Robin Greenwood: Thanks very much. Wonderful to be here with all of you and to describe some new research, that I'm doing with, with folks at State Street. So the genesis of this research is really comes out of the I think the first question you ask when somebody says that one should look at flow data, which is whose flows and why, and people typically will, then you'll as you start to think about it, you'll say, well, why do people trade? They trade. Maybe because they have information and then maybe they don't have information. Maybe they have to have, maybe they have to trade. So for example, stock going into the an index. And does that drive demand. And then who exactly is providing liquidity for those trades. And I think the overall intuition that we've had for a long time is the reason that the amazing State Street flow data is useful for investors. And one can look at it is because really two reasons. One is that there's some persistence in, you know, flows today tend to predict flows tomorrow. And then the second is of course, when those flows hit the market they impact prices. And then the question is, well, why do they impact prices. Who's trading against who? Who's providing liquidity to whom? So I think a lot of that we've done in the past really on faith, which is to say we've had those mechanisms in mind, but we haven't been able to actually understand the other participants in the market that well.

Robin Greenwood: So this is really a first attempt to try to do that. And we're going to be looking at, two additional players in the market that are interesting. One is the retail trader and the other is firms. And as as you just heard, you know, the retail trader has been in the news, certainly post pandemic, they were in the news a lot for things like GameStop. I'll show you a GameStop picture actually in a little bit. but, they have come back in the news over recent, recent periods. And actually, there's one of the things that's amazing is that over the past few years, we now have algorithms that allow us to track retail trading in the markets. And these are just estimates. So they're not precise in the way that the State Street data is very precise, because we know exactly who is trading. And we can even guess why they're trading. Retail is a little bit more challenging. But we we do have some algorithms now where we can tell on a daily basis really net net retail demand. And then second, we're going to be pairing that with data on firms issuance. And here when you think about issuance I want you to think about IPOs seasoned offerings. But most importantly M&A activity. But most importantly, and this is the thing that most that many people forget issuance for companies today is employee stock grants and employees receiving compensation.

Robin Greenwood: And then those stocks, those shares hitting the market. And that is, of course, a form of liquidity provision in the long run. So that was a long preamble. And so I'm going to just show you jp into some of the data and tell you how we do some of this. And I want to just share with you some preliminary conclusions from this analysis. I would say one of the interesting things is that we have observed is that the level of aggregation matters very much. And so there's a lot of common wisdom about what institutions do. There's a lot of common intuition about what individuals do when they trade and how one aggregates matters. So, for example, and we're not going to go deep into this today, but a lot of people have said retail traders tend to be somewhat contrarian on a daily basis. So when when a stock goes down, retail tends to be buying. That's true. On the other hand, once you aggregate, for example, at the sector level or at the industry level, retail starts to look a bit more trend following and trend chasing. So aggregation does matter. And of course we're going to be using aggregate State Street data, which is at the sector and the industry level. Okay. So why do flows matter. Of course liquidity demanders move markets where the State Street data, as you know, is a collection of trades from disparate group, disparate groups.

Robin Greenwood: Of course, if you add up all traders in the economy, the net is zero. And so we're trying to add up. Look at this in groups of individuals and institutions that matter. okay. so what are we going to be able to say today in terms of conclusions from this work? First, firms tend to systematically provide liquidity to both retail investors and institutions at Medi Horizons, which is not really shocking, I'm sure, but it's actually a useful framework for thinking about this at the Daily Horizon. Firms are not super important. You know, if I decide to increase my active weight in a stock or a or an industry to tomorrow, there's not much the firm is not going to be able to react to that. And stock is not and provide liquidity. On the other hand, when you're looking at horizons of months, quarters, years, firms are much more important for thinking about absorbing net demand from institutions and retail. Second, by and large, retail investors do not provide liquidity to institutional investors. That was something that I frankly, I had a prior that institute, that retail was sort of ultimately the set of folks who were absorbing a lot of demand. That is not true, at least in the data that we have seen. If anything, retail trades tend to be somewhat positively correlated with institutions, and retail trades tend to be very positively correlated with institutional benchmark flows.

Robin Greenwood: What does that mean? Well, it's pretty intuitive. When retail is buying, say, a particular area of the market. They tend to also be sending money to the institutions that are in that area of the market. That's a benchmark flow. And so you actually see a positive correlation between between those two very modest correlation with the active active flows. There's some predictive value from incorporating retail trading to forecast horizons at a monthly horizon. I'm going to show you a little bit of predictive stuff. We're going to do a little bit. We're going to do more of that in the months to come. And I'll be sure to share those results. Okay. So first thing, just by way of motivation, it's worth when I'm here, I'm going to be looking at the US equity market today. That's where we have great data. this is from the financial accounts of the United States, at the last quarter. So Q4 of 2023, this is from their levels tables, and this is the total value of publicly traded stocks in the US. This is 57 trillion. And of those institutions hold about half. To give you a sense. And household sector is also about half okay. Now this can be a little bit misleading. And so I just want to give an asterisk to this comment, which is that the way that the flow of funds aggregates this data, the household sector includes a lot of stuff that is not mom and pop.

Robin Greenwood: Okay. So for example, some hedge funds would be included in households. Okay. So again that's it's tricky for how you think about that. And our mapping when we think about the data isn't going to match completely to what a flow of funds mapping would be. But anyway, this is just to give you a sense of sort of how important the, the, the relative importance of households versus, institutions. And what do you see, of course, is who is on the other side of this market is of course issuers. And so understanding issuance behavior is also clearly important. So first let me get into this, which is how exactly do we estimate retail trading. So I have to tell you this has been essentially a bit of an academic food fight over the past five years or so. And so there's this very famous paper by these four authors. It's now called the BJs algorithm. And essentially what they observed was that because of Finra's regulations, retail traders have to get price improvement. And so what does that mean? Price improvement doesn't mean that you get a really great deal. It means that you as long as you get a sub penny improvement relative to mid. That's okay. From the perspective of Finra. So what is the the essence of the algorithm is to say let's look at every trade that happened every single day.

Robin Greenwood: And let's look for those sub penny increment trades. You know the things that are happening at you know, 25 point 5002 that would be at seven shares trading at that amount reasonably classified as a retail trade that day. Okay. Similarly that's on the buy side because you want to get or so that's on the sell side because you're getting price improvement up. And on the buy side it would be something just below around around nber decimal okay. So that's the basic idea of their algorithm. And then they go through and they classify every trade as a retail buy or a retail sell okay. And then they do some validation of that exercise to basically go through and say, okay, we have other sources of retail trading, for example, from brokerages and from other folks. Can we at Longer Horizons verify that what we're coming up with, which is really trade by trade, day by day, aggregates to something that looks like what retail is actually doing. Okay, now this is turned into a food fight. Why? Because people started immediately using this algorithm. And in fact, there are Wall Street shops that publish a bunch of nbers using this algorithm. Okay. In fact, if you ever by those of you in the audience who have ever bought estimates of retail or looked at folks producing analyst reports on retail trading, that is done using algorithms basically like this.

Robin Greenwood: So then other folks started looking at this and asking, well, how good is this algorithm really? And there's this fascinating paper that I would encourage you to look at by Teri Odin and some co-authors. And what they did is, I don't know how they got the approval or the money to do this, but they essentially, did I believe it's tens of thousands of random trades and using their own money essentially, you know, small trades, hundreds, $100, $1,000 and so on. And then they went after the fact and said, using this algorithm, how good is it at capturing those trades that I did as a retail trader? And what they found is it's not as good as one would like. But my overall assessment was, is that it's pretty good. And now there's this kind of escalating fight between these various authors, and there's a new set of authors, and there's some Notre Dame authors that have come into this as well. So anyway, all of that is to say, take it with a grain of salt. I think it's useful as an indicative measure of what retail is doing on any given day, but it's not a perfect measure. Okay. So what's the methodology. We're going to apply this identify retail trades separate buys and sells and then measure net retail demand for a stock as buys minus sells okay. And then we'll form a daily measure of retail demand which is scaled by shares outstanding.

Robin Greenwood: And then I'll aggregate to the sector of the industry using end of month value weights. And I'll show you some daily and monthly nbers here. Okay. Issuance is that second piece of data that we're bringing to this. How are we measuring this. This is for the US US companies at least for now. We're think about this as employee stock grants. That's the most important for the typical stock seasoned offerings IPOs and then stock based M&A activity. The one thing I'm not actually including here is IPOs in the sense that I'm just looking at existing issuers and expansions and shares outstanding rather than new entrants to the market. That's something that we may incorporate in the future. So in terms of measurement, how do we do that? It is split adjusted shares outstanding from Crisp from 2005 onwards, measured in percentage changes over one, six and 12 months. We're then aggregating to the gics sector and industry levels. And as I said, we are excluding IPOs, but they are a reasonably a form form of issuance. Last piece of data, of course, is the State Street data on institutional investors. As you know, $42 trillion under custody. Variety of different asset classes and regions. And then a large group of investors that we identified earlier mutual funds, collective funds, pension products, insurance and so on. Of course, we don't have the fed in here.

Robin Greenwood: We don't have dealer inventory. So there are players who are not in here. But I think it's a good sense of institutions. overall, you've probably seen this slide before in one of Alex's presentations. This is how do we think about the institutional flows really in two components. So we're going to define flow in a way that's actually a little bit different than academics define flow. But I'll get to that in a second. So we're going to define the total flow as essentially net purchases of the asset okay. So if institutions overall or buyers of Tesla on a given day that's a positive flow for Tesla okay. Now the real question is why are they net positive buyers that day? Is it because the institutions overall have received inflows and are expanding their overall portfolios? And they were just holders of Tesla to begin with? Or on the other hand, are they increasing their overall exposure to Tesla? Okay. So the separation between those two, the expansion of the institutional portfolio, we call that a benchmark flow. And you might reasonably think that that happens because those institutions are growing sometimes because they get funds from investors or the active flow. Is that active increase in weight. Again, we're going to be aggregating everything to the sector, sector and industry level here. And the aggregation does does matter for for how you how you think about this. Okay, so let me show you some plots that I think are just interesting.

Robin Greenwood: And I'm putting here the labels for these variables, because I know a bunch of folks in the audience use some of these indicators. So it just gives you a sense of this is my goal here is to do to tell you about research that you could easily also replicate and do for yourself, just if you were interested in understanding, this data. So what are we using here? We're using, as I said, the active and the benchmark flow. This is the sector level variable. And I'm plotting here for you the IT sector institutional flows. The reason the only reason it starts at 0 in 2005 is because the retail data starts in 2005. So everything that I'm looking at today is going to be 2005 plus okay. Right. And you can see here culative benchmark flows. Meaning overall institutions that have had what the way to interpret that picture is institutions that have exposure to it have net experienced reductions in capital over this period. On the other hand, in the beginning you can see a culative active flow. They have been sending money disproportionately disproportionately into it. But overall, by the time you get to the end of the period, you can see that they have both been net net negative. The other thing you can see in this picture, as I'm showing you both the monthly data as well as the acculation over time, you can see that there's a lot of noise in the monthly data.

Robin Greenwood: And later on I'll talk about the persistence. Okay. Now this is issuance. And I thought, we always when we think of issuance, we usually think it's positive. But of course there's lots of repurchases as well as well. Here are two examples on the complete opposite ends of the spectr. So the first one you can see is Tesla which has been a massive net issuer as you know much of that to Elon Musk himself. over this period of time and in the form of a stock grant, and then you can see Microsoft, which of course has been essentially on a path post 2005 of repurchasing, repurchasing stock on a, on a regular basis. You can see, even for a company like Microsoft, there's kind of periods when it goes quickly and then periods when that slows down. and so on. Of course, uneven net repurchases, when they engage in a capital campaign, they want to increase investment and so on, they're going to slow down their repurchasing activity. we could if this were a corporate finance class at HBS, we would spend a lot of time thinking about exactly why these firms are doing it and in what circstances and what drives the repurchases and so on. But just suffice it to say here, lots of variation at the firm level.

Robin Greenwood: Okay. Now, once you aggregate this is looking at the 12 month issuance distribution for December of 2022. And you can see the reason I show you the histogram is to say, well, plenty of firms issuing, capital into the market. And of course lots of firms repurchasing equity as well. This is a percentage change. And one of the things that I have noticed is, that the measurement matters a lot. So for example, percentage change makes it look very big when you have, say, a 50% net issuance. But of course, if that's a small issuer, the dollar amount and the impact on the overall market is less so once once you start to think about aggregation, measurement matters quite a lot. This is an example of that. So this is it. Sector Valuated issuance from 2005 through 2023. And I'll just let you do your own smell test here to see if that feels right. It feels right to me in the sense of, you know, the post-Covid boom. You can see a massive increase in it, sort of a booming, booming sector, the beginning of the meme stock era, and then lots of corresponding corresponding issuance over that period, a little bit of a decline and then another boom in the in the subsequent subsequent months. health care here, you can see it very dramatically. You can see the Covid effect of lots of securities issuing additional shares during this time.

Robin Greenwood: Then there was the essentially health care apocalypse when everything came crashing down. And we can see some recovery over the past, over the past year. So let's look at retail trading now. I find this data to be fascinating. So I guess I'm sticking with Tesla by way of example everybody knows Tesla. So this is showing you these are five day gross buys and five day gross sells. plotted over time. And you'll see there's a red line here. you may not even be able to see the blue line. Right. I think it's pretty hard to see why is that? They sit almost on top of each other. It is shocking. So in general, when you have lots of retail folks in the market buying, they're also doing lots of selling in that day. In other words, retail really tends to be in the market and out of the market day trading. and essentially net zero every single day. That green line at the bottom is the net trades. And shockingly, and this is very different than I believe the institutions, the net buys here are just a totally different scale and volatility relative to the overall activity. And this is one of the things that I have found just amazing in the data is it's actually so much easier to predict that retail is interested in trading in the market than it is to try to predict what direction they're going to go on any given day. Okay.

Robin Greenwood: Here.

Robin Greenwood: I'm trying to zoom in just so that you can see, because I thought that last picture, it's a bit hard to see, but this is zooming in on a sort of smaller time scale, and you can at least see now that the gross buys and the gross sales, and you can see how how closely they track each other on, on any given, day. That's also I'm five days is just easier. So it's a little smoothed. But one day you'll get exactly the same picture. Now of course you can acculate. This so that you can see what happens over time. And this is looking at the culative net retail purchases of Tesla over a period of over a period of time. and every single day what I'm doing is I'm measuring a percentage net purchase, okay, of the total. And then I'm just adding this up over time. The there's a a little bit misleading the picture in the following sense that because there's a lot of issuance happening, the even though this acculates to 50 to, excuse me, 10% of shares outstanding over time, it's not 10% of the new shares outstanding. Because as the share count expands, those original purchases are less than the a smaller fraction of the total going forward.

Robin Greenwood: I think you follow. Okay, but in any case, what you can see here is a very different set of units. Remember when I was showing you the five day trading of retail? That vole is massive. So they're trading a huge amount in the markets every day. But the net purchases here over time actually end up adding up to a relatively small amount. And this is for Tesla, a stock that was super retail intensive in terms of their trading. Okay, I figured it was obligatory. When you're talking about retail trading, let's just show you a picture of GameStop. So here's GameStop. Here's the retail net culative buys of GME. And the picture sort of reveals its own meme period. You could see here. And unfortunately, this data is cut off at the end of 2022. But it would be an interesting exercise to potentially look at what's happening, what's happening in retail recently. but you can see here that for some stocks like GME, the net retail demand can actually be quite sizable. Right. So in the GameStop case this is 20% of shares outstanding. That's a sizable effect for the typical stock. It's really a much smaller net effect culatively over time.

Robin Greenwood: Okay.

Robin Greenwood: So one of the things you can do is you can look at the relationship between retail vole and past and returns. And you can see here that really retail traders seem to like just volatility. So stocks are up a lot. They're in involved. Stocks are down a lot. They're involved. And so you have this U-shaped U-shaped pattern. So that's one of the things that I'd like to emphasize. I am going to show you some overall correlations and correlations with future returns. But oftentimes in this data you find these U-shaped patterns. And so sometimes even something that say it tells you that there's a positive 10% correlation. Sometimes that's actually just a you with a slightly smaller left part of the you than the right part of the view. Okay. So sometimes it's good to kind of dig into this data. These are bins, scatter plots. Those of you who do, data analysis or econometrics can know what this is. Essentially what we're doing here is we're collapsing the data into 20 equally sized blobs and then plotting the averages of the blobs. Okay. So each one of these is actually many thousands or many millions of data points compressed into one. Okay. So the reason I say that is I don't want you to when you look at a picture like this, you say, oh gosh, it's such a smooth relationship. Of course, there's tons of noise in the underlying data, but this is trying to give you a sense of, on average, what the what the patterns are.

Robin Greenwood: And this is a tool that that many people use just to get a smell test of the data. Okay. yeah. And this is once you do it, once you do this with retail net purchases and returns, it's really the same kind of picture, but it ends up being a little bit more muted in terms of of the units. Again, it's a U-shaped pattern. Okay, so let me jp into some correlations and then, tell you the kinds of some of the things that we find. So first, the first set of things that I'm going to do is aggregate everything to the sector level, and I'm going to leave out issuance for now. And the reason I'm going to leave out issuance is because I want to first look at it daily. And really, I think it's misleading to look at the relationship with, with issuance on a daily level, because we don't think of those decisions as being made really day to day. They're really over weeks and months and so on. So if you think about firms providing liquidity to players in the markets, it really is something that needs to happen over a longer period of time. So I will show it to you. But I'm going to I'm going to pause on that and show you, monthly data in a moment. Okay. So the first thing that I would point out, so this let's look at the first coln for a moment.

Robin Greenwood: So this is retail net buys. Again everything at the daily level sector okay. First thing that you'll observe is that there is a positive correlation with institutional benchmark flows. So what does this mean. This means when retail is buying net stocks and the IT sector today they are also sending money to institutions that on net have exposure in it. Okay. You know kind of intuitive right. They would be buying the tech focused mutual funds. They would be buying the ETFs that have exposure in those areas and so on. So you do see I think you'd want to see a correlation. there you see for that day's return you actually see a negative relationship okay. So on average this is at the sector level. This would be consistent with what people have called the contrarian behavior of retail investors, which is that they tend to be buying on down on down days and down down periods of time. On the other hand, it's a bit of a nuanced picture because there's a positive correlation between retail net buys and the lagged return. Okay. So they tend to be buying, when prices are up a little bit over time. But down today, retail sort of tends to be, providing liquidity, providing liquidity. You can see that as well with a lag, five day, five day return. here now when you turn next coln here is showing the active flows. And you can see that the active flows, are not well, I guess you could see that from the first coln.

Robin Greenwood: The active flows are not very correlated with retail net buys. So this was one of the things that we motivated us to look at this, which is, you know, are our institutions on average sort of trading against the retail on a daily basis. And the answer is obvious. Not not obviously. Yes. You know, they're not trading in the same direction in terms of their active trades, but they're also not offsetting in any and any meaningful sense. so again, I guess at some level it even deepens the mystery because really we think of retail and institutions as being, I think of them as being more different than they are in the data. Okay. Benchmark flows here you can see here are negatively correlated with current returns and benchmark flows. it's somewhat this is where it's, it's there's some inconsistency here because the benchmark flows tend to be negatively correlated with past returns. And retail is positively correlated with past returns. And yet benchmark flows and retail buys, net purchases are positively correlated. So, it's one way to put that is that they tend to be institutions and retail tend to be trading in the same direction, even after you control for the fact that they tend to be reacting in opposite directions to returns. Okay. So it must be something else, that, that is driving that. Okay, let me show you a couple more things. So, as I said, retail tends to be selling on days when when markets are up, this is when you aggregate to the, to the sector level. And also retail buys versus benchmark flows. This is a I think one of the strongest things to emerge from from the data is this positive correlation just in the directionality of trade. that when folks are buying more of a particular area, they're also directing capital to, to institutions. on the active side. As I said, there's a sort of negative but very weak correlation. This is just this idea that I, we thought initially that institutions might be trading against retail. But as you can see in the data, it's a very essentially non relationship here. There is it's slightly negative in a way that I was expecting. But it's very very weak in the data. Now when we bring issuance into the picture it's quite interesting. So now we're going to aggregate to the monthly level. So we were already at the sector level daily. Now we're at the monthly level. And first thing to look at again in this first coln is retail. Net buys is a function of issuance. And you do find that there's this positive correlation between our retail net purchases and issuance in that month.

Robin Greenwood: It's also a positive correlation between retail net purchases and issuance over the past 12 months. What does this mean. This means that as firms are issuing stock, retail on average is moving in the direction of absorbing it. as you can see from those pictures I showed you from Tesla, for example, they're not really doing much in terms of actually absorbing the total, but they're trading directionally in a way of absorbing the vole that is being created by firms. as you look at the active trades, there is also a positive correlation. and it's even stronger at the 12 month horizon. Some of this I want to caution you, is mechanical and has to do with the way that the active flows are calculated, but I think it's also pretty intuitive. Somebody has to accommodate issuance at longer horizons, and you can see that this is happening largely through the active positions of, of funds, surprisingly, no impact from benchmark benchmark flows. So again, I would have thought going into this, that benchmark flows would have been driving, a positive correlation with issuance, but we don't find that in the data.

Robin Greenwood: Okay.

Robin Greenwood: here's just a Ben scatters to show you. So retail purchasing and issuance so you can see a positive correlation, but it's not especially strong. yeah. So last thing that I want to show you and then I'll take a couple questions is on the persistence. So one of the reasons that indicators can be useful is because there's underlying persistence in the data. So if you see something happening today you can be pretty sure it's going to happen tomorrow. And the day after that and the day after that. And therefore that allows you to forecast forecast returns. So I'm just going to show you three sets of results really persistence of institutional flows I think you knew this. But I'm just just tell you anyway here's a simple way. There are many ways that you could do this. The simplest way is essentially to look at the correlation between the flows today and the flows tomorrow. And you're doing and then the flows today, and the flows in two days, and the flows today and the flows in three days and so on. This is done using a regression here, but you could also do it using just straight simple correlations. What do you find? You find that if you look at benchmark flows they're very persistent. In fact they're even persistent out to a year out and certainly at the daily level. So today, tomorrow and so on going forward, if you have flows into a sector or an industry, they tend to be highly persistent.

Robin Greenwood: Active flows are persistent, but it is much less. And it is really at the daily level. So once you get beyond a few days you have much less persistence in the active flows. retail trades are, very much less persistent. So they're useful. So for example, at the stock level, flows one one day are only weakly predictive of flows tomorrow. So I think that's a sort of shocking level of, lack of persistence at the individual stock level. Once you do it at the sector level, it's a bit higher. but it's retail trades today, for example, for a sector are 16% correlated with retail trades tomorrow and 9% with net buys the day after that. Still, I think I want you to take away the overall message that retail trades are much more idiosyncratic in the time series than than benchmark flows. Okay. So from a predictability perspective, looking at the institutions is actually still much more interesting than looking at the retail, because it's just so hard to figure out what the retail will do. Now, again, the asterisk here is that it's very easy to figure out what the retail is going to do if you're interested in just vole. So if you just want to know, is this an exciting stock that people will trade? You can say quite a lot, but if you want to say what direction are they going to trade next week? Next month, that's a much more challenging exercise.

Robin Greenwood: Okay.

Robin Greenwood: in terms of issuance, there's a very strange pattern, and I have to confess, I haven't we haven't fully figured this out yet, but I'll just tell you what the result is. In any case, is that the, persistence is high. It's highly issuance is highly persistent for the equal weighted series. Meaning on average, if firms are issuing in a particular area, they will continue to issue in the months ahead. Okay. On the other hand, if you do that on a valuated way, issuance is much less persistent and one, the only way that I've made sense of this to myself is to say, well, jeez, big spikes in issuance for a sector or an industry really are driven by kind of a lot of one off events, like an M&A activity or a big company coming to market or something like that, or, you know, Elon Musk's pay package being realized, right. Those that generates a massive amount of issuance in that day in that in that month. but so once you look in evaluated sense, I think that might be generally explaining why the, the persistence is less. You know, last thing I will say is we've done a little bit of experimentation, of trying to understand the patterns between this and future returns. I, I want some of this is preliminary, so I'm just going to show you some scatters rather than, go, go deep into this and there'll be more to come in this work. But we have found some, I would say, surprising amounts of relationship between retail vole and returns for sectors that I as I said, I think it makes more sense here to be looking at vole rather than net purchases because net purchases are just so volatile.

Robin Greenwood: But if you look at overall vole, retail vole and you're trying here, I'm trying to predict just the next quarter's return. So the next three months of return and is actually, those of you who run predictive regressions know how weak these results can be. It's a shockingly strong. And the data. So it's something that we're investigating a little bit more and trying to understand the relationship, between, between what drives this. We also find, I think, which is comforting, is that benchmark flows in this same period are behaving in very much the same way. again, I want to be a little bit cautious here. This is the 2005 forward period. I think this is maybe capturing a moment effect for sectors during this particular time. But so and that is being driven by by flows. This is something that we're going to continue to investigate. So I'm out of time. you know, in terms of oh my gosh, let's not in terms of conclusions and what and what's next? we are going to be doing a lot more of, of this, trying to understand these patterns, interested in your feedback and really trying to understand, I think the the big thing that we'd like to understand is can we modulate, institutional flows by looking at the other players and use that to really understand future patterns better? In other words, in the predictive sense, I think we have some promising results here, but I think lots, lots to do ahead. Looking forward to any questions. Comments.

Cayla Seder: Thank you. There are a couple of questions that have come through. Thank you. So when it comes to retail investors versus institutional investors, I think it's their time. The time frame or the time horizon of their investments is probably different. Are you seeing that in the data, or how do you think that might impact some of the persistence in the flow data?

Robin Greenwood: Oh, absolutely.I mean, I think that the those nbers that I showed you so the, the retail and I think back to that Tesla picture that I showed you on Tesla. So there's the vole kind of moves like this. But then the net demand is kind of like this. So it's very hard to predict on a day to day basis. Institutions are different. There's much more persistence. Benchmark flows are super persistent. And then, active flows are a little bit persistent. So if you add that up overall you have yeah very different horizons really at which these things are playing out. So it's weeks, months quarters for institutions. It's today versus tomorrow for retail okay great.

Cayla Seder: And okay time we have a couple more. But I'm just going to do one more.

Robin Greenwood: I'm sorry for going over.

Cayla Seder: Oh no no no. You're good. how does market volatility impact the results. Our calm periods different from days or weeks when the market moves a lot.

Robin Greenwood: That, is a great question. I don't know the answer. I would love to know the answer. I think we should put that on our list of, of of things to do. I think the volatile periods are the most interesting because those are the periods when retail actually has a big role. It's also the role, by the way, volatile periods issuance is super important. So if you, I suspect that we're going to get a lot more action in those periods and that will be an interesting place to dig in.Wonderful.

Cayla Seder: Well, we are out of time.