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On the Other Side of Hedge Fund Equity Trades

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Olga Kolokolova discusses that, trading against highly-diversified low-turnover institutional investors (quasi-indexers), hedge funds generate positive ex-post abnormal returns and avoid negative abnormal returns on their equities portfolios.

Image courtesy of interviewee. January 24, 2024

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The paper examines hedge funds, financial institutions known for exploiting market inefficiencies to generate profits for their clients while enhancing market performance. It notes that while hedge funds earn abnormal returns (alpha), someone on the other side of the trade must incur losses. The authors aim to investigate the counterparties that provide these alpha-generating opportunities for hedge funds and the possible implications of those trades for other investors.

Key findings indicate that hedge funds display distinct trading behaviors, including a preference for illiquid stocks and a tendency to engage in frequent trading, often operating as short-term investors compared to other institutional players. Additionally, they find a specific relationship between hedge funds and quasi-indexes (long-term, diversified institutions), referred to as an "alpha for beta swap." This video video suggests that hedge funds earn positive alpha when buying stocks that quasi-indexes simultaneously sell, while quasi-indexes generate negative alpha from purchasing stocks that hedge funds sell.

The paper highlights that quasi-indexes may lose money because of their risk-averse strategies, often preferring high-beta stocks that do not consistently provide high alpha. Furthermore, hedge funds utilize market anomalies—predictors of future returns—effectively to identify profitable trading opportunities while many other institutions trade in the opposite direction. This video video divergence creates sustained profitable opportunities for hedge funds.

The findings lead to two primary takeaways:
1. The market can facilitate beneficial trades between investors with differing utility functions, even if it appears detrimental for some.
2. The existence of large assets under management by non-alpha-maximizing institutions like quasi-indexes means that there will always be exploitable market anomalies that hedge funds can capitalize on for profit.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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In this paper, we look at hedge funds.

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So hedge funds, it's pretty well known.

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These are financial institutions

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and they are seen as being arbitrages.

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So what they quite, what they do,

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they exploit market efficiencies.

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And by doing so, they not only earn money for their clients,

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but they also help markets to perform better.

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So they help price discovery

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and they make our markets more efficient.

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So these are kind of the key, well part

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where we start about the hedge funds.

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Well, however, if you think about hedge funds,

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if they continuously earn, well alphas abnormal returns,

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so they, it means that there is somebody on the other side

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who actually provides 'em the offer.

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So if they earn money, somebody must be losing money.

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And even though we know a lot about the skills

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of hedge funds already from the previous literature,

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we thought that it would be really great

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to look at the other side of hedge fund epi traits

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and actually look at what are the,

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who are the counterparties who provide them

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with these opportunities to earn alpha

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and then try to uncover why they do so.

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And if they do so does it mean that there is another group

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of investors who actually lose money for the investors?

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So this was the starting point of our analysis.

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The, as I mentioned, we know really a lot already about

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what hash funds, do,

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or even though I shouldn't admit

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that they're quite secretive and we don't

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observe all the trades.

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But, at least some indirect evidence suggests, first

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of all, that hedge funds have, stock peaking

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and market bilities.

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This means that among all the stocks, they're able to,

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to pick up stocks which perform the best

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and also to turn them correctly.

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So buy them and exit the market, in the correct time.

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So we also know that hedge funds tend

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to trade more illiquid stocks

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and that they trade much more frequently than any

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other institutional investors.

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And they as an aggregate tend

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to be much more short-term investors than

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say, a pension funds.

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But we also know that sometimes hedge funds

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do front run mutual funds, for instance,

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and if mutual funds are in distress, so

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that hedge funds sometimes start selling stocks

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before mutual funds selling them.

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So that kind of early money, the kind of short positions.

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So we also know that hedge funds tend

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to trade on market anos.

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So let me just mention quickly what market anos are.

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What we usually think about the markets

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that they're information efficient, meaning that as soon

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as information becomes publicly available,

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the prices should adjust pretty

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much immediately or instantly.

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And then let's say there is no possibility

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to earn any abnormal returns

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or excessive profits, thereafter.

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But what we observed that in fact there are lots

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of predictors of future returns,

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quite often these predictors are based on accounting data,

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for instance, for profitability.

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And then months

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after this information has been released,

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most profitable firms keep

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outperforming in the stock market.

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So what we know in hedge funds that they tend

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to use this information

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and they tend to trade profitably on this

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information. So again,

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This, as I said, we know a lot of what do hedge funds,

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and how do they generate alpha,

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but it is still not really clear who the counterparts

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of hedge funds are.

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So, and that's where we try to like add to the literature.

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We proceed in several steps.

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First of all, and since we're talking about different groups

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of institutions, it's important to identify who's who.

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So we started from hedge funds

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and for these, we have a union

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of several hedge fund databases.

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So we have U Hatch, Barclay Hedge Morning Start and Task.

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We merged these databases,

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we clean them from the duplicates.

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And then we looked at the filings over 30 F form

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to security and exchange commission.

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So this is the filings which all institutions which manage

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more than 100 million have to file

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with security exchange commission,

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where they report their kind large equity positions.

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And what we did, we then matched the names

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of the hedge funds from our list to the names

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of institutions, which filed this third F form.

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And once we have done this, we then, take our time to go

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through Bloomberg and to check that all this match pairs,

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which we find are actually really hedge funds.

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And they don't have any other business on the side.

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So it's not a part of the mutual fund.

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They don't have insurance company.

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So that we identify really pure hedge fund companies.

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So it's our first step.

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And the second step is who are these other institutions,

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which we're looking at and this

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big question, how do you classify them?

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Yeah. And then one could go by their nominal labels.

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Okay, we have pension funds, we have mutual funds,

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we have ETF, yeah, but then we also have banks.

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Yeah. But then the question

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of course is if you have a mutual fund,

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what exactly does this fund do?

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Is it probably, there are active mutual funds,

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there are very passive mutual funds.

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So, and that's what we did in this stage.

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We adopted the classification of Brian Boucher,

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who suggested looking not at the nominal labels,

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but action what institutions do.

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So in particular, how frequently they rebalanced their

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portfolios and how diversified their portfolios are.

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And then he provided this very nice classification

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and I should meet, we use his database to look at it,

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and divide it in the institutions to several big groups

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and the largest, quasi indexes.

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So these institutions which have,

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very diversified portfolios who follow, long only

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and buy whole strategies largely

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and quite often trace the indices.

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So it's one group and another group which is much more

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active institutions transient.

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So these institutions,

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which also hold diversified portfolios,

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but they rebalance them much more frequently,

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so much more short-term institutions.

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So again, coming back to our certain F filings,

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we have the list of all institutions who file their holdings

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with the SSC, we first identify hedge funds,

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and the rest we classify using the,

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brands database on transcend and quasi indices.

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So once this is done, once we know the institu,

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and then I should admit that we cover around 63 to 65%

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of all institutions by doing.

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So. Of course there is a chunk

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of institutions which we cannot classify,

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and this would be, others.

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We also have them in the paper.

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Yeah, but that's kind of unclassifiable.

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So, we look at these institutions

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and with the next step we look at how they trade.

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So we estimate change in their holdings in each

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stock from quarter to quarter.

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So, and we look at it as the kind aggregate holdings

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as a group, for instance, for each stock,

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let's say which ones they priorit and Apple, Microsoft.

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Uh, so we'll take the holdings

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of a given class institutions say all hedge funds at the end

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of second quarter of year

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as a fraction of the firm which they held.

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We'll look how much of this firm the hedge funds held

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during at the end of the previous quarter.

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And we take a difference. This kind

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of gives us the rough idea of

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how much on the aggregate given types of institutions bought

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or sold in each stock.

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So the, these are kind of the first step.

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Second, we try to identify which stocks are hot

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or cold in for each of the types of institutions.

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And we looked at these changes in holdings

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and we classify them in large sales

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or large buys.

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So if this change in holdings is above 20 percentile,

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actually about 80 percentile,

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20 top changes in holdings would be, in,

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stocks which these group of institutions intensively buys

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during this quarter.

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And we, if you look on the negative changes in the bottom

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20%, the off what we'll see, we'll see that this is the,

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stocks which were intently sold by given group prostitution.

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And last step is the intersection of this

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because again, we're looking at this counterparts

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and we look want

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to identify the stocks which are traded in one direction

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by hedge funds and in the opposite direction

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by other groups of institutions.

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Yeah, so makes sense. Yeah.

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So we look on the, the largest, for instance,

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large purchases by hedge funds

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and also large sales by was the indexes.

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And this would be the stocks which are in

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our small portfolios.

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And the last step we evaluate performance of the stocks.

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They're to see whether they do go good or not so good.

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Uh, depending on whether hedge funds buy other solutions

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sell or vice versa.

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Once we, did them, as we evaluate performance

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of these portfolios and what we find out that first of all,

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we can identify the group of investors, which seems

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to really provide this extra opportunities

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to earn alpha, alpha hedge funds.

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And it looks like we understand at

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least partly the reason for this.

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And what we find, we find that between hedge funds

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and quasi indexes, this kind

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of long-term diversified index follow institutions,

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this seem to be an alpha for beta swap.

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So the hedge funds earn positive alpha on those stocks,

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which they buy while quasi sell.

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And on the opposite, so the alpha of stocks,

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which quasi indexes buy

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and hedge fundal is negative over the next

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quarter after the trade.

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Yeah. So as we look at in the future, so basically indeed,

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so it looks like quasi indexes lose abnormal returns

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where they trade against hedge funds

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or they provide the abnormal returns

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if they sell stocks to them.

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And what's happening in the parallel is

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that we have the opposite effects of the beater.

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So it looks like once that indexes,

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they purchase from hedge funds, high beater stocks,

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the market beater, and they sell

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lower beater stocks to them.

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So we call it alpha beta swap.

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So, just a side remark, if you compare hedge fund rates

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with other institutions like transient,

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we don't see this pattern whatsoever.

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So this is really this alpha beta swap, which pronounced

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between, quasi indexes and hedge funds.

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It is also very stable.

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We look at it over time

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and we find that we observe pretty much the same pattern,

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throughout, our sample, which is from 19, 94

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to 2017.

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So, and now the question is why is it happening?

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And so why, why would,

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well quasi indexes basically lose kind of money.

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And our deportation of that is that the reason is

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that they have not maximizing strategies.

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The optimization functions very different in particular

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because they cannot short sell, they cannot,

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quite often they are, they they benchmarked.

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So the only way for them to earn a bit

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of more money is take a bit of a more market risk.

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So that's why they tend to prefer high beta stocks,

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basically maximizing their own utility.

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But high beta stocks, they tend to also generate

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a smaller alpha.

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And we try to dig into this in more detail

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to see whether indeed this is just this, this,

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that they could just usually lose money

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because they buy high be of stocks.

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And we had a counterfactual example

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where we looked at the trades of quasi indexes where they

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say buy a lot of stocks,

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but hash funds do not sell these stocks to them.

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So this kind of counterfactual.

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And what we found is that we still see this beta preference.

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So quasi indexes prefer stocks with high beta,

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but there is no losses in alpha.

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So which tells us that's actually among all the

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so quasi indexes, they prefer high beta stocks

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and they want to buy high beta stocks.

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They want to sell low beta stocks while hedge funds are able

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to identify out of the pool of all the stocks,

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really profitable trades.

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And that's when they act as counterparts for wza in indexes.

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And one of these factors which we found is the,

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lottery demand factor of al.

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So what is the lottery demand?

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This, this is, so the lottery type stocks,

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this is the stocks which pretty much work like lottery.

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So from time to time with small probability,

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they may give you very high return.

11:54.915 --> 11:57.695
And these stocks on average tend to have high beater

11:58.035 --> 11:59.735
and actually lower alpha.

12:00.835 --> 12:04.335
And we found out that indeed stocks which has funds sell

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to quasi indexes,

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these are the stocks which exhibit this high exposure

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to the lottery demand factor.

12:11.385 --> 12:14.125
And this exposure to this lottery demand factor,

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explains the negative alpha of stocks, which,

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quasi indexes buy from hedge funds.

12:21.125 --> 12:24.375
Well, however, so we still have a part of stocks,

12:24.375 --> 12:27.135
which hedge funds, hedge funds buy from quasi indexes,

12:27.515 --> 12:29.815
and their positive alpha cannot be explained

12:29.815 --> 12:30.855
by the lottery demand.

12:31.195 --> 12:34.495
So there still is something else in hedge funds skills,

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really, there's a opportunity

12:35.975 --> 12:39.255
to identify really profitable traits out of those stocks,

12:39.305 --> 12:42.095
which quasi indexes, which are wishing to sell.

12:42.675 --> 12:44.415
We did lots of digging trying

12:44.415 --> 12:45.775
to figure out what is in there.

12:45.995 --> 12:47.735
So for instance, we looked at liquidity.

12:48.475 --> 12:51.175
Is it true that hedge funds just earn liquidity premium?

12:51.995 --> 12:55.455
So yes, it's true. Uh, they do buy a lot of illiquid stocks.

12:55.605 --> 12:59.535
However, if we exclude illiquid stocks from our trades,

12:59.595 --> 13:01.735
we still see this alphabet swap.

13:01.765 --> 13:04.855
It's still there. So then we looked at other things,

13:04.855 --> 13:08.175
for instance, trading of corporate events like m and a.

13:08.195 --> 13:10.295
We also know that hedge funds quite often traded on m

13:10.295 --> 13:13.375
and a, so we exclude from our sample all firms,

13:13.425 --> 13:16.255
which were involved in emerging acquisitions like two

13:16.255 --> 13:18.135
towards before the quarters after our trade.

13:18.515 --> 13:20.615
And we still find in this cleaner sample,

13:20.685 --> 13:21.775
this alpha beta swap.

13:22.595 --> 13:27.415
We looked also at contrarian versus, momentum trading

13:27.415 --> 13:28.535
because it's also known

13:28.535 --> 13:31.815
that hedge funds go into contrarian trades while like QA

13:31.815 --> 13:34.775
indexes, mutual funds quite often go into momentum trades.

13:35.355 --> 13:38.335
So that we looked at past performance

13:38.595 --> 13:41.735
and we exclude stocks with, really bad past performance

13:41.735 --> 13:43.655
or really good past performance kind of to control

13:43.655 --> 13:46.535
for this extreme contrarian versus momentum trades.

13:46.835 --> 13:49.295
And we still see this alpha for beta swap.

13:50.545 --> 13:52.125
The only thing which also add to this,

13:52.505 --> 13:55.445
but again, well largely on the side

13:55.445 --> 13:56.845
where hedge fund sell stocks

13:56.845 --> 14:00.045
to quasi indexes are trading on market omas.

14:01.025 --> 14:02.925
So because if we think about the market anos,

14:02.925 --> 14:05.685
so these are the factors which predict future returns

14:06.065 --> 14:08.085
and they kind of prescribe trading.

14:08.225 --> 14:10.805
So if you have a, a firm with high profitability,

14:10.985 --> 14:12.365
you need to buy this firm.

14:12.665 --> 14:13.685
If you have firms with low

14:13.685 --> 14:15.005
profitability, you need to sell the firms.

14:15.745 --> 14:17.885
And, it was kindly shown in the past

14:17.885 --> 14:22.405
that in general institutions as the whole tr tends

14:22.425 --> 14:25.565
to trade in the opposite direction of what, this kind

14:25.565 --> 14:27.445
of prescriptions or recipe would be

14:27.445 --> 14:28.645
according to market omas.

14:29.185 --> 14:32.165
And what we find is indeed, if we look into detail

14:32.225 --> 14:35.045
to the swap stocks according to long

14:35.145 --> 14:39.445
or short, lag of this market, anoma trades, we see

14:39.445 --> 14:40.645
that within the short lag.

14:40.665 --> 14:43.405
So these are the kind of stocks which are predicted

14:43.405 --> 14:46.085
to perform poorly by the market anomalies.

14:46.855 --> 14:49.565
There. Uh, quasi indexes tend

14:49.565 --> 14:51.885
to buy stocks while hedge funds sell to them.

14:52.025 --> 14:54.485
And this is the other part which generates again,

14:54.485 --> 14:56.645
the negative alpha for,

14:56.725 --> 14:58.165
quasi indexes when they buy stocks.

14:58.545 --> 15:00.325
Key takeaways are kind of twofold.

15:00.465 --> 15:04.285
The one probably more philosophical is that,

15:04.465 --> 15:07.845
if you observe trading in the market, even if you think

15:07.955 --> 15:11.165
that it is, bad for some institutions,

15:11.185 --> 15:13.245
in fact it may be still a win-win situation.

15:13.785 --> 15:15.285
If we have different types of investors

15:15.515 --> 15:18.645
with their not perfectly aligned utility functions,

15:18.985 --> 15:21.245
you end up having trades which benefit both sides.

15:22.315 --> 15:24.005
It's one takeaway. And the second one, if,

15:24.005 --> 15:27.125
if you look at the size of the institutions,

15:27.705 --> 15:31.445
the quasi indexes, they, they manage way larger portfolios

15:31.505 --> 15:33.525
and way more money than hedge funds or,

15:33.625 --> 15:35.285
or transit institutions together.

15:36.065 --> 15:40.405
So, and it means that while we have such large,

15:40.745 --> 15:43.245
amount of money invested with institutions which do not have

15:44.085 --> 15:46.005
explicit alpha maximizing strategies,

15:47.535 --> 15:50.045
there always will be some profitable opportunities

15:50.705 --> 15:53.605
on the table, based on market anomalies,

15:53.605 --> 15:56.485
based on some other factors which hedge funds these

15:56.485 --> 15:58.765
productive arbitrators can exploit

15:58.945 --> 16:00.725
and then earn money on this for their investors.

Hedge Funds: Market Efficiency and Counterparties

00:00:00 - In this paper, we look at hedge funds. So hedge funds, it's pretty well known. These are financial institutions and they are seen as being arbitrages. So what they quite, what they do, they exploit market efficiencies. And by doing so, they not only earn money for their clients, but they also help markets to perform better. So they help price discovery and they make our markets more efficient. So these are kind of the key, well part where we start about the hedge funds. Well, however, if you think about hedge funds,
00:00:34 - so they, it means that there is somebody on the other side who actually provides 'em the offer. So if they earn money, somebody must be losing money. And even though we know a lot about the skills of hedge funds already from the previous literature, we thought that it would be really great to look at the other side of hedge fund epi traits and actually look at what are the, who are the counterparties who provide them with these opportunities to earn alpha and then try to uncover why they do so.
00:01:04 - of investors who actually lose money for the investors? So this was the starting point of our analysis. The, as I mentioned, we know really a lot already about what hash funds, do, or even though I shouldn't admit that they're quite secretive and we don't observe all the trades. But, at least some indirect evidence suggests, first of all, that hedge funds have, stock peaking and market bilities. This means that among all the stocks, they're able to, to pick up stocks which perform the best
00:01:34 - So buy them and exit the market, in the correct time. So we also know that hedge funds tend to trade more illiquid stocks and that they trade much more frequently than any other institutional investors. And they as an aggregate tend to be much more short-term investors than say, a pension funds. But we also know that sometimes hedge funds do front run mutual funds, for instance, and if mutual funds are in distress, so that hedge funds sometimes start selling stocks
00:02:05 - So that kind of early money, the kind of short positions. So we also know that hedge funds tend to trade on market anos. So let me just mention quickly what market anos are. What we usually think about the markets that they're information efficient, meaning that as soon as information becomes publicly available, the prices should adjust pretty much immediately or instantly. And then let's say there is no possibility to earn any abnormal returns or excessive profits, thereafter.
00:02:37 - of predictors of future returns, quite often these predictors are based on accounting data, for instance, for profitability. And then months after this information has been released, most profitable firms keep outperforming in the stock market. So what we know in hedge funds that they tend to use this information and they tend to trade profitably on this information. So again, This, as I said, we know a lot of what do hedge funds, and how do they generate alpha,

Identifying and Classifying Hedge Fund Institutions

00:03:09 - of hedge funds are. So, and that's where we try to like add to the literature. We proceed in several steps. First of all, and since we're talking about different groups of institutions, it's important to identify who's who. So we started from hedge funds and for these, we have a union of several hedge fund databases. So we have U Hatch, Barclay Hedge Morning Start and Task. We merged these databases, we clean them from the duplicates.
00:03:40 - to security and exchange commission. So this is the filings which all institutions which manage more than 100 million have to file with security exchange commission, where they report their kind large equity positions. And what we did, we then matched the names of the hedge funds from our list to the names of institutions, which filed this third F form. And once we have done this, we then, take our time to go through Bloomberg and to check that all this match pairs,
00:04:11 - And they don't have any other business on the side. So it's not a part of the mutual fund. They don't have insurance company. So that we identify really pure hedge fund companies. So it's our first step. And the second step is who are these other institutions, which we're looking at and this big question, how do you classify them? Yeah. And then one could go by their nominal labels. Okay, we have pension funds, we have mutual funds, we have ETF, yeah, but then we also have banks. Yeah. But then the question
00:04:42 - what exactly does this fund do? Is it probably, there are active mutual funds, there are very passive mutual funds. So, and that's what we did in this stage. We adopted the classification of Brian Boucher, who suggested looking not at the nominal labels, but action what institutions do. So in particular, how frequently they rebalanced their portfolios and how diversified their portfolios are. And then he provided this very nice classification and I should meet, we use his database to look at it,
00:05:14 - and the largest, quasi indexes. So these institutions which have, very diversified portfolios who follow, long only and buy whole strategies largely and quite often trace the indices. So it's one group and another group which is much more active institutions transient. So these institutions, which also hold diversified portfolios, but they rebalance them much more frequently, so much more short-term institutions. So again, coming back to our certain F filings,
00:05:47 - with the SSC, we first identify hedge funds, and the rest we classify using the, brands database on transcend and quasi indices. So once this is done, once we know the institu, and then I should admit that we cover around 63 to 65% of all institutions by doing. So. Of course there is a chunk of institutions which we cannot classify, and this would be, others. We also have them in the paper. Yeah, but that's kind of unclassifiable.

Analyzing Hedge Fund Trading Strategies and Performance

00:06:18 - and with the next step we look at how they trade. So we estimate change in their holdings in each stock from quarter to quarter. So, and we look at it as the kind aggregate holdings as a group, for instance, for each stock, let's say which ones they priorit and Apple, Microsoft. so we'll take the holdings of a given class institutions say all hedge funds at the end of second quarter of year as a fraction of the firm which they held.
00:06:48 - during at the end of the previous quarter. And we take a difference. This kind of gives us the rough idea of how much on the aggregate given types of institutions bought or sold in each stock. So the, these are kind of the first step. Second, we try to identify which stocks are hot or cold in for each of the types of institutions. And we looked at these changes in holdings and we classify them in large sales or large buys.
00:07:22 - actually about 80 percentile, 20 top changes in holdings would be, in, stocks which these group of institutions intensively buys during this quarter. And we, if you look on the negative changes in the bottom 20%, the off what we'll see, we'll see that this is the, stocks which were intently sold by given group prostitution. And last step is the intersection of this because again, we're looking at this counterparts
00:07:50 - to identify the stocks which are traded in one direction by hedge funds and in the opposite direction by other groups of institutions. Yeah, so makes sense. Yeah. So we look on the, the largest, for instance, large purchases by hedge funds and also large sales by was the indexes. And this would be the stocks which are in our small portfolios. And the last step we evaluate performance of the stocks. They're to see whether they do go good or not so good. depending on whether hedge funds buy other solutions
00:08:22 - Once we, did them, as we evaluate performance of these portfolios and what we find out that first of all, we can identify the group of investors, which seems to really provide this extra opportunities to earn alpha, alpha hedge funds. And it looks like we understand at least partly the reason for this. And what we find, we find that between hedge funds and quasi indexes, this kind of long-term diversified index follow institutions, this seem to be an alpha for beta swap. So the hedge funds earn positive alpha on those stocks,
00:08:57 - And on the opposite, so the alpha of stocks, which quasi indexes buy and hedge fundal is negative over the next quarter after the trade. Yeah. So as we look at in the future, so basically indeed, so it looks like quasi indexes lose abnormal returns where they trade against hedge funds or they provide the abnormal returns if they sell stocks to them. And what's happening in the parallel is that we have the opposite effects of the beater. So it looks like once that indexes,

Understanding Alpha-Beta Dynamics in Investments

00:09:28 - the market beater, and they sell lower beater stocks to them. So we call it alpha beta swap. So, just a side remark, if you compare hedge fund rates with other institutions like transient, we don't see this pattern whatsoever. So this is really this alpha beta swap, which pronounced between, quasi indexes and hedge funds. It is also very stable. We look at it over time and we find that we observe pretty much the same pattern, throughout, our sample, which is from 19, 94
00:10:00 - So, and now the question is why is it happening? And so why, why would, well quasi indexes basically lose kind of money. And our deportation of that is that the reason is that they have not maximizing strategies. The optimization functions very different in particular because they cannot short sell, they cannot, quite often they are, they they benchmarked. So the only way for them to earn a bit of more money is take a bit of a more market risk.
00:10:32 - basically maximizing their own utility. But high beta stocks, they tend to also generate a smaller alpha. And we try to dig into this in more detail to see whether indeed this is just this, this, that they could just usually lose money because they buy high be of stocks. And we had a counterfactual example where we looked at the trades of quasi indexes where they say buy a lot of stocks,
00:11:03 - So this kind of counterfactual. And what we found is that we still see this beta preference. So quasi indexes prefer stocks with high beta, but there is no losses in alpha. So which tells us that's actually among all the so quasi indexes, they prefer high beta stocks and they want to buy high beta stocks. They want to sell low beta stocks while hedge funds are able to identify out of the pool of all the stocks, really profitable trades.
00:11:35 - And one of these factors which we found is the, lottery demand factor of al. So what is the lottery demand? This, this is, so the lottery type stocks, this is the stocks which pretty much work like lottery. So from time to time with small probability, they may give you very high return. And these stocks on average tend to have high beater and actually lower alpha.
00:12:04 - to quasi indexes, these are the stocks which exhibit this high exposure to the lottery demand factor. And this exposure to this lottery demand factor, explains the negative alpha of stocks, which, quasi indexes buy from hedge funds. Well, however, so we still have a part of stocks, which hedge funds, hedge funds buy from quasi indexes, and their positive alpha cannot be explained by the lottery demand.

Understanding Hedge Funds and Quasi Indexes

00:12:34 - really, there's a opportunity to identify really profitable traits out of those stocks, which quasi indexes, which are wishing to sell. We did lots of digging trying to figure out what is in there. So for instance, we looked at liquidity. Is it true that hedge funds just earn liquidity premium? So yes, it's true. they do buy a lot of illiquid stocks. However, if we exclude illiquid stocks from our trades, we still see this alphabet swap.
00:13:04 - for instance, trading of corporate events like m and a. We also know that hedge funds quite often traded on m and a, so we exclude from our sample all firms, which were involved in emerging acquisitions like two towards before the quarters after our trade. And we still find in this cleaner sample, this alpha beta swap. We looked also at contrarian versus, momentum trading because it's also known that hedge funds go into contrarian trades while like QA
00:13:35 - So that we looked at past performance and we exclude stocks with, really bad past performance or really good past performance kind of to control for this extreme contrarian versus momentum trades. And we still see this alpha for beta swap. The only thing which also add to this, but again, well largely on the side where hedge fund sell stocks to quasi indexes are trading on market omas. So because if we think about the market anos,
00:14:06 - and they kind of prescribe trading. So if you have a, a firm with high profitability, you need to buy this firm. If you have firms with low profitability, you need to sell the firms. And, it was kindly shown in the past that in general institutions as the whole tr tends to trade in the opposite direction of what, this kind of prescriptions or recipe would be according to market omas. And what we find is indeed, if we look into detail to the swap stocks according to long
00:14:39 - that within the short lag. So these are the kind of stocks which are predicted to perform poorly by the market anomalies. There. quasi indexes tend to buy stocks while hedge funds sell to them. And this is the other part which generates again, the negative alpha for, quasi indexes when they buy stocks. Key takeaways are kind of twofold. The one probably more philosophical is that, if you observe trading in the market, even if you think
00:15:11 - in fact it may be still a win-win situation. If we have different types of investors with their not perfectly aligned utility functions, you end up having trades which benefit both sides. It's one takeaway. And the second one, if, if you look at the size of the institutions, the quasi indexes, they, they manage way larger portfolios and way more money than hedge funds or, or transit institutions together. So, and it means that while we have such large,

Exploiting Market Anomalies for Profit

00:15:58 - explicit alpha maximizing strategies, there always will be some profitable opportunities on the table, based on market anomalies, based on some other factors which hedge funds these productive arbitrators can exploit and then earn money on this for their investors.

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