1
00:00:02,759 --> 00:00:10,560
Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the

2
00:00:10,560 --> 00:00:14,560
Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am

3
00:00:14,600 --> 00:00:18,520
Speaker 1: Eastern on Applecarplay and Android Auto with the Bloomberg Business App.

4
00:00:18,600 --> 00:00:21,880
Speaker 1: Listen on demand wherever you get your podcasts, or watch

5
00:00:21,960 --> 00:00:23,080
Speaker 1: us live on YouTube.

6
00:00:23,960 --> 00:00:26,480
Speaker 2: One of the big movers in the marketplace today is Roadblocks.

7
00:00:27,120 --> 00:00:29,840
Speaker 2: The game company stock is down eleven percent. It was

8
00:00:29,880 --> 00:00:31,920
Speaker 2: down more than twenty percent early in the trading day.

9
00:00:32,880 --> 00:00:35,840
Speaker 2: Bookings missed outlook a little bit softer than expected. Let's

10
00:00:35,840 --> 00:00:37,640
Speaker 2: get some details on now we can do that. The

11
00:00:37,680 --> 00:00:40,600
Speaker 2: man Deep singing, he's a senior technology channels for Bloomberg Intelligence.

12
00:00:40,800 --> 00:00:45,400
Speaker 2: Man Deep Talk, describe what Roadblocks does, and then what's

13
00:00:45,440 --> 00:00:46,480
Speaker 2: happening with the stock today.

14
00:00:47,440 --> 00:00:50,839
Speaker 3: I mean, this is a platform think fit as a

15
00:00:50,880 --> 00:00:55,480
Speaker 3: Bold garden where you can play any user generated game

16
00:00:55,640 --> 00:00:58,840
Speaker 3: as well as you know, the Triple A titles and

17
00:00:58,960 --> 00:01:04,440
Speaker 3: all sorts of game content hosted inside Roadblocks platform where

18
00:01:04,640 --> 00:01:09,520
Speaker 3: multiple users can play the game. And it's been quite popular,

19
00:01:09,680 --> 00:01:13,360
Speaker 3: you know, with that demographic, the nine to seventeen eighteen

20
00:01:13,480 --> 00:01:13,800
Speaker 3: year old.

21
00:01:13,880 --> 00:01:17,640
Speaker 4: It's a great way to entice somebody to do their homework.

22
00:01:18,720 --> 00:01:19,360
Speaker 4: It really is.

23
00:01:19,560 --> 00:01:22,000
Speaker 5: Yeah, the use cases beyond gaming.

24
00:01:22,040 --> 00:01:24,760
Speaker 4: You spent twenty minutes doing this, you know, after you

25
00:01:24,800 --> 00:01:25,959
Speaker 4: do your homework or whatever.

26
00:01:26,160 --> 00:01:30,880
Speaker 3: Yeah, and so I think again user growth of twenty

27
00:01:30,920 --> 00:01:33,800
Speaker 3: percent plus and anytime you see a company missing on

28
00:01:33,959 --> 00:01:38,080
Speaker 3: user growth, the reaction is pretty strong. Now, if you remember,

29
00:01:38,160 --> 00:01:41,760
Speaker 3: there was a report from Hindenberg talking about how the

30
00:01:41,880 --> 00:01:45,840
Speaker 3: user and engagement metrics are inflated, so pretty much the

31
00:01:45,920 --> 00:01:50,680
Speaker 3: company has been you know, doing the cleanup post that report,

32
00:01:50,760 --> 00:01:53,800
Speaker 3: even though they didn't really acknowledge, you know, there were

33
00:01:54,360 --> 00:01:55,640
Speaker 3: a large number of users.

34
00:01:55,920 --> 00:01:59,000
Speaker 4: Were they faking those numbers. They're just so difficult too.

35
00:02:00,360 --> 00:02:05,000
Speaker 3: With online platforms, is it's so hard to figure out exactly,

36
00:02:05,080 --> 00:02:08,320
Speaker 3: you know, what's bought activity and where are the user

37
00:02:08,680 --> 00:02:14,400
Speaker 3: metrics inflated or engagement And there's always scope to you know,

38
00:02:15,000 --> 00:02:19,079
Speaker 3: clean up and have real users being reflected on the platform.

39
00:02:19,080 --> 00:02:21,919
Speaker 3: In the case of roadblocks, brand safety is a big

40
00:02:22,000 --> 00:02:24,920
Speaker 3: aspect because you know, we were talking about kids using

41
00:02:24,960 --> 00:02:28,440
Speaker 3: the platform. So they've really, I think done a lot

42
00:02:28,560 --> 00:02:33,320
Speaker 3: since that short thesis from Hindenburg, and that's partly the

43
00:02:33,360 --> 00:02:36,480
Speaker 3: reason why they missed in terms of the user and

44
00:02:36,560 --> 00:02:40,600
Speaker 3: engagement metrics. But they called out Eastern Europe being weak

45
00:02:40,639 --> 00:02:43,640
Speaker 3: and Turkey being weak. To me, this is really the

46
00:02:43,720 --> 00:02:46,639
Speaker 3: quarter where the impact was the most obvious, and then

47
00:02:46,760 --> 00:02:49,360
Speaker 3: it will gradually get better because they've cleaned up.

48
00:02:49,480 --> 00:02:52,840
Speaker 2: So it seems like at least the partial vindication for

49
00:02:52,880 --> 00:02:54,080
Speaker 2: the Hindenburg research.

50
00:02:54,240 --> 00:02:57,320
Speaker 3: Well, I could make the case even for you know,

51
00:02:57,480 --> 00:03:01,320
Speaker 3: a platform like snap where you may have they have

52
00:03:01,400 --> 00:03:05,360
Speaker 3: shown hundred million daily active users every quarter. Now do

53
00:03:05,440 --> 00:03:07,600
Speaker 3: they really have one hundred million or is it more

54
00:03:07,720 --> 00:03:10,920
Speaker 3: ninety seven million because three million are just captured in

55
00:03:10,919 --> 00:03:14,240
Speaker 3: their metrics. So no one has a consistent approach to.

56
00:03:14,160 --> 00:03:17,720
Speaker 2: A third party like a Nielsen for TV ratings.

57
00:03:17,800 --> 00:03:20,240
Speaker 5: There's no third party to airfact, there is no third party.

58
00:03:20,400 --> 00:03:22,720
Speaker 5: It's the company that's giving you the metrics.

59
00:03:22,760 --> 00:03:26,200
Speaker 3: And I don't think there is like a consistent approach

60
00:03:26,280 --> 00:03:28,840
Speaker 3: that every company uses in terms of giving out their

61
00:03:28,880 --> 00:03:30,240
Speaker 3: daily active usercount.

62
00:03:30,520 --> 00:03:35,240
Speaker 4: Beyond the daily active users, what are the other important metrics.

63
00:03:34,840 --> 00:03:39,840
Speaker 5: That engagement also was like kind of a slight.

64
00:03:39,760 --> 00:03:42,240
Speaker 4: Message and radio it's time spent listening. Is that this

65
00:03:42,400 --> 00:03:43,200
Speaker 4: sort of the same thing.

66
00:03:43,360 --> 00:03:47,480
Speaker 3: Yeah, So again I look at you know, what they

67
00:03:47,480 --> 00:03:49,960
Speaker 3: are doing on the AI side. They are looking to

68
00:03:50,000 --> 00:03:53,200
Speaker 3: develop their own foundational model. So think of it as

69
00:03:53,320 --> 00:03:56,600
Speaker 3: you know what the LLLM companies are doing. It's a

70
00:03:56,640 --> 00:04:01,440
Speaker 3: walled garden where they will create all of tools to

71
00:04:01,640 --> 00:04:05,360
Speaker 3: enable more content. It's similar to YouTube in some sense.

72
00:04:05,400 --> 00:04:08,640
Speaker 3: YouTube has got a lot of video content, Roadblocks has

73
00:04:08,760 --> 00:04:11,640
Speaker 3: got a lot of gaming content and other things for

74
00:04:11,840 --> 00:04:15,960
Speaker 3: that demographic. So net I mean, this seems to be

75
00:04:16,920 --> 00:04:20,360
Speaker 3: justified reaction because they missed on their numbers, But has

76
00:04:20,360 --> 00:04:22,720
Speaker 3: a thesis really changed because they're losing share?

77
00:04:22,800 --> 00:04:24,240
Speaker 6: I don't think so real quick.

78
00:04:24,279 --> 00:04:27,160
Speaker 4: Have the safety concerns as a parent have they been

79
00:04:27,320 --> 00:04:28,120
Speaker 4: fully addressed?

80
00:04:28,760 --> 00:04:31,160
Speaker 3: That's what they're trying to address, and that's why you

81
00:04:31,160 --> 00:04:34,080
Speaker 3: know they missed on these metrics. So they're using a

82
00:04:34,360 --> 00:04:38,120
Speaker 3: combination of AI tools as well as just focusing more

83
00:04:38,120 --> 00:04:40,719
Speaker 3: on brand safety because they want to ramp up ADS.

84
00:04:41,000 --> 00:04:42,960
Speaker 5: And the way you ramp up ADS is you.

85
00:04:42,880 --> 00:04:46,120
Speaker 3: Want to make guarantee the advertisers that it's a safe

86
00:04:46,120 --> 00:04:47,400
Speaker 3: platform to advertise on.

87
00:04:47,480 --> 00:04:49,200
Speaker 6: Man Deep Seeing, thank you so much. We appreciate that.

88
00:04:49,200 --> 00:04:49,719
Speaker 6: Man Deep Seeing.

89
00:04:49,720 --> 00:04:52,560
Speaker 2: Senior technology channels for Bloomberg Intelligence. Join us here on

90
00:04:52,560 --> 00:04:54,000
Speaker 2: a bloomerg interactive broker studio.

91
00:04:55,680 --> 00:04:59,360
Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live

92
00:04:59,440 --> 00:05:02,800
Speaker 1: weekdays ten am Eastern on Apple coarcklay and Android Auto

93
00:05:02,920 --> 00:05:06,000
Speaker 1: with the Bloomberg Business app. Listen on demand wherever you

94
00:05:06,040 --> 00:05:09,480
Speaker 1: get your podcasts, or watch us live on YouTube.

95
00:05:10,600 --> 00:05:14,560
Speaker 2: Right next, guest, nice exposure to technology. And one of

96
00:05:14,560 --> 00:05:17,240
Speaker 2: my questions is is technology going to continue to lead

97
00:05:17,360 --> 00:05:18,000
Speaker 2: this market?

98
00:05:18,080 --> 00:05:19,599
Speaker 6: Hire have a little bit of a hiccup so far

99
00:05:19,680 --> 00:05:20,400
Speaker 6: in it.

100
00:05:20,480 --> 00:05:22,920
Speaker 4: Was the only sector to decline in the month of

101
00:05:23,000 --> 00:05:24,320
Speaker 4: January exactly, and it's.

102
00:05:24,160 --> 00:05:25,240
Speaker 6: Just kind of underperforming here.

103
00:05:25,279 --> 00:05:28,680
Speaker 2: So Nancy Tangler John's She's the CEO and CIO of

104
00:05:29,000 --> 00:05:33,280
Speaker 2: Laffer Tangler Investments based in Get this right now. She's

105
00:05:33,279 --> 00:05:37,160
Speaker 2: calling from Scottsdale, Arizona. One of my all time favorite.

106
00:05:36,800 --> 00:05:39,080
Speaker 4: Play there is snow, snow and ice on the.

107
00:05:39,000 --> 00:05:42,679
Speaker 6: Ground, snow as Royal Palms Hotel is my go to place.

108
00:05:42,720 --> 00:05:45,280
Speaker 2: They love me there lots of good stuff to do

109
00:05:45,400 --> 00:05:48,720
Speaker 2: out there, exactly. Nancy, thanks so much for joining us here.

110
00:05:49,480 --> 00:05:52,839
Speaker 2: Deep Seek was a story a couple of weeks ago.

111
00:05:52,880 --> 00:05:54,920
Speaker 2: We've now kind of forgotten about it, but I think

112
00:05:54,920 --> 00:05:56,719
Speaker 2: that costs a lot of tech investors to kind of

113
00:05:56,720 --> 00:06:00,600
Speaker 2: maybe rethink a little bit their AI trade. Here, how

114
00:06:00,600 --> 00:06:03,080
Speaker 2: did you kind of view that news and did it

115
00:06:03,560 --> 00:06:06,520
Speaker 2: kind of change in anyway your view of big tech.

116
00:06:08,120 --> 00:06:10,760
Speaker 7: Well, Paul and John, I'm dressed in sympathy for you

117
00:06:10,920 --> 00:06:14,720
Speaker 7: this morning in a down vest because it is actually cold.

118
00:06:14,520 --> 00:06:19,799
Speaker 6: Here to find cold, Nancy, Well, not really cold yet.

119
00:06:21,080 --> 00:06:22,920
Speaker 4: I was going to whip out my violins.

120
00:06:23,240 --> 00:06:26,560
Speaker 6: Plan well, listen.

121
00:06:26,680 --> 00:06:28,160
Speaker 7: I think there was a couple of things with the

122
00:06:28,200 --> 00:06:30,720
Speaker 7: Deep Seak announcement. Remember on the Friday before we had

123
00:06:30,720 --> 00:06:34,200
Speaker 7: Projects Stargate where the President had Larry Ellison, Sam Maltman,

124
00:06:34,240 --> 00:06:37,400
Speaker 7: and Masassan in the White House announcing a five hundred

125
00:06:37,440 --> 00:06:42,240
Speaker 7: billion dollar investment in AI and whether or not it's

126
00:06:42,279 --> 00:06:44,159
Speaker 7: going to be five billion dollars. We could talk about

127
00:06:44,200 --> 00:06:47,880
Speaker 7: that all for quite some time, but it generated enthusiasm.

128
00:06:48,080 --> 00:06:50,239
Speaker 7: And then the next Monday we had the story break

129
00:06:50,279 --> 00:06:55,400
Speaker 7: that Deep Seak had developed a more efficient AI model

130
00:06:55,480 --> 00:07:01,719
Speaker 7: for a five point six million I don't think that's correct,

131
00:07:01,800 --> 00:07:04,479
Speaker 7: and I think there was timing involved, And then we

132
00:07:04,480 --> 00:07:06,920
Speaker 7: had Ali Baba come out. What we know is that

133
00:07:06,960 --> 00:07:10,400
Speaker 7: the system is not as reliable, it's censored, it crashed.

134
00:07:10,560 --> 00:07:13,800
Speaker 7: And we also know that at Dagos, where this was

135
00:07:13,840 --> 00:07:16,960
Speaker 7: being talked about and for months before Deep Seek that

136
00:07:17,160 --> 00:07:22,120
Speaker 7: is that they allegedly have fifty thousand h one in

137
00:07:22,240 --> 00:07:25,200
Speaker 7: Vidio chips, which costs forty thousand dollars each. So I

138
00:07:25,200 --> 00:07:27,920
Speaker 7: think the whole thing is a bit suspect, but good

139
00:07:28,080 --> 00:07:31,120
Speaker 7: for the overall AI market because what we know from

140
00:07:31,240 --> 00:07:34,280
Speaker 7: Jevon's rule is that what you will see is the

141
00:07:34,360 --> 00:07:37,800
Speaker 7: cheaper technology gets, the more the usage and the use

142
00:07:37,840 --> 00:07:41,200
Speaker 7: case demand goes up. And we're seeing that across all

143
00:07:41,240 --> 00:07:45,200
Speaker 7: sectors in earnings reports for the last nine months and

144
00:07:45,320 --> 00:07:47,080
Speaker 7: also this most recent.

145
00:07:46,840 --> 00:07:50,520
Speaker 4: Quarter, Nancy, what do we learn from the earnings reports

146
00:07:50,600 --> 00:07:54,080
Speaker 4: so far? I mean they're kind of backward looking, I

147
00:07:54,080 --> 00:07:58,800
Speaker 4: guess more specifically the earnings calls that we've had.

148
00:08:00,520 --> 00:08:02,920
Speaker 7: Yeah, John, No, you're absolutely right. So it is backward

149
00:08:02,960 --> 00:08:05,720
Speaker 7: looking when you look at earnings beats. But what we

150
00:08:05,840 --> 00:08:09,440
Speaker 7: listen to is the guidance, and what we're hearing is

151
00:08:09,480 --> 00:08:12,160
Speaker 7: pretty optimistic. So if I'm going to look at my

152
00:08:12,200 --> 00:08:13,920
Speaker 7: notes just a little bit, because we cover a lot

153
00:08:13,920 --> 00:08:17,200
Speaker 7: of companies, but we're hearing things from companies as diverse

154
00:08:17,240 --> 00:08:22,840
Speaker 7: as next Era Energy, raytheon Xylum water treatment company, and

155
00:08:23,320 --> 00:08:26,920
Speaker 7: pro Logis, for example. So old economy companies that are

156
00:08:26,960 --> 00:08:32,600
Speaker 7: benefiting from the usage of AIRE in their well in

157
00:08:32,640 --> 00:08:36,000
Speaker 7: their businesses and then the margin improvement is showing up

158
00:08:36,040 --> 00:08:39,600
Speaker 7: in margins and we're getting guidance that's pretty optimistic. And

159
00:08:39,679 --> 00:08:42,160
Speaker 7: so you see that in the markets. What we also

160
00:08:42,160 --> 00:08:44,480
Speaker 7: see is volatility because we have Trump two point zero

161
00:08:44,800 --> 00:08:47,479
Speaker 7: and we know from Trump one point oh that volatility

162
00:08:47,880 --> 00:08:50,720
Speaker 7: will be with us. He's going to make bold statements,

163
00:08:50,760 --> 00:08:53,400
Speaker 7: some of which will come through, many of which won't,

164
00:08:53,640 --> 00:08:56,160
Speaker 7: but it'll rile the markets, and it'll rile the algos

165
00:08:56,160 --> 00:08:59,040
Speaker 7: and the short term traders. As an investor, it's great

166
00:08:59,040 --> 00:09:02,040
Speaker 7: news for me because we're Volatility is the friend of

167
00:09:02,080 --> 00:09:03,199
Speaker 7: the long term investor.

168
00:09:04,280 --> 00:09:04,520
Speaker 6: Names.

169
00:09:04,559 --> 00:09:08,360
Speaker 2: I'm looking at your twelve Best Ideas portfolio, very very

170
00:09:08,400 --> 00:09:13,920
Speaker 2: heavily weighted with the big tech names everybody knows Spotify, Amazon, Broadcom,

171
00:09:14,000 --> 00:09:17,360
Speaker 2: Microsoft and the like. Talk to us about your thesis

172
00:09:17,360 --> 00:09:18,520
Speaker 2: there behind that portfolio.

173
00:09:19,720 --> 00:09:21,880
Speaker 7: Yeah, So we run a growth strategy pall, and then

174
00:09:21,920 --> 00:09:25,040
Speaker 7: we also run a dividend growth strategy, which is more

175
00:09:25,120 --> 00:09:28,439
Speaker 7: value and we always pull from there our twelve best

176
00:09:28,440 --> 00:09:30,840
Speaker 7: ideas because there was a study done decades ago that

177
00:09:30,880 --> 00:09:34,439
Speaker 7: said optimal diversification is twelve names, and we have found

178
00:09:34,440 --> 00:09:36,839
Speaker 7: that to be true over the years. That portfolio has

179
00:09:36,880 --> 00:09:39,439
Speaker 7: done better than everything else. So we look for the

180
00:09:39,480 --> 00:09:43,600
Speaker 7: companies that are generating earning's growth at a reasonable not

181
00:09:43,720 --> 00:09:47,440
Speaker 7: a cheap, but a reasonable valuation. In determining that, we

182
00:09:47,480 --> 00:09:49,959
Speaker 7: look at relative price to sales ratio, So what am

183
00:09:49,960 --> 00:09:53,000
Speaker 7: I paying for a future unit of sales and then

184
00:09:53,040 --> 00:09:56,120
Speaker 7: the price earnings to growth ratio, And these are the

185
00:09:56,200 --> 00:10:00,040
Speaker 7: names that we have confidence in.

186
00:10:00,040 --> 00:10:03,199
Speaker 6: In fact, it has shown up in the performance.

187
00:10:03,320 --> 00:10:06,640
Speaker 7: So I think it's also got a heavy exposure to

188
00:10:06,679 --> 00:10:12,040
Speaker 7: consumer discretionary, modest exposure to industrials via Uber, and then

189
00:10:12,120 --> 00:10:16,520
Speaker 7: some one healthcare name, and I think one financial Goldman Sachs.

190
00:10:17,559 --> 00:10:20,400
Speaker 4: I learned from Paul this morning that there's high dispersion

191
00:10:20,800 --> 00:10:24,200
Speaker 4: in the market. I want to answer for a detailed

192
00:10:24,480 --> 00:10:28,000
Speaker 4: explanation of that. But does are we all writing the

193
00:10:28,000 --> 00:10:30,520
Speaker 4: same our investors all writing the same wave.

194
00:10:32,520 --> 00:10:36,000
Speaker 7: Well to some extent, you know, and that's driven by

195
00:10:36,080 --> 00:10:40,400
Speaker 7: the ETFs. John, So you know, when you see a

196
00:10:40,400 --> 00:10:42,520
Speaker 7: big sell off in a stock like Apple, a lot

197
00:10:42,520 --> 00:10:46,400
Speaker 7: of times that's because retail investors or financial advisors are

198
00:10:46,400 --> 00:10:50,760
Speaker 7: exiting ETFs, many of which own Apple, So you have

199
00:10:50,800 --> 00:10:53,840
Speaker 7: to keep that in mind. But what we're looking at

200
00:10:54,040 --> 00:10:57,360
Speaker 7: is an analogy to the nineteen nineties, and I think

201
00:10:57,400 --> 00:11:01,719
Speaker 7: we're in the early years of this anal Internet as

202
00:11:01,720 --> 00:11:05,600
Speaker 7: a technology was super cool, but we were measuring eyeballs.

203
00:11:05,960 --> 00:11:09,480
Speaker 7: I wasn't but analysts for eyeballs on a website. This

204
00:11:09,600 --> 00:11:12,400
Speaker 7: technology is much more robust. And you've heard John Chambers,

205
00:11:12,440 --> 00:11:16,080
Speaker 7: who was the CEO of the poster child of overvalued

206
00:11:16,120 --> 00:11:19,120
Speaker 7: stocks in the nineties, say that generative AI and AI

207
00:11:19,200 --> 00:11:23,640
Speaker 7: computing is more powerful than cloud computing and the Internet combined.

208
00:11:24,000 --> 00:11:28,360
Speaker 7: So in some periods of time it makes sense to

209
00:11:28,400 --> 00:11:31,200
Speaker 7: be part of the consensus as long as you're not

210
00:11:31,360 --> 00:11:34,640
Speaker 7: buying companies with multiples of one hundred times peak earnings,

211
00:11:34,640 --> 00:11:37,000
Speaker 7: which is what people were paying for Cisco in the day.

212
00:11:37,480 --> 00:11:41,720
Speaker 7: And the buyers of the infrastructure are the hyperscalers who

213
00:11:41,760 --> 00:11:43,160
Speaker 7: have robust balance sheets.

214
00:11:43,360 --> 00:11:44,000
Speaker 6: Many of them were.

215
00:11:43,960 --> 00:11:47,680
Speaker 7: Spending fifty sixty seventy five billion dollars this year in capex,

216
00:11:47,880 --> 00:11:52,120
Speaker 7: but they're still generating fifty sixty seventy five billion dollars

217
00:11:52,160 --> 00:11:57,000
Speaker 7: in pre cash flow, so these are really sustainable, strong company.

218
00:11:57,480 --> 00:11:59,319
Speaker 6: Nancy, thank you so much for joining us. Really appreciate it.

219
00:11:59,400 --> 00:12:02,920
Speaker 2: Nancy Tangler's CEO and chief investment office Laffer Tangler Investments,

220
00:12:02,960 --> 00:12:05,600
Speaker 2: and oh, by the way, forecast at high for Scotts Sale,

221
00:12:05,679 --> 00:12:07,720
Speaker 2: Arizona today seventy eight degrees.

222
00:12:08,000 --> 00:12:08,679
Speaker 6: So there you go.

223
00:12:08,800 --> 00:12:09,319
Speaker 4: That's tough.

224
00:12:09,920 --> 00:12:11,520
Speaker 6: Is they have a nice golf tournament there.

225
00:12:11,559 --> 00:12:13,199
Speaker 2: The PGA is there in Scotts Sale this week in

226
00:12:13,200 --> 00:12:14,800
Speaker 2: Phoenix area, so they're gonna have been good weather.

227
00:12:16,480 --> 00:12:20,160
Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live

228
00:12:20,240 --> 00:12:23,640
Speaker 1: weekdays at ten am Eastern on Applecarplay and Android Auto

229
00:12:23,760 --> 00:12:26,800
Speaker 1: with the Bloomberg Business App. Listen on demand wherever you

230
00:12:26,840 --> 00:12:29,840
Speaker 1: get your podcasts, or watch us live on YouTube.

231
00:12:30,440 --> 00:12:32,120
Speaker 2: All right, let's talk to these markets here. Let's talk

232
00:12:32,160 --> 00:12:34,840
Speaker 2: tech technology in these markets. This technology is still a

233
00:12:35,000 --> 00:12:38,319
Speaker 2: leader for these equity markets as it has been forever.

234
00:12:38,440 --> 00:12:41,040
Speaker 6: Shana Cisel joint says. She's the president and CEO.

235
00:12:40,880 --> 00:12:45,320
Speaker 2: Of Bondrin Capital Management, joining us here. Shana, you know,

236
00:12:45,360 --> 00:12:47,400
Speaker 2: a couple of weeks ago, it seems like a lifetime ago.

237
00:12:47,480 --> 00:12:51,240
Speaker 2: There's this thing we all learned about deep seek, and

238
00:12:51,320 --> 00:12:53,840
Speaker 2: I know you guys spend a lot of time thinking

239
00:12:53,840 --> 00:12:57,560
Speaker 2: about investing in technology broadly defined. How did you guys

240
00:12:58,200 --> 00:13:00,960
Speaker 2: think about this deep seek and what may mean for

241
00:13:01,160 --> 00:13:02,480
Speaker 2: technology investing?

242
00:13:03,760 --> 00:13:07,480
Speaker 8: So my general rule of thumb with any kind of

243
00:13:07,520 --> 00:13:12,960
Speaker 8: news is if it makes me have a very visceral

244
00:13:13,040 --> 00:13:17,720
Speaker 8: response that I should step back and read, and so

245
00:13:17,840 --> 00:13:20,360
Speaker 8: that was my initial response. The market, however, does not

246
00:13:20,600 --> 00:13:26,160
Speaker 8: have the focus and the ability to necessarily do that,

247
00:13:26,320 --> 00:13:30,240
Speaker 8: so it freaks out. As I learned more, I became

248
00:13:30,480 --> 00:13:33,679
Speaker 8: less concerned about the deep seek threat and what that

249
00:13:33,760 --> 00:13:37,360
Speaker 8: means for China as a competitor in the AI space.

250
00:13:38,200 --> 00:13:40,080
Speaker 8: There's a number of things that stood out to me

251
00:13:40,559 --> 00:13:44,200
Speaker 8: that made me think, like, Okay, this is no different

252
00:13:44,280 --> 00:13:48,040
Speaker 8: than any time a Chinese company knocks off a American

253
00:13:48,600 --> 00:13:53,040
Speaker 8: shoe brand or anything else. It's very similar, But there's

254
00:13:53,160 --> 00:13:55,400
Speaker 8: more of a concern here. If we thought TikTok was

255
00:13:55,440 --> 00:13:58,680
Speaker 8: a concern from national security perspective, this is an even

256
00:13:58,720 --> 00:14:02,199
Speaker 8: greater concern with its connection to the CCP through their app.

257
00:14:02,679 --> 00:14:06,679
Speaker 8: So while there's definitely some interesting aspects of the open

258
00:14:06,720 --> 00:14:10,079
Speaker 8: source code that can be used by Amazon or anybody

259
00:14:10,840 --> 00:14:15,640
Speaker 8: to leverage the actual app itself and the things that

260
00:14:15,920 --> 00:14:18,480
Speaker 8: related to that, I'm less concerned about in terms of

261
00:14:18,520 --> 00:14:21,120
Speaker 8: what it means for the US companies in the tech space.

262
00:14:22,280 --> 00:14:24,440
Speaker 5: This shouldn't be a surprise. It was always the.

263
00:14:24,440 --> 00:14:28,760
Speaker 8: Greatest risk to technology companies in the US was having

264
00:14:28,800 --> 00:14:32,600
Speaker 8: somebody disrupt the market. But I think it's a bit

265
00:14:32,640 --> 00:14:36,520
Speaker 8: overblown and quite frankly, I think we've been complaining about

266
00:14:36,600 --> 00:14:41,520
Speaker 8: valuations forever and this solved our problem pretty quickly.

267
00:14:41,880 --> 00:14:44,520
Speaker 4: All right, let's move to treasury yields. I heard the

268
00:14:44,920 --> 00:14:48,720
Speaker 4: new treasure secretary say yesterday that he's kind of focused

269
00:14:48,720 --> 00:14:52,720
Speaker 4: on the tenure and bringing that down. Now, I'm kind

270
00:14:52,760 --> 00:14:54,520
Speaker 4: of a student of history, and I have to go

271
00:14:54,600 --> 00:14:58,200
Speaker 4: back to the early sixties and beyond right up to

272
00:14:58,240 --> 00:15:03,120
Speaker 4: present day. The ten here seems to follow economic activity.

273
00:15:03,400 --> 00:15:05,240
Speaker 4: And if you want to bring the ten year lower,

274
00:15:05,320 --> 00:15:09,560
Speaker 4: a sure way to do it is to lessen economic growth.

275
00:15:10,640 --> 00:15:12,080
Speaker 4: I'm sure it doesn't want to do that.

276
00:15:13,200 --> 00:15:16,400
Speaker 8: Yeah, that's the conundrum here, right, You want to bring

277
00:15:16,520 --> 00:15:19,360
Speaker 8: rates down, but you know, at the end of the day,

278
00:15:19,720 --> 00:15:23,000
Speaker 8: there is correlation there between the strength of the economy

279
00:15:23,040 --> 00:15:25,160
Speaker 8: and the interst rate environment and the yield curve and

280
00:15:25,160 --> 00:15:28,000
Speaker 8: how the yeld curve behaves. You know, the FED doesn't

281
00:15:28,040 --> 00:15:31,760
Speaker 8: have the power things it has to control that. I mean,

282
00:15:31,800 --> 00:15:35,560
Speaker 8: we've seen that in recent months with the FED cutting

283
00:15:35,720 --> 00:15:41,280
Speaker 8: and yet the yield curve not behaving like rates have

284
00:15:41,320 --> 00:15:44,360
Speaker 8: been cut. In fact, it's to be expected, right, because

285
00:15:44,760 --> 00:15:46,720
Speaker 8: you want to get out of an inverted yield curve,

286
00:15:46,720 --> 00:15:49,640
Speaker 8: which requires the longer end to go up even in

287
00:15:49,680 --> 00:15:53,240
Speaker 8: a cutting cycle. But I think that the problem here

288
00:15:53,280 --> 00:15:56,760
Speaker 8: and what they're trying to kind of fix, is related

289
00:15:56,800 --> 00:16:00,560
Speaker 8: to mortgage rates in the housing market, and I don't

290
00:16:00,600 --> 00:16:04,080
Speaker 8: know that there's an easy fix to that. But I

291
00:16:04,120 --> 00:16:06,800
Speaker 8: am concerned that there's this belief that if they just

292
00:16:06,840 --> 00:16:09,680
Speaker 8: cut rates enough, they can fix this problem, because I

293
00:16:09,680 --> 00:16:14,120
Speaker 8: think it's not that black and white, and there's a

294
00:16:14,320 --> 00:16:18,200
Speaker 8: high probability of a potential mistake being made if they

295
00:16:18,240 --> 00:16:21,440
Speaker 8: go in with policy decisions thinking that it is that easy.

296
00:16:22,160 --> 00:16:22,320
Speaker 7: Chan.

297
00:16:22,400 --> 00:16:25,600
Speaker 2: I know you work with your clients talking about alternative investments. Here,

298
00:16:26,480 --> 00:16:29,480
Speaker 2: where do you see the greatest opportunity in alternatives here?

299
00:16:29,560 --> 00:16:32,480
Speaker 2: Private equity, private credit, hedge funds.

300
00:16:33,320 --> 00:16:34,880
Speaker 6: Where are you spending most of your time these days?

301
00:16:35,520 --> 00:16:39,720
Speaker 8: So more hedge fund like strategies, private equity, private credit

302
00:16:39,760 --> 00:16:42,200
Speaker 8: are kind of sensitive to the interest rate environment and

303
00:16:42,280 --> 00:16:47,040
Speaker 8: also sensitive to public market betas in the hedge fund world,

304
00:16:47,200 --> 00:16:50,880
Speaker 8: especially if you believe that there's going to be increased volatility.

305
00:16:51,120 --> 00:16:55,440
Speaker 8: Hedge funds and hedge fund like strategies are great diversifiers. So,

306
00:16:55,520 --> 00:17:00,680
Speaker 8: for example, last week, there's a ETF PTL, the ADF

307
00:17:01,240 --> 00:17:05,480
Speaker 8: us Anti Beta fund long low beta short high beta

308
00:17:05,560 --> 00:17:09,879
Speaker 8: that that product did remarkably well when we had the

309
00:17:09,920 --> 00:17:14,720
Speaker 8: big deep seek threat threat sell off, And that's exactly

310
00:17:14,760 --> 00:17:17,840
Speaker 8: what you want and those types of products, and that's

311
00:17:17,880 --> 00:17:21,439
Speaker 8: what hedge fund like strategies can do is provide you

312
00:17:21,560 --> 00:17:26,959
Speaker 8: that downside protection when the market is under significant stress.

313
00:17:27,000 --> 00:17:30,200
Speaker 8: Managed futures is another example, like a CTA et F.

314
00:17:31,040 --> 00:17:33,000
Speaker 8: And then when you move into the hedge fund space

315
00:17:33,040 --> 00:17:37,280
Speaker 8: where they can use leverage more as a tool, you

316
00:17:37,320 --> 00:17:40,200
Speaker 8: can get even greater benefit as a diversifier and as

317
00:17:40,200 --> 00:17:43,400
Speaker 8: a downside protector. But it's not sexy and people get

318
00:17:43,880 --> 00:17:46,919
Speaker 8: forget that, like these aren't meant to outperform the market.

319
00:17:46,960 --> 00:17:49,200
Speaker 8: There they serve a purpose in your portfolio.

320
00:17:49,680 --> 00:17:51,320
Speaker 5: I like to think of it as.

321
00:17:51,400 --> 00:17:54,080
Speaker 8: Like home insurance, like you hope you never have to

322
00:17:54,160 --> 00:17:56,520
Speaker 8: use it, but it's you would never not have it

323
00:17:57,359 --> 00:18:00,800
Speaker 8: because you need to be able to manage the risk.

324
00:18:00,880 --> 00:18:03,320
Speaker 8: And that's exactly how I see those types of strategies.

325
00:18:03,760 --> 00:18:06,000
Speaker 8: This year, I think is the year where these types

326
00:18:06,040 --> 00:18:10,480
Speaker 8: of strategies are really going to show their worth because

327
00:18:10,480 --> 00:18:12,040
Speaker 8: I think the markets are going to be quite polatile.

328
00:18:12,760 --> 00:18:14,959
Speaker 2: Shana, thank you so much for joining us. Always appreciate

329
00:18:15,000 --> 00:18:17,440
Speaker 2: getting a few minutes of your time. Shana Sissel, president

330
00:18:17,480 --> 00:18:21,320
Speaker 2: and CEO of a Bondrion Capital Management, joining us, you're

331
00:18:21,320 --> 00:18:22,440
Speaker 2: talking about the broader market.

332
00:18:22,880 --> 00:18:27,600
Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, Spotify,

333
00:18:27,800 --> 00:18:31,720
Speaker 1: and anywhere else you get your podcasts. Listen live each weekday,

334
00:18:31,960 --> 00:18:35,240
Speaker 1: ten am to noon Eastern on Bloomberg dot com, the

335
00:18:35,320 --> 00:18:39,199
Speaker 1: iHeartRadio app tune In, and the Bloomberg Business app. You

336
00:18:39,240 --> 00:18:42,520
Speaker 1: can also watch us live every weekday on YouTube and

337
00:18:42,720 --> 00:18:44,680
Speaker 1: always on the Bloomberg terminal