1
00:00:02,520 --> 00:00:13,760
Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is the Bloomberg

2
00:00:13,840 --> 00:00:17,920
Speaker 1: Surveillance Podcast. Catch us live weekdays at seven am Eastern

3
00:00:18,200 --> 00:00:21,240
Speaker 1: on Apple car Play or Android Auto with the Bloomberg

4
00:00:21,320 --> 00:00:24,840
Speaker 1: Business app. Listen on demand wherever you get your podcasts,

5
00:00:25,280 --> 00:00:27,120
Speaker 1: or watch us live on YouTube.

6
00:00:27,320 --> 00:00:30,840
Speaker 2: Is this the beginning of maybe something bigger? Let's talk

7
00:00:30,880 --> 00:00:33,479
Speaker 2: it over with Cameron Dawson, chief investment officer at New

8
00:00:33,560 --> 00:00:37,159
Speaker 2: Edge Wealth, joining us here in our studios Interactive Broker Studios.

9
00:00:37,200 --> 00:00:38,960
Speaker 2: So good to have you, Cameron, Thanks for being here.

10
00:00:38,840 --> 00:00:39,680
Speaker 3: Thank you for having me.

11
00:00:40,120 --> 00:00:43,000
Speaker 2: Let's talk about twenty twenty six for a minute. Let's

12
00:00:43,000 --> 00:00:45,960
Speaker 2: look into the crystal ball. What do you see as

13
00:00:46,080 --> 00:00:48,400
Speaker 2: the biggest challenges for this market.

14
00:00:48,720 --> 00:00:51,120
Speaker 3: Well, certainly as we get into twenty twenty six, we

15
00:00:51,200 --> 00:00:55,000
Speaker 3: will be trading at high valuations with high earnings expectations.

16
00:00:55,280 --> 00:00:58,080
Speaker 3: You've seen valuations just like last week they got over

17
00:00:58,160 --> 00:01:01,720
Speaker 3: twenty three times forward earnings. You have earning sexpectations for

18
00:01:01,880 --> 00:01:05,080
Speaker 3: mid teens growth that's baked in already into S and

19
00:01:05,080 --> 00:01:08,240
Speaker 3: P five hundred estimates, and so that does create a

20
00:01:08,360 --> 00:01:11,319
Speaker 3: high bar for the market to jump over. And I

21
00:01:11,319 --> 00:01:13,720
Speaker 3: think the question will be as a is there further

22
00:01:13,840 --> 00:01:17,160
Speaker 3: upside to earning sestiments because of things like AI productivity

23
00:01:17,200 --> 00:01:19,959
Speaker 3: and AI investment. I think that remains a big question mark.

24
00:01:20,319 --> 00:01:23,199
Speaker 3: And B and this is a little bit nebulous, which

25
00:01:23,240 --> 00:01:26,720
Speaker 3: is do you still have a liquidity environment that supports

26
00:01:26,800 --> 00:01:30,520
Speaker 3: even more margin or multiple expansion, because if liquidity starts

27
00:01:30,560 --> 00:01:34,600
Speaker 3: to receive, then twenty three times looks pretty darn expensive.

28
00:01:34,800 --> 00:01:37,360
Speaker 4: Particularly with the MAG seven still putting up some big numbers.

29
00:01:37,400 --> 00:01:39,959
Speaker 4: But the growth rates are decelerated for the MAC seven,

30
00:01:40,040 --> 00:01:43,600
Speaker 4: so that means the four hundred and ninety three need

31
00:01:43,720 --> 00:01:45,520
Speaker 4: to really perform.

32
00:01:46,720 --> 00:01:47,720
Speaker 5: How do you think about that?

33
00:01:48,080 --> 00:01:50,880
Speaker 3: Hope springs a tournament a lot in the broadening out

34
00:01:50,920 --> 00:01:54,280
Speaker 3: trade if that is something that market participants we're hoping

35
00:01:54,320 --> 00:01:56,960
Speaker 3: for in twenty twenty five, this idea that you would

36
00:01:56,960 --> 00:01:59,120
Speaker 3: see the four ninety three catch up even as MAG

37
00:01:59,200 --> 00:02:02,320
Speaker 3: seven dece and what ended up happening is MAG seven

38
00:02:02,400 --> 00:02:05,000
Speaker 3: surprise to the upside and four ninety three surprise to

39
00:02:05,080 --> 00:02:07,840
Speaker 3: the downside. And you didn't see that kind of catchup.

40
00:02:08,120 --> 00:02:10,960
Speaker 3: But if we look at what's going on in twenty six,

41
00:02:11,160 --> 00:02:14,120
Speaker 3: you have this law of large numbers that's pulling MAG

42
00:02:14,160 --> 00:02:16,960
Speaker 3: seven earnings growth lower, and of course secondaryvet is matter

43
00:02:17,000 --> 00:02:17,560
Speaker 3: in markets.

44
00:02:17,720 --> 00:02:19,200
Speaker 5: Do you have a name like Meta.

45
00:02:18,880 --> 00:02:22,000
Speaker 3: Decelerating from forty percent growth this year to a potential

46
00:02:22,320 --> 00:02:25,079
Speaker 3: decline of about two percent next year? And you see

47
00:02:25,120 --> 00:02:28,320
Speaker 3: that across the mag seven names, and this baking in

48
00:02:28,440 --> 00:02:31,160
Speaker 3: of the idea of the broadening out really relies on

49
00:02:31,280 --> 00:02:35,280
Speaker 3: the economy a being very strong and b these businesses

50
00:02:35,320 --> 00:02:38,880
Speaker 3: being able to generate margin expansion. And they don't necessarily

51
00:02:38,919 --> 00:02:41,160
Speaker 3: deserve the benefit of the doubt as we look at

52
00:02:41,160 --> 00:02:45,000
Speaker 3: that four ninety three because they've continuously underperformed or disappointed

53
00:02:45,040 --> 00:02:46,840
Speaker 3: on the earnings front over the last few years.

54
00:02:47,280 --> 00:02:50,320
Speaker 2: What are you looking at at outside the AI trade,

55
00:02:50,360 --> 00:02:52,640
Speaker 2: because there's a lot of good stuff out there that

56
00:02:52,680 --> 00:02:55,040
Speaker 2: we just don't talk a lot about. Is doesn't garner

57
00:02:55,040 --> 00:02:55,639
Speaker 2: the headlines.

58
00:02:55,800 --> 00:03:00,679
Speaker 3: Yeah, our equity portfolio manager j Peters has been absolutely

59
00:03:00,720 --> 00:03:03,280
Speaker 3: salbrating at some of the deals that he's starting to

60
00:03:03,320 --> 00:03:06,160
Speaker 3: see where you have had names simply get left behind

61
00:03:06,200 --> 00:03:09,040
Speaker 3: in the rally. Remember that September and October have been

62
00:03:09,360 --> 00:03:13,880
Speaker 3: incredibly low quality rallies and incredibly narrow rallies, So a

63
00:03:13,919 --> 00:03:16,680
Speaker 3: lot of junk moving higher very very fast, and then

64
00:03:16,720 --> 00:03:19,640
Speaker 3: the quality things that have been moving higher really just

65
00:03:19,720 --> 00:03:22,440
Speaker 3: been in that MAC seven. So if you can broaden

66
00:03:22,480 --> 00:03:25,760
Speaker 3: out the aperture into more value oriented names as well

67
00:03:25,800 --> 00:03:28,240
Speaker 3: as MidCap names, is where we're finding a lot of

68
00:03:28,280 --> 00:03:31,040
Speaker 3: opportunities of parts of the market again that have just

69
00:03:31,080 --> 00:03:34,200
Speaker 3: simply been left behind, but remaining very disciplined on those

70
00:03:34,280 --> 00:03:38,520
Speaker 3: quality metrics, not chasing the junk, not chasing the dumpster dives,

71
00:03:38,720 --> 00:03:40,960
Speaker 3: I think is incredibly important at this time.

72
00:03:41,440 --> 00:03:43,640
Speaker 4: The most powerful leadership in this market over the last

73
00:03:43,640 --> 00:03:48,320
Speaker 4: six months has been low quality, junkie, no profit, no revenue,

74
00:03:48,640 --> 00:03:51,600
Speaker 4: specultive parts of the market that reminds me of me

75
00:03:51,640 --> 00:03:54,720
Speaker 4: in nineteen ninety eight, nineteen ninety nine, parts of two

76
00:03:54,720 --> 00:03:56,880
Speaker 4: thousands of stuff I was selling when I was a banker.

77
00:03:57,640 --> 00:03:58,720
Speaker 4: How concerned are you about that?

78
00:03:59,040 --> 00:04:02,200
Speaker 3: Yeah, it's been some thing that has definitely caused us

79
00:04:02,200 --> 00:04:04,200
Speaker 3: to raise an eyebrow. We wrote about it over the

80
00:04:04,240 --> 00:04:07,600
Speaker 3: week and saying this is not sustainable. We looked at

81
00:04:07,640 --> 00:04:10,800
Speaker 3: the rally that you saw in for example, Korean fried

82
00:04:10,880 --> 00:04:11,800
Speaker 3: chicken stocks in.

83
00:04:11,760 --> 00:04:13,320
Speaker 5: Response freaking fried chicken.

84
00:04:13,440 --> 00:04:16,159
Speaker 3: There was a picture of Ginsen Wong eating fried chickens

85
00:04:16,360 --> 00:04:18,720
Speaker 3: one viral, and you saw this big, huge surge in

86
00:04:18,760 --> 00:04:19,440
Speaker 3: Korean stocks.

87
00:04:19,440 --> 00:04:21,480
Speaker 5: But if you look at the costp it.

88
00:04:21,600 --> 00:04:26,960
Speaker 3: Rallied twenty six percent in October alone, So you.

89
00:04:26,839 --> 00:04:29,520
Speaker 5: Talk about irrational exuberance exactly.

90
00:04:29,240 --> 00:04:31,920
Speaker 3: And the COSTPI has oftentimes had these kind of casino

91
00:04:31,960 --> 00:04:35,480
Speaker 3: capitalism types of moves. So it's a global dynamic, and

92
00:04:35,760 --> 00:04:39,520
Speaker 3: we why we remain really disciplined about qualities because what

93
00:04:39,680 --> 00:04:41,960
Speaker 3: tends to happen is these names can go up a

94
00:04:42,000 --> 00:04:44,240
Speaker 3: lot in a very short period of time, but they

95
00:04:44,240 --> 00:04:45,880
Speaker 3: can also go down a lot in a short period

96
00:04:45,920 --> 00:04:48,600
Speaker 3: of time. So from a portfolio perspective, they actually don't

97
00:04:48,600 --> 00:04:52,160
Speaker 3: add any incremental return and at the same time, they

98
00:04:52,200 --> 00:04:53,320
Speaker 3: just add to volatility.

99
00:04:53,720 --> 00:04:56,400
Speaker 2: What do you like in terms of alternative assets? I mean,

100
00:04:56,400 --> 00:04:59,400
Speaker 2: we saw bitcoin have a little bit more volatility. I

101
00:04:59,400 --> 00:05:01,400
Speaker 2: think it's back up of one hundred thousand dollars now,

102
00:05:02,160 --> 00:05:04,960
Speaker 2: but failled to its lowest level since June. Gold continues

103
00:05:05,000 --> 00:05:07,719
Speaker 2: to hang in there despite whatever is happening in the

104
00:05:07,720 --> 00:05:10,719
Speaker 2: equity market. What do you like in terms of ald assets?

105
00:05:10,800 --> 00:05:12,839
Speaker 3: So when we think about bitcoin, we see it really

106
00:05:12,920 --> 00:05:16,400
Speaker 3: as a function of liquidity, which appears to be tightening

107
00:05:16,480 --> 00:05:18,880
Speaker 3: at this time given the fact that it is broken down,

108
00:05:18,920 --> 00:05:23,279
Speaker 3: but also encapsulating some of these fears about dollar debasement.

109
00:05:23,320 --> 00:05:25,440
Speaker 3: Et cetera, which of course we know is the relationship

110
00:05:25,480 --> 00:05:29,000
Speaker 3: with gold. I would add on to that spectrum of

111
00:05:29,040 --> 00:05:31,480
Speaker 3: real assets, which we would consider gold and bitcoin to

112
00:05:31,520 --> 00:05:34,159
Speaker 3: be a part of infrastructure. Is something that we've really

113
00:05:34,279 --> 00:05:36,600
Speaker 3: liked and added to portfolios. This idea that you can

114
00:05:36,640 --> 00:05:39,440
Speaker 3: get high single digit, low double digit returns depending on

115
00:05:39,960 --> 00:05:42,839
Speaker 3: how you're positioned, and have something that's less correlated to

116
00:05:42,880 --> 00:05:45,880
Speaker 3: equity markets. We like that in a world where equity

117
00:05:45,880 --> 00:05:48,720
Speaker 3: markets could have lower forward returns. So that's been an

118
00:05:48,760 --> 00:05:52,240
Speaker 3: area where it's certainly peaked our interest over the course

119
00:05:52,279 --> 00:05:54,440
Speaker 3: of the last year and definitely into twenty twenty six.

120
00:05:54,880 --> 00:05:56,200
Speaker 5: What are we doing in the bond market here?

121
00:05:56,240 --> 00:05:58,520
Speaker 4: I could sit there and to your treasury three point

122
00:05:58,520 --> 00:06:02,120
Speaker 4: five three point six percent, that's not a bad way

123
00:06:02,120 --> 00:06:02,840
Speaker 4: to make a living here.

124
00:06:02,880 --> 00:06:04,640
Speaker 5: Do we stick there or do we take some credit version?

125
00:06:04,880 --> 00:06:07,599
Speaker 3: Yeah? I mean continuously we keep talking about how this

126
00:06:07,720 --> 00:06:11,040
Speaker 3: idea that credit really isn't compensating you for the risk

127
00:06:11,160 --> 00:06:14,960
Speaker 3: that is potentially embedded in some of these businesses and

128
00:06:15,040 --> 00:06:18,760
Speaker 3: remaining higher quality and credit doing active management within credit

129
00:06:18,800 --> 00:06:20,880
Speaker 3: to be able to make sure that the right issues

130
00:06:20,920 --> 00:06:24,560
Speaker 3: are being put into portfolios. And I think that high

131
00:06:24,640 --> 00:06:27,200
Speaker 3: yield is what is causing us also to kind of

132
00:06:27,240 --> 00:06:29,320
Speaker 3: look a little bit with a higher eyebrow at this

133
00:06:29,440 --> 00:06:32,080
Speaker 3: market because you've seen those spreads start to widen out.

134
00:06:32,320 --> 00:06:34,159
Speaker 3: The thing to remember about high yield is that it

135
00:06:34,240 --> 00:06:37,719
Speaker 3: is coincident and it is episodic, So spreads will widen

136
00:06:37,760 --> 00:06:40,599
Speaker 3: out as there is pain in other places. Not necessarily

137
00:06:40,680 --> 00:06:41,640
Speaker 3: a leading indicator.

138
00:06:42,080 --> 00:06:44,000
Speaker 2: Now we talk a lot about how this market is

139
00:06:44,200 --> 00:06:46,080
Speaker 2: priced for perfection, and all you need to do is

140
00:06:46,080 --> 00:06:49,000
Speaker 2: look at a stock like Palenteer, right, I mean, by

141
00:06:49,040 --> 00:06:51,680
Speaker 2: all metrics, that was a really good earnings report, yet

142
00:06:51,760 --> 00:06:54,359
Speaker 2: they were punished. What do you make of that? And

143
00:06:54,360 --> 00:06:56,279
Speaker 2: what's an investor to do if you're looking for those

144
00:06:56,360 --> 00:06:57,240
Speaker 2: quality names.

145
00:06:57,400 --> 00:06:59,359
Speaker 3: Yeah, a high bar is a high bar is a

146
00:06:59,440 --> 00:07:02,560
Speaker 3: high bar. And the reality is is that what might

147
00:07:02,600 --> 00:07:05,560
Speaker 3: be baked into the cell side estimates that generates some

148
00:07:05,640 --> 00:07:09,480
Speaker 3: beaten rays might not be being baked into the buy

149
00:07:09,640 --> 00:07:11,920
Speaker 3: side estimates the people who actually own the stock and

150
00:07:11,960 --> 00:07:14,800
Speaker 3: the forecast that they need to drive them to buy more.

151
00:07:14,920 --> 00:07:18,080
Speaker 3: So we think that these reactions to good earnings reports

152
00:07:18,240 --> 00:07:20,960
Speaker 3: is a reflection of a buy side estimates are probably

153
00:07:21,000 --> 00:07:24,880
Speaker 3: too high, and b the fact that positioning is probably

154
00:07:24,920 --> 00:07:29,200
Speaker 3: already very crowded in these areas. So valuations always do matter,

155
00:07:29,720 --> 00:07:33,120
Speaker 3: but they matter eventually, and it's that catalyst of what

156
00:07:33,200 --> 00:07:36,480
Speaker 3: causes evaluation to unwind. That's a really key component of

157
00:07:36,640 --> 00:07:39,960
Speaker 3: potentially driving these high flying stocks lower.

158
00:07:40,160 --> 00:07:42,680
Speaker 2: All right, but again we've got futures on the downside

159
00:07:42,760 --> 00:07:45,960
Speaker 2: right now, looking at possibly back to back down days

160
00:07:46,040 --> 00:07:49,840
Speaker 2: for Wall Street. Cameron Dawson, Chief Investment Officer knew Edge. Well,

161
00:07:49,880 --> 00:07:51,400
Speaker 2: thanks so much for stopping by the studios.

162
00:07:51,480 --> 00:07:54,600
Speaker 5: Stay with us. More from Bloomberg Surveillance coming up after this.

163
00:08:00,800 --> 00:08:04,400
Speaker 1: You're listening to the Bloomberg Surveillance podcast. Catch us Live

164
00:08:04,440 --> 00:08:07,600
Speaker 1: weekday afternoons from seven to ten am Eastern Listen on

165
00:08:07,720 --> 00:08:11,120
Speaker 1: Apple Karplay and Android Otto with the Bloomberg Business app

166
00:08:11,280 --> 00:08:12,880
Speaker 1: or what Us Live on YouTube.

167
00:08:13,000 --> 00:08:13,960
Speaker 5: Jennifer Lawless, Georgia.

168
00:08:14,000 --> 00:08:16,360
Speaker 4: She's a professor of politics and public policy at the

169
00:08:17,120 --> 00:08:21,520
Speaker 4: University of Virginia and lovely Charlottesville, Virginia. Been there many

170
00:08:21,680 --> 00:08:25,640
Speaker 4: many times, Jennifer, talk to us about what you saw

171
00:08:25,680 --> 00:08:30,080
Speaker 4: coming out of Virginia and New Jersey with democratic wins,

172
00:08:30,080 --> 00:08:32,160
Speaker 4: pretty solid wins as well.

173
00:08:32,160 --> 00:08:35,400
Speaker 5: What did you take away from those governorship races?

174
00:08:36,320 --> 00:08:37,120
Speaker 6: Well, good morning.

175
00:08:37,240 --> 00:08:39,800
Speaker 7: I think it was a good night for Democrats because

176
00:08:39,800 --> 00:08:44,040
Speaker 7: both Abigails Bamberger and Mikey Cheryl had double digit wins.

177
00:08:44,080 --> 00:08:47,720
Speaker 7: They both exceeded expectations, and they were able to, especially

178
00:08:47,760 --> 00:08:50,840
Speaker 7: in the case of Virginia, bring a long down ballot

179
00:08:50,880 --> 00:08:54,439
Speaker 7: candidates that included j Jones for Attorney General in Virginia

180
00:08:54,520 --> 00:08:57,720
Speaker 7: as well as flipping as of right now, thirteen House

181
00:08:57,760 --> 00:09:00,360
Speaker 7: of Delegate seats. So there was a moment on the

182
00:09:00,400 --> 00:09:03,800
Speaker 7: Democratic side. There was excitement for these candidates at.

183
00:09:03,679 --> 00:09:04,280
Speaker 5: The top of the.

184
00:09:04,200 --> 00:09:08,240
Speaker 7: Ticket, and that's necessary, although not sufficient, if the Democrats

185
00:09:08,240 --> 00:09:11,400
Speaker 7: are going to do well in twenty twenty six, professor.

186
00:09:12,120 --> 00:09:16,040
Speaker 2: Professor, were these races the sweep by Dems last night,

187
00:09:17,200 --> 00:09:19,560
Speaker 2: is it a verdict on Trump's second term? Do you think?

188
00:09:21,000 --> 00:09:22,480
Speaker 5: I think it's a mix of factors.

189
00:09:22,520 --> 00:09:26,200
Speaker 7: Some of it is certainly dissatisfaction with Trump, but that's

190
00:09:26,240 --> 00:09:30,240
Speaker 7: really among Democrats who were enthusiastic and independents who got

191
00:09:30,240 --> 00:09:33,720
Speaker 7: out to vote. There's not much evidence that Republicans decided

192
00:09:33,760 --> 00:09:36,719
Speaker 7: that they were no longer going to support the Republican administration.

193
00:09:37,240 --> 00:09:40,160
Speaker 7: It was just they weren't that excited about their candidates

194
00:09:40,160 --> 00:09:42,880
Speaker 7: at the top of the ticket, and Democratic turnout surge.

195
00:09:43,080 --> 00:09:46,439
Speaker 6: So it was in part a referendum on the Trump administration.

196
00:09:46,960 --> 00:09:48,400
Speaker 6: But the Democratic candidates who.

197
00:09:48,320 --> 00:09:51,760
Speaker 7: Won last night were not only running as alternatives to Trump,

198
00:09:51,920 --> 00:09:54,679
Speaker 7: they were also putting forward their own plans that would

199
00:09:54,679 --> 00:09:56,199
Speaker 7: resonate with independent voters.

200
00:09:56,840 --> 00:10:00,240
Speaker 4: It's interesting, Jennifer. I'm a resident of New Jersey and

201
00:10:00,800 --> 00:10:02,920
Speaker 4: President Trump had a very, i want to say, a

202
00:10:03,000 --> 00:10:06,160
Speaker 4: low profile in this race here in terms of supporting

203
00:10:06,200 --> 00:10:10,480
Speaker 4: the Republican candidate, mister Chittarelli, What does that tell you,

204
00:10:10,520 --> 00:10:13,160
Speaker 4: if anything, Well.

205
00:10:13,000 --> 00:10:16,960
Speaker 7: I think the Trump administration and the Republican candidate's campaigns

206
00:10:17,320 --> 00:10:21,040
Speaker 7: are aware of the liabilities associated with Donald Trump.

207
00:10:20,720 --> 00:10:21,640
Speaker 5: In blue states.

208
00:10:21,960 --> 00:10:23,000
Speaker 6: There's no question that.

209
00:10:22,920 --> 00:10:27,200
Speaker 7: Trump overperformed expectations in New Jersey in twenty twenty four,

210
00:10:27,480 --> 00:10:29,800
Speaker 7: but it's important to keep in mind that Kamala Harris

211
00:10:29,840 --> 00:10:32,839
Speaker 7: didn't really campaign there. So I think the Republicans thought

212
00:10:32,840 --> 00:10:35,200
Speaker 7: that they were stronger in some of these blue states

213
00:10:35,240 --> 00:10:38,439
Speaker 7: than they actually were, simply because there was one presidential

214
00:10:38,440 --> 00:10:41,840
Speaker 7: candidate campaigning there last year and not two. And the

215
00:10:41,880 --> 00:10:45,880
Speaker 7: outcomes in both Virginia and New Jersey suggest that Donald

216
00:10:45,920 --> 00:10:49,880
Speaker 7: Trump can be a liability, especially when Democrats are excited

217
00:10:49,920 --> 00:10:51,800
Speaker 7: and energized about their own candidates.

218
00:10:52,520 --> 00:10:54,240
Speaker 2: Triser, I want to step away from the elections for

219
00:10:54,240 --> 00:10:55,880
Speaker 2: a moment and just talk about what's going on with

220
00:10:55,880 --> 00:10:58,560
Speaker 2: the Supreme Court today, going to hear arguments over Trump's

221
00:10:58,640 --> 00:11:03,640
Speaker 2: unilateral decision too sweeping global tariffs. What's at stake here,

222
00:11:04,160 --> 00:11:08,319
Speaker 2: besides possibly trillions of dollars in refunds needing to be given.

223
00:11:08,080 --> 00:11:12,040
Speaker 7: Back, This is one of the cases where we're really

224
00:11:12,080 --> 00:11:15,079
Speaker 7: going to see what the Supreme Court thinks about presidential

225
00:11:15,160 --> 00:11:18,760
Speaker 7: power and executive power and the ability of the president

226
00:11:18,840 --> 00:11:23,000
Speaker 7: to engage in unilateral action. If we've learned anything over

227
00:11:23,040 --> 00:11:25,360
Speaker 7: the course of the last several months is that the

228
00:11:25,400 --> 00:11:28,600
Speaker 7: Supreme Court seems to be willing to grant Donald Trump

229
00:11:29,160 --> 00:11:31,760
Speaker 7: more in the way of authority than previous presidents thought

230
00:11:31,800 --> 00:11:33,080
Speaker 7: they were entitled or sought.

231
00:11:33,160 --> 00:11:35,480
Speaker 6: So this is just one more example of that. We'll

232
00:11:35,480 --> 00:11:36,479
Speaker 6: see what happens.

233
00:11:37,240 --> 00:11:40,000
Speaker 4: So, given the what we saw yesterday, just kind of

234
00:11:40,040 --> 00:11:44,199
Speaker 4: the elections here, Professor, what are the takeaways for I

235
00:11:44,200 --> 00:11:47,240
Speaker 4: don't know the Democratic Priority writ large, the Republican Party

236
00:11:47,240 --> 00:11:47,800
Speaker 4: writ large?

237
00:11:47,800 --> 00:11:49,280
Speaker 5: Are there lessons to be learned here?

238
00:11:50,440 --> 00:11:53,240
Speaker 7: I'm always a little bit leery about using a couple

239
00:11:53,240 --> 00:11:56,280
Speaker 7: of races to forecast what could be done in the future,

240
00:11:56,320 --> 00:11:57,840
Speaker 7: but I do think there are a few lessons.

241
00:11:58,240 --> 00:12:00,320
Speaker 6: The first is that despite the.

242
00:12:00,080 --> 00:12:02,800
Speaker 7: Fact that the overwhelming majority of the American people are

243
00:12:02,800 --> 00:12:05,560
Speaker 7: turned off to politics are disgusted with what's going on,

244
00:12:06,080 --> 00:12:09,200
Speaker 7: they voted in record numbers yesterday, and that bodes well

245
00:12:09,200 --> 00:12:12,239
Speaker 7: for the Democrats. It suggests that even though the Democrats

246
00:12:12,240 --> 00:12:15,240
Speaker 7: have been demoralized and have felt that they haven't had

247
00:12:15,240 --> 00:12:17,560
Speaker 7: a win in a long time, when push came to shove,

248
00:12:17,640 --> 00:12:20,079
Speaker 7: they turned out. They turned out with enthusiasm, and they won.

249
00:12:20,679 --> 00:12:23,480
Speaker 7: The second take home I think is that different kinds

250
00:12:23,480 --> 00:12:26,680
Speaker 7: of Democratic candidates win in different kinds of places, which

251
00:12:26,720 --> 00:12:29,480
Speaker 7: is not rocket science, but it's something that we often forget.

252
00:12:29,920 --> 00:12:31,640
Speaker 7: And so the kind of Democrat that can win in

253
00:12:31,640 --> 00:12:33,640
Speaker 7: New York City does not look like the kind of

254
00:12:33,679 --> 00:12:34,880
Speaker 7: Democrat who can win.

255
00:12:34,960 --> 00:12:36,199
Speaker 6: Statewide in Virginia.

256
00:12:36,400 --> 00:12:38,720
Speaker 7: That doesn't mean that Democrats can't win in both of

257
00:12:38,760 --> 00:12:42,240
Speaker 7: those places, and that should also be encouraging for the

258
00:12:42,280 --> 00:12:44,560
Speaker 7: Democratic Party heading into twenty twenty six.

259
00:12:44,920 --> 00:12:46,960
Speaker 6: And the final message I think is that.

260
00:12:47,400 --> 00:12:50,880
Speaker 7: When Donald Trump is not on the ballot, Republicans have

261
00:12:50,960 --> 00:12:55,640
Speaker 7: a tougher time. He's really what motivates turnout among Republican candidates,

262
00:12:55,760 --> 00:12:57,439
Speaker 7: and he's not going to be on the ballot in

263
00:12:57,480 --> 00:13:01,120
Speaker 7: twenty twenty six. So I think last night should have

264
00:13:01,240 --> 00:13:05,040
Speaker 7: injected some enthusiasm into the Democratic Party.

265
00:13:05,080 --> 00:13:06,520
Speaker 6: We'll see what they do with it over the course

266
00:13:06,559 --> 00:13:07,760
Speaker 6: of the next twelve months.

267
00:13:09,280 --> 00:13:10,600
Speaker 5: All right, very good.

268
00:13:10,640 --> 00:13:13,320
Speaker 4: So I guess one final question here at Monica is

269
00:13:13,480 --> 00:13:18,520
Speaker 4: just simply who is the face of this Democratic Party?

270
00:13:18,559 --> 00:13:18,719
Speaker 3: Here?

271
00:13:18,880 --> 00:13:20,880
Speaker 4: It doesn't seem to be mister Schumer, doesn't seem to

272
00:13:20,960 --> 00:13:24,120
Speaker 4: be even King Jefferies. Might it be one of these

273
00:13:24,160 --> 00:13:25,320
Speaker 4: folks who won yesterday?

274
00:13:27,160 --> 00:13:27,800
Speaker 6: It's unclear.

275
00:13:27,840 --> 00:13:31,560
Speaker 7: There was definitely a generational shift yesterday. All of these

276
00:13:31,600 --> 00:13:36,040
Speaker 7: candidates are relatively young, some younger than others, but a

277
00:13:36,080 --> 00:13:39,520
Speaker 7: lot of them have not been career politicians either. They've

278
00:13:39,559 --> 00:13:42,319
Speaker 7: been elected just for the last three or four cycles.

279
00:13:42,400 --> 00:13:44,560
Speaker 7: And so I think the face of the Democratic Party

280
00:13:45,040 --> 00:13:47,400
Speaker 7: might be just fresh faces.

281
00:13:46,920 --> 00:13:48,880
Speaker 6: And new ideas and new energy.

282
00:13:49,320 --> 00:13:52,679
Speaker 7: And as long as Jeffries and Schumer embraced that, I

283
00:13:52,720 --> 00:13:55,839
Speaker 7: think that the twenty twenty six elections could go better

284
00:13:55,840 --> 00:13:57,680
Speaker 7: than they probably anticipated a week ago.

285
00:13:59,000 --> 00:14:02,160
Speaker 4: And shutdown who's paying the cost here? I mean, we're

286
00:14:02,200 --> 00:14:04,600
Speaker 4: in day thirty something or that. I can't even keep track.

287
00:14:04,920 --> 00:14:08,679
Speaker 4: Is there anybody paying a cost here for these shutdown.

288
00:14:09,160 --> 00:14:12,560
Speaker 7: The American people are clearly paying the cost. It seems

289
00:14:12,600 --> 00:14:14,599
Speaker 7: based on last night that at least in terms of

290
00:14:14,640 --> 00:14:18,520
Speaker 7: the Virginia results, federal workers broke for spam Berger. And

291
00:14:18,800 --> 00:14:21,920
Speaker 7: it seems like they are blaming the Republicans. When the

292
00:14:21,920 --> 00:14:25,400
Speaker 7: Republicans have unified control of government, when they control the House,

293
00:14:25,440 --> 00:14:27,960
Speaker 7: the Senate, and the White House, it's hard not to

294
00:14:27,960 --> 00:14:30,880
Speaker 7: blame them for the shutdown. And it seems that at

295
00:14:30,960 --> 00:14:33,160
Speaker 7: least in terms of the ballots cast yesterday, they are

296
00:14:33,200 --> 00:14:33,920
Speaker 7: bearing the brunt.

297
00:14:34,280 --> 00:14:36,120
Speaker 5: All right, Jennifer, thank you so much. We appreciate that.

298
00:14:36,160 --> 00:14:40,200
Speaker 4: Jennifer Lolas, Professor of politics and public Policy at the

299
00:14:40,320 --> 00:14:42,800
Speaker 4: University of Virginia, to stay with us. More from Bloomberg

300
00:14:42,840 --> 00:14:44,520
Speaker 4: Surveillance coming up after this.

301
00:14:50,720 --> 00:14:54,320
Speaker 1: You're listening to the Bloomberg Surveillance podcast. Catch us live

302
00:14:54,360 --> 00:14:57,520
Speaker 1: weekday afternoons from seven to ten am Eastern Listen on

303
00:14:57,600 --> 00:15:01,280
Speaker 1: Applecarplay and Android Otto with the Work Business up, or

304
00:15:01,440 --> 00:15:02,920
Speaker 1: watch us live on YouTube.

305
00:15:03,160 --> 00:15:05,720
Speaker 4: Daniel Di Martino Booth joins us. She's one of our faves.

306
00:15:05,960 --> 00:15:09,680
Speaker 4: She's a CEO and chief strategistic QI Research. She looks

307
00:15:09,680 --> 00:15:13,000
Speaker 4: at data that I don't even know where it comes from.

308
00:15:13,320 --> 00:15:15,960
Speaker 4: And you know, she doesn't rely upon the government stuff.

309
00:15:16,000 --> 00:15:18,400
Speaker 4: She gets her own data sets. I have no idea

310
00:15:18,400 --> 00:15:20,280
Speaker 4: where that comes from. But it's really really smart and

311
00:15:20,280 --> 00:15:23,160
Speaker 4: really helpful here. Danielle, thanks so much for joining us

312
00:15:23,160 --> 00:15:26,880
Speaker 4: here again with the dearth of economic data out there

313
00:15:27,040 --> 00:15:28,920
Speaker 4: for those of us that are trying to figure out

314
00:15:28,920 --> 00:15:31,120
Speaker 4: where this economy is going? What are you looking at?

315
00:15:31,160 --> 00:15:33,920
Speaker 4: What are you seeing at there in this US economy?

316
00:15:35,280 --> 00:15:37,360
Speaker 8: So I mean, I think the good news a month

317
00:15:37,400 --> 00:15:41,359
Speaker 8: ago is that QI Research was always looking at alternative

318
00:15:41,440 --> 00:15:42,000
Speaker 8: data sets.

319
00:15:42,000 --> 00:15:43,840
Speaker 5: The Reuters poll that.

320
00:15:43,760 --> 00:15:45,960
Speaker 8: Came out that said that eighty nine percent of economists

321
00:15:45,960 --> 00:15:49,000
Speaker 8: did not trust the official data prior to the shutdown,

322
00:15:49,040 --> 00:15:51,560
Speaker 8: So I don't think that I'm necessarily alone. But anyways,

323
00:15:51,560 --> 00:15:54,960
Speaker 8: to answer your question, we look at macro Edge, which

324
00:15:55,120 --> 00:15:57,720
Speaker 8: is an alternative to Challenger. They use kind of big

325
00:15:57,800 --> 00:16:00,760
Speaker 8: data to scrape headlines. For the month of October, we

326
00:16:00,880 --> 00:16:02,920
Speaker 8: learned that it was almost one hundred and fifty five

327
00:16:02,960 --> 00:16:06,000
Speaker 8: thousand job cuts that were announced, and that helps to

328
00:16:06,080 --> 00:16:09,000
Speaker 8: kind of put into context what we heard from ADP

329
00:16:09,160 --> 00:16:12,200
Speaker 8: this morning. Because these are large companies making big job

330
00:16:12,240 --> 00:16:14,800
Speaker 8: cut announcements and yet what we saw for the month

331
00:16:14,800 --> 00:16:19,000
Speaker 8: of October is that ADP job creation was exclusively with

332
00:16:19,120 --> 00:16:22,080
Speaker 8: the largest companies. Of course, you count an employee as

333
00:16:22,080 --> 00:16:25,080
Speaker 8: being on the payroll until their severance runs out, so

334
00:16:25,120 --> 00:16:27,160
Speaker 8: we know that that's kind of a ticking time bomb.

335
00:16:27,840 --> 00:16:32,120
Speaker 8: We follow Google trends every day Americans search or don't

336
00:16:32,200 --> 00:16:35,720
Speaker 8: search to file for unemployment, and when you look at

337
00:16:35,720 --> 00:16:39,600
Speaker 8: that the five twenty twenty five trend really has gone

338
00:16:39,640 --> 00:16:42,360
Speaker 8: off of what we saw in twenty two, twenty three,

339
00:16:42,760 --> 00:16:46,720
Speaker 8: and twenty four. We also follow individual state warn notices,

340
00:16:46,920 --> 00:16:49,520
Speaker 8: which they have to provide to workers when they're going

341
00:16:49,520 --> 00:16:52,080
Speaker 8: to be laid off, and right now, in the aggregate,

342
00:16:52,160 --> 00:16:54,560
Speaker 8: on a nationwide basis, warn notices are running at the

343
00:16:54,840 --> 00:16:57,080
Speaker 8: highest pace since September two thousand and nine.

344
00:16:57,240 --> 00:16:59,160
Speaker 5: So the data has always been there. You just have

345
00:16:59,200 --> 00:16:59,880
Speaker 5: to know where to look.

346
00:17:00,360 --> 00:17:02,080
Speaker 2: And what does all this mean for the FED and

347
00:17:02,120 --> 00:17:05,080
Speaker 2: that December meeting. You know, in just a few weeks

348
00:17:05,080 --> 00:17:07,240
Speaker 2: are we going to get one more cut? Do you

349
00:17:07,280 --> 00:17:08,400
Speaker 2: think before the end of the year.

350
00:17:09,560 --> 00:17:13,240
Speaker 8: Well, I think given the headlines that we've seen subsequent

351
00:17:13,400 --> 00:17:17,639
Speaker 8: to Powell being at the podium in the beginning, that

352
00:17:17,680 --> 00:17:19,359
Speaker 8: the odds of a rate cut tick down to like

353
00:17:19,400 --> 00:17:21,720
Speaker 8: fifty five percent. Now as of today, we're back up

354
00:17:21,760 --> 00:17:24,480
Speaker 8: at seventy percent, and I think that that's because we're

355
00:17:24,520 --> 00:17:27,439
Speaker 8: not seeing, especially when you hear from companies like Cava

356
00:17:27,520 --> 00:17:30,760
Speaker 8: and Chipotle, we're not seeing companies able to pass through

357
00:17:31,200 --> 00:17:34,240
Speaker 8: higher costs to their end customers.

358
00:17:34,480 --> 00:17:36,119
Speaker 5: And that means that there's not going.

359
00:17:36,000 --> 00:17:39,040
Speaker 8: To be as much pressure on the Fed's inflation mandate

360
00:17:39,280 --> 00:17:41,760
Speaker 8: as there is going to be on its employment mandate.

361
00:17:41,800 --> 00:17:44,280
Speaker 8: And I think that that's why we've seen odds of

362
00:17:44,320 --> 00:17:47,520
Speaker 8: a subsequent December rate cut stay hover around this seventy

363
00:17:47,560 --> 00:17:51,480
Speaker 8: percent level. And Jerome Pals never defied markets when it's

364
00:17:51,520 --> 00:17:54,560
Speaker 8: been north of even fifty percent, so there is the

365
00:17:54,560 --> 00:17:56,480
Speaker 8: presumption in the markets that we will see this next

366
00:17:56,480 --> 00:17:56,880
Speaker 8: break cut.

367
00:17:57,760 --> 00:18:01,040
Speaker 4: Danielle, So we've seen a lot of I guess, headlines

368
00:18:01,119 --> 00:18:04,600
Speaker 4: on layoffs and from companies we all know and people

369
00:18:04,600 --> 00:18:08,119
Speaker 4: can relate to. Is that headline risk or does that

370
00:18:08,240 --> 00:18:11,280
Speaker 4: really reflect oh boy, we've got a labor market that's

371
00:18:11,880 --> 00:18:14,080
Speaker 4: at the margin, really kind of struggling here.

372
00:18:15,560 --> 00:18:19,040
Speaker 8: Well, I think it's the ladder of the two. Yesterday

373
00:18:19,040 --> 00:18:23,040
Speaker 8: we heard from trip Advisor, American Airlines IBM, and every

374
00:18:23,119 --> 00:18:26,720
Speaker 8: day we seem to get a new laundry list of companies,

375
00:18:26,800 --> 00:18:29,680
Speaker 8: and I think that that it's indegative, that it's more

376
00:18:29,800 --> 00:18:32,399
Speaker 8: endemic kind of At the beginning of the year, we

377
00:18:32,440 --> 00:18:37,480
Speaker 8: had this so called low hiring, low firing situation, and

378
00:18:37,800 --> 00:18:40,760
Speaker 8: Powell warned when he gave a speech a few weeks

379
00:18:40,760 --> 00:18:44,920
Speaker 8: ago that if we see a continued lack of hiring

380
00:18:45,240 --> 00:18:48,560
Speaker 8: but also see a pickup in firing, that that really

381
00:18:48,600 --> 00:18:51,240
Speaker 8: does change the construct. It's going to change the FEDS thinking.

382
00:18:51,240 --> 00:18:53,959
Speaker 8: And I think that that's where we are at this point, Danielle.

383
00:18:54,000 --> 00:18:57,159
Speaker 2: I just want to get your thoughts on AI because

384
00:18:57,320 --> 00:18:59,159
Speaker 2: we seem to be doing that every day with our guests.

385
00:18:59,440 --> 00:19:02,960
Speaker 2: And earlier we had Brad Konger on he's a chief

386
00:19:02,960 --> 00:19:06,119
Speaker 2: investment officer over at Hurdle Callahan, and he said that

387
00:19:06,200 --> 00:19:09,280
Speaker 2: he thought the AI data center boom was a big,

388
00:19:09,880 --> 00:19:13,000
Speaker 2: huge bubble. He went on record saying that what are

389
00:19:13,040 --> 00:19:13,720
Speaker 2: your thoughts on that?

390
00:19:15,040 --> 00:19:17,160
Speaker 8: Well, you know, I'm not an expert in this field,

391
00:19:17,200 --> 00:19:19,160
Speaker 8: but I would have to say that Main Capital would

392
00:19:19,160 --> 00:19:23,280
Speaker 8: have agreed. Just yesterday when they came out and they

393
00:19:23,280 --> 00:19:25,080
Speaker 8: said that they were not going to be joining with

394
00:19:25,160 --> 00:19:28,879
Speaker 8: other large investors in trying to pump up this boom.

395
00:19:29,280 --> 00:19:32,040
Speaker 8: We also know, you know, this is the day after

396
00:19:32,080 --> 00:19:34,280
Speaker 8: election day. We also know that a lot of communities

397
00:19:34,440 --> 00:19:37,480
Speaker 8: are pushing back against these data centers being in their

398
00:19:37,520 --> 00:19:40,760
Speaker 8: backyard because one of the main sources of inflation are

399
00:19:40,880 --> 00:19:44,879
Speaker 8: food prices and electricity prices. But yes, when when the

400
00:19:45,000 --> 00:19:48,399
Speaker 8: United States has ten x that of Germany or China

401
00:19:48,480 --> 00:19:51,280
Speaker 8: in terms of the number of data centers, it brings

402
00:19:51,320 --> 00:19:53,720
Speaker 8: you back to the days of fiber optics and makes

403
00:19:53,760 --> 00:19:56,800
Speaker 8: you question, is this optics two point zero? I would

404
00:19:56,800 --> 00:19:58,359
Speaker 8: agree with your guests earlier today.

405
00:19:59,119 --> 00:20:02,960
Speaker 4: Yeah, I remember those fiber optic days when that was

406
00:20:03,080 --> 00:20:07,879
Speaker 4: kind of the bomb and telecom stocks are going crazy, Danielle.

407
00:20:07,960 --> 00:20:11,320
Speaker 4: On the inflation front here, you know the headline numbers,

408
00:20:11,320 --> 00:20:13,720
Speaker 4: they're still a little bit above obviously where the FED

409
00:20:13,760 --> 00:20:16,159
Speaker 4: would like to see it. But boy, I kind of

410
00:20:16,160 --> 00:20:19,760
Speaker 4: feel like we're dodging a bullet here on this tariff inflation.

411
00:20:19,880 --> 00:20:22,879
Speaker 4: Here is that writer or is it still perhaps to

412
00:20:23,000 --> 00:20:25,919
Speaker 4: come listen to your point.

413
00:20:26,000 --> 00:20:29,760
Speaker 8: Ism came out a few days ago and said we

414
00:20:29,800 --> 00:20:32,560
Speaker 8: are no longer seeing the effect of tariffs in our

415
00:20:32,600 --> 00:20:37,600
Speaker 8: price structure. Manufacturers, factories are no longer feeling these tariffs

416
00:20:37,720 --> 00:20:40,520
Speaker 8: they've mainly come through the pipeline, And in fact, what

417
00:20:40,560 --> 00:20:43,880
Speaker 8: we're seeing now is one company after another saying they've

418
00:20:43,880 --> 00:20:46,879
Speaker 8: got too much inventory on hand because they had a

419
00:20:46,880 --> 00:20:50,479
Speaker 8: lot of stock building going on into the tariffs, and

420
00:20:50,480 --> 00:20:52,440
Speaker 8: now they're trying to work that down. And we're hearing

421
00:20:52,440 --> 00:20:55,800
Speaker 8: from one company after another, especially in the housing space,

422
00:20:56,080 --> 00:20:58,320
Speaker 8: that they've simply got too much stock on hand right

423
00:20:58,400 --> 00:21:00,879
Speaker 8: now and they're having to discount to get rid of it.

424
00:21:01,000 --> 00:21:04,640
Speaker 8: So you're actually saying the opposite effect on inflation due

425
00:21:04,640 --> 00:21:05,359
Speaker 8: to tariffs.

426
00:21:05,840 --> 00:21:10,119
Speaker 2: Do you think also that just they've been successful negotiating

427
00:21:10,320 --> 00:21:13,200
Speaker 2: with other countries or with you, whatever it is they

428
00:21:13,200 --> 00:21:14,840
Speaker 2: have to pay at the port, So I mean there's

429
00:21:14,880 --> 00:21:17,320
Speaker 2: got to be some heavy duty negotiating going on here.

430
00:21:17,480 --> 00:21:21,560
Speaker 2: It's hard to believe they're absorbing all the cost. Well, yes,

431
00:21:21,600 --> 00:21:24,240
Speaker 2: and you hear one company after another say we have

432
00:21:24,359 --> 00:21:28,000
Speaker 2: found alternatives. It's like I'm finding alternative data sets every day.

433
00:21:28,400 --> 00:21:33,439
Speaker 2: Companies are finding alternative suppliers if need be, and I

434
00:21:33,480 --> 00:21:36,440
Speaker 2: think that that is something that is probably a healthy

435
00:21:36,520 --> 00:21:39,200
Speaker 2: development because you always want to be able to whether

436
00:21:39,200 --> 00:21:42,600
Speaker 2: it's hedging your portfolio or hedging your supply lines. And

437
00:21:42,680 --> 00:21:45,960
Speaker 2: I hope to see this trend continue going forward, because

438
00:21:46,280 --> 00:21:48,440
Speaker 2: I think it could have been pursued more aggressively after

439
00:21:48,480 --> 00:21:48,960
Speaker 2: COVID and.

440
00:21:48,920 --> 00:21:49,280
Speaker 9: It was not.

441
00:21:50,040 --> 00:21:52,120
Speaker 4: Danielle, thanks so much for joining us. We always appreciate

442
00:21:52,160 --> 00:21:54,879
Speaker 4: getting a few minutes of your time. Danielle Di Martinez,

443
00:21:54,880 --> 00:21:58,000
Speaker 4: Booth's CEO and chief strategist, QI research on the smart

444
00:21:58,080 --> 00:22:00,560
Speaker 4: folks we chat with to stay with us from Bloomberg

445
00:22:00,600 --> 00:22:02,439
Speaker 4: Surveillance Coming up after this.

446
00:22:08,480 --> 00:22:12,080
Speaker 1: You're listening to the Bloomberg Surveillance podcast. Catch us live

447
00:22:12,160 --> 00:22:15,680
Speaker 1: weekday afternoons from seven to ten am Eastern Listen on Apple,

448
00:22:15,720 --> 00:22:19,040
Speaker 1: Karplay and Android Otto with the Bloomberg Business app, or

449
00:22:19,200 --> 00:22:20,840
Speaker 1: watch us live on YouTube.

450
00:22:21,240 --> 00:22:23,840
Speaker 4: Let's do some newspapers right now. It's that time of

451
00:22:23,880 --> 00:22:25,560
Speaker 4: the day we do. Lisa Matay, what do you have

452
00:22:25,600 --> 00:22:27,080
Speaker 4: for Lisa Okay? I want to start with this in

453
00:22:27,080 --> 00:22:29,240
Speaker 4: the Washington Post. This is a sign that more college

454
00:22:29,240 --> 00:22:32,920
Speaker 4: students are worried about getting a job in this economy analysts.

455
00:22:33,680 --> 00:22:36,399
Speaker 9: This is an analysis of federal data. They're choosing more

456
00:22:36,720 --> 00:22:40,120
Speaker 9: double majors, which is interesting. They have nearly five point

457
00:22:40,119 --> 00:22:43,080
Speaker 9: four million credentials, which are degrees or certificates. They were

458
00:22:43,119 --> 00:22:45,680
Speaker 9: earned by the four point eight million college and university

459
00:22:45,720 --> 00:22:48,240
Speaker 9: graduates in twenty twenty three and twenty twenty four, So

460
00:22:48,320 --> 00:22:51,520
Speaker 9: that means about twelve percent of graduates earned more than

461
00:22:51,600 --> 00:22:54,840
Speaker 9: one credential. That was compared to six to ten percent

462
00:22:54,920 --> 00:22:56,760
Speaker 9: years ago. So you see there's more of them doing

463
00:22:56,760 --> 00:22:58,960
Speaker 9: and experts are saying, you know what, the students think

464
00:22:59,160 --> 00:23:01,679
Speaker 9: more is more because there is a study that shows

465
00:23:01,680 --> 00:23:05,159
Speaker 9: graduate who add two majors, they're fifty six percent less

466
00:23:05,320 --> 00:23:07,480
Speaker 9: likely to be laid off and have their pay cut.

467
00:23:07,920 --> 00:23:10,440
Speaker 9: So these are kind of the decisions that students are making.

468
00:23:10,600 --> 00:23:13,320
Speaker 9: I had a double major when they're specially back.

469
00:23:13,200 --> 00:23:18,359
Speaker 5: In the day. But what was your double was it journalism? Communications?

470
00:23:18,880 --> 00:23:20,359
Speaker 5: Very closely aligned?

471
00:23:20,520 --> 00:23:20,720
Speaker 6: Nice?

472
00:23:20,920 --> 00:23:22,280
Speaker 2: Well, you know what for me, it would come down

473
00:23:22,320 --> 00:23:23,840
Speaker 2: to you also, how much more money is that going

474
00:23:23,880 --> 00:23:25,920
Speaker 2: to be? Most colleges will not charge you for the

475
00:23:25,960 --> 00:23:27,760
Speaker 2: double major because it still falls within why I guess,

476
00:23:27,760 --> 00:23:30,000
Speaker 2: however many credits you need to graduate, But if it

477
00:23:30,000 --> 00:23:32,080
Speaker 2: means summer classes, if it means more money, you might

478
00:23:32,119 --> 00:23:33,880
Speaker 2: want to rethink that college is expensive.

479
00:23:33,920 --> 00:23:35,439
Speaker 9: For me, it took a little summer class and it

480
00:23:35,480 --> 00:23:38,440
Speaker 9: took like a few more classes to take, but four

481
00:23:38,520 --> 00:23:39,560
Speaker 9: years they got it done.

482
00:23:39,600 --> 00:23:42,679
Speaker 5: You gotta do here. You are to do it all, right,

483
00:23:43,160 --> 00:23:43,679
Speaker 5: burger King?

484
00:23:43,760 --> 00:23:46,119
Speaker 9: Okay, yeah, so we talked about McDonald's earlier, right, so

485
00:23:46,200 --> 00:23:48,280
Speaker 9: now we're going to hit burger King. It's not about

486
00:23:48,320 --> 00:23:51,080
Speaker 9: the burgers and fries, though, It's about the change that

487
00:23:51,119 --> 00:23:52,560
Speaker 9: they're giving to customers.

488
00:23:52,920 --> 00:23:54,120
Speaker 5: This was in the Wall Street Journal.

489
00:23:54,160 --> 00:23:57,040
Speaker 9: It's actually says that it's struggling with pennies, and you

490
00:23:57,119 --> 00:24:00,639
Speaker 9: mentioned this before. They're wondering what's going to happen when

491
00:24:00,640 --> 00:24:03,280
Speaker 9: they run out of them because most of their customers

492
00:24:03,280 --> 00:24:06,720
Speaker 9: still pay with cash over at McDonald's. So one operator

493
00:24:06,760 --> 00:24:08,520
Speaker 9: is saying, you know what, I have thirty boxes of

494
00:24:08,560 --> 00:24:11,360
Speaker 9: pennies squirreled away. I hope it makes me go through

495
00:24:11,359 --> 00:24:14,600
Speaker 9: two months at least. President Trump, you know, has that

496
00:24:14,720 --> 00:24:17,119
Speaker 9: order stopping production of new pennies. So it has all

497
00:24:17,160 --> 00:24:19,760
Speaker 9: these you know, folks scrambling. So what are they doing.

498
00:24:19,760 --> 00:24:22,560
Speaker 9: In the meantime, you have cashiers who are having to

499
00:24:22,560 --> 00:24:25,919
Speaker 9: get used to like split second rounding. It's all this

500
00:24:26,119 --> 00:24:29,080
Speaker 9: math at the counter that's trying to figure a round.

501
00:24:28,920 --> 00:24:31,719
Speaker 5: Up and then the rounding up in a round up.

502
00:24:31,960 --> 00:24:36,440
Speaker 4: I go for uh breakfast under lunch on Saturdays in

503
00:24:36,560 --> 00:24:40,320
Speaker 4: Mike Town, cash only and you be surprised. Now, for

504
00:24:40,440 --> 00:24:42,199
Speaker 4: all the locals that go there, we all know it

505
00:24:42,720 --> 00:24:44,719
Speaker 4: and we're all prepared for it. But for the tourists

506
00:24:44,720 --> 00:24:47,960
Speaker 4: to come in, they're like, what cash? I mean, where

507
00:24:47,960 --> 00:24:50,960
Speaker 4: do I get cash? You know Venmo. You know, they're

508
00:24:51,080 --> 00:24:53,159
Speaker 4: freaked out because they have to deal with cash. So

509
00:24:53,280 --> 00:24:54,439
Speaker 4: it's so true. It's a dying art.

510
00:24:54,520 --> 00:24:55,120
Speaker 5: It's so true.

511
00:24:55,160 --> 00:24:56,720
Speaker 9: I mean sometimes you go, but usually they have the

512
00:24:56,760 --> 00:24:59,560
Speaker 9: big sign that says cash all right, exactly, it's to

513
00:24:59,600 --> 00:25:03,199
Speaker 9: warn you. Okay, So this last one, this has a

514
00:25:03,240 --> 00:25:06,600
Speaker 9: panic setting in for moms and dads. Okay, K Pop

515
00:25:06,720 --> 00:25:11,320
Speaker 9: Demon Hunters toys may not be here in time for Christmas.

516
00:25:11,600 --> 00:25:13,960
Speaker 9: They don't know what to do with themselves. They're struggling.

517
00:25:14,000 --> 00:25:15,480
Speaker 9: What are we How are we going to get these toys?

518
00:25:15,480 --> 00:25:18,000
Speaker 9: They're not going to get them. The Netflix said, it's

519
00:25:18,080 --> 00:25:19,960
Speaker 9: it's less. It is high demand for merch you have

520
00:25:20,080 --> 00:25:22,760
Speaker 9: like action figures, dolls, plushies. The kids want them all.

521
00:25:23,080 --> 00:25:25,679
Speaker 9: The reason why it's not happening, it's because of the

522
00:25:25,680 --> 00:25:28,679
Speaker 9: time required to produce ship the you know, the consumer products.

523
00:25:28,800 --> 00:25:30,760
Speaker 5: But no one thought it would become.

524
00:25:31,000 --> 00:25:34,520
Speaker 9: These things take like a long time in the making,

525
00:25:34,600 --> 00:25:37,680
Speaker 9: you know, to start, Like Netflix was pitching retailers about

526
00:25:37,680 --> 00:25:41,560
Speaker 9: the film years ago and they said, no, we're not interested,

527
00:25:41,680 --> 00:25:41,919
Speaker 9: you know.

528
00:25:42,080 --> 00:25:44,000
Speaker 5: And now they're now APAs Bro Mattel.

529
00:25:44,080 --> 00:25:46,719
Speaker 2: They're scrambling to uh, you know, to get these items

530
00:25:46,720 --> 00:25:48,280
Speaker 2: out there. So I wonder what the big thing is

531
00:25:48,280 --> 00:25:50,920
Speaker 2: going to be for Christmas. Then that's a good question.

532
00:25:51,119 --> 00:25:51,359
Speaker 3: Toy.

533
00:25:51,400 --> 00:25:53,320
Speaker 5: I don't know. My kids are toys now. I don't

534
00:25:53,320 --> 00:25:56,280
Speaker 5: electronics for our house. I'm not zoned in on that anymore.

535
00:25:56,320 --> 00:25:57,600
Speaker 4: I just get they get a pat on the back

536
00:25:57,600 --> 00:26:01,760
Speaker 4: of my kids. What's the demo for these kpop things?

537
00:26:01,840 --> 00:26:03,479
Speaker 4: Is it teenagers? Is it younger?

538
00:26:03,600 --> 00:26:06,760
Speaker 9: It's younger and I'm teens too. I kind of watched

539
00:26:06,760 --> 00:26:08,560
Speaker 9: it over the weekend with my daughter, and what do.

540
00:26:08,520 --> 00:26:10,040
Speaker 5: You think, Well, I haven't seen it yet.

541
00:26:10,080 --> 00:26:12,359
Speaker 9: I actually it was kind of a me fun movie

542
00:26:12,400 --> 00:26:14,879
Speaker 9: to watch, Like it's very like uplifting and kind of

543
00:26:14,920 --> 00:26:16,520
Speaker 9: like dancing to the seek.

544
00:26:17,200 --> 00:26:19,040
Speaker 5: But I do have to say it's good. But for

545
00:26:19,040 --> 00:26:21,480
Speaker 5: those kids who wanted these toys, they're going to be

546
00:26:21,520 --> 00:26:23,040
Speaker 5: a little bit bit upset about it. It's not going

547
00:26:23,080 --> 00:26:25,320
Speaker 5: to be in the stock. Thanks for them to strive towards,

548
00:26:25,359 --> 00:26:29,720
Speaker 5: you know. Okay, very good, All right, that's it. Newspapers,

549
00:26:29,960 --> 00:26:31,560
Speaker 5: Lisa Matteo, you know it, you love it.

550
00:26:31,720 --> 00:26:36,560
Speaker 1: This is the Bloomberg Surveillance Podcast, available on Apple, Spotify,

551
00:26:36,680 --> 00:26:40,960
Speaker 1: and anywhere else you get your podcasts. Listen live each weekday,

552
00:26:41,080 --> 00:26:44,320
Speaker 1: seven to ten am Easter and on Bloomberg dot Com,

553
00:26:44,440 --> 00:26:48,280
Speaker 1: the iHeartRadio app, tune In, and the Bloomberg Business app.

554
00:26:48,560 --> 00:26:51,680
Speaker 1: You can also watch us live every weekday on YouTube

555
00:26:51,960 --> 00:26:53,960
Speaker 1: and always on the Bloomberg terminal