In the chart, this is how the fund has compared to an S&P index over the past 5 years:
* 2013 - 5% over
* 2014 - 15% under (and negative overall)
* 2015 - 30% over
* 2016 - 10% over
* 2017 - About even
One exceptionally strong year, and pretty uneven otherwise. Hardly proof that these biology-derived algorithms are the secret to market-beating returns.
We have no idea what the beta of the fund is; it's difficult to talk about performance without knowledge of the fund's underlying risk
The other problem is scale - this is pretty evidently an advertising attempt in order to raise more cash. It's a lot easier to return 20% on, say, $100M AUM than it is on $1B AUM.
To be fair -- these caveats are true for almost every fund you hear about. I do think that's kind of my big issue with the article though, that this fund is just like every other fund; it's intrinsically an actively managed portfolio with the same shortcomings as any other. It's just got a good publicist!
I don't understand why 100 mil aum is different from 1 bill aum. couldn't you just divide the billion amongst 10 groups that are Chinese walled from each other? then there'd be no problems of market moving or depth or anything.
I agree, I don't believe this would pass a test for statistical significance (of outperforming the market indices).
Unless there is a way to break this data down much more finely (by 6-day window, by individual asset) in which case it's possible there's a high p-value but low-power effect. Even so, if there are more than 100 experimental firms, a single one with p-value .01 isn't evidence enough to jumping to big conclusions.
It seems like you could probably insure some great press coverage by starting several funds with wild-ass prediction mechanisms that don't change much. As long as you pick enough investments to roughly mirror the market, you can spin all the winners as incredible breakthrough techniques.
Heh, it's like that scam where you email people your predictions for who will win each football game for a particular team, where different groups get different winners. Each week some of the emails are right and some are wrong. After six weeks, 1.5% of your original folks will have seen six correct predictions, at which point you ask them for $1000 to see the 7th prediction which they are likely to pay for since "you're so accurate!".
Hah, that's excellent! I'd never heard of that specific trick, but survivor bias does seem like one of the best ways to fool scam-aware people into trusting you.
I had a b-school class do a similar exercise, in the context of the debate between passively and actively managed funds. Everyone in the class was asked to stand up, pull out a coin and flip it. If you flipped tails you sat down. Then the remaining students flipped again, repeating until there was only one person left standing. At which point the professor "interviewed" the student, asking what her method was and how she became such a skilled heads-flipper. Lots of parallels in the real world where luck is a dominating factor (stock picking, choosing a "rocket ship" company to join or invest in, general business success), yet our tendency is to seek out a cause or reason.
I like the exercise. But from a practical pedagogical standpoint (in the unlikely scenario I ever teach a class probability or survivor bias), what would be a safe point to stop the flipping? 3 students left standing? 2? What if all of the students still standing all flip tails simultaneously?
Oh, that's a good question. I'd stop at 4, or certainly anywhere under that. 4 flipping is a 1/16 chance of lesson failure, which isn't bad but is a bit uncomfortable.
Maybe you'd weight it based on class size? 4 left isn't bad in a class of 100, but it's a bit high in a class of 10.
If the winning techniques continue to generate greater risk-adjusted returns than the market for a significant amount of time, you haven't really bypassed the whole, "finding alpha" bit. Unless of course you've spun up a truly absurd number of funds, but I'd find that harder to believe than just beating the market in the first place given the capital requirements.
"Fuzzing" for alpha that way would be really inefficient.
I think I was a little unclear. I'm not actually proposing fuzzing alpha, just capitalizing on survivor bias. Basically my idea was that if you're going to spin up a 'normal' managed fund, you'll average tracking the market, with some chance of success or failure beyond that.
If that's all you're doing, you might as well go all-out on claiming to have secret sauce that produces your alpha. I'm not expecting you'll get any, just that you might as well have a media-friendly way of picking if you don't have an actually useful one.
Also there's a question of how many new hedge funds are using machine learning. If you have enough different attempts, one of them will beat the market by sheer game of chance. Picking out their specific quirks afterwards (algae etc.) is just survivor bias.
Get 25 monkeys to throw darts at S&P 500 stock symbols on January 1st of each year. The chance that at least one monkey outperforms 5 years in a row is about 55%. (The target areas need to be proportional to market capitalization etc.)
That's if you have one trade per year. How would you model this if you have 25 monkeys throwing, say, 300 darts at the board per day, for every day that the market is open (252 days), for five years?
If you're going to quantify survivorship bias, you can't use entire years as data points, because that doesn't properly represent the amount of activity that occurs. We should reason about each event, because if consistency emerges on an event basis we might not even need more than one year for our sample. The decision-making that is being empirically examined here (i.e. acumen capable of beating the market beyond chance) ostensibly functions on trading events, which means years are not the correct data point to use (and will provide an incorrectly pessimistic sample).
> That's if you have one trade per year. How would you model this if you have 25 monkeys throwing, say, 300 darts at the board per day, for every day that the market is open (252 days), for five years?
If the data is reported on a yearly basis then it's pretty much the same thing.
No it isn't, because each firm doesn't have a 50% chance of beating the market each year. Unless you're postulating that that is the case, it's not at all the same.
I can quibble about the odds of each individual trade resulting in profit or less being binary, but for the sake of argument it'll do. But a 50% chance of beating the market each year isn't supported by anything.
The grouping of data reporting doesn't suggest anything about the underlying data if it doesn't also share the same probability distribution. The trades are the events which determine if a fund will outperform on an annual basis, and we can group those trades by day, week, month, year, etc.
The part that isn't clear is how this compared to typical performance variation - then we can run our p-tests. But I suspect its pretty good, given the news worthiness of the article.
Also, hasn't been tested under bear market conditions... I'm skeptical that past returns prove future gains here. I can take any small subset of something and say its beating the market.
Was disappointed because title is misleading; I had hoped the fund was using actual Algae (i.e. computation in biological medium) to produce market decisions. Instead it is just biologists that are creating algos with their existing machine-learning knowledge. Apparently deep-learning and algae are the same thing.
Okay, I know you're joking, but this is exactly how I interpreted the title and I was so excited to see a picture of some suits staring at some green water and trying to read it like tea leaves.
> Apparently deep-learning and algae are the same thing.
That's perhaps the most annoying thing about "machine learning" in general and heuristic algorithms in particular.
These researchers hack together some rules to put together an algorithm, and only after that does the real work start: to come up with a metaphor to explain their model based on some clever story on why they decided to generate random sample points or filter training points.
In the end, the whole field starts to look like a bullshitter's ball, where everyone tries to one-up each other with the biggest bullshit metaphor to sell their an algorithm which is actually only a very minor tweak on an established age-old concept.
"We use particle swarms to generate new solutions, which are then genetically modified and subjected to a darwinian-inspired differential-evolution filter, who are then analised based on the behavior manifested by wolfpacks to search and hunt for their prey, and whose sub-optimal solutions are eliminated by following nature's resource-exhaustion megakill phenomena."
I hoped that the fund was investing in companies producing GMO algae to solve large problems: carbon sequestration, energy production, toxin remediation, etc.
There are skeptics, too. Emanuel Derman, who was among the first physicists to work on Wall Street, doubts that biologists possess secret sauce for investing. Derman rose to lead the quant risk strategies group in his 17 years at Goldman Sachs Group Inc. He found that as physicists applied their expertise of the laws of motion, atoms and mathematics to investing, their models didn’t work nearly as well as they did in a lab.
Newton’s law of gravity hasn’t changed for eons, Derman said, but human behavior in markets changes all the time, wreaking havoc on even the best models made by scientists.
“I’ve developed a lot of skepticism about anyone bringing their expertise from one field to another,” said Derman, author of the book “Models.Behaving.Badly” and a Columbia University professor of financial engineering. “They say stocks are like atoms, or like genes. But stocks are not atoms or genes. There is a resemblance, but ultimately they are very different.”
This is essentially a certain type of cognitive bias I think (halo effect?), where people take someone's high skill or talent in one area and assume it carries to another field. For example, assuming a chess grandmaster will be good at business strategy, or a great mathematician an automatically great engineer. These examples are convaluted but anecdotally I've seen it in action in recruiting.
Also 'the map is not the territory', all models will be unable to deal with all possible behaviours of the reality they are dealing with in a correct way.
I don't think the cognitive bias you're describing matches this scenario very well, because that bias appears to result when people don't acknowledge specialization versus overall intelligence.
Your first example seems like it maps well to that cognitive bias - someone assumes that a chess grandmaster has catch-all capability because they demonstrated expertise in one area, and then they flare out in an orthogonal area.
Mathematics and engineering (at least, computer science) overlap in many areas. If someone excels at mathematics it doesn't prove that they'd be good at programming, but I'd bet a significant amount of money that the mean mathematics major is more capable of programming than the mean population in general. If that holds, there's no bias in trying to cross-pollinate expertise, even if it ultimately doesn't work out.
A lot of the work that occurs in finance is legitimate mathematics and has very close ties to physics. The heat equation is directly used in Black-Scholes; securities can be modeled as stochastic processes, which means that much of the models that apply to Brownian motion also apply to them. Outside of quantitative derivatives pricing (and more recently), hedge funds can apply the same scientific computing techniques used by physicists and computational biologists to analyze vast amounts of data (more than they know what to do with).
1. That doesn't meaningfully respond to my point about cross pollinating skillsets, because we aren't distinguishing between amateurs, novices who become professionaks after expertise in other fields, and core professionals in finance/economics.
2. That bet is paraded more than it should be. Buffett bet against a "fund of funds", which is the aggregate performance of the industry. We didn't need a decade long bet to tell us most hedge funds are a poor return of capital, just as we don't need a bet to realize that the greatest n participants in many fields are mediocre.
I would have gladly taken that bet with Buffett and won if I could have chosen a single firm. But Buffett wouldn't have taken that bet, because (to his credit) he understands this point already as a savvy investor. The bet proves that the industry overall is mediocre, but it says nothing about the top firms that mostly don't even take outside capital anymore because they're so successful.
I was being cheeky when I responded to you. I agree with you on the first point.
But I think you are downplaying the significance of the bet. If it was an obvious wash like you are making it seem then why was the bet even placed? What top firms are you talking about?
1. The bet was placed because Buffett's thesis is fundamentally true - you have much better odds of receiving a good return through passive index fund investing than you do through active management. However, this bet is often used (unempirically, though not necessarily strictly inaccurately) to justify the idea that active management cannot beat the market consistently.
2. Firms like Baupost or RenTec would have handily won that bet against Buffett. But like I said, Buffett wouldn't have made that bet, because Buffett is a smart better and already knows all of this. Buffett has never argued that the market is perfectly efficient and resistant to alpha harvesting; in fact, he has publicly taken the opposite position in letters to shareholders.
I asked derman what an alternative would be to the formal models quants use now and his answer was that the whole thing should just be left up to "smart people and their gut instincts". the problem is that as soon as these smart people's gut instincts fail (which they will) and they try to investigate why you'll be right back to formal models. derman strikes me as just a guy trying to sell books.
They are overthinking this... with sufficiently low latency to your exchange, all your "quant"(and I use this term loosely) needs to do is watch the order book. When either side's inside bid/ask is about to crack, you join the opposing side with a market order, and place a stop on the next tick in your direction.
This is what 99.99% of consistently profitable quants do. It's boring, unexciting, and VERY profitable. The crux is...you must have that low latency connection to the exchange, and you must have priority routing for your orders. Bonus point if you have market maker status(but if you have that, why are you doing this in the first place?).
The deep learning craze seems to be infiltrating the minds of a few algorithmic funds, but it doesn't stay long(either they stop trying it, or they blow up their fund). Positions of any reasonable size(such as that required to move the market) are opened by human beings. Human beings operate on emotion and mob mentality in the market, so that is what you capitalize on when designing your algorithm.
Disclaimer: I'm looking at this with an ultra short term timeframe, such as 5-15 seconds being the maximum time in market per position. If these guys are targeting longer term positions, then all bets are off. The algorithms I create and maintain work in this timeframe, and the majority of my competitors algorithms are in the same timeframe.
This is what 99.99% of consistently profitable quants do.
This is entirely incorrect. Quants do not, as a rule, run liquidity providing strategies with market orders. Market orders pay for liquidity; they are the buyers for what HFT is selling.
Even though "average" is a statistically non-rigorous term, we should be out of the "5-15 second maximum" holding times mentioned by GP by at least one order of magnitude.
It's average as determined by the statistics from my own algorithm's licensees, and from my competitors. I'm sure there are plenty out there that don't fit that mold, but all I can report on is what I see - and those statistics are what I see day in and day out.
Another disclaimer, the markets I'm quoting these averages from are the ES and ZB.
It's hard to know if the returns are statistically significant given annual returns, but I'm sure he's providing more granular statistics to investors. Kinda annoying how the articles hypes this by distinguishing it from statistical models since he is obviously running some sort of statistical model as well.
In general, people have a poor understanding of how to evaluate an investment manager. It's not enough to just look at absolute returns and compare them to the S&P, you need to correct for market exposure (the beta). Even then, it is not that straightforward: this is one of the best overviews I've seen (the author of the blog, Robert Frey, was a former managing director at Renaissance Technologies, the most successful hedge fund of all time)
To make the "correcting for exposure" aspect concrete, suppose you have the opportunity to invest in a poker player that generates a 10% return on capital per year. It wouldn't really make sense to compare this return to the S&P 500 returns, because the beta is very close to 0.
> As the genome project produced reams of data, Lun saw an opportunity to break ground in computational biology and in 2006 joined the Broad Institute of MIT and Harvard, a crossroads for scientists and hedge fund managers. There Lun met senior computational biologist Nick Patterson, a former cryptographer who had spent a decade at Renaissance Technologies making mathematical models. Another Lun colleague, genomic researcher Jade Vinson, left Broad for the same pioneering quant hedge fund for 10 years.
Well he is certainly surrounded by some impressive people.
I think at this point in the search for alpha, wall street has employed applied mathematicians, physicists, code breakers, engineers, economists, chemists, computer scientists, sociologists, and now computational biologists.
Each one of them brought some new/novel mathematical techniques to the field. Do Medieval Historians learn any specialized math?
Maybe he can beat the market reliably but he's only managing $20 million. The big question every quant fund asks is can this strategy provide alpha at a salable level of investment.
A 3 year track record is plenty long enough to prove out a system and provide a track record. It's a troubling sign that there is only $20 million in his fund if.
> A 3 year track record is plenty long enough to prove out a system and provide a track record. It's a troubling sign that there is only $20 million in his fund if.
I'm curious as to why you say a 3 year track record is long enough to prove a system. I don't necessarily disagree (though I think number of trades executed in that timespan and the type of trading strategy might be as important as the timespan itself), but I'm interested in your reasoning.
It's important to note that 3 years doesn't mean 3 data points. It really depends on the funds average trade horizon. Which is, I think, exactly what you were referring to.
An HFT firm trades at such small scales that it can use its daily returns such that each year actually provides 252 data points.
On the other side of the coin, Berkshire Hathaway would need benchmark times longer than a single year.
I'm assuming the fund has holding times of around a week based on intuition and prior knowledge of alto of different fund investment structures.
The thing to understand about hedge funds is that most of them change investment strategies at some point in their lifetime such that historical records no longer really apply. This can happen for a number of reasons:
1) markets get crowded and force people to search for alpha somewhere else
2) funds get larger and existing strategies don't have the capacity to manage the new money.
3) traders leave and new traders have new ideas.
3 year is an industry goldilocks mark for comparing hedge funds. Not too long to take into account old strategies that are no longer employed and not too short that it doesn't allow the strategies to play out.
I cant' remember the exact number but Victor Haghani of LTCM fame talked about this and said it would be something like 143 years of data to know if a biased coin that comes up heads 60% of the time is biased to a 95% confidence level.
Obviously this isn't workable and as such we have to use smaller time frames.
Yeah, that all makes sense, thanks. I figured you were using that 3 year figure with an implicit acknowledgement that it would provide greater or lesser rigor depending on the number of trades and the holding time. Thanks for the resources as well.
The best hedge funds should be 2x the SPY... It would beat every body. However, clients won't pay for this. People spend so much effort and don't beat the market, but clients want people to work on stuff...
The article is pretty hyped and low on actual details. From what I can figure out, he used the same models that he used to predict stuff about Algae to predict the market.
No information about what these models are is given. But it seems more "I created a system which can predict cell changes and stock market movements" than "I used Algae to predict the stock market".
Better this guy would continue working on algae to finally invent that (definitely possible) strain that would be useful to produce cheap fuel from! He tried to play that eternal zero sum game instead... sad
It sounds like he is doing both (managing investments and doing research) simultaneously. From the article,
> Lun, who was born in Hong Kong, splits his time between his firm in Pennsylvania and lab at Rutgers, where he’s undertaken an ambitious long-term project: creating computer models that predict how cells behave, using data from blue-green algae and other sources. The models allow Lun to re-engineer genes for useful purposes: he has modified E. coli for production of bio-fuel for transportation.
Question for investing people.. say I started a hedge fund, put all the money in vanguard to achieve s&p500 benchmark return, then once a year did an options bet with a roughly 97 percent chance of a 3 percent return and a 3 percent chance of ruin. I do this for 10 years, outperformng the s&p 500 by 3 percent consistently. I have 70 percent odds of not being ruined, and I consistent outperform most hedge funds. I have good alpha and there is no way to see that my beta ain't great. Would I become a wealthy hedge fund manager?
It's a good strategy, if you can afford not to withdraw from the fund on the year (and for several years after) you 97% bet turns sour - there was an article somewhere, which showed that options on S&P singe the great depression almost a hundred years ago, would have only slightly outperformed the index, due to losing in the bear market.
> It's a good strategy, if you can afford not to withdraw from the fund on the year (and for several years after) you 97% bet turns sour
If the bet turns sour, you close the fund. The idea is that you grow the fund and take your two and twenty while your bets are winning, and you keep your prior years' two and twenty after your fund collapses.
There are ways to achieve this though - using a martingale betting strategy where you take small, high risk bets and basically keep doubling up until you win or bust, you can achieve the same risk-reward profile on most high risk markets
> In John Bogle's “The Little Book of Common Sense Investing,” he notes that the average U.S. equity fund compounded at 10 percent from 1980 through 2005, while the Vanguard 500 Index Fund made 12.3 percent. Actively managed funds did worse than average, not better as the brokers would have you believe.[1]
Lets see how it performs longer term (10 year period).
This guy looks young. Usually, it's the older Ph.D.s who veer into crackpottery.
I'm reminded of Linus Pauling: He made amazing, fundamental breakthroughs in chemistry and quantum physics, but when he applied his genius to medicine, we got orthomolecular medicine and mega-dose vitamin C as a cure-all, something which has been roundly disproven by actual evidence.
That said, investing in algae could be a good idea. It has potential as a cheap, high-volume input to synthetic food production.
Aside from whether this actually works reliably already or not, from a principal point of view it makes sense that computational biology adds something to the mix.
For the last year I've only been a glorified webdeveloper working for molecular neurobiologists at the Karolinska Institute, but from what I understand it is all about untangling vast quantities of high-dimensional data: data sets of tens to hundreds of thousands of individuals cells, where for each cell the expression levels of tens of thousands of genes are being measured (in what stage of development in which tissue was the cell harvested).
If you can find algorithms that somehow make sense of how these cell populations and genes interact and develop over time, I think it is not out of the question that the same algorithms could make some sense of the aggregate behaviour of the stock market, give a decent data set as input of course. Especially given that most of these algorithms are forms of machine learning, so don't necessarily require an a priori model of what is happening (I mean, if I understand correctly, uncovering that model is precisely what the biologists are after).
Boo, he basically got lucky twice, once on Brexit and once on Trump's election; he'll raise a bunch of money for his fund, lose much of it and be out of business in a few years when his model fails because luck isn't a strategy.
I think what he is doing is modeling equities as an interaction model and performing clustering and or community detection on a dynamic graph. On the bottom you see a bunch of triads graphs. When they say "A quant tries to make sense of time-series price data that at first look chaotic because we don’t know the different parameters and their relations. So you try to piece that together, as one would do with biological data." you could accomplish this by taking the time series data and when one equity interacts with another within some type of time series window you would compute a set of graphs and see how they cluster together.
I can't tell if this suggesting he uses models that were made to predict algae growth or if he is using algae/bio/genetic inspired optimization algorithms. But either way he is certainly not "using" algae.
I guess I'm a bit skeptical. The article doesn't address how the fund managed to lose 2% in a year when holding an S&P 500 index fund would've gotten a 10+% return, and in general we just need more data before we can conclusively say that this has an edge on the market.
I also did not understand why cells specifically provide such good insights for markets, as compared to any other complex natural system (like weather or ecological systems).
Based on the title and the summary, I thought the guy was plugging wires into algae in a Petri dish and getting some magical output that lets him beat the market. Turns out, he's crunching numbers with a computer using a mathematical model borrowed from computational biology. This doesn't sound all too different to what quant hedge funds do currently, unless I'm missing something?
Just from eyeballing the chart it looks like they made a few million more than the index, over those five years, which pays for 3ish engineers if they're taking half the performance over index and no management fees.
Many hedge funds invest in lots of things other than publically traded stocks e.g. local bonds, property companies, etc.. if you have some expectation the market will tank ala 2008, you probably don't want to keep all of your money in the market, even if you get lower returns overall..
Also, not sure about hedge funds, but alot of mutual funds exist with a specific focus - one country, sector, asset class, etc. They try to do well within that category...
Just because the 'average' does worse, doesn't mean there aren't a few that do far better if you pick the right one..
Whether you think it is good to invest in that sector over the short or long run and why is up to you based on your portfolio and expectations for the market over the holding period..
It's the same reason why companies buy licensed software even when decent open source alternatives exist--they know they can pick up a phone and yell at someone when something goes wrong. Also, there is a ton of folklore built up around the market and telling your golf buddies you are with 'so and so' is just another kind of name dropping.
I mean, you could have a "throwing money into an incinerator" ETF and call it diversification, but that doesn't make it a good call. There are plenty of other ways to diversify if an index fund with hundreds of stocks isn't already diverse enough for you.
Not if you group all trading events into single data points by year it doesn't, but that's a silly way of analyzing them if you have extraordinary performance consistency on a day by day (or trade by trade!) basis.
In the end, all that we care about is the effective annual rate of return. It doesn't matter if you did 200 trades that year or 1 - the money is the same if you have the same RoR.
The number of data points is important when looking for trends, or for cleanness of data. However, they're not showing us the data. It could be hugely volatile, or fairly linear.
It probably doesn't exist. Most funds of this type are very very paranoid about publicizing the methods they use because
1. Another firm could exploit what they are doing.
2. Another firm could trade the signal and remove their ability to profit.
3. They think being opaque makes them cool and mysterious. Which it does.
