1
00:00:06,360 --> 00:00:13,159
Speaker 1: We can trailings and I'm Eric Beltis Eric. There's this

2
00:00:13,200 --> 00:00:17,480
Speaker 1: thing happening in the world, coronavirus. Kind of concerned about it.

3
00:00:18,000 --> 00:00:22,880
Speaker 1: How concerned are you? Um, I'll be honest zero A.

4
00:00:23,239 --> 00:00:25,400
Speaker 1: I think this stuff gets blown out of proportion by

5
00:00:25,400 --> 00:00:27,120
Speaker 1: the media because they can hit Trump over the head

6
00:00:27,120 --> 00:00:28,800
Speaker 1: with it. So I think I have to divide by

7
00:00:28,840 --> 00:00:31,120
Speaker 1: two the media attention number two. If you look at

8
00:00:31,120 --> 00:00:34,080
Speaker 1: the fatality percentages and the actual cases, I think it's

9
00:00:34,080 --> 00:00:36,239
Speaker 1: a little overblown. Plus, I live in Philly, so my

10
00:00:36,280 --> 00:00:39,239
Speaker 1: immune system gets a big workout every day. And like

11
00:00:39,280 --> 00:00:42,839
Speaker 1: the consultant experts, so I'm going everywhere. I shake hands,

12
00:00:42,960 --> 00:00:46,000
Speaker 1: I ride trains, I went to the mall last weekend.

13
00:00:46,040 --> 00:00:48,400
Speaker 1: Bring it on. I'm somewhere between the two and eight

14
00:00:48,479 --> 00:00:51,920
Speaker 1: depending on the moment. But the point of this episode

15
00:00:52,080 --> 00:00:55,000
Speaker 1: is to actually like, bring E t F into this conversation.

16
00:00:55,400 --> 00:00:57,440
Speaker 1: Who are we gonna talk with? Yeah, so there is

17
00:00:57,480 --> 00:01:01,640
Speaker 1: no coronavirus et F obviously, but where we got our

18
00:01:01,680 --> 00:01:04,320
Speaker 1: next guest was I remember back in ten there was

19
00:01:04,319 --> 00:01:07,280
Speaker 1: a filing for something called the bio shares biothreat e

20
00:01:07,400 --> 00:01:09,440
Speaker 1: t F with with which is god of the ticker GERM.

21
00:01:09,560 --> 00:01:12,080
Speaker 1: Remember the time that it is, and I was like,

22
00:01:12,319 --> 00:01:15,400
Speaker 1: this is a little crazy. Come on, now. We went

23
00:01:15,440 --> 00:01:17,600
Speaker 1: back and we looked in the prospectives. We looked at

24
00:01:17,600 --> 00:01:20,119
Speaker 1: the stocks that would have held, and these stocks were

25
00:01:20,240 --> 00:01:23,959
Speaker 1: up thirty to a hundred and forty each over the

26
00:01:24,000 --> 00:01:27,959
Speaker 1: past week after the because their virus fighting biotech firms.

27
00:01:28,520 --> 00:01:29,959
Speaker 1: You know, I think this e t F might have

28
00:01:30,000 --> 00:01:32,639
Speaker 1: had its hack moment where you know, like hack launched

29
00:01:32,760 --> 00:01:36,840
Speaker 1: right before the Sony hack and return maybe triple the

30
00:01:36,920 --> 00:01:38,440
Speaker 1: S and P over the next couple of months, one

31
00:01:38,440 --> 00:01:41,240
Speaker 1: of the big success stories in et launchers. Yeah, if

32
00:01:41,240 --> 00:01:43,959
Speaker 1: you're a small issuer with a product that's niats, you

33
00:01:44,080 --> 00:01:46,760
Speaker 1: need that kind of moment. This it didn't happen for

34
00:01:46,800 --> 00:01:49,560
Speaker 1: this fund. However, the company that that issued it has

35
00:01:49,560 --> 00:01:52,920
Speaker 1: two other funds called the Vertice Life Side, Biotech Clinical

36
00:01:52,960 --> 00:01:55,960
Speaker 1: Trials et F, and the Product So they divided biotech

37
00:01:56,000 --> 00:01:58,840
Speaker 1: into clinical trials and the product side, and biotech is

38
00:01:58,880 --> 00:02:01,000
Speaker 1: just interesting. We've never covered it. So I figured this

39
00:02:01,000 --> 00:02:04,000
Speaker 1: would be a good time to discuss, uh, the virus,

40
00:02:04,520 --> 00:02:07,040
Speaker 1: what biotech companies do in order to fight it, and

41
00:02:07,080 --> 00:02:08,640
Speaker 1: a little bit about the E t F side of things.

42
00:02:09,200 --> 00:02:12,239
Speaker 1: So joining us this time, Paul Yuke and Ryanson only

43
00:02:12,400 --> 00:02:21,200
Speaker 1: of life side ventures. This time I'm joining the biotech frontier. Paul, Ryan,

44
00:02:21,200 --> 00:02:24,840
Speaker 1: Welcome to trillions. Thank you. So whose big idea was

45
00:02:25,080 --> 00:02:29,560
Speaker 1: this company? Slash ets? So this is Paul speaking Hi,

46
00:02:30,280 --> 00:02:32,520
Speaker 1: thank you for having me. So I came up with

47
00:02:32,560 --> 00:02:36,080
Speaker 1: the idea of creating some new biotech ets. You know,

48
00:02:36,160 --> 00:02:39,560
Speaker 1: at the time when we launched BBC and BBP, this

49
00:02:39,639 --> 00:02:42,040
Speaker 1: was two thousand and fourteen, there were really only two

50
00:02:42,680 --> 00:02:47,240
Speaker 1: established biotech ets and they remained the big grilla biotech

51
00:02:47,280 --> 00:02:51,400
Speaker 1: ets today IBB an XBI, and they had the lion's

52
00:02:51,400 --> 00:02:54,160
Speaker 1: share of the assets, and really, if you look at them,

53
00:02:54,200 --> 00:02:57,680
Speaker 1: I called them fairly dirty biotech ets because they were

54
00:02:57,720 --> 00:03:03,280
Speaker 1: not pure biotechnology sector funds. You take Ryan Net who's

55
00:03:03,280 --> 00:03:06,799
Speaker 1: next to me, who's a PhD, twelve years of bench

56
00:03:06,840 --> 00:03:10,320
Speaker 1: science and then additional five plus years investment banking and

57
00:03:10,360 --> 00:03:14,639
Speaker 1: investment experience. I've been twenty twenty four years in this

58
00:03:14,680 --> 00:03:17,880
Speaker 1: space now, and we wanted to create a true biotech

59
00:03:17,919 --> 00:03:21,679
Speaker 1: ETF created by sector experts. And the first and most

60
00:03:21,720 --> 00:03:23,720
Speaker 1: important thing we did is we said biotech is really

61
00:03:23,760 --> 00:03:27,720
Speaker 1: two sectors. You've got emerging let's call it risky companies

62
00:03:27,720 --> 00:03:30,919
Speaker 1: and we call those clinical trial companies. They sell zero

63
00:03:31,040 --> 00:03:35,920
Speaker 1: drugs and they are experimental. They're they're testing their yeah

64
00:03:36,000 --> 00:03:38,360
Speaker 1: and they and when they have a positive result, the

65
00:03:38,360 --> 00:03:42,720
Speaker 1: stocks can go up two in one day. On the converse,

66
00:03:42,840 --> 00:03:46,080
Speaker 1: when they fail a clinical trial, they can drop. The

67
00:03:46,120 --> 00:03:48,720
Speaker 1: other side of the equation are more established companies. You

68
00:03:48,720 --> 00:03:52,119
Speaker 1: have many of them now. They're selling today life saving medicines,

69
00:03:52,200 --> 00:03:55,520
Speaker 1: am Gen, Biogen, Regenera on Gilliad. Some of these are

70
00:03:55,520 --> 00:03:58,280
Speaker 1: now household names. What's exciting about them is they don't

71
00:03:58,320 --> 00:04:01,120
Speaker 1: have the same risk profile, but they're still growing probably

72
00:04:01,120 --> 00:04:03,880
Speaker 1: twenty plus percent revenue growth per year in an economy

73
00:04:03,960 --> 00:04:06,320
Speaker 1: that's growing two or three. So that's what's great about

74
00:04:06,320 --> 00:04:09,080
Speaker 1: those two buckets. We separate them out and that's the

75
00:04:09,120 --> 00:04:11,320
Speaker 1: biggest change that we made. I'm shocked no one else

76
00:04:11,360 --> 00:04:12,880
Speaker 1: has done that in the past five or six years.

77
00:04:13,360 --> 00:04:16,200
Speaker 1: You know, we've spoken to Brad Longkar, he's got that

78
00:04:16,400 --> 00:04:19,640
Speaker 1: cancer immunotherapy. There's been some of that. But you're right,

79
00:04:19,960 --> 00:04:22,800
Speaker 1: and biotech is an area that has a ton of assets,

80
00:04:22,880 --> 00:04:25,080
Speaker 1: and you know, I think a lot of new ETFs

81
00:04:25,120 --> 00:04:27,400
Speaker 1: come from these India issuers that see something wrong with

82
00:04:27,400 --> 00:04:30,440
Speaker 1: the big guys. Ryan, You're a PhD. I'm gonna ask

83
00:04:30,480 --> 00:04:32,760
Speaker 1: you the same question they ask Eric, how concerned on

84
00:04:32,760 --> 00:04:38,320
Speaker 1: the skill want to don Are you about the coronavirus? Yeah? So, um,

85
00:04:38,360 --> 00:04:42,560
Speaker 1: I think that obviously when new viruses present themselves, it

86
00:04:42,600 --> 00:04:45,160
Speaker 1: can be a bit scary. But obviously the world has

87
00:04:45,160 --> 00:04:49,599
Speaker 1: seen coronavirus is before, and my feeling is that it's

88
00:04:49,640 --> 00:04:55,040
Speaker 1: early days. But what we've seen in China is that

89
00:04:55,360 --> 00:04:59,719
Speaker 1: sort of rapid containment strategy that they employed has worked

90
00:04:59,720 --> 00:05:02,320
Speaker 1: pretty well in the sense the cases over time have

91
00:05:02,400 --> 00:05:06,400
Speaker 1: been coming down and the spread through the country seems

92
00:05:06,440 --> 00:05:11,520
Speaker 1: to have been relatively contained. Unfortunately, you can't keep everybody

93
00:05:11,600 --> 00:05:14,880
Speaker 1: sort of locked up, right, and so what we now

94
00:05:14,960 --> 00:05:18,280
Speaker 1: have is the emergence of I'm sure you've heard the

95
00:05:18,360 --> 00:05:22,920
Speaker 1: buzzword sort of community presentation and so that is where,

96
00:05:23,000 --> 00:05:26,520
Speaker 1: you know, you can't really track an individual case back

97
00:05:26,600 --> 00:05:31,240
Speaker 1: to travel to China or exposure in in in Korea

98
00:05:31,440 --> 00:05:34,840
Speaker 1: or elsewhere. So that essentially, I think, you know, has

99
00:05:34,880 --> 00:05:38,680
Speaker 1: presented initially in the state of Washington, and now we're

100
00:05:38,680 --> 00:05:41,880
Speaker 1: seeing you know, a number of cases emerging there. We've

101
00:05:41,880 --> 00:05:46,120
Speaker 1: seen a pretty rapid response by by our government and

102
00:05:46,160 --> 00:05:50,440
Speaker 1: by the agency's CDC and others and I imagine that

103
00:05:50,839 --> 00:05:53,680
Speaker 1: you know, we'll continue to sort of have updates as

104
00:05:53,680 --> 00:05:56,680
Speaker 1: this sort of experience plays out. So put your investment

105
00:05:56,720 --> 00:05:59,039
Speaker 1: hat on for a second. What's the opportunity that you

106
00:05:59,080 --> 00:06:02,640
Speaker 1: see here? I mean, Paul described theres to to sort

107
00:06:02,640 --> 00:06:05,200
Speaker 1: of buckets. But from a science perspective, what do you

108
00:06:05,320 --> 00:06:07,120
Speaker 1: what do you look at? Sure? I mean I think

109
00:06:07,120 --> 00:06:09,480
Speaker 1: from from the outside, when when you have an outbreak

110
00:06:09,560 --> 00:06:11,560
Speaker 1: like this, um you think about, you know, what are

111
00:06:11,560 --> 00:06:16,000
Speaker 1: the needs in order to sort of contain And initially

112
00:06:16,480 --> 00:06:18,960
Speaker 1: we heard that there was essentially a lack of resources

113
00:06:19,000 --> 00:06:22,320
Speaker 1: on the diagnostic side, and so the CDC really had

114
00:06:22,440 --> 00:06:24,960
Speaker 1: one of the only kits that was being used here

115
00:06:24,960 --> 00:06:27,520
Speaker 1: in the States, and there was a shortage. We were

116
00:06:27,560 --> 00:06:30,960
Speaker 1: not testing patients as rapid or potential patients as rapidly

117
00:06:31,040 --> 00:06:33,760
Speaker 1: as we could. And so over the course of the

118
00:06:33,839 --> 00:06:36,000
Speaker 1: last week or so, I think we've we've seen a

119
00:06:36,080 --> 00:06:38,760
Speaker 1: number of press releases from some of these smaller cap

120
00:06:39,040 --> 00:06:43,000
Speaker 1: biotechnology and diagnostics companies that have said, hey, look we're

121
00:06:43,000 --> 00:06:47,680
Speaker 1: working on these next generation diagnostics. It turns out that

122
00:06:48,000 --> 00:06:54,080
Speaker 1: these kits that help diagnose patients are not necessarily sophisticated.

123
00:06:54,360 --> 00:06:58,839
Speaker 1: It's really just about sort of implementing the manufacturing UM

124
00:06:58,920 --> 00:07:02,599
Speaker 1: and and getting them out to uh, to the clinics. UM.

125
00:07:02,640 --> 00:07:05,280
Speaker 1: So I think there's been a real push. So you know,

126
00:07:05,320 --> 00:07:07,720
Speaker 1: we've we certainly have been looking at companies that we

127
00:07:07,760 --> 00:07:10,520
Speaker 1: think that you know, are on the forefront of putting

128
00:07:10,560 --> 00:07:13,560
Speaker 1: new diagnostics out there. UM. In addition, you know, we're

129
00:07:13,560 --> 00:07:21,440
Speaker 1: looking at companies that have means of of sterilizing nursing homes, hospitals, schools,

130
00:07:22,200 --> 00:07:25,560
Speaker 1: subway cars for example. And there's some interesting technologies out

131
00:07:25,600 --> 00:07:29,040
Speaker 1: there that we've come across and against small cap companies

132
00:07:29,280 --> 00:07:36,200
Speaker 1: where they have hydrogen peroxide based mists or sprays for decontamination. UM.

133
00:07:36,240 --> 00:07:39,680
Speaker 1: And then finally, obviously there's um, you know, there's therapeutics

134
00:07:39,680 --> 00:07:43,000
Speaker 1: and vaccines, and that's probably you know, closest to home

135
00:07:43,040 --> 00:07:45,160
Speaker 1: to what you know, Paul and I do. And so

136
00:07:45,200 --> 00:07:47,480
Speaker 1: there have been a number of companies that have announced

137
00:07:47,720 --> 00:07:52,080
Speaker 1: that they're working on either vaccines or they've identified potential

138
00:07:52,160 --> 00:07:55,920
Speaker 1: therapeutics by looking at the components of of some of

139
00:07:55,960 --> 00:08:00,640
Speaker 1: the of the blood of infected patients and so UM.

140
00:08:00,680 --> 00:08:04,040
Speaker 1: You know, those companies as they continue to sort of

141
00:08:04,440 --> 00:08:07,880
Speaker 1: move those early stage products through development, I'm sure we're

142
00:08:07,880 --> 00:08:10,280
Speaker 1: going to hear more and more about them. We'll go

143
00:08:10,320 --> 00:08:13,600
Speaker 1: into German a minute. But what are in terms of BBC.

144
00:08:13,640 --> 00:08:17,360
Speaker 1: Are these companies that that do these things? Are they

145
00:08:17,400 --> 00:08:20,200
Speaker 1: in BBC or BBP that's the clinical trials or the

146
00:08:20,240 --> 00:08:24,120
Speaker 1: product stage? And which companies are they? So the BBC

147
00:08:24,280 --> 00:08:28,840
Speaker 1: and BBP are very specifically biotechnology drug companies. You know,

148
00:08:28,880 --> 00:08:32,320
Speaker 1: the term biotech is used colloquially UM to cover a

149
00:08:32,360 --> 00:08:36,440
Speaker 1: wide range of approaches, whether their business orienters sometimes not.

150
00:08:36,520 --> 00:08:40,959
Speaker 1: Even business cloning. For example, animals is sometimes considered biotech.

151
00:08:41,040 --> 00:08:43,240
Speaker 1: We don't consider that a biotech investment. We consider a

152
00:08:43,320 --> 00:08:46,240
Speaker 1: drug that's put into a human to be a biotech stock.

153
00:08:46,559 --> 00:08:50,120
Speaker 1: In this case, the last segmented companies Ryan mentioned, which

154
00:08:50,120 --> 00:08:52,520
Speaker 1: I think is potentially the most lucrative on an ongoing

155
00:08:52,559 --> 00:08:56,360
Speaker 1: basis financially UM. These are the medicines that will cure

156
00:08:56,760 --> 00:09:00,840
Speaker 1: affected patients or vaccinate and prevent in action of the

157
00:09:00,840 --> 00:09:02,800
Speaker 1: broader population. I think that's going to be the big

158
00:09:02,800 --> 00:09:06,920
Speaker 1: business here for corona virus and other similar types of outbreaks.

159
00:09:07,200 --> 00:09:09,120
Speaker 1: There are a handful of those stocks which happen to

160
00:09:09,160 --> 00:09:13,520
Speaker 1: be in BBC and BBP because they're strong scientific players.

161
00:09:13,559 --> 00:09:17,120
Speaker 1: Let's take a look at Maderna. It's a leading biotech

162
00:09:17,200 --> 00:09:20,200
Speaker 1: company about a ten billion dollar market cap with a

163
00:09:20,320 --> 00:09:26,000
Speaker 1: real leadership position in RNA therapeutic technology. They jumped very

164
00:09:26,080 --> 00:09:28,720
Speaker 1: quickly to said to say, we can move and and

165
00:09:28,760 --> 00:09:31,880
Speaker 1: develop a vaccine to prevent coronavirus outbreaks in the future,

166
00:09:31,920 --> 00:09:35,959
Speaker 1: like within weeks, within weeks, within weeks. So that company,

167
00:09:36,360 --> 00:09:38,520
Speaker 1: they're not selling any drugs. They're in clinical trust for

168
00:09:38,600 --> 00:09:41,520
Speaker 1: other compounds, but they are in BBC, the C standing

169
00:09:41,559 --> 00:09:46,040
Speaker 1: for clinical. On the other side, Gilead, which has a drug,

170
00:09:46,360 --> 00:09:50,000
Speaker 1: an anti viral drug that was already in testing for

171
00:09:50,080 --> 00:09:53,360
Speaker 1: other coronavirus. Let's keep in minding coronavirus has been been

172
00:09:53,400 --> 00:09:56,320
Speaker 1: around for a while. It's a type of a virus.

173
00:09:56,440 --> 00:09:59,800
Speaker 1: Common colds are caused by some forms of coronavirus, also

174
00:10:00,200 --> 00:10:03,560
Speaker 1: stars and merser coronaviruses. So Gilliad had a drug wasn't

175
00:10:03,600 --> 00:10:07,440
Speaker 1: that wasn't testing for prior forms of coronavirus. This drug

176
00:10:07,520 --> 00:10:10,920
Speaker 1: looks like it works in one patient tested in the US.

177
00:10:10,960 --> 00:10:14,400
Speaker 1: That patient recovered very quickly, so they're studying it very

178
00:10:14,480 --> 00:10:17,679
Speaker 1: quickly in corona patients and the stock is reacting very

179
00:10:17,679 --> 00:10:19,520
Speaker 1: well because it looks like this could be a winning

180
00:10:19,559 --> 00:10:21,640
Speaker 1: agent in this war. Talk to me more about the

181
00:10:21,640 --> 00:10:25,319
Speaker 1: science of this and how quickly things have improved, because

182
00:10:25,320 --> 00:10:28,319
Speaker 1: you've mentioned mers and stars, like when when we're developing

183
00:10:28,320 --> 00:10:32,320
Speaker 1: treatments at that stage, it was taking months years to

184
00:10:32,400 --> 00:10:35,680
Speaker 1: get something and here we're seeing it just in like

185
00:10:35,720 --> 00:10:39,400
Speaker 1: a matter of weeks, where there's potential solution that's even

186
00:10:39,440 --> 00:10:44,000
Speaker 1: been identified. Right, this all plays too how quickly biotech

187
00:10:44,160 --> 00:10:46,160
Speaker 1: is moving. You know, when I first entered the industry,

188
00:10:46,800 --> 00:10:49,920
Speaker 1: the rule of thumb was it would take thirteen years

189
00:10:50,240 --> 00:10:54,160
Speaker 1: to move from that pre clinical phase to f D approval.

190
00:10:54,200 --> 00:10:56,720
Speaker 1: That was just the rule. Thirteen years, maybe twelve if

191
00:10:56,720 --> 00:11:00,719
Speaker 1: you're very fast. We now have examples of drug hugs

192
00:11:00,760 --> 00:11:05,840
Speaker 1: that got from lightbulb moment. Aha scientists says, let's try

193
00:11:05,880 --> 00:11:10,000
Speaker 1: this to f D approval and dosing in a commercial patient,

194
00:11:10,200 --> 00:11:13,160
Speaker 1: a patient who paid insurance company paid the bill in

195
00:11:13,240 --> 00:11:15,800
Speaker 1: four and a half years, and targeted cancer effect. Ryan

196
00:11:15,840 --> 00:11:18,520
Speaker 1: worked on one of those drugs that was approved targeted

197
00:11:18,559 --> 00:11:22,120
Speaker 1: cancer and is selling today treating patients. So that if

198
00:11:22,120 --> 00:11:24,599
Speaker 1: you think about that speed thirteen going to four and

199
00:11:24,640 --> 00:11:28,840
Speaker 1: a half, that's science that's regulatory abouties. The FDA has

200
00:11:28,880 --> 00:11:32,720
Speaker 1: now created pathways where you can move, or accelerated approval,

201
00:11:32,800 --> 00:11:37,040
Speaker 1: breakthrough approval designations, UM, new clinical trial designs that say

202
00:11:37,040 --> 00:11:40,480
Speaker 1: you don't have to do sometimes things that don't make

203
00:11:40,480 --> 00:11:43,480
Speaker 1: sense in a clinical trial. And for all of those reasons,

204
00:11:43,559 --> 00:11:46,240
Speaker 1: we can react much more quickly today to an outbreak

205
00:11:46,240 --> 00:11:49,240
Speaker 1: than we could with stars or abola. What you just

206
00:11:49,320 --> 00:11:51,319
Speaker 1: described make me want to invest in biotech, and I

207
00:11:51,400 --> 00:11:54,920
Speaker 1: think that's a lot of what people are attracted to. UM,

208
00:11:55,040 --> 00:11:58,800
Speaker 1: help me explain the relentlessness of this sector because every

209
00:11:58,880 --> 00:12:00,959
Speaker 1: year we have the leaderboard, know which ETFs are like

210
00:12:01,000 --> 00:12:02,679
Speaker 1: at the top and the top twenty, it's usually like

211
00:12:02,720 --> 00:12:04,760
Speaker 1: they come and go right, they have their moment because

212
00:12:04,760 --> 00:12:07,000
Speaker 1: you're usually very concentrative. But biotechs on there a lot.

213
00:12:07,480 --> 00:12:09,640
Speaker 1: And you look at XBI that's the equal weighted biotech.

214
00:12:09,720 --> 00:12:11,880
Speaker 1: It's up three, D and six over the past ten years.

215
00:12:12,440 --> 00:12:15,640
Speaker 1: The S and ps up two and even this health

216
00:12:15,679 --> 00:12:18,559
Speaker 1: care sectors are only up two, so they're up a

217
00:12:18,600 --> 00:12:21,520
Speaker 1: lot already. Right, UM, how much of this is priced in?

218
00:12:21,679 --> 00:12:23,720
Speaker 1: I mean, how much are we pricing in the future

219
00:12:23,840 --> 00:12:27,560
Speaker 1: expectations versus what has already happened. So I think today

220
00:12:27,800 --> 00:12:30,240
Speaker 1: what you're asking about is UM, you know, we look

221
00:12:30,240 --> 00:12:32,840
Speaker 1: at things ultimately as net present values. I mean, we're

222
00:12:32,880 --> 00:12:35,680
Speaker 1: both of us here sitting here are former bankers, and

223
00:12:35,760 --> 00:12:38,599
Speaker 1: you've got to boil things down to DCF analysis. And

224
00:12:39,080 --> 00:12:42,679
Speaker 1: in a typical it's called a widget manufacturing business. UM,

225
00:12:42,920 --> 00:12:45,360
Speaker 1: when we project out cash flows, we'll see something like

226
00:12:45,800 --> 00:12:48,800
Speaker 1: sixty of the value in the near term casules that

227
00:12:48,840 --> 00:12:52,600
Speaker 1: you project out in the terminal value. In biotech, it's

228
00:12:52,640 --> 00:12:56,439
Speaker 1: flipped around dramatically, so in most cases it's maybe in

229
00:12:56,480 --> 00:12:59,559
Speaker 1: the near term casual terminal value, so it's really all

230
00:12:59,640 --> 00:13:03,080
Speaker 1: terminal value. Having said that, today what we see is

231
00:13:03,120 --> 00:13:07,120
Speaker 1: real tangible evidence that drug will work. Companies are often

232
00:13:07,160 --> 00:13:11,720
Speaker 1: acquired after they show in a phase two study that's

233
00:13:11,760 --> 00:13:15,319
Speaker 1: maybe two years before they're marketed. UM, then they're acquired.

234
00:13:15,400 --> 00:13:17,440
Speaker 1: That's when a drug company knows it's going to be

235
00:13:17,640 --> 00:13:20,720
Speaker 1: a real drug. So Gillyad two days ago announced the

236
00:13:20,720 --> 00:13:24,319
Speaker 1: acquisition of forty seven fts V ticker f ts V,

237
00:13:24,760 --> 00:13:28,000
Speaker 1: which is in one of our funds. And the reason

238
00:13:28,400 --> 00:13:30,960
Speaker 1: they paid five billion dollars for that company is not

239
00:13:31,000 --> 00:13:33,880
Speaker 1: because they're selling today, because they're not, but it's because

240
00:13:34,040 --> 00:13:37,319
Speaker 1: the evidence from the early clinical trials are now tantalizing

241
00:13:37,800 --> 00:13:40,360
Speaker 1: and show with very high probability that this will be

242
00:13:40,400 --> 00:13:44,480
Speaker 1: a major drug, a major new drug class. And I'm

243
00:13:44,480 --> 00:13:46,800
Speaker 1: looking at your fun BBC. It's equal weighted, right, and

244
00:13:47,040 --> 00:13:50,600
Speaker 1: XBI is equal weated. We've written about XBI in particular

245
00:13:50,600 --> 00:13:53,920
Speaker 1: because it's crushing IBB and we feel like the equal

246
00:13:53,960 --> 00:13:56,800
Speaker 1: waiting gives bigger waitings to the small companies and it's

247
00:13:56,800 --> 00:13:59,760
Speaker 1: gotten what we call m and a pop um. Talk

248
00:14:00,000 --> 00:14:02,920
Speaker 1: at the importance of that, is that why you equal weighted,

249
00:14:03,160 --> 00:14:04,760
Speaker 1: so that you'd give a little more waiting to the

250
00:14:04,760 --> 00:14:08,160
Speaker 1: potential targets because unless you have inside information, you don't

251
00:14:08,200 --> 00:14:10,480
Speaker 1: know who to own. But here you're putting your eggs

252
00:14:10,520 --> 00:14:12,679
Speaker 1: in an equal basket to get that kind of pop

253
00:14:12,720 --> 00:14:14,360
Speaker 1: Is that was that the goal in the design of

254
00:14:14,360 --> 00:14:17,440
Speaker 1: the product. That is probably the biggest reason for the

255
00:14:17,480 --> 00:14:20,520
Speaker 1: difference between the XBI and the IBB, the IBB being

256
00:14:20,520 --> 00:14:23,240
Speaker 1: market weighted. If you think about what are the largest

257
00:14:23,240 --> 00:14:26,480
Speaker 1: companies in the space, they dominate the market, CAB, gilead

258
00:14:26,760 --> 00:14:31,200
Speaker 1: am Gen, regen Eran, and these are companies that are

259
00:14:31,320 --> 00:14:34,000
Speaker 1: unlikely to be acquired anytime soon, and if they are

260
00:14:34,600 --> 00:14:39,440
Speaker 1: relatively modest premium because they're becoming big pharma like the

261
00:14:39,480 --> 00:14:43,480
Speaker 1: difference between a MRK and a visor and ultimately an

262
00:14:43,480 --> 00:14:47,160
Speaker 1: AM genery Gilead is blurring. But the companies like forty seven,

263
00:14:47,200 --> 00:14:49,160
Speaker 1: I mean, we're talking about a stock that's gone up

264
00:14:49,560 --> 00:14:53,080
Speaker 1: fifteen to twenty fold in the last six to twelve months.

265
00:14:53,080 --> 00:14:56,560
Speaker 1: Fifteen to twenty x, not percentage points, and that's fairly

266
00:14:56,600 --> 00:15:00,920
Speaker 1: typical for a small cap biotech company that a shows

267
00:15:00,960 --> 00:15:04,520
Speaker 1: that a trial goes from being high risk to to oh,

268
00:15:04,600 --> 00:15:07,320
Speaker 1: this works as cures patients, and then be going to

269
00:15:07,560 --> 00:15:10,120
Speaker 1: a big farmer company or multiple big farmer companies saying

270
00:15:10,160 --> 00:15:12,320
Speaker 1: we have to own that in our pipeline and that

271
00:15:12,320 --> 00:15:15,280
Speaker 1: those are the valuation inflects that happen in really in

272
00:15:15,320 --> 00:15:18,360
Speaker 1: the whole sector. But unless you equal weight, as you said, Eric,

273
00:15:18,560 --> 00:15:20,600
Speaker 1: you're not going to capture that alpha. So that's why

274
00:15:20,640 --> 00:15:29,520
Speaker 1: we do it. So let's talk about GERM for a second,

275
00:15:29,520 --> 00:15:35,000
Speaker 1: because great ticker not on the market right now, and

276
00:15:35,160 --> 00:15:37,080
Speaker 1: so my question is sort of like, in the middle

277
00:15:37,120 --> 00:15:38,640
Speaker 1: of all of this, do you kind of like hit

278
00:15:38,760 --> 00:15:41,320
Speaker 1: your forehead and go doll like we had the thing

279
00:15:41,360 --> 00:15:44,440
Speaker 1: and it's not out right now. So GERM, just for

280
00:15:44,480 --> 00:15:46,760
Speaker 1: a little bit of background, GERM is an index that

281
00:15:46,800 --> 00:15:49,720
Speaker 1: we have created and we created it back in two

282
00:15:49,760 --> 00:15:53,880
Speaker 1: thousand and sixteen, and the overarching index is called the

283
00:15:54,040 --> 00:15:58,760
Speaker 1: Bioshares bio Threat Index, and it's designed to invest in

284
00:15:58,840 --> 00:16:03,320
Speaker 1: companies that can protect and guard against a wide variety

285
00:16:03,360 --> 00:16:06,160
Speaker 1: of biological threats. Now, first and foremost of course, that

286
00:16:06,320 --> 00:16:12,760
Speaker 1: is disease outbreaks, Bola and that's existing and new so Bola, sars, coronavirus.

287
00:16:12,760 --> 00:16:16,800
Speaker 1: Now also biological warfare, sarin gas war. I mean, you

288
00:16:16,840 --> 00:16:18,720
Speaker 1: just don't know what's going to happen in the future,

289
00:16:18,760 --> 00:16:23,240
Speaker 1: and so protecting homeland security in the borders, things like chlorox,

290
00:16:23,320 --> 00:16:26,360
Speaker 1: I mean, we think about very basic industries. Clorox has

291
00:16:26,400 --> 00:16:29,840
Speaker 1: been a big component of that index. Clorox went up

292
00:16:29,880 --> 00:16:32,800
Speaker 1: every day of the Corona outbreak because they are selling

293
00:16:33,200 --> 00:16:36,560
Speaker 1: every unit that they can make, and so there's a

294
00:16:36,560 --> 00:16:39,000
Speaker 1: wide variety of aspects that go into the Germ Index.

295
00:16:39,040 --> 00:16:42,200
Speaker 1: But the mechanics, no one knows better than Eric of

296
00:16:42,440 --> 00:16:45,480
Speaker 1: launching an independent et F is it's expensive and it

297
00:16:45,520 --> 00:16:47,400
Speaker 1: sits out there for a while, sometimes until you have

298
00:16:47,440 --> 00:16:50,560
Speaker 1: an outbreak. So we're in high gear right now, um

299
00:16:50,600 --> 00:16:54,440
Speaker 1: with respect to potentially getting something out there that's investible.

300
00:16:54,800 --> 00:16:57,520
Speaker 1: You know, we all love to invest and the problem

301
00:16:57,680 --> 00:17:00,920
Speaker 1: with investing in coronavirus doxes are so little. These stocks

302
00:17:01,160 --> 00:17:04,800
Speaker 1: are generally small cap. They trade fifty to a hundred

303
00:17:04,800 --> 00:17:08,160
Speaker 1: million shares a day today whereas they traded fifty thousand

304
00:17:08,400 --> 00:17:11,760
Speaker 1: six months ago, and the volatility is insane. So you

305
00:17:11,800 --> 00:17:14,399
Speaker 1: need to invest in a basket, and we're seeking to

306
00:17:14,440 --> 00:17:16,880
Speaker 1: create that kind of a basket. And in the Germ index,

307
00:17:17,000 --> 00:17:18,560
Speaker 1: you know, some of the tickers you send over that

308
00:17:18,600 --> 00:17:21,480
Speaker 1: would be in here are include something like Cleveland bio

309
00:17:21,560 --> 00:17:25,919
Speaker 1: Labs via Biotechnology Co Diagnostics, which, by the ways, up

310
00:17:25,920 --> 00:17:29,520
Speaker 1: three last week when the market fell eleven, I think

311
00:17:29,520 --> 00:17:31,560
Speaker 1: they had to halt it, right, it was halt on

312
00:17:31,600 --> 00:17:34,760
Speaker 1: the upside um? Those are small. We look, those are

313
00:17:34,760 --> 00:17:37,520
Speaker 1: all small microcap. Would you balance that out with a

314
00:17:37,600 --> 00:17:41,000
Speaker 1: Clorox or a Gilliad or somebody? So, what would what

315
00:17:41,080 --> 00:17:42,680
Speaker 1: does this index look like? And how much was it

316
00:17:42,760 --> 00:17:44,960
Speaker 1: up last week? We've been told not to talk about

317
00:17:44,960 --> 00:17:47,720
Speaker 1: the index right now just because it's uh, lawyers, come on,

318
00:17:48,600 --> 00:17:51,760
Speaker 1: but lawyers, clients. No, the the index is fun. Let's

319
00:17:51,800 --> 00:17:54,280
Speaker 1: think about it as a It does have a lot

320
00:17:54,359 --> 00:17:57,159
Speaker 1: of a lot of larger companies in there, and some

321
00:17:57,240 --> 00:17:59,520
Speaker 1: of them did very well. Some of them didn't do

322
00:17:59,680 --> 00:18:03,160
Speaker 1: as well, but overall, the let's say you're to date,

323
00:18:03,400 --> 00:18:07,000
Speaker 1: you're talking about double digit out performance over the SMP.

324
00:18:07,560 --> 00:18:10,280
Speaker 1: So the way the index works is it is modified

325
00:18:10,320 --> 00:18:12,840
Speaker 1: market cap weight with a four point nine percent maximum

326
00:18:12,840 --> 00:18:16,359
Speaker 1: waiting about fifty four stocks currently in the index, and

327
00:18:16,440 --> 00:18:19,600
Speaker 1: it has several different buckets, not just coronavirus or other

328
00:18:19,680 --> 00:18:23,359
Speaker 1: virus companies, but also companies that will assist in sterilization.

329
00:18:23,840 --> 00:18:27,280
Speaker 1: Um think about companies, hospital cleaning companies like Sterocycles actually

330
00:18:27,280 --> 00:18:31,520
Speaker 1: done very well. Companies that make disposable gloves or mass

331
00:18:31,560 --> 00:18:34,320
Speaker 1: like these are companies that everyone knows Kimberly Clark, three

332
00:18:34,600 --> 00:18:37,200
Speaker 1: M chlorox as we mentioned. And then of course the

333
00:18:37,280 --> 00:18:39,760
Speaker 1: drug companies like Gilead, which was already a leader in

334
00:18:39,800 --> 00:18:43,200
Speaker 1: anti virals and very quickly they were able to jump

335
00:18:43,280 --> 00:18:46,080
Speaker 1: on and help with this outbreak. Okay, Ryan, I want

336
00:18:46,080 --> 00:18:49,480
Speaker 1: to bring you back in, especially on the investor beware

337
00:18:49,800 --> 00:18:52,879
Speaker 1: side of this. Right, like biotech, for all the promise

338
00:18:52,920 --> 00:18:56,080
Speaker 1: out there, it's also had bus moments and and big ones.

339
00:18:56,119 --> 00:18:59,080
Speaker 1: You know in the Thoronos question is probably still hangs

340
00:18:59,119 --> 00:19:02,960
Speaker 1: over the industry to some extent. Where do you come

341
00:19:03,000 --> 00:19:05,640
Speaker 1: down in terms of like how an investor should approach

342
00:19:06,040 --> 00:19:09,159
Speaker 1: what could be you know, massive returns but also like

343
00:19:09,200 --> 00:19:12,760
Speaker 1: a total bust. Yeah, for sure. And I think with

344
00:19:12,960 --> 00:19:15,760
Speaker 1: the stocks that we were just discussing, you know, a

345
00:19:15,800 --> 00:19:19,920
Speaker 1: NETF approach would certainly be what I would recommend. There's

346
00:19:19,960 --> 00:19:24,080
Speaker 1: just a ton of volatility in these names, as Paul mentioned, Um,

347
00:19:24,320 --> 00:19:28,480
Speaker 1: you know, companies like co Diagnostics or Cleveland, or even

348
00:19:28,640 --> 00:19:31,399
Speaker 1: in the names like in Ovo Um we've seen in

349
00:19:31,520 --> 00:19:35,679
Speaker 1: just in the past day have gone up to and

350
00:19:35,720 --> 00:19:41,360
Speaker 1: they'll come down to in in less time, and and

351
00:19:41,400 --> 00:19:45,239
Speaker 1: certainly Um for for the average retail investor investor out there,

352
00:19:45,240 --> 00:19:47,879
Speaker 1: it's it's it's quite challenging to be able to time

353
00:19:48,400 --> 00:19:51,399
Speaker 1: those traits. So that's why we're so excited about putting

354
00:19:51,440 --> 00:19:54,480
Speaker 1: together this Jeremy TF because we think, you know, certainly

355
00:19:54,960 --> 00:19:58,560
Speaker 1: provides an opportunity for folks to get involved in a

356
00:19:58,600 --> 00:20:02,040
Speaker 1: relatively de risked way. Paul, I see you at conferences

357
00:20:02,080 --> 00:20:04,480
Speaker 1: at the booth there sometimes, you know, and I consider

358
00:20:04,520 --> 00:20:08,480
Speaker 1: you an indie issuer. Um. Besides obviously what you've already

359
00:20:08,520 --> 00:20:10,560
Speaker 1: proven here, which is that indie issuers are very close

360
00:20:10,600 --> 00:20:13,120
Speaker 1: to the ground, their local they know their material, very well,

361
00:20:13,520 --> 00:20:17,000
Speaker 1: in some cases better than the bigger issuers, but they

362
00:20:17,080 --> 00:20:19,320
Speaker 1: typically also have a lot of other things going on

363
00:20:19,600 --> 00:20:22,359
Speaker 1: in their business life. So talk about these ETFs are

364
00:20:22,400 --> 00:20:25,520
Speaker 1: just the small sliver of your whole operation. What else

365
00:20:25,560 --> 00:20:29,200
Speaker 1: do you do? Yes? So, Life Size Partners is headquartered

366
00:20:29,200 --> 00:20:31,080
Speaker 1: in New York City. We have about a hundred and

367
00:20:31,119 --> 00:20:34,840
Speaker 1: seventy employees globally at this point. We're a leading consultancy

368
00:20:34,960 --> 00:20:37,919
Speaker 1: to the biotechnology sector as well as the broader healthcare space.

369
00:20:38,680 --> 00:20:41,399
Speaker 1: We have a number of different business units that can

370
00:20:41,440 --> 00:20:46,879
Speaker 1: provide executive search, investor relations, consulting, investment banking. As you know,

371
00:20:47,280 --> 00:20:50,320
Speaker 1: the operations of an e t F that's passively managed

372
00:20:50,400 --> 00:20:54,240
Speaker 1: like ours doesn't require a lot of day to day UM. Really,

373
00:20:54,280 --> 00:20:56,040
Speaker 1: the business once the funds have been up and running

374
00:20:56,040 --> 00:20:58,080
Speaker 1: in ours have been out there for over five years,

375
00:20:58,200 --> 00:21:01,040
Speaker 1: is to make sure that the indexes are maintained properly

376
00:21:01,119 --> 00:21:03,879
Speaker 1: and regular ausly. UM. So we do that and twice

377
00:21:03,920 --> 00:21:06,840
Speaker 1: here we rebounce. What Ryan and I spend the vast

378
00:21:06,920 --> 00:21:09,520
Speaker 1: majority of our time doing is seeking out private companies,

379
00:21:09,960 --> 00:21:12,080
Speaker 1: and so we we run a venture fund called LIFESID.

380
00:21:12,160 --> 00:21:15,320
Speaker 1: Venture Partners were out there generally looking to invest maybe

381
00:21:15,400 --> 00:21:19,200
Speaker 1: one year or two years before these companies become public

382
00:21:19,280 --> 00:21:21,320
Speaker 1: through an I p O, in which case they would

383
00:21:21,440 --> 00:21:24,639
Speaker 1: enter most likely the BBC et F And what size

384
00:21:24,680 --> 00:21:27,159
Speaker 1: of investment are you guys looking on the VC So

385
00:21:27,680 --> 00:21:31,160
Speaker 1: we're generally participating in this round that's called a crossover

386
00:21:31,240 --> 00:21:33,240
Speaker 1: round or maybe a Series B or C round. Those

387
00:21:33,280 --> 00:21:36,439
Speaker 1: are usually fifty to a hundred million dollar rounds. Today

388
00:21:36,480 --> 00:21:39,720
Speaker 1: we're writing ten ish million dollar checks. Um, we're just

389
00:21:40,200 --> 00:21:42,320
Speaker 1: we just had our first clothes on our latest fund,

390
00:21:42,880 --> 00:21:45,280
Speaker 1: our second fund, which is a two million dollar fund.

391
00:21:46,000 --> 00:21:48,119
Speaker 1: But we would join as a syndicate member across you know,

392
00:21:48,160 --> 00:21:49,639
Speaker 1: we'll lead a couple of deals a here, but we'll

393
00:21:49,720 --> 00:21:52,280
Speaker 1: join you know, three to five other investors in that round.

394
00:21:52,920 --> 00:21:54,399
Speaker 1: So if I zoom out a little bit and just

395
00:21:54,600 --> 00:21:57,800
Speaker 1: think about all the various industries and sectors that have

396
00:21:57,920 --> 00:22:01,359
Speaker 1: sort of had moments and you know, maybe reached a

397
00:22:01,440 --> 00:22:03,800
Speaker 1: certain plateau and like we've seen tech just over the

398
00:22:03,920 --> 00:22:07,480
Speaker 1: last twenty years just boom in a big way. Biotechs

399
00:22:07,560 --> 00:22:10,440
Speaker 1: had those boom moments, but it hasn't reached maybe the

400
00:22:10,560 --> 00:22:13,880
Speaker 1: same exponential growth that we've seen from the tech industry.

401
00:22:14,320 --> 00:22:16,640
Speaker 1: And if you know about Moore's lawn sort of what's

402
00:22:16,880 --> 00:22:20,440
Speaker 1: happening in sort of the tech industry, Like maybe there's

403
00:22:21,000 --> 00:22:23,240
Speaker 1: a thought that we could see a plateau, there is

404
00:22:23,359 --> 00:22:28,760
Speaker 1: this biotech moment. I don't know that this is biotex

405
00:22:28,880 --> 00:22:31,000
Speaker 1: moment per se. I think it's been, you know, a

406
00:22:31,119 --> 00:22:35,000
Speaker 1: more of a gradual curve up, and I think moments

407
00:22:35,160 --> 00:22:39,399
Speaker 1: like outbreaks like this is the industry's chance to shine

408
00:22:39,440 --> 00:22:42,280
Speaker 1: and really show everyone else what's going on. One of

409
00:22:42,359 --> 00:22:44,879
Speaker 1: my first bosses in the industry, um, a senior banker.

410
00:22:44,960 --> 00:22:48,560
Speaker 1: Goldman once said, Um, this was back in the early Now,

411
00:22:48,600 --> 00:22:50,160
Speaker 1: it's probably in the late nine You said, there's really

412
00:22:50,200 --> 00:22:53,600
Speaker 1: no biotech sector. In my view, biotech companies are really

413
00:22:53,680 --> 00:22:55,760
Speaker 1: just small pharma companies. And I think that's a view

414
00:22:55,800 --> 00:22:58,040
Speaker 1: that was long held for a while, and I think

415
00:22:58,240 --> 00:23:01,480
Speaker 1: people really understanding that that things have changed so dramatically

416
00:23:01,560 --> 00:23:04,880
Speaker 1: that the DNA of a biotech company is so utterly

417
00:23:05,000 --> 00:23:07,960
Speaker 1: different than that of a farmer company. Eric, you said

418
00:23:07,960 --> 00:23:11,280
Speaker 1: you're from Philly. Philly is one of the original homes

419
00:23:11,320 --> 00:23:15,600
Speaker 1: of those mainline farmer companies Smith Klein, Beach, um Roan

420
00:23:15,680 --> 00:23:18,440
Speaker 1: poolong Roarer, and these are companies that all along the

421
00:23:18,520 --> 00:23:21,359
Speaker 1: main line of near the Philadelphia area, they've now merged

422
00:23:21,440 --> 00:23:23,879
Speaker 1: and merged and these are in some cases fifty or

423
00:23:23,880 --> 00:23:27,040
Speaker 1: a hundred year old businesses. These are people who join

424
00:23:27,080 --> 00:23:30,280
Speaker 1: a company and will stay and retire and have their pensions.

425
00:23:30,680 --> 00:23:34,320
Speaker 1: In biotech, it's very different. These are superstar scientists, superstar

426
00:23:34,400 --> 00:23:37,600
Speaker 1: executives who are free agents and and will solve a problem,

427
00:23:37,680 --> 00:23:40,440
Speaker 1: sell the company, move on to the next. And it's

428
00:23:40,480 --> 00:23:43,800
Speaker 1: a it's a dynamic industry and it's growing faster and faster.

429
00:23:44,800 --> 00:23:47,520
Speaker 1: Besides GERM maybe coming out at some point, do you

430
00:23:47,560 --> 00:23:49,879
Speaker 1: have any other that are you know, farther down the

431
00:23:49,960 --> 00:23:52,360
Speaker 1: road that are sort of cutting edge kind of indexes

432
00:23:52,400 --> 00:23:56,200
Speaker 1: that could btfs? Yeah, we are. You know, we're constantly

433
00:23:56,280 --> 00:23:59,680
Speaker 1: looking to see what other indexes that we can innovate

434
00:23:59,800 --> 00:24:01,840
Speaker 1: on within healthcare. And there are a lot of other

435
00:24:02,400 --> 00:24:04,520
Speaker 1: spaces that I think that are investable that are not

436
00:24:04,680 --> 00:24:06,760
Speaker 1: served by the current market. There's nothing I would say

437
00:24:07,080 --> 00:24:10,760
Speaker 1: earlier than the bio threat index. Um, we're being very selective.

438
00:24:10,800 --> 00:24:14,200
Speaker 1: We've got a lot on our plate. Last thought, Ryan,

439
00:24:14,320 --> 00:24:16,439
Speaker 1: you didn't give me a number scale of one to ten.

440
00:24:16,520 --> 00:24:19,520
Speaker 1: Where are we on the coronavirus? Here's what he wants

441
00:24:19,520 --> 00:24:21,480
Speaker 1: you to do. He wants you because you're an expert

442
00:24:21,560 --> 00:24:24,960
Speaker 1: PhD to overrule sort of embarrass me for my I

443
00:24:25,000 --> 00:24:27,399
Speaker 1: don't care. Yes, this is what's going on. So the

444
00:24:27,480 --> 00:24:30,520
Speaker 1: higher the number, the better he'll feel. But don't don't listen,

445
00:24:30,520 --> 00:24:32,920
Speaker 1: don't do it just for him. Yeah. I think we're

446
00:24:32,920 --> 00:24:37,240
Speaker 1: somewhere around the five or six. Yeah. Yeah, you just

447
00:24:37,320 --> 00:24:41,440
Speaker 1: made Joel very happy. I'm still a point to Paul Ryan.

448
00:24:41,480 --> 00:24:50,040
Speaker 1: Thanks for going, Thank you, thank you, Thanks for listening

449
00:24:50,119 --> 00:24:52,240
Speaker 1: to Trillions. Until next time. You can find us on

450
00:24:52,240 --> 00:24:56,280
Speaker 1: the Bloomberg Terminal, Bloomberg dot com, Apple Podcast, Spotify, and

451
00:24:56,320 --> 00:24:58,680
Speaker 1: where else you'd like to listen. We'd love to hear

452
00:24:58,680 --> 00:25:01,760
Speaker 1: from you. We're on Twitter, I'm at Joel Webber Show,

453
00:25:02,160 --> 00:25:05,240
Speaker 1: He's at Rick Faultunos, and you can learn more about

454
00:25:05,280 --> 00:25:08,760
Speaker 1: Life's Side Partners at Life side Partners dot com. This

455
00:25:08,880 --> 00:25:12,640
Speaker 1: episode of Trillions was produced by Magnus Hendrickson. Francesca Levy

456
00:25:12,760 --> 00:25:14,840
Speaker 1: is the head of Bloomberg Podcast. Bye