Speaker 1: Hello, and welcome to another episode of the Odd Lots podcast.
Speaker 1: I'm Joe Wisenthal and I'm Tracy Hallaway. So I don't
Speaker 1: know what day people are going to be listening to
Speaker 1: this episode, but um, you know, the stock market hit
Speaker 1: a record high yesterday. Yeah, it's true. So all the
Speaker 1: losses that we saw during the COVID crisis have basically
Speaker 1: been raised and markets are back where they were before
Speaker 1: all of this happened. Yeah, it's essentially six months from
Speaker 1: the pre crisis peak to this one. So I think
Speaker 1: the SUP peaked at um February on February fifteen, and
Speaker 1: then we saw the the new peak yesterday, August eighteen.
Speaker 1: And in a sense, it really feels like we've compressed
Speaker 1: this sort of gigantic cycle into an extremely short period
Speaker 1: of time. Yeah, that's true. And I was looking at
Speaker 1: the latest fund managers survey from Bank of America and
Speaker 1: it showed that I think fund managers have completely flipped
Speaker 1: from thinking that we're in a recession to thinking that
Speaker 1: we're in the early stages of a fresh economic cycle.
Speaker 1: And if they're right to your point, it does suggest
Speaker 1: that we've just seen, you know, one of the shortest
Speaker 1: recessions of all time. Yeah. I mean, you could make
Speaker 1: the argument that the recession recession in terms of the
Speaker 1: shrinking of growth was done by the end of March,
Speaker 1: when most data points started turning up. And while the
Speaker 1: overall level of economic activity is still very depressed, and
Speaker 1: of course unemployment rate is still above ten percent, so
Speaker 1: hardly time to be declaring victory. We have seen steady
Speaker 1: improvement on a host of economic data points basically since
Speaker 1: end of March early April. That's true, but I also
Speaker 1: feel like there's something kind of weird going on with
Speaker 1: the data. Like there's the old stock first flow argument,
Speaker 1: which we're seeing everywhere, but particularly in p M I.
Speaker 1: So even when we get a big rebound in p
Speaker 1: m I s, it doesn't necessarily mean that we're getting
Speaker 1: back to the levels that we saw pre crisis. But
Speaker 1: you're also seeing just sort of weird indicators that are
Speaker 1: happening simultaneously. And I think one of our colleagues pointed
Speaker 1: out a really good one recently, and that was intentions
Speaker 1: to buy a house surging at the same time as
Speaker 1: mortgage delinquencies, which I mean never happens in an economic crisis.
Speaker 1: Now it's really weird. But I think because of all
Speaker 1: the weirdness that we're seeing this sort of contrary indicators,
Speaker 1: because there's this weird gap between pieces of change which
Speaker 1: have been very fast and unexpected versus levels which are
Speaker 1: still very bad levels. And then also just the fact
Speaker 1: that it's so compressed, there's probably never been more demand
Speaker 1: for sort of alternative real time data points and this
Speaker 1: feeling that the official economic data points that we get
Speaker 1: monthly jobs report, monthly retail sales report, they just there's
Speaker 1: not enough of them. They're not timely enough to get
Speaker 1: a sense of what's going on, given how fast the
Speaker 1: changes have been both on the downturn and the rebound. Yeah. Absolutely,
Speaker 1: and I mean, just on a very simple basis, everyone
Speaker 1: wants to know what's going on with the recovery, right,
Speaker 1: and everyone's tracking to what degree the economy has reopened,
Speaker 1: and some of the most useful indicators for that are
Speaker 1: arguably alternative economic indicators like um like open table reservations,
Speaker 1: things like that. Yeah, totally. I mean, that's like one
Speaker 1: of the things we've been watching the most. It's like
Speaker 1: open table they could keep track of people making reservations
Speaker 1: or doing in seeding dining, so if you want to
Speaker 1: sort of understand how behavior has changed or how people
Speaker 1: are doing different things. Um, due to the virus, that's
Speaker 1: been one of the sort of key data points, not
Speaker 1: something that people were really tracking before as far as
Speaker 1: I know, on a meaningful level. So I think that's
Speaker 1: really important. I mean, I think obviously real time alternative
Speaker 1: data has never been more in demand than what we've
Speaker 1: seen over the last six months. But I don't think
Speaker 1: it's going away now. It's kind of another one of
Speaker 1: these things where real time data points of a range
Speaker 1: of things will sort of be part of the conversation
Speaker 1: for a long time, even if and when we get
Speaker 1: back to something resembling a normal economy. Yeah, I think
Speaker 1: that's right. So today we're going to be talking all
Speaker 1: about alternative data, what it's showing, and more importantly, how
Speaker 1: investors actually use it in their process. And so we're
Speaker 1: going to be speaking with Ben Brightholtz. He's a data
Speaker 1: scientist at Arbor Data Science, which is part of Arbor
Speaker 1: Research and Trading. I've been following their stuff. They do
Speaker 1: some really interesting things with looking at Google search trends
Speaker 1: for lots of different keywords and trying to divine an
Speaker 1: economic significance from them. So let's talk more about that. Ben,
Speaker 1: thank you very much for joining us. Yeah, thank you
Speaker 1: very much. Joe, happy to be here. So let's just
Speaker 1: start a big picture. What do you do? What is
Speaker 1: arbor data science? Talk to us a little bit about
Speaker 1: your work. Sure, So over the years we've gotten more
Speaker 1: and more into essentially this idea of filling the gaps
Speaker 1: between latent economic data and the econ data that can
Speaker 1: be distorted like we've seen with unemployment data as of late,
Speaker 1: and also really trying to help our customers and the
Speaker 1: investment space in general deal with surveys that have been
Speaker 1: more or less leading indicators for quite some time. They've
Speaker 1: kind of fallen flat on their face. And this is
Speaker 1: something that's taken place well before UM even the current
Speaker 1: episode we're going through now, looking back to the financial crisis,
Speaker 1: with really the polarization of the country and the world
Speaker 1: on a political space, and really the advent of social
Speaker 1: media has created really this bifurcation and in sentiment it
Speaker 1: could be republic and democrat or it can be more
Speaker 1: or less group think. Based on UM, the use of Facebook, Twitter,
Speaker 1: we create all these small microcosms we essentially live within
Speaker 1: and that is ultimately distorted the ability of survey data,
Speaker 1: for example, to have this leading nature that it used
Speaker 1: to have really for decades um. And that's posed a
Speaker 1: significant problem for investors that are in putting this either
Speaker 1: on a subjective level or within their own modeling to
Speaker 1: then project board where they think financial markets will go
Speaker 1: in the future. I have a really basic question, which
Speaker 1: is what's the difference between big data and a large
Speaker 1: set of data? So big data is such a misnomer
Speaker 1: and um nasty term, you know most I think big
Speaker 1: data is a term that's kind of slowly gone away
Speaker 1: that I think the initial idea is that it's it's
Speaker 1: unstructured data. That's for example, you can find all this
Speaker 1: wonderful information on a Bloomberg terminal, all right, and it
Speaker 1: comes you can download it via a p I or
Speaker 1: access at via via your your Windows or your terminal,
Speaker 1: all nice, clean and easy to use, ready to input.
Speaker 1: And big data um to me. Uh, this day and age,
Speaker 1: especially with alternative data, has to do with more or
Speaker 1: less unstructured kind of ugly data. So this, for example
Speaker 1: could be all just like us talking right now, or
Speaker 1: when you are all on TV, you have all of
Speaker 1: this this text, this closed captioning that exists out there,
Speaker 1: and let's say, for example, it's in fifteen second increments,
Speaker 1: and it can be ugly, it can be have plenty
Speaker 1: of errors within the data within the closed captioning um
Speaker 1: And essentially we have to use algorithms and different processes
Speaker 1: in order to take that unstructured data and make it
Speaker 1: something useful and really turn it into something that's more
Speaker 1: or less numerical in order to benchmark against financial markets,
Speaker 1: econ data, overall sentiment and so on. So, you know,
Speaker 1: big data is kind of a word. I think that's
Speaker 1: somewhat going away. But to me, again, it means somewhat
Speaker 1: of an unstructured data set. So I'm thinking about what
Speaker 1: you scribed as the problem with surveys, and uh, you know,
Speaker 1: I think it's either the University of Michigan Consumer Sentiment
Speaker 1: survey or the conference board one. There's one of these
Speaker 1: data points that we have it on the Bloomberg terminal,
Speaker 1: and it's like they say, it is now a good
Speaker 1: time to buy a washing machine? Is now a good
Speaker 1: time to buy a car. There's even one that's one
Speaker 1: of my favorites. Is now a good time to buy
Speaker 1: a vacuum cleaner. But I guess what you're doing is
Speaker 1: you don't have to ask people is now a good
Speaker 1: time to buy a vacuum cleaner, because in if you
Speaker 1: know how to find the data, you can just look
Speaker 1: at searches for vacuum cleaners and that's presumably a lot
Speaker 1: more reliable than asking people into survey whether or now
Speaker 1: is a good time to buy a vacuum cleaner. Right, So,
Speaker 1: the within surveys, there's and there's a plenty of studies
Speaker 1: on this as of late showing that respondents will not
Speaker 1: provide really honest answers relating to their financial hardships. So
Speaker 1: there's there's large gaps and you know, our things better
Speaker 1: now or worse? Are you going to spend do you
Speaker 1: have the money to spend here moving forward on a vacuum,
Speaker 1: on a new washing machine? And so on? And there's
Speaker 1: always been a gap, for example, example, between the web
Speaker 1: based responses and phone based and we saw this too
Speaker 1: with the election. That's a whole another other topic, but
Speaker 1: um on a web based survey and individuals are typically
Speaker 1: much more honest than they are regarding financial hardship than
Speaker 1: they are on the telephone or basically being put on
Speaker 1: the spot. So the idea here between behind search activity
Speaker 1: and this is something that I think that has improved
Speaker 1: in most recent years, is yes, we can get ahead
Speaker 1: of this intention of consumers and we're not necessarily we're
Speaker 1: not really going to lie to that little window on Google. Um.
Speaker 1: You know, we might lie maybe sometimes to our girlfriends
Speaker 1: or our boyfriends or husbands or wives. Um, but you know,
Speaker 1: what we put into that search window is really truly
Speaker 1: what we're seeking and what we're actually trying to query.
Speaker 1: There's no no one really looking over our shoulder. So
Speaker 1: our belief is that search activity, um, really, over the
Speaker 1: past five six years has become kind of a great
Speaker 1: estimate or indication of the consumers intentions of what they
Speaker 1: plan to do. Am I going to buy a wash machine?
Speaker 1: Or if I'm in distress, what does it mean that
Speaker 1: by default on my credit card payment or I don't
Speaker 1: pay my credit card payment? Or what if I need
Speaker 1: to go out and search and find a bankruptcy lawyer.
Speaker 1: These are the type of things we can pick up on, uh,
Speaker 1: you know, within this information to then create a kind
Speaker 1: of um, you know, overall look at the consumer. And
Speaker 1: this can be all the way from the you know
Speaker 1: up towards the United States, the complete um, you know,
Speaker 1: country level, it can be worldwide, and it can be
Speaker 1: drilled down all the way down to a metropolitan area UM.
Speaker 1: And again, the whole idea there is trying to get
Speaker 1: the most honest representation of the individual. And I'll also
Speaker 1: say that the growth UM in the Internet and really
Speaker 1: access to the Internet, both mobile and on the PC,
Speaker 1: has been a big boon for search activity, so that
Speaker 1: you now have fifty of the world having Internet access
Speaker 1: and using it on an active basis. That's more than
Speaker 1: four and a half billion individuals, which has really doubled,
Speaker 1: if not tripled, since the financial crisis. So I think
Speaker 1: early efforts of using search activity UM is, I know,
Speaker 1: a lot of it pre crisis kind of fell on
Speaker 1: its face and kind of faded away. Google used to
Speaker 1: have these curated indices um. I think they had twenty
Speaker 1: five of them, kind of showing how the economy, economy
Speaker 1: was moving um here and there. I think that that
Speaker 1: what didn't work as well because we didn't have the
Speaker 1: ubiquity of Google searches and really Internet access. And as
Speaker 1: that improves, this type of information becomes that much more important.
Speaker 1: I think to the investing process. How much do you
Speaker 1: think the the unusual or the extreme circumstances surrounding the
Speaker 1: coronavirus crisis are are distorting survey responses? And I asked
Speaker 1: that because again, I've seen a lot of criticism of
Speaker 1: the p m I s recently, and one of of
Speaker 1: things people are saying about those surveys at the moment
Speaker 1: is that respondents aren't really judging their experiences on a
Speaker 1: month to month basis, but they're sort of responding by
Speaker 1: comparing now to a period of relative normality. So everything's
Speaker 1: getting skewed. Do you think the unusual nous of of
Speaker 1: our current circumstances might be skewing survey data as well? Yes,
Speaker 1: I think so. I think it's it's a combination. Like
Speaker 1: you said earlier with stock flow, it's what type of
Speaker 1: reaction have we had over the past couple of months.
Speaker 1: UM I think has been more reflective within the survey data,
Speaker 1: and we're seeing that breakdown between surge activity UM and surveys.
Speaker 1: And we also have this big group think are almost
Speaker 1: circular reference that occurs within a lot of the sentiment data.
Speaker 1: So we all look to the equity market. We all
Speaker 1: know that we can use the equity market essentially forecast
Speaker 1: where consumer a confidence will be for the next month UM,
Speaker 1: and I think a lot of that's feeding into some
Speaker 1: of the more rosy consumer confidence numbers as well as
Speaker 1: the UM eyes and again that's some somewhat of distortion
Speaker 1: UM and why we seem to't like to rely on
Speaker 1: the search activity for the most part. So let's talk
Speaker 1: a little bit more about that search activity. How do
Speaker 1: you take how do you get the data? First of all,
Speaker 1: what does Google make available? And then how do you
Speaker 1: present it in a form so that it's usable because
Speaker 1: there's obviously seasonality factors the you know, you can't just
Speaker 1: look at searches for a vacation and see whether they
Speaker 1: go up or down because people don't vacation at the
Speaker 1: same uh at the same pace all year round. So
Speaker 1: how do you get the data from Google? What's that
Speaker 1: process like? And then what do you do to actually
Speaker 1: put it in a format such that it's not just
Speaker 1: OLiS for investors, like just describe it overall? How work? Sure,
Speaker 1: so we are able to access just like anybody else
Speaker 1: via Google Trends, which there is an API to be
Speaker 1: able to grab that information, and what we do is
Speaker 1: we avoid using the specific search terms. So if we're
Speaker 1: just going to say wash machine or vacuum UM, that
Speaker 1: will include specifically that exact term UM. And we know
Speaker 1: that there can be multiple variations of those actual text terms,
Speaker 1: and so we want to pick up on that. The
Speaker 1: beauty is Google curates and creates two different types of
Speaker 1: groupings of search activity. And they do this for you know,
Speaker 1: each and every country essentially, which is going to take
Speaker 1: care of the major language barriers and issues that we'd
Speaker 1: run into as well. And so that is they create categories,
Speaker 1: which there are roughly und forty plus different categories, everything
Speaker 1: from accounting services all the way out to urban transportation
Speaker 1: which would be things like uber and lift. And then
Speaker 1: they have topics and that can be anything from inflation
Speaker 1: or those talking about disinflation, or gold bugs or bitcoin um.
Speaker 1: And that's going to then be more encompassing and based
Speaker 1: on their mapping of a numerous new it could be hundreds,
Speaker 1: if not thousands of thousands in certain cases, of different
Speaker 1: search terms and phrases that then get housed underneath those
Speaker 1: individual UM topics. We can I stop you and ask
Speaker 1: you a quick question right there, Sure the data the
Speaker 1: year able to draw. Just to make clear, is that
Speaker 1: the granular within those hundreds or thousands of terms, you're
Speaker 1: able to get data for each one of those. You
Speaker 1: could see beyond just the sort of general category. Yeah,
Speaker 1: so we can drill down. There are ways to drill
Speaker 1: down within the individual categories. We understand what the actual
Speaker 1: searches are within those categories, but in order to create
Speaker 1: a more encompassing indication of what the consumer business is
Speaker 1: looking for or thinking about, we do then pull in
Speaker 1: that search trend. Essentially, that's going to be an aggregation
Speaker 1: of all of all those searches underneath a given topic
Speaker 1: or underneath a given category. And like I said that,
Speaker 1: one of the greatest things about the way that Google
Speaker 1: set this up is that you are then able to say,
Speaker 1: let's look at urban transportation uber and LIFT, and let's
Speaker 1: look at it not just here in the US. Let's
Speaker 1: go to UH somewhere like Germany, let's go to Australia,
Speaker 1: or let's go to Japan UM and they take care of,
Speaker 1: fortunately a lot of the language barriers in that urban
Speaker 1: transportation that is translated into into um, you know, Japanese
Speaker 1: or um you know whatever is being German, and so on. UM,
Speaker 1: so getting into the course of how we then digest
Speaker 1: and use that information. Like you said, is there's a
Speaker 1: high degree of seasonality. Of course, it could be like
Speaker 1: with clothing with back to school, or can be accounting
Speaker 1: services coming into March, April and October. UM. So we
Speaker 1: do decomposition where we'll we'll break down each individual topic
Speaker 1: or categories search activity into three components, and that is
Speaker 1: it's overall trend component. You think of it as like
Speaker 1: as kind of a slower moving average trend of that
Speaker 1: search activity. And then we have the seasonality that we're
Speaker 1: able to then strip out. And then we also have
Speaker 1: this thing we call the residual or the shock. What's
Speaker 1: interesting about the experience that we've seen here in UM
Speaker 1: with COVID nineteen is we were never so interested in
Speaker 1: the shock component and the very quick um shifts in
Speaker 1: search activity either positive or negative until COVID hit, when
Speaker 1: we saw it's just substantial breaks from these trends and
Speaker 1: what would be expected by seasonality. That can be anything
Speaker 1: from the searching for you know, physical policy news, economic news,
Speaker 1: how individuals are searching on the line, then for groceries
Speaker 1: UM and making those type of consumer staples purchases. But
Speaker 1: getting back to it, the idea is to break it
Speaker 1: down into those three components that we get idea of
Speaker 1: what is the you know, the long term trend UM
Speaker 1: and shift really potentially in search activity. How does that
Speaker 1: relate then to to what we're seeing within financial markets
Speaker 1: and overall economic data. And then what are these shock
Speaker 1: components and regarding those big distortions or shifts away from
Speaker 1: those underlying trends, what does that have to tell us
Speaker 1: about how things may be abruptly changing in the near
Speaker 1: term UM and what that can mean? I mean, of
Speaker 1: course for potential volatility UM and equity markets, uncertainty in
Speaker 1: general UM from the consumer base UM and so on.
Speaker 1: And so what we do is we pull down those
Speaker 1: three pieces of information that then gets used within our
Speaker 1: written content as well within our own models and our
Speaker 1: clients models UM and so on. So correct me if
Speaker 1: I'm wrong. But the data that you're using is mostly
Speaker 1: public data. If investors all have access to the same data,
Speaker 1: how are they using that to actually generate outperformance? How
Speaker 1: do they differentiate how they're using the data versus how
Speaker 1: another fund or another investor might be using the data? Right?
Speaker 1: So I mean that's that's the question we get. We
Speaker 1: probably get the most most is since we do deal
Speaker 1: mainly again with with public forms of data, there's plenty
Speaker 1: of alternative data that is private and the credit card
Speaker 1: space and spending UM and so on. Is we try
Speaker 1: to uncover data we think that is underutilized UM. And
Speaker 1: in this case with all of our dealings specifically with
Speaker 1: fixed income portfolio managers, pension fund managers UM and the like,
Speaker 1: the use of search activity on a broader scale, on
Speaker 1: a country by country, even a metro by metro level,
Speaker 1: we believe has been under appreciated UM and non internalized
Speaker 1: the extent that it could be. Now, like you said, once,
Speaker 1: think something like this gets over used or gets used
Speaker 1: as a key benchmark potentially to filling the latent gaps
Speaker 1: between economic data. Potentially some that alpha creation could UM
Speaker 1: evaporate UM and that would mean we have to move
Speaker 1: on to some additional data sources for this time being.
Speaker 1: In all our communications, the front offices of of investment managers,
Speaker 1: banks and so on have not been heavy users of
Speaker 1: the search activity. I think that early uses of it
Speaker 1: prior to the crisis and during the crisis kind of
Speaker 1: fell flat. Again. Maybe the ubiquity of actual Internet usage
Speaker 1: and those young too old that we're using Google was
Speaker 1: not there as of yet. And what we've seen over
Speaker 1: the years, really since two thousand eleven two thousand and twelve,
Speaker 1: search activities ability to fill the gap and really take
Speaker 1: the place of surveys has improved markedly year after year UM.
Speaker 1: And that's something we can measure UM statistically and VR
Speaker 1: modeling for essentially those turning points as to when maybe
Speaker 1: search activity loses its flare loses its ability UH to
Speaker 1: then forecast and now cast via g d P retail
Speaker 1: sales inflation UM and the like. But we're not there yet.
Speaker 1: So obviously the demand for this data, and you mentioned
Speaker 1: maybe search data is sort of relatively newly being incorporated
Speaker 1: into investment processes, but for years we've been hearing about
Speaker 1: satellite looking at parking lots in Walmart, or satellite looking
Speaker 1: at trained or credit card data that's been out there
Speaker 1: is a thing for a while. How intense is the
Speaker 1: search basically for new data sources, either on the bi side,
Speaker 1: the investor side, or you as sort of a data
Speaker 1: vendor so to speak, to just constantly be coming up
Speaker 1: with something that's relatively underappreciated. What does that process look
Speaker 1: like the use of alternative data within the investment world.
Speaker 1: You know, really the investment world was very late to
Speaker 1: using alternative data UM, you know, compared to healthcare, even education,
Speaker 1: UM and the like. And we initially saw this, you know,
Speaker 1: in our routines of going out to big banks, for example,
Speaker 1: and discussing with their teams, UM, you know, how they're
Speaker 1: utilizing alternative data. It was almost always in the back office.
Speaker 1: So it could be UM, you know, anything to do
Speaker 1: with their customer relations. It could chatbots in terms of
Speaker 1: creating natural better language, natural language processing and ployment data
Speaker 1: for that. It could be trade matching all kinds of
Speaker 1: different things that were done in the back office. They
Speaker 1: were trying to basically bring in machine learning, bringing better
Speaker 1: data to create better predictions. And it could have to
Speaker 1: do again with their customers, which customers to call and
Speaker 1: not call, who's going to potentially provide the best UM,
Speaker 1: best avenue for new business UM and so on. But
Speaker 1: what we've seen, i'd say, you know, starting roughly inen,
Speaker 1: we started to see a UM with the advent of
Speaker 1: more alternative data available via numerous vendors, the increase in
Speaker 1: transfer to the front office has happened rapidly UM. And
Speaker 1: I would say that now with COVID nineteen and the
Speaker 1: inability for econ data to keep up with the actual
Speaker 1: UM happenings of the economy UM, and really the needs
Speaker 1: of investors to understand that just what's going on with
Speaker 1: how rapidly things are changing. UM. The demand is just intense,
Speaker 1: and so it calls to us and and calls I
Speaker 1: know too many of our competitors in similar, similar alternative
Speaker 1: data providers. UM has just shot to the moon and
Speaker 1: you can see that again. Bloomberg of course offers some
Speaker 1: of this alternative data. There's plenty of other repositories to
Speaker 1: grab it, but I would say that the degree of
Speaker 1: interest is increased tenfold UM since it's it's beginnings in
Speaker 1: what's been your favorite alternative data set during the crisis,
Speaker 1: Like what has either surprised you or what has been
Speaker 1: most useful in judging the direction of the overall economy.
Speaker 1: We've been benchmarking a lot. The mobility data that's available
Speaker 1: via Apple UM and to cart lab is another one.
Speaker 1: Google and Benjy benchmark in that off of search activity,
Speaker 1: and I've been absolutely shocked at how well. Search activity
Speaker 1: has been able to predict two things, and that's been
Speaker 1: retail sales on a month over month basis and also
Speaker 1: inflation on a month over month basis. A lot of
Speaker 1: our kind of point forecasts looking forward, based on what
Speaker 1: we believe are the most unique and important search activity
Speaker 1: have done very well UM in predicting the rebound in May,
Speaker 1: for example, the heavy damage done to transportation, energy, UM
Speaker 1: and apparel within March and April. To CPI for example,
Speaker 1: we had UM noticed the heavy degree of rebound in
Speaker 1: all three of those categories, in particular UM within apparel,
Speaker 1: which ultimately lead to rebound in overall apparel spending in May,
Speaker 1: which then ultimately translated to higher inflation UM it was
Speaker 1: reported in June. And so the search activity that we've
Speaker 1: been able to use most utilized, which I think Joe
Speaker 1: featured in a CHARTUM a number of weeks ago, has
Speaker 1: to do with a series of key categories, and that
Speaker 1: can be everything from beauty and fitness, which is we
Speaker 1: found to be a highly leading indicator UM as well
Speaker 1: as just the general public searching for economic news and
Speaker 1: physical policy news revolving around welfare and unemployment and jobless
Speaker 1: benefits welfare and unemployment. Unemployment itself has been a highly
Speaker 1: leading indicator. And then also UM, one of the things
Speaker 1: we picked up on very early was the incredible drive
Speaker 1: for home improvement that really began in the final weeks
Speaker 1: of March. UM. And what we had seen was this
Speaker 1: effervent search activity UM, you know, looking across all the
Speaker 1: major metros and all the major states of the United States,
Speaker 1: heavy degree of need for our need, a desire to
Speaker 1: place appliances, to paint their homes, to get a new roof,
Speaker 1: new side, new carpeting, UM. And this is something that
Speaker 1: really took place ahead of the Hares acting signed on
Speaker 1: March seven, It began really two weeks before that, which
Speaker 1: I think was a leading indicator that the consumer would
Speaker 1: be stronger and potentially spend more UM than those uh,
Speaker 1: the naysayers. And then that we had expected UM to
Speaker 1: see given the calls for a recession and potential depression,
Speaker 1: given the full stop to the economy. And it's really
Speaker 1: striking just this week, UH, we've seen home depot and
Speaker 1: lows post extraordinary sales. Home improvement has just been one
Speaker 1: of the monster stories of this recovery. How much spending
Speaker 1: and how sustained that's been I just want to drill
Speaker 1: a little bit further down. I mean, it's clear that
Speaker 1: like okay, if someone identified that trend at the end
Speaker 1: of March and I saw what was going on, there
Speaker 1: were huge investment opportunities because like again, like I said,
Speaker 1: home depot lows, it's that are huge beneficiaries that their
Speaker 1: stocks about extraordinary runs due to this, uh desire for
Speaker 1: people that like renovate and fix things in their home
Speaker 1: while they're working from home and so forth. How then,
Speaker 1: do in your clients and when you talk to them,
Speaker 1: how do they actually make a decision by or sell
Speaker 1: based on the data and the context that you're giving
Speaker 1: that What is the the you know, that's sort of
Speaker 1: the last mild question, so to speak. They can get
Speaker 1: the data from you, but then how are they actually
Speaker 1: using it to form of you and take a risk
Speaker 1: both on a subjective then also on an algorithmic basis.
Speaker 1: We have many, many clients that are effectively now casting,
Speaker 1: and so they're now casting either econ data, the econ environment,
Speaker 1: and then as well the financial the impact on the
Speaker 1: on the actual financial market in terms of producing their
Speaker 1: own actual forecasts of where things will be one week
Speaker 1: to six weeks to twelve weeks later. So the search
Speaker 1: activity UM is one that we found provides a lead time. UM.
Speaker 1: That's more you know, kind of medium term as opposed
Speaker 1: to ultra high frequency short term. So uh, you know,
Speaker 1: within the searches, just like survey data, UM, we're not
Speaker 1: going to be able to help someone UM if if
Speaker 1: effectively make a decision for that day. You know, what
Speaker 1: is the next twenty four hours of economic activita? People
Speaker 1: buying more watch machines, they buying more cars, m Are
Speaker 1: they buying more apparel? It's not That's not exactly how
Speaker 1: it works. It's a more immediate term, medium term focus
Speaker 1: of UM. Varying lead times typically from one week to
Speaker 1: eight weeks. So we have things, for example, like apparel
Speaker 1: UM that will have a lead time of days to
Speaker 1: a week UM, and then we'll have things like building
Speaker 1: materials or roofing that will have a lead time of
Speaker 1: seven to eight weeks. So then what our customers and
Speaker 1: our clients are doing is taking that information in understanding
Speaker 1: those lead times and then either in putting it to
Speaker 1: their own subjective decision making process in order to affect
Speaker 1: their decision. It could be a risk management one in
Speaker 1: regards to their actual book or their position, determine if
Speaker 1: there's something that could be disruptive to their position, or
Speaker 1: it could be on the flip side, someone that's actually UM,
Speaker 1: you know, using on a more tactical basis, that is,
Speaker 1: and in putting it to their own now casting forecasting
Speaker 1: process and that coming up with their own conclusion UM
Speaker 1: of how will that supports or doesn't support their their
Speaker 1: general idea. But UM with this data, along with a
Speaker 1: lot of the natural language processing data that we work with,
Speaker 1: UM does not have a high frequency basis. This is
Speaker 1: something that's more medium term, if not long term in nature.
Speaker 1: Then last thing like where do you see what's the
Speaker 1: next big thing for you? In terms of I just
Speaker 1: thinking back to when you said, Okay, at some point
Speaker 1: the search data will get more used, the it will
Speaker 1: get more modified, the alpha from having access to it
Speaker 1: will theoretically diminish. What are the next frontiers in terms
Speaker 1: of data that you think are interesting and potentially still
Speaker 1: underappreciated or underutilized at this point. So I think the
Speaker 1: advent of mobility data, for example with discard Dicart labs
Speaker 1: that's able to zero in on specific retailers and look
Speaker 1: at the actual foot traffic UM that's occurring coming to
Speaker 1: them going away from them. It can be also down
Speaker 1: to you know, parks UM specific locations within different metros
Speaker 1: or rural areas. I think this mobility data, which we
Speaker 1: don't have a high degree of historical data to work with,
Speaker 1: is something that moving forward will become more and more
Speaker 1: of the leading indicator that think individuals will seek for. Unfortunately,
Speaker 1: you know, Apple, Google and the cart labs have you know,
Speaker 1: they sell this data, so it's not necessarily publicly available.
Speaker 1: But I think as they build a larger and larger
Speaker 1: track record in order to benchmark that against anything begun
Speaker 1: search activity, survey information of how consumers are operating, where
Speaker 1: they're moving, and what they're doing. I think that is
Speaker 1: more or less the kind of the cutting edge and
Speaker 1: leading edge of understanding the consumer and now and then
Speaker 1: how they're interacting with retail, interacting with people around them
Speaker 1: using urban transportation UM and so on. And obviously in
Speaker 1: this environment of COVID nineteen with how much we were
Speaker 1: not moving around in March and April UM, I think
Speaker 1: it'll be critical UM here moving forward to get a
Speaker 1: better grasp on how much of a revival um economies.
Speaker 1: There's economies are seeing and how mobile people have become
Speaker 1: or it will be one of the fun ones we
Speaker 1: didn't talk about, but I know it's uh, it's definitely um,
Speaker 1: you know, fringe too, just because it's it's such a
Speaker 1: strange space. Is that the Twitter sentiment is one that's
Speaker 1: become I think, more and more useful UM in terms
Speaker 1: of gauging actual investor sentiment. It's been pretty wild to
Speaker 1: watch the number of economists and even formal central bankers
Speaker 1: that have popped up on Twitter that use it pretty voraciously.
Speaker 1: We even have like Christia Freeland, Um, you just took
Speaker 1: over a finance minister in Canada. There's just such noteworthy individuals,
Speaker 1: and it's it's become something that's become more and more predictive,
Speaker 1: I think, not necessarily a direction of equity markets, but
Speaker 1: more or less a gauge of uncertainty UM and you know,
Speaker 1: financial market volatility. So we've built a lot of algorithms too.
Speaker 1: It's just like another thing where it's kind of like
Speaker 1: because I know people were like interested that ten years ago,
Speaker 1: but it wasn't enough interest. There weren't enough people on
Speaker 1: there for Twitter or social media to be representative, but
Speaker 1: sort of like kind of like search where you can
Speaker 1: actually get a big enough cross section that it's meaningful exactly.
Speaker 1: So that's the what's absolutely wild is the number of
Speaker 1: people provide providing original content and the speed by which
Speaker 1: they are actually tweeting has accelerated just demonstably. So we
Speaker 1: saw this incredible crescendo in tweeting and Twitter activity in
Speaker 1: fin twitt through really the middle of March, and it's
Speaker 1: just held there ever since. With this COVID you know,
Speaker 1: pandemic everyone at home, UM and really grasping for information.
Speaker 1: So it's been fun to be able to break down
Speaker 1: all the different opponents of Twitter, which we do into
Speaker 1: is based on clustering prior to the financial crisis. We
Speaker 1: break it down into primables, bears, primatists, UM, economy US
Speaker 1: UM and the like UH, and then able to grab
Speaker 1: out you know, how are they feeling about liquidity in
Speaker 1: the market, how are they feeling about the equity market?
Speaker 1: COVID nineteen UM, I had the consumer UM and so on,
Speaker 1: And it's amazing pulling in the information, like you said,
Speaker 1: prior to just three or four years ago, its ability
Speaker 1: to actually get ahead of and forecast, you know, volatility
Speaker 1: and maybe a little bit of financial market direction is
Speaker 1: improved significantly. So it's it's it's been an interesting space
Speaker 1: to dabble into. Ben. That was great, Ben bright Hole,
Speaker 1: I really appreciate you joining us. This feels like such
Speaker 1: a big area and there's such a clear explanation of
Speaker 1: how it all worked. Thank you for coming on. All right,
Speaker 1: Thanks Joe be so much fun. That's really interesting. Yeah,
Speaker 1: I thought that was great. You know, I do feel
Speaker 1: like just from us from a media perspective, we you've
Speaker 1: never used alternative real time data as much as we
Speaker 1: have over the last six months, and so I thought
Speaker 1: it was great to hear how it's actually collected and
Speaker 1: then how it's actually used to put into an investment process. Yeah,
Speaker 1: it's funny like thinking back to this now, but I
Speaker 1: remember in I guess it would have been February telling
Speaker 1: someone about how we were tracking movie bookings in our
Speaker 1: theater bookings in South Korea because of the COVID outbreak there,
Speaker 1: and the person I was telling it to just thought
Speaker 1: it was like so unusual and so amazing. But of
Speaker 1: course now everywhere around the world and especially in the US,
Speaker 1: people are looking at all sorts of those kinds of things,
Speaker 1: from restaurant bookings to the mobility data that Ben was
Speaker 1: talking about. Um, it's kind of become normal. Yeah, And
Speaker 1: I'm really fascinated by the sort of you know, the
Speaker 1: speed with which sort of alpha deterior rates. So you
Speaker 1: can imagine the first person who really discovers that search
Speaker 1: indications for certain terms has some predictive value. There's a
Speaker 1: lot of money to be made in that. But look,
Speaker 1: I mean we're talking about it on the podcast that
Speaker 1: Ben's active, and pretty soon you have to figure that
Speaker 1: will be table stakes that people will be searching for
Speaker 1: the next, the next thing, that that is a process
Speaker 1: that will essentially never stop. Yeah, I think that's right.
Speaker 1: But also I think what becomes clearer from speaking with
Speaker 1: Ben is that understanding the data, how it's collected, and
Speaker 1: how you can actually apply it is really really important.
Speaker 1: So even with something like the mobility data, it's very
Speaker 1: useful at the moment, but I think it's benchmark to
Speaker 1: early January or something like that. So it's really good
Speaker 1: to be aware when the summer comes around that the
Speaker 1: benchmark that you're comparing the data to might not be
Speaker 1: you know, completely applicable to warmer weather. So there's all
Speaker 1: these quirks in each data set that you really have
Speaker 1: to get to know. Yeah, totally. I mean even with
Speaker 1: the Google Day it a just having the sort of
Speaker 1: experience to adjust for seasonality it takes. Those are all
Speaker 1: things that if you were to say, if I were
Speaker 1: to just look on Google trends and look at that vacations,
Speaker 1: it would be hard for me to get much signal
Speaker 1: unless I really like understood the data and had experience
Speaker 1: working with it. Mm hmm, yeah exactly. All right, shall
Speaker 1: we leave it there? Yeah, okay, this has been another
Speaker 1: episode of the ad Thoughts podcast. I'm Tracy Alloway. You
Speaker 1: can follow me on Twitter at Tracy Alloway and I'm
Speaker 1: Joe Wisn't Thought. You can follow me at the Stalwarts,
Speaker 1: and you should follow our guest Ben Brightholtz on Twitter.
Speaker 1: He posts tons of interesting charts from the uh the
Speaker 1: Arbor research work that they do. Follow him at Ben Brightholtz.
Speaker 1: Follow our producer on Twitter, Laura Carlson. She's at Laura M. Carlson.
Speaker 1: Followed the Bloomberg head of podcast, Francesco Leavi at Francesca
Speaker 1: Today and check out all of our podcasts at Bloomberg
Speaker 1: unto the handle at podcast I for listening.