665 --> 00:18:56,505 when you put it that way. 5 to 7% doesn't sound like a lot. But 298 00:18:56,505 --> 00:19:00,205 I can imagine, you know, in these, you know, in in Frank's 299 00:19:00,345 --> 00:19:03,565 Bank, acquisition scenario. 300 00:19:04,039 --> 00:19:07,559 Yeah. That's, you know, that's across thousands of 301 00:19:07,559 --> 00:19:11,000 employees. Yeah. That can be a large number of 302 00:19:11,000 --> 00:19:14,299 people. Well, there was also the rock stars. You know? 303 00:19:14,520 --> 00:19:17,399 Yeah. I don't know how it is now, but back then, you know, Wall Street 304 00:19:17,399 --> 00:19:21,025 was very aggressive about getting you know, they would basically go to the top 305 00:19:21,025 --> 00:19:24,805 trader at, let's say, BT Bankers Trust, and say, hey. 306 00:19:25,505 --> 00:19:28,705 We know you're feeling a bit uncertain now. Why don't you have a conversation with 307 00:19:28,705 --> 00:19:32,385 us? Right? And you can you you'll make more you'll make, 308 00:19:32,385 --> 00:19:35,840 like, 20% more or twice as much and bring anyone you want over 309 00:19:35,840 --> 00:19:39,220 to. Right? So the I suspect the numbers are actually higher, 310 00:19:39,760 --> 00:19:43,280 but the published numbers in terms of layoffs are probably 5 to 311 00:19:43,280 --> 00:19:45,460 7%. But I think the star performers, 312 00:19:47,054 --> 00:19:50,515 I think you kinda lose the star performers almost right away. Right? 313 00:19:50,575 --> 00:19:54,174 Yeah. Yeah. You're you're perfectly right. That that's what 314 00:19:54,174 --> 00:19:58,015 economists call moral hazard, which 315 00:19:58,015 --> 00:20:01,390 means the employees employees you lose. It's not 316 00:20:01,390 --> 00:20:05,170 just a matter of numbers. You lose the best 317 00:20:05,230 --> 00:20:08,370 employees, those with the best alternative 318 00:20:09,309 --> 00:20:12,929 outside, and you are left with those without 319 00:20:13,325 --> 00:20:17,165 any or very attractive alternatives. So the 320 00:20:17,165 --> 00:20:20,925 degradation of the work workforce is much 321 00:20:20,925 --> 00:20:24,765 more serious than just the numbers. Yeah. Yeah. You 322 00:20:24,765 --> 00:20:28,520 know, I have an experience like that too. I I just, for some reason, 323 00:20:28,520 --> 00:20:31,580 it escaped me earlier, but I was a manager 324 00:20:32,200 --> 00:20:35,960 at Unisys, and I was managing the, the data 325 00:20:35,960 --> 00:20:39,260 engineering team. We called it the ETL for extract, 326 00:20:39,320 --> 00:20:43,025 transform, and load, team. There were about 40 people who 327 00:20:43,025 --> 00:20:46,705 were a combination of full time workers and 328 00:20:46,705 --> 00:20:49,605 then an extended collection of subcontractors. 329 00:20:51,025 --> 00:20:54,705 And we went through a merger and I'll spill the beans on this one too 330 00:20:54,705 --> 00:20:57,960 with Molina Healthcare that was headquartered in, 331 00:20:58,120 --> 00:21:01,020 out in California. And 332 00:21:02,040 --> 00:21:05,880 we had some of that. In fact, my my boss who was a 333 00:21:05,880 --> 00:21:09,660 director, he was a fantastic example of this, 334 00:21:09,800 --> 00:21:13,325 definitely a high performer, published 5 books, 335 00:21:14,265 --> 00:21:18,045 a known entity in the data field, and just an excellent, 336 00:21:18,585 --> 00:21:22,185 leader in my opinion. In 337 00:21:22,185 --> 00:21:25,325 the ramp up to the merger, 338 00:21:26,240 --> 00:21:29,920 or it actually was an acquisition. In the ramp up to that, he 339 00:21:30,000 --> 00:21:33,300 when he got wind of it, he began putting out feelers, 340 00:21:33,760 --> 00:21:36,900 for, you know, making a move to another company. And eventually, 341 00:21:37,520 --> 00:21:41,205 he did. And this was excuse me. His move, him 342 00:21:41,205 --> 00:21:44,965 leaving was a huge hit to the company, a 343 00:21:44,965 --> 00:21:48,725 huge loss. And he did this months before the deal 344 00:21:48,725 --> 00:21:52,565 was concluded, like a full quarter ahead of time. And does that I'm 345 00:21:52,565 --> 00:21:56,350 curious. Does that count in the 5 to 7%? Would his 346 00:21:56,350 --> 00:21:59,890 leaving count in that, or would you would it be post acquisition? 347 00:22:01,230 --> 00:22:04,670 In in some cases, it it is included. In other 348 00:22:04,670 --> 00:22:08,055 cases, it it may not be included. It all depends on the 349 00:22:08,295 --> 00:22:12,135 relative timing of acquisition announcement versus k. The 350 00:22:12,135 --> 00:22:15,415 fiscal year end. Because as you probably know, 351 00:22:15,655 --> 00:22:19,495 companies don't disclose the number of employees all the time. I 352 00:22:19,495 --> 00:22:23,259 think right now, they, you know, provide this number once a year in 353 00:22:23,259 --> 00:22:27,039 their annual report. So there's always some discrepancy, 354 00:22:28,059 --> 00:22:31,419 in the number of in the exact number of employees, you know, 355 00:22:31,419 --> 00:22:34,965 between fiscal year end and, the announcement of the 356 00:22:34,965 --> 00:22:38,805 acquisition. Gotcha. But on average, it should be, you know, 357 00:22:38,805 --> 00:22:42,645 around that number. You mentioned the importance of 358 00:22:42,645 --> 00:22:46,240 losing key talent. Frank also made the key point here. We 359 00:22:46,240 --> 00:22:49,679 completely agree with you. Actually, in one of the chapters in your 360 00:22:49,679 --> 00:22:53,039 book, we have a graph showing, clear 361 00:22:53,039 --> 00:22:56,559 evidence, supporting this effect of 362 00:22:56,559 --> 00:23:00,400 losing talent. We document that after the acquisition is 363 00:23:00,400 --> 00:23:03,915 completed, 2 to 3 years down the road, there is a 364 00:23:03,915 --> 00:23:07,055 clear pattern of declining employee productivity. 365 00:23:07,995 --> 00:23:11,295 So that's normally a sign of losing key talent. 366 00:23:12,235 --> 00:23:15,860 You know, you know, you have lost the most important human capital 367 00:23:15,920 --> 00:23:19,540 component of your combined workforce, and there's no way, 368 00:23:20,160 --> 00:23:23,600 your workforce productivity is gonna be as strong as they used to 369 00:23:23,600 --> 00:23:27,060 be. So that's clearly a consequence, 370 00:23:27,795 --> 00:23:31,315 on the on the employee side after mergers, acquisitions are 371 00:23:31,315 --> 00:23:35,095 completed. So I wanna mention we're recording this on the 18th 372 00:23:35,235 --> 00:23:38,835 October 2024, and the book is 373 00:23:38,835 --> 00:23:42,055 named the m and a, m ampersand a, 374 00:23:42,600 --> 00:23:46,120 failure trap. And the subtitle is why most 375 00:23:46,120 --> 00:23:49,900 mergers and acquisitions fail and how the few succeed. 376 00:23:50,360 --> 00:23:53,820 And that book is due out according to Amazon today. 377 00:23:54,200 --> 00:23:57,935 They're projecting November 15th. So a little less than a 378 00:23:57,935 --> 00:24:01,535 month from now is when that book is due to be available. Is that accurate 379 00:24:01,535 --> 00:24:04,915 as far as you know? Yes. Excellent. 380 00:24:05,455 --> 00:24:08,995 Now I'm gonna buy the book. So I wanna know more. 381 00:24:09,520 --> 00:24:13,360 Thank you. Thank you, William. Yes. We have one say 382 00:24:13,360 --> 00:24:17,120 say 1. Yes. We 383 00:24:17,120 --> 00:24:18,900 made it. Make it 2. 384 00:24:22,725 --> 00:24:26,185 Your order is going to be the most special one because it's the first one. 385 00:24:26,645 --> 00:24:30,485 And, and since you bought the book, you can all, you can 386 00:24:30,485 --> 00:24:33,225 also give us, high recommendation. 387 00:24:34,360 --> 00:24:37,559 Okay. As for And we'll do that. Yeah. Yeah. Well, both Frank and I, you 388 00:24:37,559 --> 00:24:41,080 may not know this, but Frank and I are published. We've written I Frank, you've 389 00:24:41,080 --> 00:24:43,820 written a couple. Right? Couple 3? 390 00:24:44,760 --> 00:24:48,299 3. 3. Yeah. Mhmm. And I've been involved 391 00:24:48,615 --> 00:24:51,835 either as the sole author or a member of a team for 14. 392 00:24:52,695 --> 00:24:56,295 But I started way before Frank to be fair. That's a great 393 00:24:56,295 --> 00:25:00,135 number. Well, it warms my 394 00:25:00,135 --> 00:25:03,930 heart to hear smart people say that, but I have to share. I 395 00:25:03,930 --> 00:25:07,450 have to share that it has way more to do with insomnia than 396 00:25:07,450 --> 00:25:09,710 intelligence. Just just so you know. 397 00:25:13,450 --> 00:25:14,990 That's even more incredible. 398 00:25:17,385 --> 00:25:21,065 I I recall holding, my my youngest is 399 00:25:21,065 --> 00:25:24,745 17 years old now. But when he was a baby, I did 400 00:25:24,745 --> 00:25:28,184 that year. I wrote 2 at the same time. I just wrote chapters in a 401 00:25:28,184 --> 00:25:32,020 book on a team, just a few chapters, but I'll never do that again. 402 00:25:32,020 --> 00:25:35,700 And I haven't since. But I was holding him and had, you know, 403 00:25:35,700 --> 00:25:39,380 my arm had his head in my arm here and holding the bottle, feeding 404 00:25:39,380 --> 00:25:43,145 him at, like, 2 AM. And I'm typing on the laptop with 405 00:25:43,205 --> 00:25:46,505 my other hand. True story. 406 00:25:48,005 --> 00:25:51,525 That's quite a story. Yeah. This looks like an an amazing 407 00:25:51,525 --> 00:25:55,365 book. I've yeah. I'm a data, you know, a data weenie, being a 408 00:25:55,365 --> 00:25:57,145 data engineer, and I've worked around financial data of all my career. 409 00:26:03,559 --> 00:26:07,160 What we did at Unisys was Medicaid, driven data. 410 00:26:07,160 --> 00:26:10,679 And so you get a lot of finance in there. So we get it you 411 00:26:10,679 --> 00:26:14,505 know, we dabbled in that part of it, and there's just so much financial 412 00:26:14,505 --> 00:26:18,285 data out there. And I've seen so many ways to analyze it 413 00:26:18,665 --> 00:26:21,965 and then ways to, you know, not intentionally, 414 00:26:22,265 --> 00:26:25,570 but misanalyze it. You you look at the data, 415 00:26:26,110 --> 00:26:29,950 an old story intentionally and intentionally. Well, I imagine there's some 416 00:26:29,950 --> 00:26:33,650 intent. I was trying to be nice, Frank. But 417 00:26:33,790 --> 00:26:37,390 I have an old story that I share with data engineers. It's 418 00:26:37,390 --> 00:26:41,015 not, you know, it's not a real life story, but it's an analogy of 419 00:26:41,015 --> 00:26:44,795 the misapplication of thinking that sometimes goes along 420 00:26:44,795 --> 00:26:47,995 with this. It's kind of a, you know, getting the cart before the horse or 421 00:26:47,995 --> 00:26:51,375 miss you know, misunderstanding cause and effect. And 422 00:26:51,675 --> 00:26:55,370 the analogy that I use is, if you analyze 423 00:26:55,430 --> 00:26:59,110 the altitudes of aircraft in flight, you 424 00:26:59,110 --> 00:27:02,870 will find that the altitudes drop as they near 425 00:27:02,870 --> 00:27:06,550 an output sorry, an output, an airport, and everybody says, 426 00:27:06,550 --> 00:27:10,155 well, duh. And I'm like, so one conclusion 427 00:27:10,215 --> 00:27:14,055 you could draw from that is in placing airports, someone 428 00:27:14,055 --> 00:27:17,575 did an analysis of this data and said, where the craft are 429 00:27:17,575 --> 00:27:21,095 lowest, we'll build an airport there. And we all know that's not 430 00:27:21,095 --> 00:27:24,679 true. You know? But Yeah. Yeah. That happens. That kind of thinking 431 00:27:24,679 --> 00:27:28,520 happens a lot in analysis. And I'm wondering if that 432 00:27:28,520 --> 00:27:32,140 kind of mistaken analysis, if mistaken cause and effect 433 00:27:32,760 --> 00:27:36,485 plays into some of the thinking early on. Is 434 00:27:36,485 --> 00:27:39,705 that any of that leading to the 75% 435 00:27:40,485 --> 00:27:43,945 failure or failure to achieve result rate? 436 00:27:45,285 --> 00:27:48,804 There are lots of studies that are done by particularly done by, 437 00:27:49,445 --> 00:27:53,060 consultants, and they are based on, 438 00:27:53,760 --> 00:27:57,460 simple correlations. For example, 439 00:27:59,280 --> 00:28:02,820 companies, high on the ranking of, 440 00:28:03,520 --> 00:28:07,325 ESG, made it through the COVID 441 00:28:08,345 --> 00:28:12,045 disaster better than, others. Gotcha. 442 00:28:13,465 --> 00:28:16,925 I, with a group of, other researchers, 443 00:28:18,910 --> 00:28:22,290 rather than looking at just the correlation between 444 00:28:22,350 --> 00:28:25,870 ESG and success, we used a big 445 00:28:25,870 --> 00:28:29,650 model that looked at, that had 446 00:28:30,110 --> 00:28:33,684 representation of the industry, other 447 00:28:33,825 --> 00:28:37,285 variables there. Turns out that, 448 00:28:38,065 --> 00:28:41,905 most of these high up on the ranking of, 449 00:28:42,304 --> 00:28:45,205 ESG, were high-tech companies. 450 00:28:45,919 --> 00:28:49,440 They were extreme they were extremely successful as we 451 00:28:49,440 --> 00:28:53,200 know, many of them. Yeah. And this, 452 00:28:53,200 --> 00:28:56,820 of course, was reflected in share prices and profits 453 00:28:56,880 --> 00:29:00,275 and others. And they also had the means 454 00:29:00,575 --> 00:29:04,255 to contribute to the community and do other things 455 00:29:04,255 --> 00:29:07,315 that those who rank companies on on ESG 456 00:29:07,615 --> 00:29:11,135 like. So this is a this is a 457 00:29:11,135 --> 00:29:14,580 clear example in statistics of the 458 00:29:14,580 --> 00:29:18,280 missing correlated variable. The variable 459 00:29:18,340 --> 00:29:21,700 that that really went in was the 460 00:29:21,700 --> 00:29:25,140 industry of, of, this. And and and these 461 00:29:25,140 --> 00:29:28,280 these, people who just ran the simple correlation, 462 00:29:29,785 --> 00:29:33,545 missed it. That's why we built we built a 463 00:29:33,545 --> 00:29:36,845 humongous model of 43 variables 464 00:29:37,385 --> 00:29:40,685 that attempts to take everything into account. 465 00:29:41,670 --> 00:29:45,050 And then when when one variable 466 00:29:45,350 --> 00:29:49,030 indicates success or failure, for example, in your 467 00:29:49,030 --> 00:29:52,170 case of Deutsche Bank, we have a variable 468 00:29:52,550 --> 00:29:56,325 of foreign acquisition. This variable 469 00:29:57,025 --> 00:30:00,485 comes out after the estimation with a negative 470 00:30:00,705 --> 00:30:04,385 coefficient, meaning it detracts. All the 471 00:30:04,545 --> 00:30:07,765 all other things equal, it detracts from the acquisition, 472 00:30:09,110 --> 00:30:12,250 success. So we can say with with, 473 00:30:13,750 --> 00:30:17,370 fair certainty that, 474 00:30:17,990 --> 00:30:21,610 this is indeed a contributing factor because we accounted 475 00:30:21,750 --> 00:30:25,235 for, for most of the others. Yeah. 476 00:30:25,855 --> 00:30:29,615 Yeah. Brooke is absolutely right about, the special care we 477 00:30:29,615 --> 00:30:33,055 take to ensure that we're not just documenting simple 478 00:30:33,055 --> 00:30:36,835 correlation. We're actually, you know, the identifying 479 00:30:37,615 --> 00:30:41,399 the cause and effect relationship, In most 480 00:30:41,399 --> 00:30:44,919 of all performance related variables, we 481 00:30:44,919 --> 00:30:48,299 make very careful adjustment for industry average 482 00:30:48,520 --> 00:30:52,279 performance, at the same time. So this removes a 483 00:30:52,279 --> 00:30:56,025 lot of confounding factors from our analysis and gives 484 00:30:56,025 --> 00:30:59,325 us a lot of confidence in the validity of our results. 485 00:31:00,985 --> 00:31:04,665 That makes perfect sense. And I can see, and 486 00:31:04,665 --> 00:31:07,945 you've got the word trap in the title of your book. I can see the 487 00:31:07,945 --> 00:31:10,605 trap of, you know, making 488 00:31:11,570 --> 00:31:15,330 a correlation, which is a valid thing. It's a valid point in my example 489 00:31:15,330 --> 00:31:18,230 about the planes and the airports. It's a valid example. 490 00:31:18,690 --> 00:31:22,530 Apparently, you know, what you're sharing with me is you're seeing this, and somebody just 491 00:31:22,530 --> 00:31:25,750 picking up and focusing on a single correlation 492 00:31:26,515 --> 00:31:30,294 and making that the driving metric. And 493 00:31:31,155 --> 00:31:34,934 that that makes perfect sense. And I as you were explaining that, 494 00:31:35,395 --> 00:31:38,915 both of you, I thought of, books I've read 495 00:31:38,915 --> 00:31:42,640 about Warren Buffett's, and his partner, and I can't 496 00:31:42,880 --> 00:31:46,720 nobody remembers his well, it's Charlie. Charlie Munger. Charlie Munger. Right. 497 00:31:46,720 --> 00:31:50,559 Him and Charlie work together, and they look at the fundamentals. And they 498 00:31:50,559 --> 00:31:54,365 just over and over again, they just pour through probably all 499 00:31:54,365 --> 00:31:58,044 of the things that y'all are recommending, you know, for 500 00:31:58,044 --> 00:32:01,745 people who are interested in a merger or an acquisition. You probably recommended 501 00:32:01,885 --> 00:32:05,424 the same stuff. It's, you know, the fundamentals of 502 00:32:05,725 --> 00:32:09,570 what makes a business, you know, stable. And as you 503 00:32:09,570 --> 00:32:13,170 mentioned, Baruch, about, Deutsche Bank, that 504 00:32:13,170 --> 00:32:17,010 foreign acquisitions number, that's not something I would have thought of. But, 505 00:32:17,010 --> 00:32:20,684 you know, if it's stored in a data table somewhere, then I'd I'd look at 506 00:32:20,684 --> 00:32:24,445 the data, of course. Mhmm. But it's not I'm not a business mind. 507 00:32:24,445 --> 00:32:28,125 I am a I'm an engineer, for better or worse. As someone who 508 00:32:28,125 --> 00:32:31,885 lived through it, like, there definitely was a lot of disconnect between American business 509 00:32:31,885 --> 00:32:35,480 culture and German business culture. Like, it was a very That makes sense. It was 510 00:32:35,560 --> 00:32:38,920 I mean, it was a massive disconnect. You know? Yeah. The joke we had at 511 00:32:38,920 --> 00:32:42,360 the time, I think Chrysler was bought by Mercedes or Daimler Group that year. 512 00:32:42,360 --> 00:32:46,200 Daimler. Around that same time. And the joke was, thank God that that happened 513 00:32:46,200 --> 00:32:49,420 because we would be the biggest cross Atlantic disaster. 514 00:32:51,325 --> 00:32:55,085 You know, everybody was so focused on we were a distant 515 00:32:55,085 --> 00:32:58,924 second compared to what's going on there. And that, I mean, if you Chrysler's never 516 00:32:58,924 --> 00:33:02,765 really recovered from that. Well, the the joke I heard about that 517 00:33:02,765 --> 00:33:06,549 is, you know, how do they pronounce Daimler Chrysler in Germany? 518 00:33:07,090 --> 00:33:10,929 And it was they call it Daimler. Yeah. It's slightly Chrysler is slightly 519 00:33:12,450 --> 00:33:15,730 yeah. It's true, though. Like and, you know, one card says take over, and the 520 00:33:15,730 --> 00:33:19,065 other side of the card in English says merger. Right? Like, it's it's it's, 521 00:33:19,225 --> 00:33:22,904 you know, a lot of people had a good laugh at 522 00:33:22,904 --> 00:33:25,784 that, but I mean, there was a lot of truth to that. And also too, 523 00:33:25,784 --> 00:33:29,304 like, there's a funny meme going around about this, where it was a 524 00:33:29,304 --> 00:33:32,924 professor basically saying a 100% of the people who don't understand 525 00:33:33,310 --> 00:33:36,690 the difference between causation and correlation will die. 526 00:33:40,590 --> 00:33:43,950 That's a good meme. Yes. I'll have to dig it up and and reshare 527 00:33:43,950 --> 00:33:47,330 it. This was this was, 528 00:33:48,030 --> 00:33:51,465 many, many years ago, and I took it to University of 529 00:33:51,465 --> 00:33:54,904 Chicago, a statistics course. One of the 530 00:33:54,904 --> 00:33:57,884 first example in the first class was, 531 00:33:59,865 --> 00:34:03,644 the instructor showing a very high correlation between 532 00:34:03,945 --> 00:34:07,190 lung cancer and living in, 533 00:34:07,590 --> 00:34:11,429 Arizona. No way. Of course of 534 00:34:11,429 --> 00:34:14,409 course, the correlation is there, but that's not the causation. 535 00:34:15,270 --> 00:34:18,965 Arizona's weather is very good for the lungs. And that's 536 00:34:18,965 --> 00:34:22,804 why lung patients are going to a 537 00:34:22,804 --> 00:34:26,185 result. Oh. So, the causation is 538 00:34:26,804 --> 00:34:30,405 exactly the opposite direction than what the 539 00:34:30,405 --> 00:34:34,150 correlation seems to show. Yeah. His his next 540 00:34:34,150 --> 00:34:37,989 example is that more people die in hospitals than at 541 00:34:37,989 --> 00:34:41,449 home, which means that which means that hospitals 542 00:34:41,590 --> 00:34:45,030 are extremely dangerous to people. I have to try to 543 00:34:45,030 --> 00:34:48,795 avoid try to avoid them. That's those are 544 00:34:48,795 --> 00:34:52,554 really good examples. And I I one of the examples I read a 545 00:34:52,554 --> 00:34:55,514 long time ago, I was gonna say it was from the it may have been 546 00:34:55,514 --> 00:34:59,275 from World War 2, but I'm not a 100% positive of that. 547 00:34:59,275 --> 00:35:02,575 But there were aircraft engaged in combat, 548 00:35:03,080 --> 00:35:06,920 and they wanted to reinforce aircraft to make them survive, you 549 00:35:06,920 --> 00:35:10,520 know, the engagements better. And since they were 550 00:35:10,520 --> 00:35:14,200 pointing out, the bullet holes are showing up in these patterns, and they 551 00:35:14,200 --> 00:35:17,820 noticed that, you know, there's some here and there's some that we need to reinforce 552 00:35:17,880 --> 00:35:21,724 those areas. And someone thankfully pointed out that, wait, these planes 553 00:35:21,724 --> 00:35:25,404 are making it back. We need to put the reinforcement where 554 00:35:25,404 --> 00:35:29,025 the where the bullet holes are not. You know? So 555 00:35:29,565 --> 00:35:33,325 yeah. Survivor bias. Right? I think that's That's yeah. Yeah. That's 556 00:35:33,325 --> 00:35:35,839 it. That's true. But, yeah, great examples. 557 00:35:39,420 --> 00:35:42,000 So you have to be careful with analyzing data, 558 00:35:43,819 --> 00:35:47,055 particularly in our case, and that's 559 00:35:47,454 --> 00:35:50,175 straight, into the topic of your, 560 00:35:51,295 --> 00:35:54,974 of your, podcast. Mhmm. 561 00:35:55,135 --> 00:35:58,434 I let I let, Feng briefly 562 00:35:58,734 --> 00:36:02,560 describe the many databases sources 563 00:36:02,619 --> 00:36:05,600 that we use and converge, 564 00:36:06,940 --> 00:36:10,640 to get this kind of a sample and statistical model. 565 00:36:11,260 --> 00:36:15,075 Yeah. Yeah. So this is, really, the most 566 00:36:15,075 --> 00:36:18,595 important part about how we did our research to write this 567 00:36:18,595 --> 00:36:22,295 book. Everything, as Brooke mentioned earlier, is data driven. 568 00:36:22,994 --> 00:36:26,435 Our main conclusions are supported by, you know, 569 00:36:26,435 --> 00:36:30,020 analysis using large sample, not just a couple of, 570 00:36:30,260 --> 00:36:34,099 case studies, some anecdotal evidence. No. To reach 571 00:36:34,099 --> 00:36:37,560 that level, we pull data 572 00:36:37,700 --> 00:36:41,140 from a large number of sources starting 573 00:36:41,140 --> 00:36:44,944 from a mainstream mergers acquisition database, 574 00:36:46,125 --> 00:36:49,885 which gives a lot of details about both the acquiring company and 575 00:36:49,885 --> 00:36:52,464 a target company, the time of the announcement, 576 00:36:53,805 --> 00:36:57,050 the terms of the deal, and other interesting 577 00:36:57,270 --> 00:37:01,110 details like exactly what the the acquiring company CEO 578 00:37:01,110 --> 00:37:04,010 said about, his or her expectations 579 00:37:04,710 --> 00:37:08,390 for the forthcoming acquisition and so on. So we 580 00:37:08,390 --> 00:37:11,130 use that as the starting point to, 581 00:37:12,515 --> 00:37:15,655 collect as much data as needed. As Brooke mentioned, 582 00:37:16,355 --> 00:37:19,955 you know, we try to avoid simple correlation kind 583 00:37:19,955 --> 00:37:23,655 of scenario. So, in addition to industry, 584 00:37:24,835 --> 00:37:28,220 level adjustment, we also look at entire 585 00:37:28,220 --> 00:37:31,900 history of the acquiring company and the target company, you know, 586 00:37:31,900 --> 00:37:35,420 3 to 5 years before they get to the point of making a 587 00:37:35,420 --> 00:37:39,040 deal. Try to understand the circumstances of the acquisition. 588 00:37:40,380 --> 00:37:43,925 And then that is completed by 589 00:37:43,985 --> 00:37:47,605 using financial statement data, which is obtained 590 00:37:47,745 --> 00:37:51,345 from the company's financial statements, across multiple 591 00:37:51,345 --> 00:37:54,565 years, both before the acquisition and after the acquisition. 592 00:37:55,390 --> 00:37:59,150 Of course, stock price, information plays a huge role in 593 00:37:59,150 --> 00:38:02,830 understanding, both investors' immediate 594 00:38:02,830 --> 00:38:06,610 reaction to the acquisition news, and the performance 595 00:38:06,990 --> 00:38:10,725 of the combined entity after the acquisition is 596 00:38:10,885 --> 00:38:14,485 completed over several years down the road. Not just a couple of 597 00:38:14,485 --> 00:38:18,325 months, not just 1 year. We actually track, 3 to 4 598 00:38:18,325 --> 00:38:22,005 years after the acquisition is completed in 599 00:38:22,005 --> 00:38:25,520 order to obtain, a more robust and a 600 00:38:25,520 --> 00:38:29,360 consistent view of how the value of the company has been 601 00:38:29,360 --> 00:38:32,880 affected by the acquisition, is that value creation or 602 00:38:32,880 --> 00:38:36,640 value destruction? Alright. I also mentioned earlier 603 00:38:36,640 --> 00:38:40,445 about, you know, employee turnover. You asked you 604 00:38:40,445 --> 00:38:44,125 made a lot of good points about how mergers acquisition may 605 00:38:44,125 --> 00:38:47,805 affect, employees, not just everyday employee, but also 606 00:38:47,805 --> 00:38:51,185 key talent, of each organization. So 607 00:38:51,640 --> 00:38:55,260 we obtained very detailed employee turnover data 608 00:38:55,320 --> 00:38:58,840 from a database that is, I think, based 609 00:38:58,840 --> 00:39:02,520 on LinkedIn, information. So the original source is 610 00:39:02,520 --> 00:39:05,880 LinkedIn, which is probably, the most 611 00:39:05,880 --> 00:39:09,615 comprehensive database nowadays on employee 612 00:39:09,615 --> 00:39:13,375 turnover, very detailed real time employee turnover, not 613 00:39:13,375 --> 00:39:16,915 just, you know, once a quarter, once a year kind of information. 614 00:39:17,135 --> 00:39:20,115 So, we had very detailed, 615 00:39:21,069 --> 00:39:24,690 you know, in details a very detailed data 616 00:39:25,150 --> 00:39:28,750 on the trend of employee turnover. We look at it month by 617 00:39:28,750 --> 00:39:32,289 month to see exactly, how employees 618 00:39:32,670 --> 00:39:36,375 decide to stay or leave, once 619 00:39:36,375 --> 00:39:40,215 the merger news, comes out. So that gives 620 00:39:40,215 --> 00:39:43,735 you a snapshot of, the variety of databases we 621 00:39:43,735 --> 00:39:47,355 use, to, you know, conduct our analysis 622 00:39:47,655 --> 00:39:51,190 and then to provide our evidence. It's it's really a very, 623 00:39:51,190 --> 00:39:54,550 very comprehensive process. But you mentioned 624 00:39:54,550 --> 00:39:58,090 LinkedIn, and, I'm pretty sure the grain 625 00:39:58,390 --> 00:40:02,145 of their, to and from dates of employment, That 626 00:40:02,145 --> 00:40:05,905 that is a monthly drain that that they store that data in. That's 627 00:40:05,905 --> 00:40:09,665 something a data engineer would pick up on. But I I 628 00:40:09,665 --> 00:40:12,725 love the way you're describing how you acquired your data 629 00:40:13,185 --> 00:40:16,690 and, you know, in that it was a very 630 00:40:16,690 --> 00:40:20,210 macro process. You were looking at as many companies as you could 631 00:40:20,210 --> 00:40:24,050 find. I like that part of it. I like the time span that 632 00:40:24,050 --> 00:40:27,590 you applied going 3 to 4 years after the merger acquisition 633 00:40:27,970 --> 00:40:31,765 occurred. It it really reminds me I mean, I'm more excited about 634 00:40:31,765 --> 00:40:35,605 reading the book now because it reminds me of the business books that 635 00:40:35,605 --> 00:40:39,445 I learned the most from. And I I won't mention the other books, 636 00:40:39,445 --> 00:40:42,265 but there's only a handful of them that take that approach. 637 00:40:42,940 --> 00:40:46,560 And I I think it bodes well for the success of your book. 638 00:40:47,100 --> 00:40:50,940 So I'm I'm curious how, if how 639 00:40:50,940 --> 00:40:54,780 and if you, encountered data 640 00:40:54,780 --> 00:40:57,520 that you either decided was out of bounds? 641 00:40:58,515 --> 00:41:02,194 Did you did you have limits on that? Did you run into 642 00:41:02,194 --> 00:41:03,895 any data quality issues? 643 00:41:05,954 --> 00:41:09,714 Yeah. In some cases, because we require the post 644 00:41:09,714 --> 00:41:13,540 acquisition performance information to be available for 645 00:41:14,000 --> 00:41:17,600 3 to 4 years after the acquisition. You know, 646 00:41:17,600 --> 00:41:21,280 some companies don't survive that long. Actually, we have seen 647 00:41:21,280 --> 00:41:24,740 cases where the acquiring company later on, became 648 00:41:24,825 --> 00:41:28,204 too weak and eventually being acquired by other company. 649 00:41:28,585 --> 00:41:32,204 So those cases were probably not fully captured. 650 00:41:32,744 --> 00:41:36,505 We also don't have full information on some of the 651 00:41:36,505 --> 00:41:40,090 private targets. We don't know everything about their 652 00:41:40,090 --> 00:41:43,630 performance, before the acquisition like sales, 653 00:41:43,690 --> 00:41:47,450 profitability, and so on. And, of course, these private targets 654 00:41:47,450 --> 00:41:51,050 don't even have stock price information. So you 655 00:41:51,050 --> 00:41:54,775 can't see how investors react, the investor of the 656 00:41:54,775 --> 00:41:58,535 target company reacts to the news of acquisition. You can't even 657 00:41:58,535 --> 00:42:02,215 measure, this frequently used metric called, 658 00:42:02,455 --> 00:42:06,155 acquisition premium. You know, in in case of, a publicly 659 00:42:06,215 --> 00:42:09,700 traded company acquiring another publicly traded company, you 660 00:42:09,700 --> 00:42:12,760 can easily measure this acquisition premium 661 00:42:13,140 --> 00:42:16,900 by comparing the stock price of the target before 662 00:42:16,900 --> 00:42:20,120 the acquisition use, with the deal, 663 00:42:20,980 --> 00:42:24,825 the the the acquisition price that the acquiring company decides to pay. 664 00:42:24,825 --> 00:42:28,185 But in the case of a private target, you really cannot do that 665 00:42:28,185 --> 00:42:32,025 because, you know, they don't have stock treated, on the open 666 00:42:32,025 --> 00:42:35,785 market. So we had to be creative. Brooke 667 00:42:35,785 --> 00:42:39,480 and I developed a measure relating the acquisition price 668 00:42:39,480 --> 00:42:43,080 to the sales number of the target, which 669 00:42:43,080 --> 00:42:46,680 is actually very useful information because this 670 00:42:46,680 --> 00:42:50,060 allows us to get around this private target issue and 671 00:42:50,395 --> 00:42:53,835 make the metric much more comparable. And we 672 00:42:53,835 --> 00:42:57,595 actually developed a lot of insights from using this, different 673 00:42:57,595 --> 00:43:00,415 measure of acquisition premium. Cool. 674 00:43:02,810 --> 00:43:06,410 That's interesting. That's interesting. I like the fact that you take a data 675 00:43:06,410 --> 00:43:10,090 driven approach to this. Right? Because you listen to Bloomberg or whatever, they always 676 00:43:10,090 --> 00:43:13,770 show the rah rah. Look how great this merger is 677 00:43:13,770 --> 00:43:17,305 gonna be. It makes sense in this point of view. And if you're lucky, 678 00:43:17,305 --> 00:43:20,985 maybe they'll spend 10 seconds on, like, the detractors of it and things like that. 679 00:43:20,985 --> 00:43:24,825 But, you know, looking at this data all up, like, 680 00:43:24,825 --> 00:43:28,665 it it seems that and also think, too, the other thing to 681 00:43:28,665 --> 00:43:32,470 double click on is, if it's a private company, it's probably 682 00:43:32,470 --> 00:43:35,690 going to be way smaller. So I think a bigger fish eating a smaller fish 683 00:43:36,390 --> 00:43:39,930 is less likely to have indigestion, so to speak. 684 00:43:40,230 --> 00:43:42,809 Whereas if 2 big fish eat each other, 685 00:43:44,565 --> 00:43:46,825 there there's a lot of territorial fighting. 686 00:43:49,445 --> 00:43:52,885 Yeah. That's that's exactly, what Brooke mentioned earlier. 687 00:43:53,445 --> 00:43:56,964 Acquisition of a larger target is much more difficult to succeed 688 00:43:56,964 --> 00:44:00,790 because the integration process can become very contentious. 689 00:44:01,490 --> 00:44:04,930 Fight of egos and, a lot of, you 690 00:44:04,930 --> 00:44:08,370 know, emotional issues can get into the way to 691 00:44:08,370 --> 00:44:11,190 prevent the integration to be fully successful. 692 00:44:12,055 --> 00:44:15,595 Right. That Right. That makes sense. And it it gives me hope as a, 693 00:44:15,815 --> 00:44:19,655 you know, as a smaller company that maybe one day someone will come 694 00:44:19,655 --> 00:44:23,415 along. And I keep up with a touch of newsletters on this, not 695 00:44:23,415 --> 00:44:27,115 not a lot. I really didn't start looking into it until we started approaching, 696 00:44:27,840 --> 00:44:31,680 the 10 year mark. And one of the things that 697 00:44:31,680 --> 00:44:35,380 shocked me was the size of of companies. 698 00:44:35,520 --> 00:44:38,880 And and when I talk about the size, I mean, how small 699 00:44:38,880 --> 00:44:42,020 companies are, revenue wise. I mean, 700 00:44:42,444 --> 00:44:46,065 I I saw one newsletter that was talking that a 701 00:44:47,085 --> 00:44:50,605 I don't know how big of a segment this is for targets of 702 00:44:50,605 --> 00:44:54,285 acquisition, but they were half a1000000 to a1000000 and a half in gross 703 00:44:54,285 --> 00:44:57,960 sales. And that was shocking to me. I was like, I would be thinking they 704 00:44:57,960 --> 00:45:01,660 were looking at 10, 20,000,000, you know, size companies. 705 00:45:01,720 --> 00:45:05,480 But according to this one newsletter, it 706 00:45:05,480 --> 00:45:09,215 was a hot thing, you know, going after companies that that size 707 00:45:09,215 --> 00:45:13,055 in revenue. And I was shocked. Can you 708 00:45:13,055 --> 00:45:16,495 still hear me? Yeah. I can still hear you. No problem. We you 709 00:45:16,495 --> 00:45:20,095 disappeared a little on the video, but Yeah. Because I I I got the 710 00:45:20,095 --> 00:45:23,775 phone call. No. I wonder. But if you if you can hear 711 00:45:23,775 --> 00:45:27,430 me, that's that's okay. Yeah. That's good. We can hear you. Strong. 712 00:45:27,430 --> 00:45:31,029 Yeah. Yeah. Yeah. So so speaking of small 713 00:45:31,029 --> 00:45:34,490 acquisitions, what you said is exactly chewing 714 00:45:34,630 --> 00:45:38,250 some specialty sectors. Like, in our book, we mentioned 715 00:45:38,845 --> 00:45:42,605 large pharmaceutical companies acquiring much, much 716 00:45:42,605 --> 00:45:46,285 smaller, biotech firms in order to beef up their 717 00:45:46,285 --> 00:45:50,125 product pipeline. You know, the smaller size of 718 00:45:50,125 --> 00:45:53,830 this target is really misleading, you know, when you mentioned 719 00:45:53,830 --> 00:45:57,450 sales because, these are basically start up companies 720 00:45:57,510 --> 00:45:59,770 and they focus on developing technology. 721 00:46:01,030 --> 00:46:04,790 Especially if you look at the earnings, many of them don't have profit for 722 00:46:04,790 --> 00:46:08,275 decades. But that doesn't mean they're not valuable. We 723 00:46:08,275 --> 00:46:11,735 actually have some cases showing that a large pharmaceutical 724 00:46:11,875 --> 00:46:15,715 companies are often willing to pay a very high premium to 725 00:46:15,715 --> 00:46:19,475 acquire these, startup biotech firms because they see the value 726 00:46:19,475 --> 00:46:23,160 there. So, you know, acquisitions coming all color 727 00:46:23,160 --> 00:46:27,000 and shades. It's it's a huge phenomenon no matter 728 00:46:27,000 --> 00:46:30,840 what type of industry you look at, not just in tech industries. If you 729 00:46:30,840 --> 00:46:34,540 look at some of the highly matured industry like food, 730 00:46:34,600 --> 00:46:38,355 energy, Every year, you see large and small 731 00:46:38,355 --> 00:46:41,875 deals all the time. So that's that's what really, you know, 732 00:46:41,875 --> 00:46:44,535 interest Brooke and I when we decide to, 733 00:46:45,395 --> 00:46:48,455 write a book on this topic because it's ubiquitous 734 00:46:49,080 --> 00:46:52,440 and affects everybody, not just shareholders, affects 735 00:46:52,440 --> 00:46:55,980 employees. In some cases, affects consumers, 736 00:46:56,200 --> 00:46:59,960 customers as well because, you know, a merged company 737 00:46:59,960 --> 00:47:03,745 may decide to increase price in order to show, 738 00:47:04,065 --> 00:47:07,425 the value of the acquisition. Right? Or decrease their 739 00:47:07,425 --> 00:47:11,265 services or downgrade their services. Move one 740 00:47:11,265 --> 00:47:14,625 of the levers on the seesaw there. Yeah. Yeah. 741 00:47:14,625 --> 00:47:18,260 Yeah. We have we have, on this point, we have 742 00:47:18,260 --> 00:47:22,040 a a brief chapter in the book, titled 743 00:47:22,340 --> 00:47:25,960 killer acquisitions. And these are the cases. 744 00:47:26,260 --> 00:47:29,785 Yeah. And we give examples. These are the cases in which 745 00:47:29,845 --> 00:47:33,224 the acquisition is made, basically, 746 00:47:33,525 --> 00:47:37,204 to kill the target in this case too. I've heard of 747 00:47:37,204 --> 00:47:40,920 that. Yeah. Yeah. The most the most probably the most 748 00:47:41,079 --> 00:47:44,619 the most famous case is Visa, 749 00:47:45,559 --> 00:47:49,180 trying to acquire Visa Visa debit, 750 00:47:49,400 --> 00:47:53,079 not Visa credit. Visa debit, which has 751 00:47:53,079 --> 00:47:56,895 a huge market share. I think they have 70% of, 752 00:47:57,775 --> 00:48:01,455 all the all the US market in this case. And here 753 00:48:01,455 --> 00:48:05,055 comes, a small start up, which 754 00:48:05,055 --> 00:48:08,115 is much more efficient in obtaining data, 755 00:48:09,135 --> 00:48:12,960 linking to customers and things like this. Mhmm. 756 00:48:13,260 --> 00:48:16,860 And they, they try to, they try to 757 00:48:16,860 --> 00:48:20,700 acquire this company, with the with 758 00:48:20,700 --> 00:48:24,300 the clear it was. It it came out in an email from the 759 00:48:24,300 --> 00:48:27,200 CEO with a clear intention to basically, 760 00:48:28,835 --> 00:48:32,674 terminate the, the product. The whole 761 00:48:32,674 --> 00:48:36,515 thing the whole thing was litigated by Department of 762 00:48:36,515 --> 00:48:38,934 Justice and then Visa retreated. 763 00:48:40,355 --> 00:48:44,020 But, we quote a study on the pharmaceutical 764 00:48:44,640 --> 00:48:47,380 industry, a very, very in-depth, 765 00:48:48,160 --> 00:48:51,780 study that, that 766 00:48:51,840 --> 00:48:55,140 track the products of the acquired company 767 00:48:55,760 --> 00:48:58,485 match with the products of the buying, 768 00:48:59,425 --> 00:49:02,725 company, they concluded about 70% 769 00:49:03,505 --> 00:49:06,325 of acquisition in the pharmaceutical industry, 770 00:49:07,425 --> 00:49:11,160 killer acquisitions. Because if you look after 771 00:49:11,160 --> 00:49:14,840 the acquisition, all of a sudden, you see that the product 772 00:49:14,840 --> 00:49:18,060 of the of the target disappears. 773 00:49:19,480 --> 00:49:23,240 And Gotcha. What are what are regulators' thoughts on that? 774 00:49:23,240 --> 00:49:26,595 Like, I imagine that Very, very negative. 775 00:49:26,735 --> 00:49:30,415 Very negative. In this case, of course, of pharmaceuticals, it 776 00:49:30,415 --> 00:49:34,115 affects health of people. Right. Yeah. It 777 00:49:34,255 --> 00:49:36,835 it harms it harms, innovation. 778 00:49:38,950 --> 00:49:42,410 And, this this is this is an interesting chapter. 779 00:49:43,830 --> 00:49:47,590 Killer acquisitions. I'm so looking forward on 780 00:49:47,590 --> 00:49:51,190 its own. Right? I'm I'm definitely looking forward to this. 781 00:49:51,190 --> 00:49:55,005 November 18th, you say? It's 15th. 15th. 15th, 15th. November 782 00:49:55,005 --> 00:49:58,545 15th, according to Amazon. Oh, no. Actually, now it just changed. 783 00:49:59,085 --> 00:50:02,365 I am not making this up. 13th is what I'm seeing now. It's nice. Oh, 784 00:50:02,365 --> 00:50:06,180 nice. Okay. 3 to 6. I maybe I misread it before. I thought you said 785 00:50:06,180 --> 00:50:09,700 13, but it says 13 now. That's the, the date given by the 786 00:50:09,700 --> 00:50:13,160 publisher. Yeah. So the the book is now being, 787 00:50:13,619 --> 00:50:17,220 I believe, produced in the last, phase of 788 00:50:17,220 --> 00:50:20,835 production. And then at the end of the month, we'll leave the warehouse. And 789 00:50:21,234 --> 00:50:25,075 around 13th November, it will be available for shipping. Yeah. 790 00:50:25,075 --> 00:50:28,835 Very cool. And then What I'll do what I'll do is I 791 00:50:28,835 --> 00:50:32,675 will put the link to the Amazon, page for 792 00:50:32,675 --> 00:50:36,460 the book. I'll put that on a calendar note and schedule it for, like, 5 793 00:50:36,460 --> 00:50:40,299 AM or something on 13th. And so I can go over and buy it right 794 00:50:40,299 --> 00:50:43,819 away. You you you really wanna be the first one to order. I 795 00:50:44,460 --> 00:50:47,855 well, I won't order it, but I'll buy it first once it's once it's released. 796 00:50:47,855 --> 00:50:51,375 I would I don't do the preorder so often because and what I'll do is 797 00:50:51,375 --> 00:50:55,214 I'll check probably on 10th to see if it's available by Kindle because I 798 00:50:55,214 --> 00:50:58,974 know sometimes they send those out a little earlier. Oh, yeah. That's true. And, yeah, 799 00:50:58,974 --> 00:51:02,660 I'll grab it then for sure. But, yeah, those If you're going to buy 800 00:51:02,660 --> 00:51:06,359 it, what are you going to do with the baby? No. That's true. 801 00:51:06,579 --> 00:51:09,559 That's a good thought. I don't I don't think the baby's a baby anymore. 802 00:51:09,940 --> 00:51:12,839 No. He's he's driving now. So 803 00:51:14,660 --> 00:51:18,465 that's a good good point, though, Baruch. Thanks. Thanks for reminding me. I need 804 00:51:18,465 --> 00:51:20,785 to stay on top of that sort of stuff, and I need all the help 805 00:51:20,785 --> 00:51:24,625 I could get. Yeah. We think we 806 00:51:24,625 --> 00:51:27,765 ran out of time for questions, but that's fine. I think this was an exciting 807 00:51:28,305 --> 00:51:32,040 conversation that I think explains a lot of what we're seeing in in 808 00:51:32,040 --> 00:51:35,720 our careers where we we start one company. You're also starting to see a 809 00:51:35,720 --> 00:51:39,560 pattern of, you know, let's say Microsoft buying LinkedIn. LinkedIn has 810 00:51:39,560 --> 00:51:43,255 largely been left alone. Yeah. Yeah. Yeah. 811 00:51:43,255 --> 00:51:46,775 Well, they were doing a lot right to start with. Right. Right. Right. 812 00:51:46,775 --> 00:51:50,234 Right. I think that's that's an interesting thing is that, you know, 813 00:51:50,295 --> 00:51:53,734 smart companies, they know if it's if it's big enough and it's doing the right 814 00:51:53,734 --> 00:51:57,275 thing on its own Mhmm. Leave them alone. Yeah. That's that's 815 00:51:57,700 --> 00:52:01,220 that's the story of Google and YouTube. Yeah. 816 00:52:01,220 --> 00:52:04,980 Yeah. Yeah. Yeah. Yeah. I'm here in the country. I I live out in 817 00:52:04,980 --> 00:52:08,280 the woods in Virginia, and we say if it ain't broke, don't fix it. 818 00:52:08,740 --> 00:52:12,500 Yeah. But on on the other hand May I may I 819 00:52:12,500 --> 00:52:16,154 mention one thing that, didn't come up with, in 820 00:52:16,154 --> 00:52:19,755 the discussion? We developed in this 821 00:52:19,755 --> 00:52:23,434 book and with about a large chapter to 822 00:52:23,434 --> 00:52:26,494 it something which I think is really unique, 823 00:52:27,099 --> 00:52:29,920 and that's a a 10 factor 824 00:52:30,619 --> 00:52:33,920 scorecard for acquisitions. Nice. 825 00:52:34,460 --> 00:52:38,060 Everyone knows that, lending decisions, credit 826 00:52:38,060 --> 00:52:41,760 card decisions, largely made by looking at 827 00:52:42,545 --> 00:52:45,925 at, the credit scores of people, 828 00:52:47,745 --> 00:52:51,205 we developed, based on the 10 most 829 00:52:51,265 --> 00:52:54,325 influential variables of our model, 830 00:52:55,140 --> 00:52:58,599 we developed a very easy to use, 831 00:52:58,740 --> 00:53:02,579 friendly to use scorecard that, you 832 00:53:02,579 --> 00:53:06,180 can you can before the acquisition, you 833 00:53:06,180 --> 00:53:09,805 can get a a a the likelihood 834 00:53:10,265 --> 00:53:13,645 of success of this acquisition, a percentage, 835 00:53:14,585 --> 00:53:18,365 which will indicate the likelihood of success. 836 00:53:20,460 --> 00:53:23,980 I guess that this would be very useful, both 837 00:53:23,980 --> 00:53:27,500 to managers in somehow early 838 00:53:27,500 --> 00:53:30,000 screening of several acquisition candidates 839 00:53:31,180 --> 00:53:33,855 and to, investors who are 840 00:53:34,815 --> 00:53:38,335 often asked to vote on acquisitions without 841 00:53:38,335 --> 00:53:42,035 any information. Mhmm. So this 842 00:53:42,175 --> 00:53:45,935 this, acquisition scope, is something 843 00:53:45,935 --> 00:53:49,475 which is, really unique to our book. Yeah. 844 00:53:49,910 --> 00:53:53,750 Yeah. And I would say as a entrepreneur who, you know, wouldn't 845 00:53:53,750 --> 00:53:57,590 mind somebody sweeping in and acquiring the company, this could 846 00:53:57,590 --> 00:54:01,190 help me improve my score. Yes. You know, make me a more 847 00:54:01,190 --> 00:54:04,835 attractive target for acquisition. Yep. You know, I'm not not saying 848 00:54:04,835 --> 00:54:08,214 any of my customers listen. I'm not selling. Yeah. But, 849 00:54:08,994 --> 00:54:12,675 well, we'll let you know if that happens. But the, but, 850 00:54:12,675 --> 00:54:16,375 yeah, I mean, it's a an I think all around, that's just great. 851 00:54:17,340 --> 00:54:21,120 And I look again, one more reason to look forward to the book coming out. 852 00:54:22,460 --> 00:54:25,980 Awesome. Cool. Well, this has been a great yeah. Yeah. This is 853 00:54:25,980 --> 00:54:29,740 great. I'm I'm really glad we got into this, and you've answered a lot of 854 00:54:29,740 --> 00:54:33,525 my questions about how acquisitions get 855 00:54:33,525 --> 00:54:37,285 approved, who wins, and who loses. Usually, it's the employees and the 856 00:54:37,285 --> 00:54:41,045 customers, and and who wins. And turns 857 00:54:41,045 --> 00:54:44,645 out that the people calling the shots are the winners. Funny how that works. I 858 00:54:44,645 --> 00:54:47,250 know it technically speaking, it's a correlation. But 859 00:54:48,770 --> 00:54:52,610 I see what you did there, Frank. You see what I did there? Where can 860 00:54:52,610 --> 00:54:55,170 people find out more about the book? Do you have a does the web the 861 00:54:55,170 --> 00:54:57,190 book have a website, or do you guys have LinkedIn 862 00:54:58,930 --> 00:55:02,625 or anything? Not yet. Maybe maybe 863 00:55:02,625 --> 00:55:06,405 maybe we should, create it. Okay. Yeah. Amazon 864 00:55:07,105 --> 00:55:10,165 Amazon gives, a short 865 00:55:11,505 --> 00:55:14,940 description of the book. Okay. Mhmm. 866 00:55:15,020 --> 00:55:18,720 And and the endorsement. We have some great endorsement, 867 00:55:20,460 --> 00:55:24,140 about this book. And, yeah. But may maybe the 868 00:55:24,140 --> 00:55:27,820 first, place to go is really Amazon and get 869 00:55:27,820 --> 00:55:31,315 the description of the book. Awesome. It is the 870 00:55:31,315 --> 00:55:34,855 m ampersandamanda failure trap. 871 00:55:36,195 --> 00:55:39,095 That's Got it. We'll make sure to put a link in the show notes. 872 00:55:40,355 --> 00:55:43,335 And anything else, Andy? No, sir. 873 00:55:44,060 --> 00:55:47,820 Alright. Well, with that, well, let's And that's a wrap for today's episode 874 00:55:47,820 --> 00:55:51,500 of data driven. We hope you enjoyed this deep dive into the 875 00:55:51,500 --> 00:55:55,260 data behind mergers and acquisitions, whether it's a friendly merger or 876 00:55:55,260 --> 00:55:58,925 an Uber name and take over. A huge thank you to our 877 00:55:58,925 --> 00:56:02,385 guests, Baruch Lev and Foam Gu, for their fascinating 878 00:56:02,605 --> 00:56:05,965 insights. If you've ever wondered why so many mergers 879 00:56:05,965 --> 00:56:09,725 fail, now you know data doesn't lie. Be sure to check out 880 00:56:09,725 --> 00:56:13,509 their upcoming book, the m and a failure trap, for even more 881 00:56:13,509 --> 00:56:17,269 data driven revelations. As always, thanks for tuning 882 00:56:17,269 --> 00:56:20,950 in. Don't forget to subscribe, leave a review, and 883 00:56:20,950 --> 00:56:23,769 join us next time for more data centric discussions. 884 00:56:24,470 --> 00:56:24,970 Cheers.