9 ms·

Rich Formula: Quant Trading (2015)

108 points by luu 9y ago

dogruck 9y ago
Article is from October, 2015.
- sctb 9y ago
  Thanks! We've updated the headline.
cheez 9y ago
It seems possible that mathematical breakthroughs are no longer being published, as they are now trade secrets/matters of national security. I wasn't surprised when Tao was beaten by a hedge funder.
- sabujp 9y ago
  maybe tao lost his edge
- xstartup 9y ago
  There are always better people around who want neither fame nor money.
- dsacco 9y ago
  This has been the case for a long time in applied mathematics and computer science (not so much pure mathematics). There are hedge funds using work that is not only unpublished, but also unknown to research labs like FAIR and Google Brain. The easiest way to be scouted by one of those funds is to publish research that looks like you’re on the verge of re-discovering their work.
  - joshuamorton 9y ago
    Do you have any proof of this, or is this or is it just your opinion that the comparatively smaller groups of researchers at hedge funds are well ahead of academia and the rest of industry?
    - dsacco 9y ago
      1. I don't have proof I can share publicly, 2. It's not just my opinion, and 3. I didn't say they're "well ahead" unilaterally. This isn't unique to finance; industry labs in tech also often have novel results in applied mathematics and computer science that are ahead of academia and other industry labs. You don't have to believe me but it's not exactly a controversial topic. Not everything is published or patented.
      - joshuamorton 9y ago
        I mean I have little doubt that there are trade secrets that these companies have. Specific algorithms and models. And yeah, industry labs are often ahead here. But I read your claim as saying that there are broad methods and approaches that they hide. And that's, while possible, more peculiar. Most of the tech industry labs don't keep their theoretical research secret. Practically anything that could be published is. As for 3, the way you described the "rediscovery" made it sound like those Labs were a number of steps ahead, so I hope you pardon my misunderstanding.
        dsacco 9y ago
        At the highest level there are broad approaches which are kept secret in the financial industry, but the reason that's peculiar is because their efficacy is inherently antagonistic to publicity. Tech firms (mostly) don't lose utility of their trade secrets if they're exposed, they just lose first mover advantages on those techniques. But if everyone is aware of your techniques in finance, your techniques cease to have an edge. Like I said in the original comment: this isn't (to my knowledge at least) pure mathematics that's being kept secret. But there are absolutely families of techniques and algorithms whose applications to finance are nontrivial, non-incremental and very well guarded.
chollida1 9y ago
Two interesting inside baseball tidbits here that I thik are worth pointing out: > From inception Two Sigma’s early funds, like Eclipse and Spectrum, focused on trading stocks globally. Eclipse was faster, changing positions within weeks, while Spectrum had a longer-term horizon closer to one month. > The duo eventually used their algorithms to create programs that operate outside the global stock markets, like the trend-following Compass funds that bet on futures markets. In 2014 another important fund, Horizon, was folded into Spectrum, which had diversified its offering beyond stocks. One of Two Sigma’s least visible funds is its Partners Fund, an internal fund of funds, fueled mostly by capital from the founders. Most, all?, of the largest hedge funds are actually partnerships that encompass multiple funds. From an internal perspective this allows each fund to focus on a few core competencies, like trend following vs market making vs global macro. Each of these strategies will do well at certain times and do poorly during others. From a firms's perspective this allows for diversification, which is almost always good. From an employee's perspective it allows them to get paid for their work and be insulated a little bit from the performance of their peers in other funds. And in some cases like Steve Cohen's SAC it allows the good ideas to retroactively be put into the blessed fudn while the bad ideas are shuffled to the lesser feeder funds. Yes, this is illegal, yes it happens. From an outsider's perspective its usually means that there is one well performing fund that is closed for new money while there are several lesser performing funds that are open to outside money. Even RenTech has funds that are open to outside money and they don't perform anywhere near as well as their master fund that is for employee's only. > Two Sigma researchers spend time testing existing models, and each researcher is expected to come up with two or three new models per year. These are presented to Overdeck in a white paper that is typically less than ten pages long. Since Two Sigma’s trading models can change its forecast in seconds, lots of back-testing goes into each model. It’s not unlike the way Amazon exhaustively tests various Web-page changes in real time to ensure optimal clicks and purchases. At Two Sigma headquarters the model builders, who need to write code, sit with the engineers and collaborate with them all the time. From the strategy development side, often idea's have a half life of anywhere from months in the HFT space to years in the global macro space. For idea's I've since come to believe that idea generation is equal parts people, ie brain power, and platform, ie the ability to iterate. RenTech is a perfect example of this. They had two people leave who were very high up and go to another fund. They had 2-3 years of poor performance away from the huge backtesting platform that RenTech had built. it's not like these Phd's suddenly forgot everything. Its the ability to iterate quickly on idea's that is the key once the bar has been met for math and intellect. As they say, its not the algorithm you use but the features that produce your alpha. If you want to make money in the markets focus all your time on feature engineering. Fun reading: https://medium.com/@63ey5f4uw3k42v1exp7/chronology-mercer-medallion-fund-9aa719ceeb4f https://medium.com/@63ey5f4uw3k42v1exp7/chronology-mercer-me...
module0000 9y ago
Quant trading is harder than people assume - really, really, hard. Nearly everything you can think of has already been done, and is being done within latency limits you are priced out of as a retail guy. So you have to get creative, and if you want it to keep working, you can't tell anyone about it. Ever.
- anonu 9y ago
  Agreed. And it's not just latency that puts the average guy at a disadvantage. It's also access to the market and access to data which are hard to obtain. The beauty in all this is that 1000s of people keep trying... But only a select few, less than 1/100 of 1 percent of market participants succeed in making reasonable profits
- thisisit 9y ago
  I think it is important to understand that low latency = HFT. Because HFT is data driven it is part of Quant trading. But not all Quant Trading requires or is being done at low latency limits.
  - jkaptur 9y ago
    A great point. When I worked in the industry, the trading (of exotic products) was actually done over the phone and via e-mail. Very much quant-driven, but about as low-frequency as it gets!
- Xcelerate 9y ago
  > Nearly everything you can think of has already been done I find this hard to believe. There's entire fields of research based around AI now and an endless number of ideas that have yet to be tried/implemented. There simply aren't enough people working at hedge funds to cover them all.
  - pakitan 9y ago
    A more accurate statement will be "Nearly everything you can think of has already been done or will be done soon and since this is a zero-sum game, the expected profit in the long term from quant trading is approaching zero"
anonu 9y ago
Tldr for this 2015 article: 2 sigma uses tons of data and processing power to extract any alpha from the markets. They've done it consistently since 2004. People leave 2 sigma and sometimes email themselves code. Bad idea as the company will come down hard on you with lawsuits.
- amerine 9y ago
  Email themselves code? That’s an astonishingly stupid way to try stealing something. What did these people expect?
  - anonu 9y ago
    Most financial firms have decent lockdown... Generally firewalls prevent file sharing sites, web mail, connecting to any external port that's not http or https. I've seen some places prevent direct IP connections (need a hostname to connect). USB and SD card slots are disabled... Not to say that there still aren't dozens of ways to get around all this if you really wanted to.
fibers 9y ago
python pandas is a digital blood diamond after what they did to Gao
- slow_donkey 9y ago
  What do you mean by that? I can't find any connection between him and pandas
polskibus 9y ago
Quants have worked in finance for ages. What's so unusual about Two Sigma? Headcount?
- tomp 9y ago
  Marketing strategy.
tntn 9y ago
I'll just leave this here: "Our definition of success has become narrow, boring, and limited. If we want young people to be creative and innovative, we need to reward them for it." from "Skip The Hedge Fund: We Need Young People To Take Risks And Build Inspiring Things" at https://www.fastcompany.com/3026586/skip-the-hedge-fund-we-need-young-people-to-take-risks-and-build-inspiring-things https://www.fastcompany.com/3026586/skip-the-hedge-fund-we-n....
SirLJ 9y ago
Quant trading is easy with small accounts < 10 millions, the more money you trade, the harder is to find an edge...
- sputknick 9y ago
  Can you elaborate on this? That sounds like the opposite of the way it would work.
  - SirLJ 9y ago
    Many examples: faster in and out of the market, better fills, will not end up owning a company by mistake (e.g. bought all shares), etc... The bigger you get (in billions) the closer the returns will be to the market average...
  - praulv 9y ago
    Typically it means you can invest in niche markets. These market would be out of reach for larger players as they would struggle to find liquidity for their portfolio size. On the other end, smaller players would not be able to adjust their positions sufficiently granularly due to minimum lot/clip sizes specified on the market - similar to a quantisation effect if you don't sample at a sufficiently high frequency.
  - kevstev 9y ago
    In general, large companies have a lot more data around them, a lot more coverage from analysts and journalists and the like, and generally have a lot more eyes on them. These companies are a lot more efficiently priced. You can do the legwork yourself- getting on management calls, reading industry news, poring over the financial statements to find strength or weakness that are not widely known. So lets say you are a 10 billion dollar fund. You do your research and find that there is a small $100 million dollar company that doesn't have much debt, is growing rapidly, and the management seems good and there are good tailwinds for the industry as whole. You have 100x the deployable capital than the company's market capitalization. Even if the stock price doubles or triples in price, your potential upside for the firm as whole is a relative drop in the bucket. You can't deploy more than a few million into this company without moving the market. For a large fund, it isn't worth wasting time chasing these small opportunities. In fact, "scaling" is a hard problem in funds. Hence, there is actually a lot of opportunity out there for small scale investors who are willing to look under rocks that bigger guys don't see enough potential opportunity in.
downandout 9y ago
Could somebody explain why so much effort is being put into quant strategies, when it seems that real-world information gathering would be a much easier way to gain an edge over others? Let’s say you pay to place a camera on a building next to a given company’s factory, and use analysis software to count the number of trucks coming and going from the factory to predict their order flow and earnings. This kind of thing is harder to scale up, but also gives an edge because not everyone else is doing it. In an age when all hedge funds have the resources to hire the best and brightest engineers and buy the fastest processing hardware, it seems that none of them will have an edge if they are all starting with the same publicly available data.
- smallnamespace 9y ago
  This is exactly what some quant funds like RenTech and DE Shaw have done—they look for data sets that show signals before they hit the market. You can view a traditional market as a process where information from the real world flows in, and people can either make money by predicting how others will react to that information, or reacting slightly faster (HFT), or understanding the patterns the information tells you (e.g. stat arb). But even better if you can get the information before they hit the markets, hence the search for more and more data sets that have predictive value. This changes the actual market (the exchange) from the place where prices changes reflect different sets of participants' views being hashed out (the market is performing information processing mediated by price signals), to one where the actual predictive signals are monopolized by private parties before they hit the market. But note that this is exactly the path that the large tech platforms' ad exchanges have gone down as well. The price per click/conversion of a user and the outcomes of real-time bidding are hidden from everyone except the particular participants of the transactions; we're moving from a world where public markets (no matter their limitations) reflect the sum of public information in real time, to one where the thinking and computation are moved into hidden platforms. At some point, of course, some bright person is going to create an exchange/marketplace for data signals, and then the financialization/reification of another abstraction will take place.
- scott00 9y ago
  Their category "alpha capture" probably incorporates this sort of thing. I think the article gave the example of proprietary survey data being in that category. I think that's probably what they call anything that doesn't come from publicly available data.
madengr 9y ago
These guys averaged a ~9% return over the last few years. The S&P 500 has exceeded that. So what’s so special? Seems that passive investing works just as well.
- dsacco 9y ago
  > These guys averaged a ~9% return over the last few years. Who are "these guys"? The funds discussed in the article have average annual returns well above 9%.
  - madengr 9y ago
    Overdeck and Seigel. “The firm’s biggest fund, Spectrum, has earned an annual average return of 9.4% net of fees since 2004.” The S&P 500 has averaged 9% for 80 years. Joe Blow can buy index fund and do just as well as the quant clients. Sure, these guys are averaging 14% before fees, and it’s a great way to get rich. But after fees the client might as well just passively invest.
    - desu_ 9y ago
      You need to consider the following elements: -You compare the S&P and two Six Sigma funds over different timelines. The index yielded much less than 9% on 2004-2015 (you can halve the performance). -What’s the common feature between: the S&P, the formal idea of passive indexing, and the emergence of passive investment funds? None of them are 80 years old. -Retail customers (Joe Blow) rarely have access to hedge fund products directly (they might still be exposed through pension funds, sovereign wealth funds, etc. but it makes the allocation change almost out of their hands). -Stating performance figures alone is mostly a Bloomberg-reader thing. In practice, there is usually at least an attempt to incorporate some kind of risk measure when selecting hedge funds. I am not even anti-passive funds but you are memeing a bit too hard.
  - deleted 9y ago
    [deleted]
- FundThrowaway 9y ago

108 points by luu 9y ago

dogruck 9y ago

Article is from October, 2015.

sctb 9y ago
Thanks! We've updated the headline.

cheez 9y ago

It seems possible that mathematical breakthroughs are no longer being published, as they are now trade secrets/matters of national security. I wasn't surprised when Tao was beaten by a hedge funder.

sabujp 9y ago
maybe tao lost his edge
xstartup 9y ago
There are always better people around who want neither fame nor money.
dsacco 9y ago
This has been the case for a long time in applied mathematics and computer science (not so much pure mathematics). There are hedge funds using work that is not only unpublished, but also unknown to research labs like FAIR and Google Brain. The easiest way to be scouted by one of those funds is to publish research that looks like you’re on the verge of re-discovering their work.
- joshuamorton 9y ago
  Do you have any proof of this, or is this or is it just your opinion that the comparatively smaller groups of researchers at hedge funds are well ahead of academia and the rest of industry?
  - dsacco 9y ago
    1. I don't have proof I can share publicly, 2. It's not just my opinion, and 3. I didn't say they're "well ahead" unilaterally. This isn't unique to finance; industry labs in tech also often have novel results in applied mathematics and computer science that are ahead of academia and other industry labs. You don't have to believe me but it's not exactly a controversial topic. Not everything is published or patented.
    - joshuamorton 9y ago
      I mean I have little doubt that there are trade secrets that these companies have. Specific algorithms and models. And yeah, industry labs are often ahead here. But I read your claim as saying that there are broad methods and approaches that they hide. And that's, while possible, more peculiar. Most of the tech industry labs don't keep their theoretical research secret. Practically anything that could be published is. As for 3, the way you described the "rediscovery" made it sound like those Labs were a number of steps ahead, so I hope you pardon my misunderstanding.
      - dsacco 9y ago
        At the highest level there are broad approaches which are kept secret in the financial industry, but the reason that's peculiar is because their efficacy is inherently antagonistic to publicity. Tech firms (mostly) don't lose utility of their trade secrets if they're exposed, they just lose first mover advantages on those techniques. But if everyone is aware of your techniques in finance, your techniques cease to have an edge. Like I said in the original comment: this isn't (to my knowledge at least) pure mathematics that's being kept secret. But there are absolutely families of techniques and algorithms whose applications to finance are nontrivial, non-incremental and very well guarded.

chollida1 9y ago

Two interesting inside baseball tidbits here that I thik are worth pointing out: > From inception Two Sigma’s early funds, like Eclipse and Spectrum, focused on trading stocks globally. Eclipse was faster, changing positions within weeks, while Spectrum had a longer-term horizon closer to one month. > The duo eventually used their algorithms to create programs that operate outside the global stock markets, like the trend-following Compass funds that bet on futures markets. In 2014 another important fund, Horizon, was folded into Spectrum, which had diversified its offering beyond stocks. One of Two Sigma’s least visible funds is its Partners Fund, an internal fund of funds, fueled mostly by capital from the founders. Most, all?, of the largest hedge funds are actually partnerships that encompass multiple funds. From an internal perspective this allows each fund to focus on a few core competencies, like trend following vs market making vs global macro. Each of these strategies will do well at certain times and do poorly during others. From a firms's perspective this allows for diversification, which is almost always good. From an employee's perspective it allows them to get paid for their work and be insulated a little bit from the performance of their peers in other funds. And in some cases like Steve Cohen's SAC it allows the good ideas to retroactively be put into the blessed fudn while the bad ideas are shuffled to the lesser feeder funds. Yes, this is illegal, yes it happens. From an outsider's perspective its usually means that there is one well performing fund that is closed for new money while there are several lesser performing funds that are open to outside money. Even RenTech has funds that are open to outside money and they don't perform anywhere near as well as their master fund that is for employee's only. > Two Sigma researchers spend time testing existing models, and each researcher is expected to come up with two or three new models per year. These are presented to Overdeck in a white paper that is typically less than ten pages long. Since Two Sigma’s trading models can change its forecast in seconds, lots of back-testing goes into each model. It’s not unlike the way Amazon exhaustively tests various Web-page changes in real time to ensure optimal clicks and purchases. At Two Sigma headquarters the model builders, who need to write code, sit with the engineers and collaborate with them all the time. From the strategy development side, often idea's have a half life of anywhere from months in the HFT space to years in the global macro space. For idea's I've since come to believe that idea generation is equal parts people, ie brain power, and platform, ie the ability to iterate. RenTech is a perfect example of this. They had two people leave who were very high up and go to another fund. They had 2-3 years of poor performance away from the huge backtesting platform that RenTech had built. it's not like these Phd's suddenly forgot everything. Its the ability to iterate quickly on idea's that is the key once the bar has been met for math and intellect. As they say, its not the algorithm you use but the features that produce your alpha. If you want to make money in the markets focus all your time on feature engineering. Fun reading: https://medium.com/@63ey5f4uw3k42v1exp7/chronology-mercer-medallion-fund-9aa719ceeb4f https://medium.com/@63ey5f4uw3k42v1exp7/chronology-mercer-me...

module0000 9y ago

Quant trading is harder than people assume - really, really, hard. Nearly everything you can think of has already been done, and is being done within latency limits you are priced out of as a retail guy. So you have to get creative, and if you want it to keep working, you can't tell anyone about it. Ever.

anonu 9y ago
Agreed. And it's not just latency that puts the average guy at a disadvantage. It's also access to the market and access to data which are hard to obtain. The beauty in all this is that 1000s of people keep trying... But only a select few, less than 1/100 of 1 percent of market participants succeed in making reasonable profits
thisisit 9y ago
I think it is important to understand that low latency = HFT. Because HFT is data driven it is part of Quant trading. But not all Quant Trading requires or is being done at low latency limits.
- jkaptur 9y ago
  A great point. When I worked in the industry, the trading (of exotic products) was actually done over the phone and via e-mail. Very much quant-driven, but about as low-frequency as it gets!
Xcelerate 9y ago
> Nearly everything you can think of has already been done I find this hard to believe. There's entire fields of research based around AI now and an endless number of ideas that have yet to be tried/implemented. There simply aren't enough people working at hedge funds to cover them all.
- pakitan 9y ago
  A more accurate statement will be "Nearly everything you can think of has already been done or will be done soon and since this is a zero-sum game, the expected profit in the long term from quant trading is approaching zero"

anonu 9y ago

Tldr for this 2015 article: 2 sigma uses tons of data and processing power to extract any alpha from the markets. They've done it consistently since 2004. People leave 2 sigma and sometimes email themselves code. Bad idea as the company will come down hard on you with lawsuits.

amerine 9y ago
Email themselves code? That’s an astonishingly stupid way to try stealing something. What did these people expect?
- anonu 9y ago
  Most financial firms have decent lockdown... Generally firewalls prevent file sharing sites, web mail, connecting to any external port that's not http or https. I've seen some places prevent direct IP connections (need a hostname to connect). USB and SD card slots are disabled... Not to say that there still aren't dozens of ways to get around all this if you really wanted to.

fibers 9y ago

python pandas is a digital blood diamond after what they did to Gao

slow_donkey 9y ago
What do you mean by that? I can't find any connection between him and pandas

polskibus 9y ago

Quants have worked in finance for ages. What's so unusual about Two Sigma? Headcount?

tomp 9y ago
Marketing strategy.

tntn 9y ago

I'll just leave this here: "Our definition of success has become narrow, boring, and limited. If we want young people to be creative and innovative, we need to reward them for it." from "Skip The Hedge Fund: We Need Young People To Take Risks And Build Inspiring Things" at https://www.fastcompany.com/3026586/skip-the-hedge-fund-we-need-young-people-to-take-risks-and-build-inspiring-things https://www.fastcompany.com/3026586/skip-the-hedge-fund-we-n....

SirLJ 9y ago

Quant trading is easy with small accounts < 10 millions, the more money you trade, the harder is to find an edge...

sputknick 9y ago
Can you elaborate on this? That sounds like the opposite of the way it would work.
- SirLJ 9y ago
  Many examples: faster in and out of the market, better fills, will not end up owning a company by mistake (e.g. bought all shares), etc... The bigger you get (in billions) the closer the returns will be to the market average...
- praulv 9y ago
  Typically it means you can invest in niche markets. These market would be out of reach for larger players as they would struggle to find liquidity for their portfolio size. On the other end, smaller players would not be able to adjust their positions sufficiently granularly due to minimum lot/clip sizes specified on the market - similar to a quantisation effect if you don't sample at a sufficiently high frequency.
- kevstev 9y ago
  In general, large companies have a lot more data around them, a lot more coverage from analysts and journalists and the like, and generally have a lot more eyes on them. These companies are a lot more efficiently priced. You can do the legwork yourself- getting on management calls, reading industry news, poring over the financial statements to find strength or weakness that are not widely known. So lets say you are a 10 billion dollar fund. You do your research and find that there is a small $100 million dollar company that doesn't have much debt, is growing rapidly, and the management seems good and there are good tailwinds for the industry as whole. You have 100x the deployable capital than the company's market capitalization. Even if the stock price doubles or triples in price, your potential upside for the firm as whole is a relative drop in the bucket. You can't deploy more than a few million into this company without moving the market. For a large fund, it isn't worth wasting time chasing these small opportunities. In fact, "scaling" is a hard problem in funds. Hence, there is actually a lot of opportunity out there for small scale investors who are willing to look under rocks that bigger guys don't see enough potential opportunity in.

downandout 9y ago

Could somebody explain why so much effort is being put into quant strategies, when it seems that real-world information gathering would be a much easier way to gain an edge over others? Let’s say you pay to place a camera on a building next to a given company’s factory, and use analysis software to count the number of trucks coming and going from the factory to predict their order flow and earnings. This kind of thing is harder to scale up, but also gives an edge because not everyone else is doing it. In an age when all hedge funds have the resources to hire the best and brightest engineers and buy the fastest processing hardware, it seems that none of them will have an edge if they are all starting with the same publicly available data.

smallnamespace 9y ago
This is exactly what some quant funds like RenTech and DE Shaw have done—they look for data sets that show signals before they hit the market. You can view a traditional market as a process where information from the real world flows in, and people can either make money by predicting how others will react to that information, or reacting slightly faster (HFT), or understanding the patterns the information tells you (e.g. stat arb). But even better if you can get the information before they hit the markets, hence the search for more and more data sets that have predictive value. This changes the actual market (the exchange) from the place where prices changes reflect different sets of participants' views being hashed out (the market is performing information processing mediated by price signals), to one where the actual predictive signals are monopolized by private parties before they hit the market. But note that this is exactly the path that the large tech platforms' ad exchanges have gone down as well. The price per click/conversion of a user and the outcomes of real-time bidding are hidden from everyone except the particular participants of the transactions; we're moving from a world where public markets (no matter their limitations) reflect the sum of public information in real time, to one where the thinking and computation are moved into hidden platforms. At some point, of course, some bright person is going to create an exchange/marketplace for data signals, and then the financialization/reification of another abstraction will take place.
scott00 9y ago
Their category "alpha capture" probably incorporates this sort of thing. I think the article gave the example of proprietary survey data being in that category. I think that's probably what they call anything that doesn't come from publicly available data.

madengr 9y ago

These guys averaged a ~9% return over the last few years. The S&P 500 has exceeded that. So what’s so special? Seems that passive investing works just as well.

dsacco 9y ago
> These guys averaged a ~9% return over the last few years. Who are "these guys"? The funds discussed in the article have average annual returns well above 9%.
- madengr 9y ago
  Overdeck and Seigel. “The firm’s biggest fund, Spectrum, has earned an annual average return of 9.4% net of fees since 2004.” The S&P 500 has averaged 9% for 80 years. Joe Blow can buy index fund and do just as well as the quant clients. Sure, these guys are averaging 14% before fees, and it’s a great way to get rich. But after fees the client might as well just passively invest.
  - desu_ 9y ago
    You need to consider the following elements: -You compare the S&P and two Six Sigma funds over different timelines. The index yielded much less than 9% on 2004-2015 (you can halve the performance). -What’s the common feature between: the S&P, the formal idea of passive indexing, and the emergence of passive investment funds? None of them are 80 years old. -Retail customers (Joe Blow) rarely have access to hedge fund products directly (they might still be exposed through pension funds, sovereign wealth funds, etc. but it makes the allocation change almost out of their hands). -Stating performance figures alone is mostly a Bloomberg-reader thing. In practice, there is usually at least an attempt to incorporate some kind of risk measure when selecting hedge funds. I am not even anti-passive funds but you are memeing a bit too hard.
- deleted 9y ago
  [deleted]
FundThrowaway 9y ago