Redefining Value Investing in a Magnificent Seven Dominated World | Jacob Pozharny

[00:00:00] The classical measurements for valuation, for quality, they've degraded a lot in terms of the ability to use them for stock picking.

[00:00:10] And we got to thinking, what's happening?

[00:00:14] The implication of the research that we've applied to our portfolios is we're actually seeing price to book as more of a risk factor in our portfolio construction.

[00:00:26] For the low intangible industries, we use classical valuation methods.

[00:00:34] The classical financial statement analysis continues to be quite valid.

[00:00:37] But for the high intangibles, what we're finding is that sentiment-based analysis is more important.

[00:00:44] Welcome to Excess Returns, where we focus on what works over the long term in the markets.

[00:00:48] Join us as we talk about the strategies and tactics that can help you become a better long-term investor.

[00:00:53] Jack Forehand is a principal at Validia Capital Management.

[00:00:56] Justin Carbonneau is a managing director at Life and Liberty Indexes.

[00:00:59] No information on this podcast should be construed as investment advice.

[00:01:03] Securities discussed in the podcast may be holdings of clients of Validia Capital.

[00:01:06] Hey guys, this is Justin.

[00:01:08] In this episode of Excess Returns, Jack and I sit down with Jacob Pozharny, partner at Bridgeway Capital Management,

[00:01:12] to discuss the importance of intangible assets like intellectual property, brand value, and human capital,

[00:01:16] and how assessing intangibles plays into the development of various investment strategies.

[00:01:20] We talk to Jacob about intangible intensity and explore what it means, how it's measured,

[00:01:25] and how it's influencing the way some portfolios are built at Bridgeway.

[00:01:28] We also discuss how intangibles challenge traditional value investing frameworks,

[00:01:32] the implications for global and emerging markets, and the growing role of AI in investing.

[00:01:36] As always, thank you for listening.

[00:01:38] Please enjoy this discussion with Bridgeway's Jacob Pozharny.

[00:01:41] Jacob, thank you very much for joining us today.

[00:01:44] Well, thank you for the invitation.

[00:01:47] I've been monitoring and listening to a lot of the stuff that you've been putting out,

[00:01:53] really enjoying it, and appreciate the interest in the research.

[00:01:57] Yeah, thank you so much.

[00:02:00] So one of the things that I think has come to light in really the past decade,

[00:02:06] although it's been happening for a lot longer than that,

[00:02:08] is the increased importance of intangible assets in terms of company valuations and economic growth.

[00:02:17] And so what we wanted to do with you today,

[00:02:20] based on a lot of the research that you've put out

[00:02:22] and based on some of the investment strategies that you run,

[00:02:26] is talk about how you look at intangible assets

[00:02:30] and get at this idea of intangible intensity

[00:02:34] and trying to understand what it is, how it's measured,

[00:02:38] how you utilize that in your investment process

[00:02:40] and in building equity portfolios at Bridgeway.

[00:02:45] So I think this will be a really good discussion

[00:02:50] and something that our audience has, to some extent,

[00:02:54] heard us talk about in the past,

[00:02:55] but you've done a lot of really interesting research on this topic.

[00:02:59] So maybe for us to start,

[00:03:03] for those people that maybe have heard of intangible assets

[00:03:07] but might not know exactly what they are on a company's balance sheet,

[00:03:11] can you just kind of walk us through what they are

[00:03:15] and I guess their importance?

[00:03:17] Sure.

[00:03:18] So intangible assets are just like company assets,

[00:03:23] but they're assets that are difficult to measure

[00:03:26] but very important in explaining the market value of a company.

[00:03:32] So companies develop intangible assets

[00:03:35] to enhance their operational efficiency,

[00:03:38] to reduce costs, to drive their revenue growth,

[00:03:42] but these investments actually decrease the net income

[00:03:47] and they have a tendency to negatively impact book value,

[00:03:52] although they're designed to be additive to growth.

[00:03:56] So this naturally complicates classical valuation metrics,

[00:04:00] classical quality metrics,

[00:04:02] and a lot of the stock screens that folks are used to using.

[00:04:06] So new economy industries, knowledge-based industries

[00:04:11] are much more impacted by the growth of intangibles

[00:04:14] compared to old economy industries.

[00:04:17] Like examples of intangibles would be intellectual property,

[00:04:24] proprietary algorithms.

[00:04:25] Like Google's PageRank algorithm is an example of an intangible asset.

[00:04:31] It's something that's very useful, very productive,

[00:04:34] but it's hard to value.

[00:04:36] TikTok's For You recommendation engine is a proprietary algorithm.

[00:04:41] Very hard to value, very important to the business.

[00:04:44] Brand equity is another example of an intangible asset.

[00:04:47] Think Coca-Cola, Nike, Disney.

[00:04:49] Customer relationships are another element of intangible assets.

[00:04:55] Think of the relationship between the client and Amazon,

[00:04:59] the client and Costco.

[00:05:00] These are material things, but they're very hard to value

[00:05:04] in order to get at explaining the market value.

[00:05:08] Interestingly, a lot of the introductory textbooks,

[00:05:11] when you first start learning about accounting,

[00:05:13] they actually attribute a lot of the difference between book value

[00:05:19] and market value in terms of intangible assets.

[00:05:24] So they're a critical element in understanding valuations.

[00:05:29] That is an excellent overview.

[00:05:31] Thank you very much.

[00:05:32] I think that that's perfect.

[00:05:33] And as your examples were spot on,

[00:05:36] and when you start actually discussing it,

[00:05:38] you can see how in sort of the world we live in,

[00:05:43] how a lot of really successful businesses,

[00:05:45] you know, have these,

[00:05:46] can have these intangible assets that are,

[00:05:49] you know, a key to the,

[00:05:52] how they might be valued by investors in the market.

[00:05:55] What is the idea behind intangible intensity?

[00:06:03] Yeah.

[00:06:04] So what motivated our research in intangible intensity

[00:06:10] is recognizing around 2016, 2017,

[00:06:14] that the classical stock screens,

[00:06:18] the classical measurements for valuation,

[00:06:20] for quality,

[00:06:21] they've degraded a lot in terms of the ability

[00:06:26] to use them for stock picking.

[00:06:29] And we got to thinking what's happening.

[00:06:32] And then as we're researching this,

[00:06:33] we recognize that a lot of really smart people

[00:06:36] have been doing really good work on this.

[00:06:39] I came across back then a book by Baruch Flaib

[00:06:42] titled End of Accounting

[00:06:43] that was very influential in my thinking.

[00:06:47] Our research on intangible capital intensity

[00:06:50] expands on a couple of the chapters

[00:06:52] that he presented in that book.

[00:06:57] So why don't I define

[00:06:58] how we look at intangible capital intensity

[00:07:01] and then I could tell you how we use it.

[00:07:05] So the challenge in defining intangible capital intensity

[00:07:11] is that a lot of this stuff isn't really reported.

[00:07:17] IFRS accounting, US gap accounting

[00:07:19] doesn't require intangible assets

[00:07:22] to actually be reported.

[00:07:24] There are certain elements

[00:07:25] that we used in our research

[00:07:28] to help us guide

[00:07:33] where intangible assets are.

[00:07:35] So there are three elements

[00:07:36] in our research that we focused on.

[00:07:38] The reason we focused on these items

[00:07:41] is that they were available in the balance sheet,

[00:07:44] they were available in income statement.

[00:07:46] And what we tried to do is extend our research

[00:07:49] into international and emerging economies.

[00:07:52] And all of these elements,

[00:07:54] these three elements were actually available

[00:07:55] for our analysis

[00:07:57] and competing the intangible capital intensity metric.

[00:08:00] So we focused on three items,

[00:08:01] as I mentioned.

[00:08:02] One is capitalized intangible asset

[00:08:07] excluding goodwill.

[00:08:08] And I can get into why we excluded goodwill

[00:08:10] a bit later.

[00:08:12] The second is innovation capital,

[00:08:16] which is proxied by R&D expenses.

[00:08:20] And the third is organizational capital.

[00:08:23] And this is a proxy by selling,

[00:08:26] general and administrative expenses,

[00:08:29] SG&A expenses.

[00:08:31] So these three items actually available

[00:08:33] in the balance sheet,

[00:08:34] they're available in the income statement.

[00:08:36] And we were able to pull this

[00:08:39] for a fairly substantial universe

[00:08:42] of about 15 countries,

[00:08:45] U.S. developed economies,

[00:08:46] as well as emerging markets.

[00:08:48] So we created a measurement based on this.

[00:08:52] We looked at the average value

[00:08:55] of companies and industries

[00:08:57] of each of these measurements.

[00:08:58] We rank these on a month-end basis.

[00:09:02] And then we looked at the average ranking.

[00:09:04] And that naturally tended

[00:09:07] to differentiate certain industries.

[00:09:10] And that differentiation

[00:09:11] was very consistent over time.

[00:09:14] I think we did this research

[00:09:16] from 1994 to 2018 in terms of data.

[00:09:20] And that categorization

[00:09:21] appeared very consistent,

[00:09:22] very robust.

[00:09:24] So that was the primary way

[00:09:27] we identified intangible capital

[00:09:30] intensity by the ranking.

[00:09:31] And then we grouped them

[00:09:33] into highest to lowest

[00:09:36] intangible capital intensity

[00:09:37] by industry.

[00:09:39] Is it a ratio?

[00:09:41] Is it a ratio?

[00:09:43] Or is it...

[00:09:44] Yeah, how do you actually rank?

[00:09:47] Sure.

[00:09:47] So in terms of the definition

[00:09:50] of capitalized intangible assets,

[00:09:53] that is measured relative

[00:09:54] to total assets.

[00:09:56] R&D expenses are measured

[00:09:58] relative to total revenue.

[00:09:59] As a G&A expenses

[00:10:01] are also relative

[00:10:02] to total revenue.

[00:10:03] So for every industry,

[00:10:06] we looked at the monthly averages.

[00:10:07] And then we ranked

[00:10:09] those monthly averages

[00:10:12] for each industry.

[00:10:13] And then we did an average

[00:10:15] of those rankings.

[00:10:17] And we charted that through time

[00:10:19] to naturally categorize industries

[00:10:21] into high intangible

[00:10:23] intense industries

[00:10:24] and low intangible

[00:10:25] intense industries.

[00:10:26] When you look at intangible

[00:10:28] intensity across the whole economy,

[00:10:29] I assume it's been

[00:10:30] an upward line, right?

[00:10:31] It's been moving up over time?

[00:10:32] It certainly has increased.

[00:10:34] And it's interesting

[00:10:35] how different aspects

[00:10:39] of these measurements

[00:10:40] have increased.

[00:10:41] So with the capitalized

[00:10:44] intangible intensity

[00:10:45] from 1994 to 2018,

[00:10:50] that measurement

[00:10:51] as a proportion of assets

[00:10:52] actually doubled.

[00:10:55] So it was a substantial increase.

[00:10:57] Obviously,

[00:10:57] this varies from country

[00:10:58] to country,

[00:10:59] industry to industry,

[00:11:00] talking about generalities.

[00:11:02] R&D expenses

[00:11:05] also increased

[00:11:06] quite substantially.

[00:11:08] They increased by 50%

[00:11:09] on average

[00:11:10] from 1994

[00:11:11] to 2018

[00:11:13] as a proportion

[00:11:14] of total revenue.

[00:11:16] as G&A expenses

[00:11:18] actually remained

[00:11:19] quite stable.

[00:11:21] Obviously,

[00:11:22] variations existed

[00:11:23] from industry

[00:11:23] and from across countries,

[00:11:25] but that's an element

[00:11:27] that actually remained

[00:11:28] quite stable

[00:11:28] over our research period.

[00:11:30] How much of this

[00:11:31] is a technology thing

[00:11:32] and how much of this

[00:11:33] is intangible intensity

[00:11:34] has actually been increasing

[00:11:35] in other industries

[00:11:35] where people might not

[00:11:36] think it has been?

[00:11:38] We're seeing this

[00:11:39] consistently increasing

[00:11:41] across most of

[00:11:43] the knowledge-based industries.

[00:11:44] So it's not just technology.

[00:11:47] You have software.

[00:11:48] You have biotech.

[00:11:50] There is substantial increases

[00:11:52] in terms of financials,

[00:11:56] the technology,

[00:11:57] the algorithms

[00:11:58] that are being used

[00:11:59] there were quite influential.

[00:12:00] So it's not just

[00:12:01] in the technology space.

[00:12:03] It's across

[00:12:03] different industries.

[00:12:05] We actually put together

[00:12:06] a table

[00:12:07] of different industries

[00:12:09] ranked by intangible

[00:12:11] capital intensity

[00:12:12] from highest to lowest.

[00:12:13] So pharma,

[00:12:16] software,

[00:12:17] telecom

[00:12:17] were some of the

[00:12:19] highest.

[00:12:21] Semiconductors,

[00:12:22] obviously,

[00:12:23] another one.

[00:12:23] But it's not just

[00:12:25] technology.

[00:12:26] It's anything

[00:12:27] that's new economy

[00:12:28] that's knowledge-based.

[00:12:29] And do you find that

[00:12:31] high intangible

[00:12:32] intensity companies

[00:12:33] have higher

[00:12:33] profitability metrics

[00:12:34] like higher return

[00:12:35] on equity

[00:12:35] or higher earnings

[00:12:36] growth or things

[00:12:37] like that?

[00:12:37] We find that

[00:12:39] the measurements

[00:12:40] of ROE,

[00:12:42] the measurements

[00:12:43] of book value,

[00:12:44] the measurements

[00:12:44] of earnings

[00:12:45] are very unstable.

[00:12:47] and what's most

[00:12:48] important,

[00:12:49] and we put out

[00:12:50] this paper

[00:12:51] in a financial

[00:12:52] analyst journal.

[00:12:53] I probably should,

[00:12:54] very important to mention

[00:12:55] that this is a

[00:12:57] collaboration

[00:12:58] between myself,

[00:13:00] Amitabh Dugar,

[00:13:01] and Andrew Birkin.

[00:13:03] We put this research

[00:13:05] together

[00:13:06] in this publication.

[00:13:07] And what we find

[00:13:08] is that

[00:13:09] the explanatory

[00:13:11] power of fundamentals

[00:13:13] in terms of price action

[00:13:15] hasn't really been

[00:13:17] affected

[00:13:18] for the low

[00:13:19] intangible industries.

[00:13:20] So,

[00:13:21] for the low

[00:13:22] intangible industries,

[00:13:23] fundamentals continue

[00:13:25] to explain

[00:13:26] price action

[00:13:27] quite well.

[00:13:28] But for the

[00:13:29] high intangible

[00:13:30] industries,

[00:13:31] we find that

[00:13:32] fundamentals,

[00:13:33] quality measurements,

[00:13:35] valuation measurements

[00:13:36] are affected

[00:13:37] and they have

[00:13:38] a much lower

[00:13:39] explanatory power

[00:13:41] in terms of

[00:13:43] explaining

[00:13:43] future price action.

[00:13:45] That's actually

[00:13:46] what I was going

[00:13:46] to get into next

[00:13:46] because I was going

[00:13:47] to ask you about

[00:13:47] the price to book.

[00:13:48] And one of the things

[00:13:49] I've always struggled

[00:13:50] with the price to book

[00:13:51] is obviously,

[00:13:51] you know,

[00:13:52] Microsoft or Google's

[00:13:53] price to book

[00:13:54] may not be that

[00:13:54] meaningful,

[00:13:55] but at the bottom

[00:13:56] of the barrel,

[00:13:56] it seems like

[00:13:57] price to book

[00:13:57] would be more

[00:13:58] meaningful.

[00:13:59] So,

[00:13:59] I always think

[00:13:59] like a straight

[00:14:00] standard price to book

[00:14:02] strategy might not

[00:14:02] be that infected

[00:14:03] by intangibles.

[00:14:05] Is that right?

[00:14:07] We see book value

[00:14:08] as a very noisy

[00:14:10] measurement.

[00:14:10] As a matter of fact,

[00:14:11] the implication

[00:14:14] of the research

[00:14:15] that we've

[00:14:17] applied to our

[00:14:18] portfolios

[00:14:19] is we're actually

[00:14:20] seeing price to book

[00:14:21] as more of a risk

[00:14:23] factor

[00:14:23] in our portfolio

[00:14:24] construction

[00:14:25] rather than

[00:14:27] an alpha factor.

[00:14:28] So,

[00:14:28] it's changed

[00:14:29] our thinking

[00:14:29] quite dramatically

[00:14:30] in terms of how

[00:14:31] we actually build

[00:14:32] out the portfolios.

[00:14:33] How does that

[00:14:34] change things

[00:14:35] when you look at it

[00:14:35] as a risk factor

[00:14:36] versus an alpha factor?

[00:14:37] You are able

[00:14:38] to still tilt

[00:14:39] on value,

[00:14:40] but you're not

[00:14:41] necessarily tilting

[00:14:42] on value

[00:14:43] based on book.

[00:14:44] It actually

[00:14:45] improves the

[00:14:46] consistency

[00:14:47] of the

[00:14:48] risk-adjusted

[00:14:49] returns

[00:14:50] when you do that

[00:14:51] because we see

[00:14:52] that price to book

[00:14:54] is a bit more

[00:14:56] of a risk element.

[00:14:57] I wouldn't say

[00:14:58] this is true

[00:14:58] prior to

[00:15:00] perhaps 2007,

[00:15:01] but after 2007

[00:15:03] it's overwhelmingly

[00:15:04] true and it's

[00:15:05] consistent

[00:15:06] with the type

[00:15:07] of growth

[00:15:07] we've seen

[00:15:08] in intangibles

[00:15:09] that we've observed

[00:15:10] in this publication.

[00:15:11] So,

[00:15:11] when you look

[00:15:12] at valuation,

[00:15:13] do you need

[00:15:13] two strategies?

[00:15:14] So,

[00:15:14] do you need

[00:15:14] to look at

[00:15:15] the high

[00:15:15] intangible

[00:15:16] companies

[00:15:16] with one

[00:15:16] set of

[00:15:17] metrics

[00:15:17] and the

[00:15:17] low

[00:15:17] intangible

[00:15:18] companies

[00:15:18] with a

[00:15:18] different

[00:15:18] set of

[00:15:19] metrics?

[00:15:20] That's

[00:15:20] actually

[00:15:20] exactly

[00:15:21] where we

[00:15:22] started

[00:15:22] thinking

[00:15:22] about this.

[00:15:23] So,

[00:15:23] a lot

[00:15:23] of our

[00:15:24] competitors

[00:15:24] and we've

[00:15:25] explored

[00:15:26] this to

[00:15:27] a great

[00:15:27] deal,

[00:15:27] they make

[00:15:28] adjustments

[00:15:30] to different

[00:15:31] measurements

[00:15:32] of

[00:15:32] intangibles

[00:15:32] and we

[00:15:33] certainly

[00:15:34] do make

[00:15:35] these types

[00:15:36] of adjustments

[00:15:36] on an

[00:15:37] industry-by-industry

[00:15:38] basis,

[00:15:38] but I think

[00:15:39] what we do

[00:15:40] a bit differently

[00:15:41] is we use

[00:15:42] completely

[00:15:42] different

[00:15:42] hearing

[00:15:43] for our

[00:15:44] stock

[00:15:45] selection

[00:15:45] for high

[00:15:46] intangible

[00:15:46] and low

[00:15:47] intangible

[00:15:47] stocks.

[00:15:48] And this

[00:15:49] is applied

[00:15:49] across

[00:15:50] our global

[00:15:51] strategies,

[00:15:52] across

[00:15:52] developed

[00:15:53] markets,

[00:15:53] and across

[00:15:53] emerging

[00:15:54] markets.

[00:15:56] Can you

[00:15:56] talk about

[00:15:57] how that

[00:15:57] might work

[00:15:57] in the

[00:15:57] real world?

[00:15:58] Like,

[00:15:58] for your

[00:15:58] traditional

[00:15:59] value

[00:15:59] companies,

[00:16:00] you might

[00:16:00] use maybe

[00:16:01] a composite

[00:16:01] of value

[00:16:02] metrics,

[00:16:02] and then

[00:16:02] for the

[00:16:03] high

[00:16:03] intangible

[00:16:03] companies,

[00:16:04] you might

[00:16:04] use something

[00:16:04] totally

[00:16:05] different.

[00:16:05] Can you

[00:16:05] talk about,

[00:16:05] I mean,

[00:16:05] I don't

[00:16:06] want you

[00:16:06] to give

[00:16:06] away your

[00:16:06] secrets,

[00:16:07] but can

[00:16:07] you talk

[00:16:08] about some

[00:16:08] like the

[00:16:09] different

[00:16:09] types of

[00:16:09] metrics you

[00:16:10] might use

[00:16:10] across the

[00:16:11] spectrum?

[00:16:11] For sure.

[00:16:12] So,

[00:16:13] most of our

[00:16:14] stock selection

[00:16:15] processes

[00:16:16] vary quite

[00:16:17] significantly

[00:16:18] from one

[00:16:19] industry

[00:16:19] to another,

[00:16:20] but in

[00:16:20] general

[00:16:21] terms for

[00:16:22] the low

[00:16:23] intangible

[00:16:24] industries,

[00:16:24] we use

[00:16:25] classical

[00:16:27] valuation

[00:16:27] methods.

[00:16:29] Classical

[00:16:29] financial

[00:16:30] statement

[00:16:30] analysis

[00:16:31] continues

[00:16:31] to be

[00:16:31] quite

[00:16:32] valid,

[00:16:32] but for

[00:16:33] the high

[00:16:33] intangibles,

[00:16:34] what we're

[00:16:34] finding is

[00:16:35] that sentiment

[00:16:36] based analysis

[00:16:37] is more

[00:16:38] important.

[00:16:39] So,

[00:16:40] we have

[00:16:40] very specific

[00:16:42] ways that

[00:16:42] we define

[00:16:43] sentiment

[00:16:44] internally,

[00:16:45] but in

[00:16:47] very general

[00:16:47] terms,

[00:16:48] we tend

[00:16:49] to prefer

[00:16:50] to look

[00:16:50] at growth

[00:16:51] of the

[00:16:52] fundamentals,

[00:16:53] forecasts of

[00:16:55] the fundamentals,

[00:16:55] revisions to

[00:16:56] the fundamentals.

[00:16:57] We find

[00:16:58] that's much

[00:16:59] more important

[00:16:59] in understanding

[00:17:01] how to

[00:17:02] price stocks

[00:17:03] in the

[00:17:04] high intangible

[00:17:05] industries.

[00:17:05] The key

[00:17:06] here in

[00:17:07] using

[00:17:07] sentiment

[00:17:08] based

[00:17:08] analysis

[00:17:09] is to

[00:17:10] make sure

[00:17:10] that when

[00:17:11] you're using

[00:17:11] sentiment

[00:17:12] based

[00:17:12] analysis,

[00:17:13] you are

[00:17:14] looking at

[00:17:15] information

[00:17:15] that isn't

[00:17:16] priced in.

[00:17:17] And we

[00:17:17] have another

[00:17:17] set of

[00:17:18] processes

[00:17:18] to

[00:17:19] understand

[00:17:20] what

[00:17:21] sentiment

[00:17:21] is priced

[00:17:22] in to

[00:17:23] the stock

[00:17:23] and what

[00:17:24] sentiment

[00:17:24] is not

[00:17:25] priced

[00:17:25] into the

[00:17:26] stock.

[00:17:26] So,

[00:17:26] naturally,

[00:17:27] the

[00:17:27] opportunities

[00:17:28] are in

[00:17:29] sentiment

[00:17:29] that isn't

[00:17:30] priced in.

[00:17:31] Yeah,

[00:17:32] that's really

[00:17:32] interesting.

[00:17:32] So,

[00:17:32] do you

[00:17:32] couple that

[00:17:33] at all

[00:17:34] with

[00:17:34] valuation

[00:17:34] on the

[00:17:35] high intangible

[00:17:35] stocks,

[00:17:36] or have

[00:17:36] you found

[00:17:36] valuation

[00:17:37] is not

[00:17:37] that useful

[00:17:38] there?

[00:17:39] I continue

[00:17:40] to be a

[00:17:40] strong believer

[00:17:41] in value

[00:17:42] and quality

[00:17:42] metrics.

[00:17:43] This isn't

[00:17:44] an issue

[00:17:44] for the

[00:17:45] high intangible

[00:17:47] stocks that

[00:17:48] we just

[00:17:48] throw out

[00:17:49] valuation

[00:17:50] methods.

[00:17:51] We don't

[00:17:52] do that.

[00:17:52] We just

[00:17:53] emphasize

[00:17:53] sentiment-based

[00:17:55] analysis.

[00:17:55] In aggregate,

[00:17:56] when we

[00:17:57] combine our

[00:17:59] stock selection

[00:17:59] to low

[00:18:00] intangibles

[00:18:01] and high

[00:18:01] intangibles,

[00:18:02] the portfolio

[00:18:02] in aggregate

[00:18:03] becomes fairly

[00:18:04] balanced,

[00:18:05] but we're

[00:18:05] across all

[00:18:06] industries.

[00:18:07] There are

[00:18:08] different

[00:18:08] metrics of

[00:18:09] valuations and

[00:18:10] quality that

[00:18:11] we do look

[00:18:11] at.

[00:18:12] And do you

[00:18:13] think about

[00:18:13] each one of

[00:18:14] these having

[00:18:14] a certain

[00:18:15] portion of

[00:18:15] your portfolio?

[00:18:16] So,

[00:18:16] do you think

[00:18:16] we want to

[00:18:17] have a

[00:18:17] certain

[00:18:17] number of

[00:18:17] high intangible

[00:18:18] companies and

[00:18:19] we want to

[00:18:19] have a

[00:18:19] certain number

[00:18:20] of low

[00:18:20] intangible

[00:18:20] companies?

[00:18:21] Is that

[00:18:21] the way

[00:18:21] you think

[00:18:21] about it?

[00:18:24] Obviously,

[00:18:24] the high

[00:18:25] intangible

[00:18:26] and low

[00:18:27] intangible

[00:18:28] industry

[00:18:28] weighting

[00:18:29] changes

[00:18:29] based on

[00:18:30] investment

[00:18:31] universe.

[00:18:32] We see

[00:18:33] stock selection

[00:18:34] and portfolio

[00:18:34] construction as

[00:18:35] two different

[00:18:36] elements.

[00:18:36] At the

[00:18:37] portfolio

[00:18:37] construction

[00:18:38] level,

[00:18:38] we want to

[00:18:39] make sure

[00:18:40] that we are

[00:18:40] building out

[00:18:41] a well-balanced

[00:18:42] portfolio.

[00:18:43] I think an

[00:18:43] element that

[00:18:44] differentiates our

[00:18:46] return stream

[00:18:46] is that

[00:18:47] because we

[00:18:49] have very

[00:18:49] different

[00:18:50] gearing for

[00:18:51] high intangible

[00:18:52] and low

[00:18:52] intangible,

[00:18:53] when we put

[00:18:53] it together

[00:18:54] in an

[00:18:55] aggregate

[00:18:55] portfolio,

[00:18:57] different

[00:18:58] animals are

[00:18:59] working with

[00:19:00] each other

[00:19:01] and against

[00:19:01] each other,

[00:19:02] so it's

[00:19:02] important to

[00:19:03] combine them

[00:19:03] in a fairly

[00:19:04] uniform way to

[00:19:05] develop consistency

[00:19:07] across alpha

[00:19:09] signals that we

[00:19:09] believe add

[00:19:10] value to the

[00:19:11] portfolio over

[00:19:12] time.

[00:19:13] I'm curious,

[00:19:14] when you look

[00:19:14] at intangible

[00:19:15] intensity

[00:19:15] internationally,

[00:19:16] has it

[00:19:16] grown less?

[00:19:17] I mean,

[00:19:17] I would guess

[00:19:17] based on just

[00:19:18] what I think,

[00:19:19] like with more

[00:19:19] technology companies

[00:19:20] in the U.S.,

[00:19:21] it would have

[00:19:21] grown more

[00:19:21] here, but

[00:19:22] is that

[00:19:22] true?

[00:19:23] We actually

[00:19:23] found intangible

[00:19:26] intensity to

[00:19:26] grow more for

[00:19:28] international

[00:19:29] companies compared

[00:19:30] to the U.S.

[00:19:31] I was a bit

[00:19:32] surprised by this

[00:19:33] result, but I

[00:19:34] think it's all

[00:19:34] about where

[00:19:36] you start,

[00:19:37] right?

[00:19:37] I mean,

[00:19:37] if you're

[00:19:37] starting at a

[00:19:38] higher level,

[00:19:38] you would expect

[00:19:40] a bit less

[00:19:41] growth.

[00:19:41] If you're

[00:19:41] starting from

[00:19:42] a lower

[00:19:42] level, you

[00:19:43] would expect

[00:19:43] a bit higher

[00:19:44] growth.

[00:19:44] But that was

[00:19:45] a bit of a

[00:19:46] surprising result

[00:19:47] for us.

[00:19:48] Was that

[00:19:48] because you,

[00:19:49] did you adjust

[00:19:49] for industry?

[00:19:50] So in other

[00:19:50] words, did you

[00:19:50] account for the

[00:19:51] fact that the

[00:19:51] U.S. has more

[00:19:52] technology so it

[00:19:53] should have more

[00:19:54] intangible growth

[00:19:54] and that's maybe

[00:19:55] like you,

[00:19:56] Europe or

[00:19:57] international

[00:19:57] companies have

[00:19:58] grown more on

[00:19:58] an intra-industry

[00:19:59] basis?

[00:20:00] This isn't in

[00:20:01] the paper, but we

[00:20:02] naturally looked

[00:20:03] at this as part

[00:20:04] of our analysis.

[00:20:05] So we did

[00:20:05] adjust for it.

[00:20:06] Even after the

[00:20:07] adjustment, we can

[00:20:08] still make this

[00:20:08] type of observation.

[00:20:09] I want to take

[00:20:10] this forward.

[00:20:11] AI is something

[00:20:12] everybody's talking

[00:20:13] about right now.

[00:20:14] It's in the news.

[00:20:15] Everybody's thinking

[00:20:15] about what it's

[00:20:16] going to mean

[00:20:16] for our lives,

[00:20:17] what it's going

[00:20:17] to mean for

[00:20:17] investing, for

[00:20:19] all kinds of

[00:20:19] different things.

[00:20:20] How do you

[00:20:21] think about that

[00:20:22] AI when you

[00:20:23] think about

[00:20:24] intangibles?

[00:20:24] It would seem

[00:20:25] like intangibles

[00:20:25] are going to be

[00:20:26] going up much,

[00:20:27] much more here

[00:20:27] in a world of

[00:20:28] AI.

[00:20:28] Do you think

[00:20:29] that's the case?

[00:20:30] This is a big

[00:20:31] topic.

[00:20:32] I need to take

[00:20:32] a drink.

[00:20:33] You're going to

[00:20:33] spend the rest

[00:20:33] of the time

[00:20:34] on this, right?

[00:20:36] Okay.

[00:20:36] So it's a really

[00:20:38] good question.

[00:20:38] I've been thinking

[00:20:39] about this a lot.

[00:20:41] So the

[00:20:42] accounting

[00:20:43] framework,

[00:20:44] it fails

[00:20:46] to capture

[00:20:47] the true

[00:20:47] value of

[00:20:48] AI-related

[00:20:50] assets.

[00:20:51] And we've been

[00:20:51] thinking about

[00:20:52] this on

[00:20:53] industry-by-industry

[00:20:55] basis.

[00:20:55] So in

[00:20:56] industrials,

[00:20:57] for example,

[00:20:57] AI optimizes

[00:20:59] manufacturing,

[00:21:00] supply chains,

[00:21:01] but these

[00:21:02] intangibles,

[00:21:03] like patents

[00:21:04] and different

[00:21:06] types of

[00:21:06] algorithms,

[00:21:07] training programs,

[00:21:08] they complicate

[00:21:10] profitability

[00:21:10] metrics because

[00:21:11] it's very

[00:21:11] hard to

[00:21:12] assess the

[00:21:13] value of

[00:21:14] these AI-related

[00:21:16] assets.

[00:21:16] In the media

[00:21:17] space,

[00:21:18] AI enhances

[00:21:21] content

[00:21:22] personalization,

[00:21:23] it enhances

[00:21:24] ad placement

[00:21:25] ability, but

[00:21:26] again, how

[00:21:27] do you

[00:21:27] value the

[00:21:28] firm's

[00:21:29] ability to

[00:21:30] value these

[00:21:31] AI-related

[00:21:32] expenses,

[00:21:33] AI-related

[00:21:34] assets?

[00:21:34] It's really

[00:21:35] tough to do.

[00:21:36] In the

[00:21:37] software space,

[00:21:38] there's heavy

[00:21:39] R&D

[00:21:39] investments that

[00:21:40] are AI-driven.

[00:21:42] It distorts

[00:21:43] short-term

[00:21:43] profits with

[00:21:44] the objective

[00:21:45] of growing

[00:21:46] long-term

[00:21:47] growth.

[00:21:49] So we

[00:21:50] definitely see

[00:21:51] it as

[00:21:51] particularly

[00:21:52] important to

[00:21:53] look at

[00:21:54] right now.

[00:21:54] In the

[00:21:55] finance space,

[00:21:56] AI improves

[00:21:57] fraud detection,

[00:21:58] risk management,

[00:21:59] but these

[00:22:00] proprietary

[00:22:01] algorithms are

[00:22:02] very difficult

[00:22:03] to value.

[00:22:03] So we

[00:22:05] definitely see

[00:22:06] it as

[00:22:06] something that's

[00:22:07] particularly

[00:22:07] important to

[00:22:08] look at as

[00:22:10] AI expenses

[00:22:12] increase and

[00:22:13] they can

[00:22:13] materially impact

[00:22:14] the profitability

[00:22:16] of firms

[00:22:18] that look at

[00:22:18] them.

[00:22:19] Yeah, to

[00:22:19] your point,

[00:22:20] the MAG7

[00:22:21] firms are

[00:22:21] spending

[00:22:21] outrageous

[00:22:22] amounts of

[00:22:23] money right

[00:22:23] now on

[00:22:24] AI, and

[00:22:24] you think

[00:22:25] about how

[00:22:25] do I

[00:22:26] value that

[00:22:26] in terms

[00:22:27] of the

[00:22:27] intangible

[00:22:27] asset

[00:22:28] they're

[00:22:28] creating.

[00:22:28] That seems

[00:22:29] like there

[00:22:30] could be a

[00:22:31] very wide

[00:22:31] range there,

[00:22:32] and that's

[00:22:32] a very

[00:22:32] difficult thing

[00:22:33] to figure

[00:22:33] out,

[00:22:34] particularly

[00:22:34] with a

[00:22:34] new

[00:22:34] technology

[00:22:35] that we

[00:22:35] are really

[00:22:35] seeing for

[00:22:36] the first

[00:22:36] time.

[00:22:37] Absolutely.

[00:22:38] I think

[00:22:40] that a

[00:22:40] lot of

[00:22:41] the value

[00:22:42] of intangibles,

[00:22:43] especially

[00:22:44] for the

[00:22:46] technology

[00:22:47] firms,

[00:22:48] explain a

[00:22:49] lot of

[00:22:50] the difference

[00:22:50] between

[00:22:51] market value

[00:22:52] and book

[00:22:53] value.

[00:22:54] You receive

[00:22:54] huge appreciations

[00:22:56] of market

[00:22:56] value.

[00:22:57] They look

[00:22:58] much more

[00:22:58] expensive

[00:22:59] based on

[00:22:59] earnings,

[00:23:00] but these

[00:23:00] AI expenses

[00:23:02] are actually

[00:23:03] negative to

[00:23:04] net income.

[00:23:04] Naturally,

[00:23:06] that distorts

[00:23:06] the P.E.

[00:23:08] ratios of

[00:23:08] these companies.

[00:23:09] I think this

[00:23:10] is another

[00:23:11] motivation for

[00:23:12] us to

[00:23:13] have a

[00:23:13] contextual

[00:23:14] stock

[00:23:14] selection

[00:23:15] process and

[00:23:16] have different

[00:23:16] gearing in

[00:23:18] our stock

[00:23:18] selection for

[00:23:19] high intangible

[00:23:20] and low

[00:23:21] intangible

[00:23:21] stocks.

[00:23:23] To some

[00:23:23] extent,

[00:23:23] this relates

[00:23:24] back to the

[00:23:24] tangible

[00:23:24] world.

[00:23:25] When a

[00:23:25] company makes

[00:23:26] a tangible

[00:23:26] investment,

[00:23:27] sometimes those

[00:23:28] work out

[00:23:28] exceptionally

[00:23:29] well,

[00:23:29] sometimes those

[00:23:29] work out

[00:23:30] exceptionally

[00:23:30] poorly.

[00:23:31] It's the

[00:23:31] same thing

[00:23:31] in the

[00:23:32] intangible

[00:23:32] world.

[00:23:34] It's hard

[00:23:34] just because

[00:23:35] somebody makes

[00:23:36] an investment,

[00:23:36] you don't

[00:23:36] know how

[00:23:37] it's going

[00:23:37] to work

[00:23:37] out.

[00:23:37] It can

[00:23:38] be

[00:23:38] challenging.

[00:23:39] Absolutely.

[00:23:40] That's why

[00:23:40] we spend

[00:23:41] a lot

[00:23:42] more time

[00:23:42] focused on

[00:23:44] sentiment

[00:23:44] analysis

[00:23:45] for the

[00:23:46] high intangible

[00:23:47] stocks.

[00:23:47] We actually

[00:23:48] noticed that

[00:23:49] because of

[00:23:50] that,

[00:23:51] the portfolios

[00:23:52] are much

[00:23:53] more nimble.

[00:23:54] They have

[00:23:54] higher turnover

[00:23:55] expectations

[00:23:56] for the

[00:23:57] high intangible

[00:23:57] industries

[00:23:58] compared to

[00:23:58] the low

[00:23:59] intangible

[00:23:59] industries.

[00:24:00] Because

[00:24:01] sentiment is

[00:24:01] something that

[00:24:02] is much

[00:24:02] faster moving,

[00:24:03] the information

[00:24:04] is much

[00:24:04] more dynamic.

[00:24:05] You may not

[00:24:06] have looked

[00:24:06] at this yet,

[00:24:06] but do you

[00:24:07] think in the

[00:24:07] future we

[00:24:07] might use

[00:24:08] AI itself

[00:24:09] to try to

[00:24:09] value the

[00:24:10] intangible

[00:24:11] assets?

[00:24:11] In other

[00:24:12] words,

[00:24:12] not starting

[00:24:12] from investments

[00:24:13] on income

[00:24:14] statements or

[00:24:15] balance sheets

[00:24:15] or anything

[00:24:16] like that,

[00:24:16] but maybe

[00:24:17] starting

[00:24:17] from in the

[00:24:17] real world,

[00:24:18] could we

[00:24:18] use AI to

[00:24:19] figure out

[00:24:19] what someone's

[00:24:20] brand's

[00:24:20] worth or

[00:24:21] someone's

[00:24:21] intellectual

[00:24:22] property or

[00:24:22] something like

[00:24:22] that?

[00:24:24] That's a

[00:24:26] great question.

[00:24:27] In our

[00:24:27] current

[00:24:28] process,

[00:24:29] we're not

[00:24:30] using machine

[00:24:31] learning or

[00:24:32] AI for stock

[00:24:33] selection.

[00:24:33] We are using

[00:24:34] machine learning

[00:24:35] for risk

[00:24:36] modeling.

[00:24:38] We can talk

[00:24:39] about that a

[00:24:40] bit later.

[00:24:40] We're not ready

[00:24:41] to use AI and

[00:24:43] machine learning

[00:24:43] for actual

[00:24:44] stock selection.

[00:24:45] This is an

[00:24:46] area of

[00:24:46] research for

[00:24:47] us.

[00:24:48] NLP

[00:24:48] techniques,

[00:24:50] natural language

[00:24:50] processing,

[00:24:51] textual

[00:24:51] processing.

[00:24:52] The Q&A

[00:24:54] that you

[00:24:55] hear on

[00:24:56] quarterly

[00:24:57] calls,

[00:24:58] I think it

[00:24:59] does give

[00:25:00] you a lot

[00:25:01] of information

[00:25:02] of what

[00:25:03] companies are

[00:25:04] doing in

[00:25:05] terms of

[00:25:05] AI that

[00:25:06] can be

[00:25:07] used in

[00:25:08] terms of

[00:25:08] stock

[00:25:09] selection.

[00:25:10] Again,

[00:25:10] this is an

[00:25:10] area of

[00:25:11] research.

[00:25:11] This is

[00:25:12] actually a

[00:25:13] big focus

[00:25:14] for us in

[00:25:15] 2025 and

[00:25:16] a bit in

[00:25:16] 2024.

[00:25:18] Yeah,

[00:25:18] it's an

[00:25:19] interesting

[00:25:19] time.

[00:25:19] We're

[00:25:19] quants

[00:25:20] too.

[00:25:20] It's

[00:25:20] an

[00:25:20] interesting

[00:25:20] time to

[00:25:20] be a

[00:25:21] quant

[00:25:21] right now

[00:25:21] because we

[00:25:21] have all

[00:25:22] these tools

[00:25:23] available to

[00:25:23] us.

[00:25:23] By the same

[00:25:24] token,

[00:25:24] we want to

[00:25:24] rely on

[00:25:25] what we

[00:25:26] know has

[00:25:26] worked over

[00:25:26] time.

[00:25:27] If we

[00:25:27] believe that

[00:25:28] a factor

[00:25:28] should have

[00:25:29] an explanation

[00:25:30] for it

[00:25:30] to work,

[00:25:31] some people

[00:25:32] believe that

[00:25:32] now and

[00:25:32] some people

[00:25:33] don't.

[00:25:33] But if

[00:25:33] we believe

[00:25:34] that,

[00:25:34] then we

[00:25:34] want to

[00:25:34] start with

[00:25:35] that as

[00:25:35] a core.

[00:25:35] We don't

[00:25:49] have to

[00:25:50] know what

[00:25:51] is

[00:25:52] an

[00:25:53] concern

[00:25:54] in

[00:25:54] applying

[00:25:55] that type

[00:25:55] of

[00:25:55] technology

[00:25:56] is that

[00:25:57] there's

[00:25:58] an element

[00:25:58] of a

[00:25:58] look-ahead

[00:25:59] bias.

[00:26:00] So when

[00:26:00] you're

[00:26:01] looking at

[00:26:01] trying to

[00:26:04] assess what

[00:26:06] your

[00:26:06] stimulation

[00:26:07] would have

[00:26:07] done in

[00:26:08] 2020 and

[00:26:08] you're using

[00:26:09] an LLM

[00:26:10] that's

[00:26:10] fitted in

[00:26:11] 2024,

[00:26:12] LLM

[00:26:13] knows what

[00:26:15] happened

[00:26:15] in 2020,

[00:26:17] 2021,

[00:26:17] 2022.

[00:26:18] So how

[00:26:19] do you

[00:26:19] really trust

[00:26:20] what it

[00:26:20] is that

[00:26:21] it's telling

[00:26:21] you?

[00:26:22] I think

[00:26:22] there needs

[00:26:23] to be

[00:26:23] a specific

[00:26:24] accounting

[00:26:25] taxonomy

[00:26:25] and applying

[00:26:26] NLP

[00:26:27] techniques.

[00:26:28] And this

[00:26:28] is something

[00:26:28] we've been

[00:26:29] thinking about

[00:26:30] a lot.

[00:26:30] And I'm

[00:26:30] sure a

[00:26:31] lot of

[00:26:31] quants have

[00:26:32] been.

[00:26:32] It's a

[00:26:33] really

[00:26:33] interesting

[00:26:34] question.

[00:26:34] Because in

[00:26:36] theory,

[00:26:37] these

[00:26:37] things know

[00:26:37] everything

[00:26:38] historically.

[00:26:39] We've

[00:26:39] been taught

[00:26:40] there's a

[00:26:40] certain way

[00:26:40] we test

[00:26:41] things.

[00:26:41] We test

[00:26:42] things out

[00:26:43] of sample

[00:26:43] and we do

[00:26:44] all this

[00:26:44] other stuff.

[00:26:45] But I

[00:26:45] wonder,

[00:26:46] do these

[00:26:46] things negate

[00:26:47] that because

[00:26:47] they know

[00:26:48] everything?

[00:26:48] It's not

[00:26:49] like you

[00:26:49] can go

[00:26:49] back and

[00:26:50] create an

[00:26:50] LLM and

[00:26:51] say,

[00:26:51] only know

[00:26:52] what you

[00:26:52] would have

[00:26:52] known in

[00:26:53] 2014 or

[00:26:54] something like

[00:26:54] that,

[00:26:54] or in

[00:26:54] 1972 or

[00:26:55] anything like

[00:26:56] that.

[00:26:56] So it's

[00:26:56] an interesting

[00:26:57] balance how

[00:26:58] to use

[00:26:58] these things

[00:26:59] and couple

[00:26:59] it with

[00:27:00] the way

[00:27:00] we've

[00:27:01] tested

[00:27:01] things

[00:27:01] historically,

[00:27:01] I think.

[00:27:02] I think

[00:27:03] it's going

[00:27:03] to be one

[00:27:03] of the

[00:27:04] most

[00:27:04] interesting

[00:27:04] challenges

[00:27:05] for

[00:27:05] quants

[00:27:06] in the

[00:27:06] next

[00:27:06] decade.

[00:27:08] It's a

[00:27:09] very

[00:27:09] exciting

[00:27:09] area of

[00:27:10] research.

[00:27:12] So I want

[00:27:12] to talk

[00:27:13] about this.

[00:27:13] You actually

[00:27:13] have used

[00:27:14] this research

[00:27:14] in long-short

[00:27:15] investing in

[00:27:16] a fund you

[00:27:16] guys run.

[00:27:17] And I

[00:27:17] want to talk

[00:27:18] a little bit

[00:27:18] about that

[00:27:19] because that

[00:27:19] seems like a

[00:27:19] very interesting

[00:27:21] context to

[00:27:21] use this

[00:27:22] intangible

[00:27:22] research on

[00:27:23] a long-short

[00:27:23] basis.

[00:27:24] So can you

[00:27:25] just talk a

[00:27:25] little bit

[00:27:25] about how

[00:27:26] you've done

[00:27:26] that?

[00:27:28] Sure.

[00:27:28] I'm not

[00:27:28] going to...

[00:27:29] Compliance has

[00:27:30] warned me

[00:27:31] that I

[00:27:32] can't talk

[00:27:32] about any

[00:27:33] funds that

[00:27:34] we have,

[00:27:34] but what

[00:27:35] I can

[00:27:35] talk about

[00:27:36] is our

[00:27:39] absolute

[00:27:39] return

[00:27:40] platform,

[00:27:40] what it

[00:27:41] is we're

[00:27:41] doing in

[00:27:42] general.

[00:27:43] But before

[00:27:44] getting into

[00:27:44] the details

[00:27:45] of the stock

[00:27:46] selection,

[00:27:46] maybe I

[00:27:46] can just

[00:27:47] talk about

[00:27:47] a bit

[00:27:48] what that

[00:27:49] is for us,

[00:27:50] what does

[00:27:50] an absolute

[00:27:50] return

[00:27:51] strategy

[00:27:51] mean for

[00:27:51] us?

[00:27:52] So what

[00:27:53] we've

[00:27:54] done with

[00:27:55] our

[00:27:55] absolute

[00:27:56] strategy,

[00:27:56] absolute

[00:27:57] return

[00:27:57] strategy

[00:27:58] portfolios

[00:27:59] is that

[00:28:00] we try

[00:28:01] to build

[00:28:01] a return

[00:28:02] stream

[00:28:02] that is

[00:28:03] agnostic

[00:28:04] to market

[00:28:06] direction.

[00:28:07] A lot

[00:28:08] of folks

[00:28:08] call this

[00:28:09] a market

[00:28:09] neutral

[00:28:09] strategy.

[00:28:12] I'm very

[00:28:12] wary of

[00:28:13] calling strategies

[00:28:14] like these

[00:28:14] market neutral

[00:28:15] because even

[00:28:16] when you

[00:28:18] build a

[00:28:19] strategy

[00:28:19] that has

[00:28:20] zero

[00:28:20] beta,

[00:28:21] it is

[00:28:22] zero beta

[00:28:23] going backwards,

[00:28:24] not zero

[00:28:25] beta going

[00:28:25] forwards,

[00:28:26] beta isn't

[00:28:27] necessarily

[00:28:27] predictive

[00:28:28] going forwards.

[00:28:28] So I like

[00:28:29] to think of

[00:28:30] our return

[00:28:30] streams

[00:28:30] as agnostic

[00:28:31] to overall

[00:28:32] market

[00:28:33] direction.

[00:28:34] Our

[00:28:35] strategies

[00:28:35] have equal

[00:28:37] amounts of

[00:28:38] longs and

[00:28:38] shorts.

[00:28:39] Typically at

[00:28:40] the time

[00:28:40] every balance

[00:28:41] are net

[00:28:42] zero,

[00:28:42] gross 200%,

[00:28:44] so 100

[00:28:45] longs by

[00:28:45] 100 shorts.

[00:28:46] I think

[00:28:47] where we

[00:28:48] might be

[00:28:48] different from

[00:28:49] other folks

[00:28:50] is that

[00:28:51] our gross

[00:28:52] exposure

[00:28:52] is proportional

[00:28:54] to our

[00:28:55] stock

[00:28:55] selection

[00:28:56] efficacy.

[00:28:57] So we're

[00:28:58] finding a lot

[00:28:58] more opportunities

[00:28:59] in small

[00:29:00] caps and

[00:29:01] mid caps.

[00:29:02] We're

[00:29:02] finding a lot

[00:29:02] of opportunity

[00:29:03] in terms of

[00:29:04] stock selection

[00:29:05] efficacy

[00:29:06] outside of

[00:29:07] the U.S.

[00:29:08] and emerging

[00:29:09] economies.

[00:29:10] So our

[00:29:10] gross exposure

[00:29:11] is proportional

[00:29:13] to that

[00:29:14] opportunity.

[00:29:14] So in our

[00:29:17] global

[00:29:17] strategy,

[00:29:18] we can have

[00:29:19] as little

[00:29:19] as 15%

[00:29:20] of our

[00:29:20] gross

[00:29:21] exposure

[00:29:21] in the

[00:29:22] U.S.

[00:29:22] It varies

[00:29:23] based on

[00:29:24] opportunity,

[00:29:25] but what we're

[00:29:26] trying to do

[00:29:26] is we're

[00:29:26] trying to

[00:29:27] tie gross

[00:29:28] exposure

[00:29:29] to our

[00:29:29] overall

[00:29:30] stock

[00:29:30] selection

[00:29:31] opportunity.

[00:29:31] We're

[00:29:32] targeting

[00:29:33] about a

[00:29:34] 10%

[00:29:34] annualized

[00:29:35] volatility

[00:29:37] over our

[00:29:37] market

[00:29:38] cycle.

[00:29:39] We're

[00:29:39] trying,

[00:29:39] our

[00:29:40] simulations

[00:29:40] indicate

[00:29:41] that we

[00:29:41] can

[00:29:41] deliver

[00:29:43] between

[00:29:43] 12%

[00:29:44] and 15%

[00:29:45] of

[00:29:45] annualized

[00:29:46] return

[00:29:46] at that

[00:29:47] type of

[00:29:47] wall.

[00:29:48] That seems

[00:29:49] to be a

[00:29:49] marketable

[00:29:50] strategy.

[00:29:52] What we

[00:29:53] typically do

[00:29:54] in our

[00:29:55] strategies

[00:29:55] is we

[00:29:55] invest

[00:29:56] in a

[00:29:56] very

[00:29:57] diversified

[00:29:58] way.

[00:29:59] We're

[00:29:59] invested

[00:29:59] in 35

[00:30:01] different

[00:30:01] countries,

[00:30:02] 11

[00:30:02] different

[00:30:02] sectors,

[00:30:04] typically

[00:30:04] all

[00:30:05] sectors.

[00:30:06] So

[00:30:07] 250 to

[00:30:08] 300

[00:30:08] longs

[00:30:09] versus

[00:30:10] about

[00:30:10] 300

[00:30:11] to

[00:30:11] 350

[00:30:12] shorts.

[00:30:13] We're

[00:30:14] doing

[00:30:14] this

[00:30:14] with

[00:30:15] single

[00:30:16] security

[00:30:17] positions.

[00:30:17] So we're

[00:30:18] applying our

[00:30:18] stock

[00:30:19] selection

[00:30:19] quite

[00:30:20] directly.

[00:30:21] We're

[00:30:21] taking our

[00:30:22] research

[00:30:22] based on

[00:30:23] intangible

[00:30:23] capital

[00:30:24] intensity

[00:30:24] and we

[00:30:25] built out

[00:30:26] a contextual

[00:30:27] stock

[00:30:27] selection

[00:30:28] process.

[00:30:28] We

[00:30:29] emphasize

[00:30:29] fundamentals

[00:30:30] for the

[00:30:31] low

[00:30:31] intangible

[00:30:31] industries.

[00:30:32] We're

[00:30:32] emphasizing

[00:30:33] sentiment

[00:30:33] for the

[00:30:34] high

[00:30:34] intangibles

[00:30:35] and each

[00:30:36] industry

[00:30:36] has different

[00:30:37] measurements

[00:30:38] of what

[00:30:39] we

[00:30:41] categorize

[00:30:41] as value,

[00:30:42] quality,

[00:30:43] sentiment,

[00:30:43] and trend.

[00:30:44] We're

[00:30:44] trying to

[00:30:45] emphasize

[00:30:45] the return

[00:30:46] source that's

[00:30:47] idiosyncratic

[00:30:48] in nature

[00:30:48] and we're

[00:30:49] trying to

[00:30:49] cap

[00:30:50] systematic

[00:30:51] exposures

[00:30:52] in the

[00:30:52] portfolio.

[00:30:53] What's

[00:30:54] interesting

[00:30:55] for us

[00:30:55] is that

[00:30:56] we have

[00:30:57] I would

[00:30:58] say

[00:30:58] 85%

[00:30:59] of our

[00:30:59] process

[00:31:00] to be

[00:31:00] somewhat

[00:31:01] systematic,

[00:31:02] 15%

[00:31:03] is

[00:31:04] discretionary,

[00:31:05] so we

[00:31:05] constantly

[00:31:06] are looking

[00:31:07] at the

[00:31:07] assumptions

[00:31:08] that our

[00:31:09] models have

[00:31:10] and whenever

[00:31:11] there's an

[00:31:12] assumption

[00:31:12] failure,

[00:31:13] whenever

[00:31:13] there's an

[00:31:14] externality

[00:31:15] to the

[00:31:15] process,

[00:31:16] we tend

[00:31:16] to

[00:31:18] mute

[00:31:19] our

[00:31:20] stock

[00:31:20] selection

[00:31:21] preferences.

[00:31:21] So we

[00:31:22] grade

[00:31:22] our

[00:31:23] stocks

[00:31:23] into

[00:31:24] A

[00:31:24] through

[00:31:24] F

[00:31:24] category

[00:31:25] and

[00:31:27] there's

[00:31:27] an

[00:31:27] externality,

[00:31:29] we

[00:31:29] will

[00:31:29] mute

[00:31:29] that

[00:31:30] A

[00:31:30] graded

[00:31:31] as

[00:31:31] a

[00:31:31] C

[00:31:31] and

[00:31:32] most

[00:31:32] likely

[00:31:32] you

[00:31:32] won't

[00:31:33] get

[00:31:33] into

[00:31:33] either

[00:31:33] the

[00:31:34] long

[00:31:34] and

[00:31:34] short

[00:31:34] portfolio.

[00:31:36] Can you

[00:31:36] think of

[00:31:37] a specific

[00:31:37] example

[00:31:38] where

[00:31:39] that may

[00:31:40] have

[00:31:40] happened,

[00:31:41] that

[00:31:41] externality

[00:31:41] came in

[00:31:42] and you

[00:31:42] guys

[00:31:42] as a team

[00:31:43] sort of

[00:31:43] had to

[00:31:44] say,

[00:31:44] you know,

[00:31:44] we're

[00:31:45] sort of

[00:31:45] moving

[00:31:46] away

[00:31:46] from

[00:31:46] systematic

[00:31:47] and

[00:31:47] layering

[00:31:47] in a

[00:31:48] discretionary

[00:31:48] decision

[00:31:49] here?

[00:31:50] It

[00:31:51] happens

[00:31:51] all

[00:31:52] the

[00:31:52] time.

[00:31:52] would

[00:31:52] say

[00:31:53] that

[00:31:53] more

[00:31:54] than

[00:31:54] half

[00:31:54] of

[00:31:55] the

[00:31:56] time

[00:31:56] of

[00:31:56] portfolio

[00:31:57] management

[00:31:57] time

[00:31:57] is

[00:31:57] actually

[00:31:58] devoted

[00:31:58] to

[00:31:59] studying

[00:31:59] externalities

[00:32:00] to

[00:32:00] the

[00:32:00] process.

[00:32:01] Meme

[00:32:02] stocks

[00:32:02] are

[00:32:02] an

[00:32:03] example

[00:32:03] of

[00:32:03] that.

[00:32:05] Fundamentals

[00:32:05] are

[00:32:06] almost

[00:32:06] irrelevant.

[00:32:07] Whenever

[00:32:07] there's

[00:32:07] M&A

[00:32:08] activity,

[00:32:09] the

[00:32:10] earnings

[00:32:11] of

[00:32:12] the

[00:32:12] company

[00:32:13] that

[00:32:13] is

[00:32:13] buying

[00:32:13] and

[00:32:14] being

[00:32:14] bought,

[00:32:15] it

[00:32:16] becomes

[00:32:16] totally

[00:32:16] unpredictable.

[00:32:17] It's not

[00:32:18] predictive

[00:32:18] what's

[00:32:19] going

[00:32:19] to

[00:32:21] cash

[00:32:22] flow

[00:32:22] in

[00:32:23] the

[00:32:23] following

[00:32:23] year

[00:32:24] or

[00:32:24] two.

[00:32:24] We

[00:32:25] tend

[00:32:25] to

[00:32:25] mute

[00:32:26] meme

[00:32:26] stocks.

[00:32:27] tend

[00:32:27] to

[00:32:27] mute

[00:32:28] anything

[00:32:28] that's

[00:32:29] going

[00:32:29] through

[00:32:29] M&A

[00:32:30] activity.

[00:32:31] There's

[00:32:31] government

[00:32:32] regulations.

[00:32:34] The

[00:32:35] Brazilian

[00:32:35] government

[00:32:35] loves

[00:32:36] to cap

[00:32:37] the

[00:32:38] amount

[00:32:38] different

[00:32:39] utilities

[00:32:39] can charge.

[00:32:40] Obviously,

[00:32:41] that has

[00:32:41] an effect

[00:32:41] on

[00:32:42] earnings.

[00:32:42] Whenever

[00:32:43] there's

[00:32:43] government

[00:32:44] regulation,

[00:32:44] we

[00:32:45] really

[00:32:45] question

[00:32:46] the

[00:32:47] underlying

[00:32:47] assumptions

[00:32:48] of

[00:32:48] our

[00:32:48] investment

[00:32:49] process.

[00:32:50] There's

[00:32:51] lots

[00:32:51] of

[00:32:52] examples

[00:32:53] just

[00:32:54] working

[00:32:54] through

[00:32:55] one

[00:32:55] this

[00:32:55] more.

[00:32:57] You

[00:32:58] got

[00:32:59] the

[00:32:59] issue

[00:32:59] of

[00:32:59] systematic

[00:33:00] strategies

[00:33:00] versus

[00:33:01] discretionary

[00:33:01] strategies.

[00:33:02] Where do

[00:33:03] you think

[00:33:03] human beings

[00:33:04] can be

[00:33:04] better than

[00:33:05] computers?

[00:33:06] Where do

[00:33:06] you think

[00:33:06] are the

[00:33:07] areas where

[00:33:08] you're a

[00:33:09] quant like

[00:33:09] me,

[00:33:09] so you

[00:33:09] probably

[00:33:10] believe

[00:33:10] in

[00:33:10] most

[00:33:10] cases

[00:33:11] systematic

[00:33:11] strategies

[00:33:12] are

[00:33:12] better,

[00:33:12] but what

[00:33:13] are the

[00:33:13] areas

[00:33:14] where you

[00:33:14] think

[00:33:14] maybe a

[00:33:15] human

[00:33:15] being

[00:33:15] is

[00:33:15] still

[00:33:15] better?

[00:33:18] Systematic

[00:33:19] process,

[00:33:20] every

[00:33:20] systematic

[00:33:21] process

[00:33:21] has

[00:33:22] underlying

[00:33:23] assumptions.

[00:33:24] The

[00:33:24] human

[00:33:25] needs

[00:33:25] to

[00:33:25] understand

[00:33:26] what

[00:33:26] those

[00:33:27] assumptions

[00:33:27] are

[00:33:28] and

[00:33:28] question

[00:33:29] the

[00:33:30] stock

[00:33:31] selection

[00:33:31] preferences.

[00:33:32] Do

[00:33:33] the

[00:33:33] assumptions

[00:33:34] hold

[00:33:34] of the

[00:33:35] model?

[00:33:36] You

[00:33:36] know

[00:33:36] what

[00:33:36] those

[00:33:36] assumptions

[00:33:37] are

[00:33:37] because

[00:33:37] you

[00:33:38] built

[00:33:38] the

[00:33:38] model.

[00:33:38] So it's

[00:33:40] absolutely

[00:33:41] critical,

[00:33:42] in my

[00:33:42] opinion,

[00:33:43] to apply

[00:33:43] quantitative

[00:33:43] strategy

[00:33:44] effectively

[00:33:45] to

[00:33:45] constantly

[00:33:46] keep in

[00:33:47] mind what

[00:33:48] the

[00:33:48] assumptions

[00:33:48] of your

[00:33:49] models

[00:33:49] are

[00:33:50] and

[00:33:51] make

[00:33:51] sure

[00:33:51] you

[00:33:52] throw

[00:33:52] out

[00:33:52] the

[00:33:53] stock

[00:33:53] picks

[00:33:54] that

[00:33:54] fail

[00:33:56] when

[00:33:56] assumptions

[00:33:57] fail.

[00:33:59] Yeah,

[00:34:00] it's

[00:34:00] interesting.

[00:34:00] I don't

[00:34:00] know if

[00:34:01] you'll

[00:34:01] agree

[00:34:01] with

[00:34:01] this,

[00:34:01] but one

[00:34:01] of

[00:34:08] the

[00:34:08] strategy

[00:34:11] ultimately

[00:34:11] you

[00:34:12] can't

[00:34:12] completely

[00:34:12] get

[00:34:13] human

[00:34:13] decision

[00:34:14] making

[00:34:14] and

[00:34:14] human

[00:34:14] emotion

[00:34:15] out

[00:34:15] of

[00:34:15] this

[00:34:15] process.

[00:34:16] I

[00:34:17] don't

[00:34:17] like

[00:34:18] to

[00:34:18] change

[00:34:18] strategies

[00:34:19] very

[00:34:19] often.

[00:34:21] It

[00:34:21] would

[00:34:21] be

[00:34:21] unusual

[00:34:22] for

[00:34:23] me

[00:34:38] fail.

[00:34:39] This

[00:34:39] is

[00:34:39] particularly

[00:34:40] important

[00:34:41] when

[00:34:41] you're

[00:34:41] running

[00:34:41] a long

[00:34:42] short

[00:34:42] strategy

[00:34:42] because

[00:34:43] the

[00:34:43] return

[00:34:44] asymmetry

[00:34:44] between

[00:34:45] longs

[00:34:45] and

[00:34:45] shorts

[00:34:46] is

[00:34:46] quite

[00:34:47] substantial.

[00:34:48] We're

[00:34:48] very

[00:34:48] careful,

[00:34:50] especially

[00:34:51] looking at

[00:34:51] the

[00:34:52] assumptions

[00:34:52] of the

[00:34:53] stock

[00:34:53] picks

[00:34:54] on the

[00:34:54] short

[00:34:54] side

[00:34:55] because

[00:34:55] the

[00:34:57] potential

[00:34:57] loss

[00:34:58] from

[00:34:58] shorts

[00:34:58] could

[00:34:59] be

[00:34:59] quite

[00:35:00] sizable.

[00:35:01] So

[00:35:02] we're

[00:35:02] constantly

[00:35:03] evaluating

[00:35:04] it.

[00:35:05] Less

[00:35:06] concerned

[00:35:06] about

[00:35:07] model

[00:35:07] assumption

[00:35:08] failures

[00:35:08] on the

[00:35:09] long

[00:35:09] side

[00:35:09] than

[00:35:09] I

[00:35:09] am

[00:35:10] on

[00:35:10] the

[00:35:10] short

[00:35:10] side.

[00:35:11] You

[00:35:12] mentioned

[00:35:12] gross

[00:35:12] exposure

[00:35:13] before

[00:35:13] and

[00:35:13] just

[00:35:13] for

[00:35:13] people

[00:35:13] who aren't

[00:35:13] familiar

[00:35:14] with it,

[00:35:14] can you

[00:35:14] define

[00:35:15] what

[00:35:15] gross

[00:35:15] and

[00:35:15] net

[00:35:15] exposure

[00:35:16] are?

[00:35:17] Sure.

[00:35:17] So

[00:35:18] for

[00:35:18] folks

[00:35:18] that

[00:35:18] are

[00:35:19] into

[00:35:20] just

[00:35:20] long

[00:35:21] investments

[00:35:22] they're

[00:35:22] familiar

[00:35:23] with

[00:35:23] the

[00:35:23] notion

[00:35:23] of

[00:35:24] active

[00:35:24] share.

[00:35:25] Active

[00:35:25] share

[00:35:25] is

[00:35:26] the

[00:35:26] sum

[00:35:27] of

[00:35:28] the

[00:35:28] exposures

[00:35:29] the

[00:35:30] overweights

[00:35:30] and the

[00:35:31] underweights

[00:35:31] some

[00:35:32] people

[00:35:32] divide

[00:35:32] it

[00:35:32] by

[00:35:33] two

[00:35:33] some

[00:35:33] people

[00:35:33] don't.

[00:35:34] Gross

[00:35:34] exposure

[00:35:35] is

[00:35:35] basically

[00:35:35] the

[00:35:36] same

[00:35:36] thing

[00:35:36] as

[00:35:36] active

[00:35:37] exposure

[00:35:37] except

[00:35:38] for

[00:35:38] a

[00:35:38] long

[00:35:39] short

[00:35:39] strategy

[00:35:39] the

[00:35:40] exposure

[00:35:40] on

[00:35:41] the

[00:35:41] long

[00:35:41] side

[00:35:42] is

[00:35:42] the

[00:35:42] stock

[00:35:43] value

[00:35:45] as

[00:35:45] a

[00:35:45] percent

[00:35:46] of

[00:35:46] total

[00:35:47] portfolio

[00:35:47] on

[00:35:48] the

[00:35:48] short

[00:35:48] side

[00:35:49] it's

[00:35:49] the

[00:35:49] short

[00:35:50] value

[00:35:51] so

[00:35:51] if

[00:35:51] you

[00:35:51] sum

[00:35:52] up

[00:35:52] all

[00:35:52] the

[00:35:52] long

[00:35:53] and

[00:35:53] short

[00:35:53] value

[00:35:54] that

[00:35:54] gives

[00:35:55] you

[00:35:55] gross

[00:35:56] exposure

[00:35:56] as

[00:35:56] a

[00:35:56] percent

[00:35:57] of

[00:36:03] long

[00:36:12] short

[00:36:14] short

[00:36:14] strategies

[00:36:16] that's

[00:36:19] actually

[00:36:20] really

[00:36:20] interesting

[00:36:21] question

[00:36:21] my

[00:36:22] opinion

[00:36:23] is

[00:36:23] that

[00:36:23] long

[00:36:23] short

[00:36:24] strategies

[00:36:24] allow

[00:36:25] you

[00:36:26] to

[00:36:26] mitigate

[00:36:28] systematic

[00:36:29] exposures

[00:36:30] much

[00:36:31] more

[00:36:33] with

[00:36:33] a

[00:36:34] long

[00:36:34] only

[00:36:34] strategy

[00:36:35] the

[00:36:36] underweights

[00:36:36] you

[00:36:37] take

[00:36:37] on

[00:36:37] are

[00:36:37] limited

[00:36:38] by

[00:36:38] your

[00:36:39] index

[00:36:40] the

[00:36:41] underweights

[00:36:41] relative

[00:36:41] to the

[00:36:42] index

[00:36:42] obviously

[00:36:42] are

[00:36:43] limited

[00:36:43] by

[00:36:43] the

[00:36:43] index

[00:36:45] value

[00:36:45] in

[00:36:46] a

[00:36:46] long

[00:36:46] short

[00:36:46] strategy

[00:36:47] you

[00:36:47] don't

[00:36:47] have

[00:36:48] that

[00:36:48] type

[00:36:48] of

[00:36:48] restriction

[00:36:49] so

[00:36:49] we're

[00:36:50] able

[00:36:51] to

[00:36:51] mitigate

[00:36:53] country

[00:36:54] exposures

[00:36:54] by making

[00:36:55] sure

[00:36:55] that

[00:36:56] the

[00:36:56] net

[00:36:56] exposures

[00:36:57] across

[00:36:58] countries

[00:36:59] is

[00:37:00] around

[00:37:00] zero

[00:37:01] we

[00:37:01] don't

[00:37:01] like

[00:37:02] taking

[00:37:02] large

[00:37:02] sector

[00:37:03] exposures

[00:37:03] so

[00:37:04] we

[00:37:04] mitigate

[00:37:05] that

[00:37:05] type

[00:37:05] of

[00:37:05] systematic

[00:37:06] risk

[00:37:06] by

[00:37:07] making

[00:37:08] sure

[00:37:08] the

[00:37:08] sum

[00:37:08] of

[00:37:09] our

[00:37:09] longs

[00:37:09] and

[00:37:09] some

[00:37:09] of

[00:37:10] our

[00:37:10] shorts

[00:37:10] in

[00:37:10] each

[00:37:10] of

[00:37:11] the

[00:37:11] sectors

[00:37:11] is

[00:37:12] roughly

[00:37:12] equal

[00:37:13] we

[00:37:14] don't

[00:37:14] like

[00:37:14] to

[00:37:14] have

[00:37:15] signs

[00:37:16] exposure

[00:37:16] we

[00:37:17] don't

[00:37:17] like

[00:37:17] to

[00:37:17] have

[00:37:17] beta

[00:37:18] exposure

[00:37:19] and

[00:37:19] it's

[00:37:19] fairly

[00:37:20] straightforward

[00:37:20] and

[00:37:21] a

[00:37:21] long

[00:37:21] short

[00:37:21] strategy

[00:37:22] to

[00:37:23] mitigate

[00:37:23] those

[00:37:24] types

[00:37:24] of

[00:37:24] systematic

[00:37:24] exposures

[00:37:25] as

[00:37:26] I

[00:37:33] we

[00:37:33] tend

[00:37:34] to

[00:37:34] not

[00:37:34] take

[00:37:35] large

[00:37:35] price

[00:37:36] to

[00:37:36] book

[00:37:37] exposures

[00:37:38] and

[00:37:38] we

[00:37:38] control

[00:37:38] that

[00:37:39] with

[00:37:39] our

[00:37:39] long

[00:37:39] portfolio

[00:37:40] matching

[00:37:41] our

[00:37:41] short

[00:37:42] portfolio

[00:37:42] on a

[00:37:43] quintile

[00:37:44] basis

[00:37:44] for

[00:37:44] price

[00:37:45] to

[00:37:45] book

[00:37:45] that

[00:37:46] said

[00:37:47] we're

[00:37:47] able

[00:37:48] to

[00:37:48] emphasize

[00:37:48] the

[00:37:49] alpha

[00:37:50] signals

[00:37:52] as

[00:37:52] represented

[00:37:53] by

[00:37:53] our

[00:37:53] long

[00:37:53] and

[00:37:54] our

[00:37:54] shorts

[00:37:54] are

[00:37:54] there

[00:37:54] any

[00:37:55] other

[00:37:55] unique

[00:37:56] risks

[00:37:56] you

[00:37:56] have

[00:37:56] to

[00:37:56] think

[00:37:57] about

[00:37:57] with

[00:37:57] a

[00:37:57] long

[00:37:57] short

[00:37:58] strategy

[00:37:58] I

[00:38:15] systematic

[00:38:16] exposures

[00:38:16] quite

[00:38:17] well

[00:38:17] as

[00:38:18] I

[00:38:18] mentioned

[00:38:18] we

[00:38:18] don't

[00:38:19] use

[00:38:19] machine

[00:38:19] learning

[00:38:20] for

[00:38:20] the

[00:38:21] alpha

[00:38:21] build

[00:38:22] but

[00:38:22] we

[00:38:23] do

[00:38:23] use

[00:38:23] machine

[00:38:24] learning

[00:38:24] for

[00:38:25] our

[00:38:25] risk

[00:38:25] modeling

[00:38:26] so

[00:38:26] with

[00:38:26] our

[00:38:27] risk

[00:38:27] modeling

[00:38:27] what we

[00:38:28] also

[00:38:28] tend

[00:38:28] to

[00:38:28] do

[00:38:29] is

[00:38:29] identify

[00:38:30] systematic

[00:38:31] drivers

[00:38:31] so

[00:38:31] what we

[00:38:32] tend

[00:38:32] to

[00:38:32] do

[00:38:32] is

[00:38:33] we

[00:38:33] look

[00:38:33] at

[00:38:33] higher

[00:38:34] frequency

[00:38:37] data

[00:38:37] elements

[00:38:38] higher

[00:38:38] frequency

[00:38:38] prices

[00:38:40] and

[00:38:41] we're

[00:38:41] based

[00:38:42] on

[00:38:42] how

[00:38:42] global

[00:38:43] prices

[00:38:44] move

[00:38:44] across

[00:38:46] the

[00:38:46] 35

[00:38:47] countries

[00:38:47] that we

[00:38:48] invest

[00:38:48] in

[00:38:48] on a

[00:38:49] daily

[00:38:50] basis

[00:38:50] we're able

[00:38:50] to pick

[00:38:51] up

[00:38:51] latent

[00:38:52] risks

[00:38:52] using

[00:38:54] we have

[00:38:55] our own

[00:38:56] version

[00:38:56] of this

[00:38:57] but

[00:38:57] the

[00:38:57] classical

[00:38:59] quant

[00:38:59] technique

[00:39:00] is using

[00:39:00] singular

[00:39:01] value

[00:39:01] decomposition

[00:39:02] to

[00:39:02] identify

[00:39:05] eigenvectors

[00:39:05] that are

[00:39:06] drivers

[00:39:07] of overall

[00:39:07] systematic

[00:39:08] risks

[00:39:08] we have

[00:39:09] our own

[00:39:10] flavor

[00:39:10] of this

[00:39:11] but

[00:39:11] what it

[00:39:12] allows

[00:39:12] us to

[00:39:12] do

[00:39:12] is

[00:39:13] identify

[00:39:13] latent

[00:39:14] risks

[00:39:14] in the

[00:39:15] process

[00:39:15] that's

[00:39:15] particularly

[00:39:16] useful

[00:39:16] in our

[00:39:17] long

[00:39:17] short

[00:39:18] strategies

[00:39:18] because

[00:39:18] we're

[00:39:19] able

[00:39:19] to

[00:39:19] pick

[00:39:19] up

[00:39:20] more

[00:39:20] fluid

[00:39:21] less

[00:39:21] known

[00:39:22] risks

[00:39:23] the

[00:39:23] classical

[00:39:24] risk

[00:39:24] models

[00:39:25] aren't

[00:39:25] necessarily

[00:39:26] going

[00:39:26] to

[00:39:26] be

[00:39:26] able

[00:39:26] to

[00:39:26] pick

[00:39:27] up

[00:39:27] geopolitical

[00:39:28] risks

[00:39:28] or

[00:39:29] risks

[00:39:29] to

[00:39:30] stocks

[00:39:30] coming

[00:39:30] from

[00:39:31] tariffs

[00:39:32] that

[00:39:33] could

[00:39:34] be

[00:39:34] in

[00:39:34] place

[00:39:35] but

[00:39:35] as soon

[00:39:36] as

[00:39:36] there's

[00:39:36] for

[00:39:37] example

[00:39:37] activity

[00:39:39] geopolitical

[00:39:40] in nature

[00:39:41] between

[00:39:41] Taiwan

[00:39:42] and China

[00:39:42] there are

[00:39:43] certain

[00:39:43] stocks

[00:39:44] that

[00:39:44] are

[00:39:44] tied

[00:39:45] that

[00:39:45] trade

[00:39:46] between

[00:39:46] China

[00:39:47] and Taiwan

[00:39:49] that light up as red in our latent risk models

[00:39:54] we can

[00:39:55] dig in

[00:39:56] we have to identify how to interpret that data

[00:39:59] but it's typically tied to lots of news coming out on geopolitical tensions

[00:40:06] between Taiwan and China for example as Trump won and there was a recognition

[00:40:14] that tariffs most likely are going to be in place that are much higher than they are now

[00:40:20] there's certain stocks that are trade related that started blinking red based on our machine learned risk model

[00:40:27] so we are identifying these types of systematic risks

[00:40:31] we're trying to understand those systematic risks

[00:40:34] and at the portfolio construction level we're trying to mitigate these latent systematic risks using a risk model

[00:40:39] so it's particularly valuable to do that especially when you're dealing with long short strategies

[00:40:45] so before you joined Bridgeway in 2018 you spent what looks like maybe 20 years approximately

[00:40:53] focused on international equities

[00:40:56] and international strategies

[00:40:57] and I'm just curious

[00:41:00] sort of at a high level like

[00:41:02] because we know that the US just pretty much since the financial crisis has been like

[00:41:06] almost one of the best markets you

[00:41:09] could have invested in

[00:41:10] and I think international exposure in general probably across most US investor portfolios

[00:41:15] and maybe even institutions is

[00:41:17] is

[00:41:19] probably

[00:41:20] certainly lower than it was

[00:41:22] let's say

[00:41:24] pre-financial crisis

[00:41:25] so I'm just wondering like do you have any opinion on

[00:41:28] the importance of international diversification whether

[00:41:31] and

[00:41:32] and maybe in addition to that like how you kind of view developed versus EM

[00:41:37] and sort of where those should be

[00:41:40] you know

[00:41:40] in investors portfolios just to help with overall dirt

[00:41:43] and you know

[00:41:44] to add on to that

[00:41:45] we've had on

[00:41:46] we asked this question to a lot of guests

[00:41:48] and then some people say

[00:41:48] well

[00:41:49] just buy the S&T 500

[00:41:50] 40% of the revenues come from outside the US

[00:41:52] others say no

[00:41:53] you know

[00:41:54] you want to be in these

[00:41:56] some of these international markets

[00:41:58] for the diversification benefits that

[00:42:01] you know they bring to the table

[00:42:02] you know

[00:42:04] that's a very interesting question

[00:42:06] obviously I've been asked this question before

[00:42:08] I have no idea

[00:42:11] if the US is going to outperform international or not

[00:42:15] what I know is right now

[00:42:18] the US is about 60%

[00:42:22] of the market value globally

[00:42:24] across developed and emerging economies

[00:42:27] and the US produces about a third

[00:42:30] of total revenue of companies

[00:42:32] in MSCI, ACQUI, IMI for example

[00:42:35] so I mean my question is

[00:42:38] how much more growth

[00:42:41] how much more outperformance

[00:42:42] can you expect from the US

[00:42:45] relative to international

[00:42:47] with that type of revenue market value disparity

[00:42:53] in terms of what excites me

[00:42:56] about investing outside of the US

[00:42:59] for me

[00:43:00] and it's really an expression

[00:43:02] of our long short strategy

[00:43:03] for me what's interesting

[00:43:05] is the opportunity for investing

[00:43:08] in markets that are less efficient

[00:43:10] in the United States

[00:43:11] present a lot more stock selection opportunity

[00:43:14] for example in the US

[00:43:16] I think most plants agree

[00:43:17] the stock selection opportunity

[00:43:19] in small caps

[00:43:21] is far greater than it is

[00:43:23] in mega cap names

[00:43:25] information is just

[00:43:26] much more faster moving

[00:43:28] in the larger names

[00:43:30] it doesn't give you an opportunity

[00:43:31] to really make money

[00:43:32] based on your alpha

[00:43:34] international that gets even

[00:43:36] more emerging markets

[00:43:39] the stock selection opportunities

[00:43:41] is even greater

[00:43:42] so I had a choice

[00:43:44] of what type of strategy

[00:43:45] I wanted to run

[00:43:46] we do have a long

[00:43:47] only international strategy

[00:43:48] in our choice

[00:43:50] my choice was to actually focus

[00:43:51] on emerging market small caps

[00:43:53] why is that

[00:43:54] for a long only strategy

[00:43:55] because I see the greatest

[00:43:57] alpha opportunity

[00:43:58] to exist in the least efficient markets

[00:44:01] so what we've done

[00:44:02] with our long short strategy

[00:44:03] is recognize

[00:44:06] that we don't want to play the game

[00:44:07] of predicting

[00:44:09] what's going to perform

[00:44:10] the US or international

[00:44:12] what we've done is

[00:44:14] we've structured

[00:44:15] our gross exposure

[00:44:16] to be proportional

[00:44:17] to the stock selection opportunity

[00:44:20] so we're seeing

[00:44:21] a lot more opportunity

[00:44:22] in stock selection

[00:44:23] in the small cap space

[00:44:25] so our gross exposure

[00:44:26] is much more proportional

[00:44:28] to small caps

[00:44:30] we have

[00:44:31] substantially more

[00:44:32] small caps

[00:44:33] and mid caps

[00:44:34] in our gross exposure

[00:44:35] than large caps

[00:44:36] because they're

[00:44:37] less efficient

[00:44:38] for that matter

[00:44:39] we have

[00:44:40] investments internationally

[00:44:42] in developed markets

[00:44:43] and emerging markets

[00:44:44] that are quite

[00:44:45] significant

[00:44:46] because we see

[00:44:47] stock selection opportunities

[00:44:48] the opportunity

[00:44:49] to make money

[00:44:50] between our longs

[00:44:51] and shorts

[00:44:51] is much greater

[00:44:53] in places

[00:44:54] where markets

[00:44:55] are less efficient

[00:44:56] that's why we've created

[00:44:57] this absolute return

[00:45:00] platform

[00:45:01] do you do any

[00:45:02] currency hedging

[00:45:03] at all

[00:45:04] within that

[00:45:05] because

[00:45:06] we are typically

[00:45:08] more or less

[00:45:09] country neutral

[00:45:10] we don't take on

[00:45:12] too much currency

[00:45:13] exposure

[00:45:14] in our long

[00:45:15] only strategies

[00:45:16] my preference

[00:45:17] is not to hedge

[00:45:19] out currencies

[00:45:20] hedging out currencies

[00:45:21] are very expensive

[00:45:22] things to do

[00:45:23] and I think

[00:45:24] that if an investor

[00:45:25] really wants to

[00:45:26] diversify

[00:45:27] they want to

[00:45:28] diversify

[00:45:29] into

[00:45:29] currencies

[00:45:30] and into

[00:45:31] international equities

[00:45:33] so my preference

[00:45:33] is not to hedge

[00:45:34] currencies

[00:45:35] but that's just

[00:45:36] my opinion

[00:45:36] yeah I remember

[00:45:37] at one point

[00:45:38] we were sub-advising

[00:45:39] mutual funds

[00:45:40] in Canada

[00:45:41] and Jack

[00:45:42] correct me

[00:45:42] if I'm gonna

[00:45:42] if I misspeak here

[00:45:43] but during the

[00:45:44] financial crisis

[00:45:46] because the dollar

[00:45:47] was so strong

[00:45:48] and they were

[00:45:50] the Canadian bank

[00:45:51] was holding

[00:45:52] US stocks

[00:45:53] like our strategy

[00:45:54] actually did

[00:45:54] very well

[00:45:56] like relatively

[00:45:56] speaking

[00:45:57] because the dollar

[00:45:58] helped prop up

[00:45:59] yeah so basically

[00:45:59] you're right

[00:46:00] the dollar

[00:46:00] like for those

[00:46:01] types of strategies

[00:46:01] was a huge boost

[00:46:03] in 2008

[00:46:03] but it was a huge

[00:46:04] headwind in 2009

[00:46:05] so effectively

[00:46:06] like the returns

[00:46:07] of that type of strategy

[00:46:08] you had significantly

[00:46:08] less losses in 2008

[00:46:09] but you had significantly

[00:46:10] less gains in 2009

[00:46:11] because the dollar

[00:46:12] went one way on you

[00:46:13] and then went

[00:46:13] the other way

[00:46:13] let me ask you

[00:46:15] do you think

[00:46:18] are there like

[00:46:19] emerging

[00:46:21] data sets

[00:46:23] that you're

[00:46:24] you know

[00:46:25] that you see

[00:46:26] out there

[00:46:26] that might be

[00:46:28] potentially undermined

[00:46:29] or untapped

[00:46:30] or I mean

[00:46:31] is there anything

[00:46:32] that you guys

[00:46:32] are looking at

[00:46:33] without giving away

[00:46:34] too much of

[00:46:34] like your research

[00:46:35] and development

[00:46:36] but that you think

[00:46:37] could be

[00:46:39] interesting

[00:46:39] and you mentioned

[00:46:40] some of the text

[00:46:41] based analysis

[00:46:41] and we've had people

[00:46:42] on the podcast

[00:46:42] that do do

[00:46:43] machine learning

[00:46:43] and things like

[00:46:45] that on patents

[00:46:46] and applications

[00:46:47] and human capital

[00:46:48] and stuff

[00:46:49] but I'm just curious

[00:46:50] is there anything

[00:46:50] that sort of

[00:46:51] gets you excited

[00:46:52] about alternative

[00:46:53] data sets in here

[00:46:54] yeah

[00:46:55] so we continue

[00:46:56] to extend

[00:46:58] our research

[00:46:59] and our machine

[00:47:00] learning

[00:47:00] risk modeling

[00:47:01] techniques

[00:47:01] I'm not comfortable

[00:47:03] making that a stock

[00:47:05] selection model

[00:47:05] quite yet

[00:47:06] but that's an area

[00:47:08] of research

[00:47:08] for us

[00:47:09] that is really

[00:47:11] proven out

[00:47:11] in terms of

[00:47:12] improving the

[00:47:13] consistency

[00:47:13] of our portfolios

[00:47:14] I think what's

[00:47:17] surprising me

[00:47:18] in terms of

[00:47:19] the little attention

[00:47:21] that it gets

[00:47:21] is prime brokers

[00:47:24] have a massive

[00:47:25] short availability

[00:47:26] data set

[00:47:27] it's daily

[00:47:28] it tells you

[00:47:29] what the availability

[00:47:31] is across every stock

[00:47:34] in the investable

[00:47:36] universe

[00:47:37] it tells you

[00:47:38] what the price

[00:47:39] of a shorting

[00:47:40] of particular stock

[00:47:41] is

[00:47:41] I've been really

[00:47:43] surprised by the

[00:47:44] lack of academic

[00:47:46] research in this area

[00:47:47] I've spent a lot

[00:47:48] of time studying

[00:47:49] this

[00:47:49] I want to put

[00:47:51] something out

[00:47:52] but prime brokers

[00:47:53] are very guarded

[00:47:55] of their

[00:47:56] short availability

[00:47:57] data set

[00:47:58] so

[00:47:58] I think that

[00:47:59] might explain

[00:48:00] why there

[00:48:01] isn't as

[00:48:02] more publications

[00:48:04] in that space

[00:48:05] but that's an area

[00:48:07] that is

[00:48:08] very valuable

[00:48:09] to look at

[00:48:10] and

[00:48:10] the classical

[00:48:11] things that you

[00:48:12] would expect

[00:48:13] to look at

[00:48:13] in that data set

[00:48:14] they don't pan out

[00:48:16] so you have to

[00:48:16] get pretty creative

[00:48:17] in

[00:48:18] you have to

[00:48:19] get into the

[00:48:19] mindset of the

[00:48:20] shorter

[00:48:20] the person who

[00:48:22] does take on

[00:48:23] the shorts

[00:48:23] to

[00:48:25] understand

[00:48:25] how to use

[00:48:26] that data set

[00:48:27] effectively

[00:48:28] I'm not going to

[00:48:28] get into specific

[00:48:30] metrics

[00:48:30] that I think

[00:48:31] are interesting

[00:48:32] but I

[00:48:32] think it's

[00:48:33] a data set

[00:48:35] that's very

[00:48:36] interesting

[00:48:36] in terms of

[00:48:38] I think the

[00:48:39] future

[00:48:39] and

[00:48:40] improving

[00:48:41] stock

[00:48:41] selection

[00:48:42] models

[00:48:42] is in

[00:48:43] natural

[00:48:44] language

[00:48:45] processing

[00:48:46] I think

[00:48:46] it's in

[00:48:47] textual

[00:48:48] analysis

[00:48:48] I think

[00:48:50] the biggest

[00:48:50] challenge

[00:48:51] to quants

[00:48:51] is

[00:48:53] trying to

[00:48:53] figure out

[00:48:54] how to

[00:48:54] do that

[00:48:55] without

[00:48:56] using

[00:48:57] too much

[00:48:58] data mining

[00:48:59] without

[00:48:59] using

[00:49:00] look ahead

[00:49:00] bias

[00:49:01] without

[00:49:01] using

[00:49:02] open source

[00:49:03] LLM

[00:49:04] models

[00:49:06] in

[00:49:07] the

[00:49:07] classical

[00:49:08] way

[00:49:08] so

[00:49:08] that's

[00:49:09] an

[00:49:09] area

[00:49:09] of

[00:49:09] research

[00:49:10] that

[00:49:10] I'm

[00:49:11] very

[00:49:11] excited

[00:49:11] about

[00:49:12] and

[00:49:12] getting

[00:49:13] into

[00:49:13] that

[00:49:14] in

[00:49:14] the

[00:49:15] big

[00:49:15] way

[00:49:15] in

[00:49:16] the

[00:49:16] coming

[00:49:16] months

[00:49:17] You talked

[00:49:18] about the

[00:49:18] more advanced

[00:49:19] short

[00:49:19] data

[00:49:19] have you

[00:49:20] found

[00:49:20] a standard

[00:49:21] reported

[00:49:21] short

[00:49:21] interest

[00:49:22] data

[00:49:22] is that

[00:49:23] something

[00:49:23] over your

[00:49:23] career

[00:49:24] you found

[00:49:24] any value

[00:49:24] in

[00:49:25] I haven't

[00:49:25] done a lot

[00:49:25] of work

[00:49:26] with

[00:49:26] that

[00:49:26] but

[00:49:26] I've

[00:49:26] always

[00:49:26] wondered

[00:49:26] if

[00:49:27] there's

[00:49:27] value

[00:49:27] in

[00:49:27] that

[00:49:28] in

[00:49:28] any

[00:49:28] way

[00:49:29] every

[00:49:29] data

[00:49:30] piece

[00:49:30] I look

[00:49:31] at

[00:49:31] is

[00:49:31] valuable

[00:49:32] I'm

[00:49:32] not

[00:49:32] sure

[00:49:32] I

[00:49:33] would

[00:49:33] rank

[00:49:33] that

[00:49:34] as

[00:49:34] particularly

[00:49:35] high

[00:49:37] the

[00:49:37] data

[00:49:38] set

[00:49:38] is

[00:49:38] distributed

[00:49:39] in

[00:49:39] a

[00:49:39] really

[00:49:40] funky

[00:49:40] way

[00:49:40] so

[00:49:41] whenever

[00:49:42] you

[00:49:42] have

[00:49:42] funky

[00:49:42] distributions

[00:49:43] and

[00:49:43] data

[00:49:44] sets

[00:49:44] it's

[00:49:44] hard

[00:49:44] to

[00:49:45] create

[00:49:46] alpha

[00:49:46] out

[00:49:47] of

[00:49:47] them

[00:49:47] what

[00:49:47] I'm

[00:49:48] talking

[00:49:48] about

[00:49:48] is

[00:49:48] getting

[00:49:49] to

[00:49:49] the

[00:49:49] prime

[00:49:50] broker

[00:49:50] getting

[00:49:51] them

[00:49:51] to

[00:49:52] share

[00:49:52] that

[00:49:52] short

[00:49:53] availability

[00:49:53] data

[00:49:54] the

[00:49:55] hedge

[00:49:55] funds

[00:49:55] out

[00:49:56] there

[00:49:56] that

[00:49:56] have

[00:49:56] access

[00:49:57] to

[00:49:57] that

[00:49:57] have

[00:49:58] an

[00:49:58] edge

[00:49:58] over

[00:49:59] the

[00:49:59] long

[00:50:00] only

[00:50:00] investors

[00:50:00] who

[00:50:01] don't

[00:50:01] have

[00:50:01] access

[00:50:01] to

[00:50:02] that

[00:50:02] data

[00:50:02] set

[00:50:02] and

[00:50:03] that's

[00:50:04] something

[00:50:04] that

[00:50:05] I would

[00:50:06] encourage

[00:50:06] getting

[00:50:07] and getting

[00:50:07] creative

[00:50:08] with

[00:50:09] I'm

[00:50:10] just

[00:50:10] curious

[00:50:10] like

[00:50:11] what

[00:50:11] your

[00:50:11] day

[00:50:12] looks

[00:50:12] like

[00:50:12] because

[00:50:13] one

[00:50:13] of

[00:50:13] the

[00:50:14] things

[00:50:14] that

[00:50:14] has

[00:50:14] kind

[00:50:14] of

[00:50:14] struck

[00:50:15] me

[00:50:15] in

[00:50:15] this

[00:50:16] conversation

[00:50:16] is

[00:50:17] I

[00:50:17] feel

[00:50:18] like

[00:50:18] there's

[00:50:18] a lot

[00:50:19] of

[00:50:19] deep

[00:50:20] thinking

[00:50:20] happening

[00:50:21] at

[00:50:21] the

[00:50:21] firm

[00:50:22] and

[00:50:22] a lot

[00:50:22] of

[00:50:24] conversation

[00:50:24] around

[00:50:27] these

[00:50:27] types

[00:50:27] of

[00:50:52] because

[00:50:52] I

[00:50:52] think

[00:50:52] for

[00:50:52] anyone

[00:50:53] that

[00:50:53] is

[00:50:54] listening

[00:50:54] to

[00:50:54] this

[00:50:55] that

[00:50:56] wants

[00:50:56] to

[00:50:57] you

[00:50:57] know

[00:50:57] maybe

[00:50:58] pursue

[00:50:59] investment

[00:50:59] management

[00:51:00] I

[00:51:00] think

[00:51:00] this

[00:51:00] is

[00:51:01] a

[00:51:01] good

[00:51:01] I

[00:51:02] think

[00:51:02] how

[00:51:02] you're

[00:51:02] going

[00:51:02] to

[00:51:02] respond

[00:51:03] and

[00:51:03] obviously

[00:51:04] all

[00:51:04] firms

[00:51:04] are

[00:51:04] different

[00:51:04] Bridgeway

[00:51:05] has

[00:51:05] a

[00:51:05] certain

[00:51:05] culture

[00:51:06] and

[00:51:07] that's

[00:51:07] different

[00:51:07] than

[00:51:07] the

[00:51:07] other

[00:51:08] firm

[00:51:08] down

[00:51:08] the

[00:51:08] street

[00:51:09] but

[00:51:09] anyway

[00:51:09] so

[00:51:10] do

[00:51:10] you

[00:51:10] get

[00:51:10] what

[00:51:11] I'm

[00:51:11] asking

[00:51:11] I

[00:51:12] do

[00:51:13] my

[00:51:14] preference

[00:51:15] in

[00:51:15] hiring

[00:51:15] is

[00:51:16] not

[00:51:16] to

[00:51:16] pigeonhole

[00:51:17] people

[00:51:17] into

[00:51:18] different

[00:51:19] types

[00:51:19] of

[00:51:20] operational

[00:51:20] tasks

[00:51:21] or

[00:51:21] research

[00:51:22] tasks

[00:51:22] my

[00:51:23] preference

[00:51:23] in

[00:51:24] hiring

[00:51:24] is

[00:51:25] to

[00:51:25] have

[00:51:26] portfolio

[00:51:27] managers

[00:51:27] have

[00:51:28] the

[00:51:28] capability

[00:51:29] of

[00:51:29] doing

[00:51:30] data

[00:51:31] analysis

[00:51:32] financial

[00:51:32] research

[00:51:33] as well

[00:51:34] as

[00:51:34] implementing

[00:51:35] the

[00:51:35] research

[00:51:36] and

[00:51:36] being

[00:51:36] accountable

[00:51:37] for

[00:51:37] the

[00:51:38] research

[00:51:38] that

[00:51:38] they

[00:51:38] implement

[00:51:39] in

[00:51:39] portfolios

[00:51:40] so

[00:51:40] I'm

[00:51:41] always

[00:51:41] looking

[00:51:41] to

[00:51:42] bring

[00:51:43] on

[00:51:43] people

[00:51:44] who

[00:51:44] can

[00:51:44] do

[00:51:44] their

[00:51:45] own

[00:51:45] data

[00:51:45] analytics

[00:51:46] who

[00:51:47] are

[00:51:47] able

[00:51:47] to

[00:51:48] read

[00:51:48] academic

[00:51:49] literature

[00:51:50] and

[00:51:50] who

[00:51:50] are

[00:51:50] able

[00:51:50] to

[00:51:51] apply

[00:51:51] that

[00:51:52] in

[00:51:52] the

[00:51:52] portfolio

[00:51:52] construction

[00:51:53] framework

[00:51:54] in

[00:51:54] terms

[00:51:55] of

[00:51:55] my

[00:51:56] time

[00:51:56] is

[00:51:57] much

[00:51:57] more

[00:51:58] focused

[00:51:58] on

[00:51:58] overseeing

[00:52:00] how

[00:52:00] portfolios

[00:52:01] are

[00:52:01] implemented

[00:52:02] and

[00:52:02] focused

[00:52:03] on

[00:52:03] research

[00:52:04] and

[00:52:04] development

[00:52:05] of

[00:52:05] new

[00:52:05] ideas

[00:52:06] but

[00:52:06] as

[00:52:07] somebody

[00:52:07] comes

[00:52:08] into

[00:52:08] the

[00:52:09] firm

[00:52:09] their

[00:52:10] focus

[00:52:11] is

[00:52:11] much

[00:52:12] more

[00:52:12] in terms

[00:52:13] of

[00:52:13] understanding

[00:52:13] the

[00:52:14] data

[00:52:14] it's

[00:52:14] something

[00:52:15] that

[00:52:15] I

[00:52:15] had

[00:52:15] to

[00:52:15] do

[00:52:16] decades

[00:52:17] ago

[00:52:17] when I

[00:52:18] got

[00:52:18] into

[00:52:18] the

[00:52:18] industry

[00:52:19] it was

[00:52:19] very

[00:52:20] important

[00:52:20] for me

[00:52:20] to

[00:52:20] understand

[00:52:21] the

[00:52:21] complexities

[00:52:21] especially

[00:52:22] with

[00:52:23] international

[00:52:23] data

[00:52:25] understanding

[00:52:26] how to

[00:52:27] process

[00:52:27] that

[00:52:27] understanding

[00:52:28] the

[00:52:28] financial

[00:52:29] literature

[00:52:29] this is

[00:52:30] something

[00:52:30] that at

[00:52:31] the

[00:52:31] beginning

[00:52:32] of my

[00:52:32] career

[00:52:32] I

[00:52:32] spent

[00:52:33] a lot

[00:52:33] of

[00:52:33] time

[00:52:33] with

[00:52:34] and

[00:52:34] this

[00:52:34] is

[00:52:34] something

[00:52:35] that

[00:52:35] I

[00:52:35] encourage

[00:52:36] folks

[00:52:37] when we

[00:52:37] bring

[00:52:38] them

[00:52:38] on

[00:52:38] to

[00:52:39] focus

[00:52:39] on

[00:52:39] as

[00:52:40] they

[00:53:03] have

[00:53:04] on

[00:53:04] a

[00:53:04] weekly

[00:53:04] basis

[00:53:05] are

[00:53:05] informative

[00:53:06] and

[00:53:07] productive

[00:53:08] and

[00:53:09] this has

[00:53:09] been

[00:53:09] great

[00:53:09] Jacob

[00:53:09] really

[00:53:10] appreciate

[00:53:11] it

[00:53:11] we'd like

[00:53:11] to ask

[00:53:12] our guests

[00:53:13] one

[00:53:13] standard

[00:53:14] closing

[00:53:15] question

[00:53:15] and that

[00:53:15] is

[00:53:16] based on

[00:53:17] your

[00:53:17] experience

[00:53:17] in the

[00:53:18] market

[00:53:18] if you

[00:53:18] could

[00:53:18] teach

[00:53:19] your

[00:53:19] average

[00:53:19] investor

[00:53:19] one

[00:53:20] lesson

[00:53:20] what would

[00:53:20] that

[00:53:21] be

[00:53:22] I

[00:53:23] would

[00:53:23] say

[00:53:23] it's

[00:53:24] to

[00:53:24] stay

[00:53:24] humble

[00:53:24] I

[00:53:26] think

[00:53:27] that

[00:53:27] investors

[00:53:28] forget

[00:53:29] often

[00:53:29] that

[00:53:30] when

[00:53:30] they

[00:53:30] trade

[00:53:31] they

[00:53:32] trade

[00:53:32] with

[00:53:32] somebody

[00:53:33] on

[00:53:33] the

[00:53:33] other

[00:53:34] side

[00:53:34] who

[00:53:34] may

[00:53:34] have

[00:53:35] substantially

[00:53:36] more

[00:53:36] information

[00:53:37] and

[00:53:37] substantially

[00:53:38] more

[00:53:38] skills

[00:53:39] than

[00:53:39] they

[00:53:39] do

[00:53:39] whenever

[00:53:40] I

[00:53:51] makes

[00:53:51] a lot

[00:53:51] of

[00:53:52] sense

[00:53:52] and

[00:53:53] in

[00:53:53] terms

[00:53:53] of

[00:53:53] staying

[00:53:54] humble

[00:53:55] for

[00:53:55] quants

[00:53:56] I

[00:53:56] think

[00:53:56] it's

[00:53:56] important

[00:53:57] to

[00:53:57] always

[00:53:58] question

[00:53:58] the

[00:53:58] assumptions

[00:53:59] of

[00:53:59] the

[00:53:59] models

[00:54:00] that

[00:54:00] they

[00:54:00] use

[00:54:01] because

[00:54:02] if

[00:54:03] you're

[00:54:03] blindly

[00:54:04] following

[00:54:04] your

[00:54:05] stock

[00:54:06] screen

[00:54:06] without

[00:54:06] understanding

[00:54:07] the

[00:54:07] assumptions

[00:54:08] of

[00:54:08] that

[00:54:08] stock

[00:54:08] screen

[00:54:09] I

[00:54:09] think

[00:54:09] you

[00:54:10] can

[00:54:10] get

[00:54:10] into

[00:54:10] some

[00:54:11] hot

[00:54:12] water

[00:54:13] that's

[00:54:13] great

[00:54:14] thank

[00:54:14] you

[00:54:14] very much

[00:54:14] Jacob

[00:54:15] we

[00:54:15] very

[00:54:15] much

[00:54:15] appreciate

[00:54:15] it

[00:54:16] well

[00:54:17] thank

[00:54:17] you

[00:54:17] for

[00:54:17] the

[00:54:17] interest

[00:54:17] in

[00:54:18] the

[00:54:18] research

[00:54:21] thanks

[00:54:21] so

[00:54:21] much

[00:54:21] for

[00:54:22] tuning

[00:54:22] in

[00:54:22] to

[00:54:22] this

[00:54:22] episode

[00:54:23] of

[00:54:23] excess

[00:54:23] returns

[00:54:24] you

[00:54:24] can

[00:54:24] follow

[00:54:25] Jack

[00:54:25] on

[00:54:25] twitter

[00:54:26] at

[00:54:26] practical

[00:54:27] quant

[00:54:27] and

[00:54:28] follow

[00:54:28] me

[00:54:28] on

[00:54:28] twitter

[00:54:28] at

[00:54:29] jj

[00:54:29] carbone

[00:54:30] if

[00:54:31] you

[00:54:31] found

[00:54:31] this

[00:54:31] discussion

[00:54:32] interesting

[00:54:32] and

[00:54:32] valuable

[00:54:33] please

[00:54:33] subscribe

[00:54:34] in

[00:54:34] either

[00:54:34] iTunes

[00:54:35] or

[00:54:35] on

[00:54:35] YouTube

[00:54:35] or

[00:54:36] leave

[00:54:36] a