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gnarsed
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ridge/rida down small. rief up.
what factor moves did you think would be most painful? |
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gnarsed
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| Total Posts: 95 |
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ridge/rida down small. rief up.
what factor moves did you think would be most painful? |
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leftskew
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| Total Posts: 25 |
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| It has been a while since I analyzed last, but I thought they had exposure to the anti-beta/low vol one and quality, and I don't think those factors have performed well recently. But I wouldn't be surprised if they were making changes. |
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zee4
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| Total Posts: 90 |
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| Are these numbers YTD or Feb only? Regardless, -5.87 and -7.86 do look ugly. |
Бухарский |
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chika
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These are YTD performance based on a few funds I track
what is the data source for this table? |
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zee4
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| Looks like HSBC Global Markets report to me. |
Бухарский |
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elf
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| RIDGE is down 1.6% in Feb, down just under 7% YTD. |
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jslade
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https://www.quora.com/How-reliant-is-Renaissance-technologies-on-HFT
Appending here for the record
James Baker, same math as Renaissance Tech from same source
Updated 5 years ago · Upvoted by Tom Groves, One of the founding partners at LindenGrove Capital, a
Macro hedge fund and Jack Wei, Hedge Fund Analyst · Author has 200 answers and 4.7M answer views
What are the investment strategies of James Simons/Renaissance Technologies? I
understand he employs complex mathematical models, along with statistical analyses,
to predict non-equilibrium changes.
Originally Answered: What are the investment strategies of James Simons/Renaissance Technologies?
I have known Jim Simons, Bob Mercer and Peter Brown since 1965, 1974, and 1979,
respectively. Renaissance has also hired senior researchers who had formerly worked for
me for years. None of these people has ever told me anything about Renaissance's
investment strategies. My observations below have been obtained entirely from publicly
available records.
In particular, the core strategy is publicly known. It's the details that are proprietary. There
are millions of details, and they are essential to the performance. However, the question
was about strategy, so that is what I will try to answer.
The core strategy is portfolio-level statistical arbitrage carried to the limit and executed
extremely well. Basically, portfolios of long and short positions are created that hedge out
market risk, sector risk and any other kind of risk that Renaissance can statistically predict.
The extreme degree of hedging reduces that net rate of return but the volatility of the
portfolio is reduced by an even greater factor. The standard deviation of the value of the
portfolio at a future date is much lower than its expected value. Therefore, with a large
number of trades the law of large numbers assures that the probability of a loss is very
small. In such a situation, leverage multiplies both the expected return and the volatility by
the same multiple, so even with a high leverage the probability of a loss remains very small.
The general properties of the strategy can be deduced from the statement of Renaissance
for the Hearing of the Senate Permanent Subcommittee on Investigations, dated July 22,
2014.
[https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=6&ved=0CEcQFjAFa
hUKEwjVl_LOifrHAhWJ1h4KHTBAAj8&url=http%3A%2F%2Fwww.hsgac.senate.gov%2Fdow
nload%2F%3Fid%3Db81368f4-6448-4867-8572-f2340418d029&usg=AFQjCNGlNbiS9DOE_N
jQ8Ks_BT_X54dIPw&sig2=s0RdwL0a8afNv6lUBfyxVQ&cad=rja]
Renaissance collects "all publicly available data [they] can that [they] believe might bear on
the movement of prices of tradable instruments--news stories, analysts' reports, energy
reports, crop reports, weather reports, regulatory findings, accounting data, and, of course,
quotes and trades from markets around the world."
Their models "use this data to make predictions about future price changes."
The hearing was specifically about the Medallion fund, about which the statement says "The
model developed by Renaissance for Medallion makes predictions that are profitable only
slightly more often than not."
With these properties, there were two reasons that Renaissance would like to have a call
option on the portfolio that it has designed: leverage and protection against Black Swan
events.
Leverage is needed because, unleveraged, the rate of return of the portfolio is low. However,
because the volatility is much less than the expected return there is no limit to how high the
leverage could be without increasing the probability of a loss, at least according to the
models. Through years of use and refinement, Renaissance knows that its models are very
reliable. However, they also know that there is always the risk of something happening that
is not covered by the models, in particular something that is outside prior experience, which
is called a "Black Swan" event.
Thus, a call option is ideal: it can provide high leverage and can provide protection both
against the very low probability of a loss greater than the option premium and also against
the unknown probability of a possibly catastrophic loss due to a Black Swan event.
We know all this because these are the business reasons for Renaissance accepting
Deutsche Bank's proposal of barrier options. Basically, Deutsche Bank, and later Barclays,
sold the equivalent of a call option to Renaissance on the reference portfolio that
Renaissance designed.
Of course, writing an uncovered call on the Renaissance portfolio would be equivalent to
betting against Renaissance at high leverage, which would seem to be a foolish thing to do.
The banks covered these options by buying all of the securities in the portfolio. Thus the
bank's position was equivalent to a covered call. In other words, the banks' profits and risks
were essentially equivalent to writing a put option, which is a bullish position. Because the
volatility was very low the probability of a loss for the bank was low and the probability of a
loss greater than the option premium was even lower.
Except for the Black Swan risk. The probability of a Black Swan risk is unknown. Part of the
premium paid by Renaissance and earned by the banks was equivalent to insurance against
Black Swan risk. I don't know if the amounts of the premiums were publicly disclosed.
There were many more details in the statements and the testimony at the hearings.
However, discussion of further details would detract from the important points that I have
made above. In particular, the hearings themselves were about tax issues not about
investment strategies. Renaissance explicitly asserted, under oath, that its "models do not
factor in tax rates when making trading decisions." Therefore, tax issues, although they
might be very important, are not part of the "investment strategy" at least as reflected in the
models, so they are outside the scope of this particular discussion.
[Edit (added in answer to a comment): The reference portfolio was highly dynamic. There
were thousands of trades per day. To accomplish this, the banks gave RenTech's computers
direct access to execute trades through the banks' trading desks.
This arrangement was part of what created controversy about what should be the proper
tax treatment for this particular case. However, I am not a tax lawyer and will not try to
analyze those issues. However, if you want to hear more details on the automatic execution
of the trades, and questions about how much human interaction was present, that is all
discussed in the live testimony before the subcommittee: [Hearings| Homeland Security &
Governmental Affairs]
Are the consistent high annual returns of Jim Simon's RenTech Medallion Fund due to
their ability to execute trades at high frequency?
Yes and No
Yes, Medallion Fund does do hundreds of thousands of trades per year. In some cases they
trade in and out of a given security position within seconds, though some positions are held
much longer.
No, in the sense that Medallion's success does not appear to be due to them executing
trades faster than others so that they can get ahead of price movements caused by large
institutional trades, which is the practice that causes "high frequency trading" to be
controversial.
As Sundeep Bhat has said, no outsider knows the details of what they do. All employees
sign a very strong non-disclosure agreement that apparently no employee or former
employee has ever violated. However, the broad strategy of what they do can be deduced
from public statements by Jim Simons, the founder of Renaissance Technologies, and Peter
Brown, one of the current co-directors. This includes Peter Brown's testimony under oath at
a US Senate hearing.
Basically, Medallion Fund does statistical arbitrage. They gather enormous quantities of
data of all types and use statistical prediction to find instances in which one collection of
securities or commodities can be hedged against another. An example used by Peter Brown
is based on the statistical observation that markets tend to do slightly better on days that
the weather is fair than on days when the weather is bad. Thus, if the weather is fair in New
York and bad in Paris, you can statistically make a profit by being long on securities traded
in New York and simultaneously short on securities in Paris. They do this even if their
predictions indicate that the market in Paris will go up, but merely less so than New York.
This only works if the securities in Paris are precisely balanced with securities in New York.
They won't be the identical securities, so it will be a very complex statistical balance
involving many securities or commodities.
However, this weather effect is very small. The system works by finding a very large number
of such small opportunities. In addition, hedging very different entities against each other
requires precisely balancing complex portfolios against each other rather than hedging
individual securities. That's the only way to get precise balance. They have a program with a
million lines of code to manage all this, and, yes, they do thousands of trades a day.
However, the large number of trades is a consequence of them needing to take advantage
of a large number of small hedging opportunities and having multiple securities and
commodities involved in each hedge.
Thanks for the A2A.
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"Learning, n. The kind of ignorance distinguishing the studious." |
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jslade
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| Total Posts: 1246 |
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Brandon Smietana, They call me "Flash Crash"
Answered 10 years ago · Upvoted by Andy Manoske, Associate at GGV Capital and Martijn Sjoorda,
Former COO asset management firm · Author has 495 answers and 1.7M answer views
How likely is it that Renaissance Technologies is a Ponzi scheme? Given how large an
outlier their main fund (Medallion) is in terms of extremely good performance over
many years?
Originally Answered: How likely is it that Renaissance Technologies is a Ponzi scheme?
Renaissance Technologies is not even the most profitable quantitative fund. These funds
are all doing so well that they do not take outside investors and do not want you to know
they exist. Its surprising that Renaissance Technologies has a website and releases its
return information at all, because most of these funds do not.
Renaissance Technologies is just one of hundred of there firms, although it is one of the
larger and older ones. However there are diminishing rates of return to on increasing
amounts of capital, so from that perspective Renaissance Technologies is not even
generating the highest rate of return for firms doing similar things.
When it comes down to it, the market is extremely inefficient. Up until a few years ago, most
trading decisions were being made by humans. We still do not even know how to price most
of the newer financial assets and there are still fundamental breakthroughs in mathematical
modeling of financial markets occurring yearly.
Most of the information in SEC filings and about companies is not used for pricing and
modeling by most firms. Most firms have no way to adjust the pricing of assets based upon
news reports. The majority of information that is relevant to financial markets is information
that we are only now integrating into our financial models.
Renaissance Technologies' 30% year rate of return is the kind of returns that someone using
a naive back-propagation trained neural network generating trading signals would get in
2002.
Today you can still generate great returns by taking something that a human trader once did
and doing it faster. For instance, by using NLP algorithms to process news reports and
using that information to predict price swings caused by news announcements.
A human might have an idea about the direction a stock will move and how fast, in response
to a new article. However the human cannot process the article and make a trading decision
in under 5 seconds, like we can with a computer algorithm. Also the trader has an intuition,
but is not going to be as accurate as the algorithm.
If we can quantify the price movement and integrate historical data on the past behavior of
the asset and other similar companies share prices responding to similar news reports,
then we can determine the likely magnitude of the price movement. We can also quantify
the uncertainty in our estimate of the magnitude of the price movement.
If you can process data faster, integrate more data or make more accurate predictions of
market behavior with better algorithms, you are going to see great rates of return. This is
merely what Renaissance Technologies does; they use data and mathematics to create
mathematical models of market behavior. It should be obvious that any firm with better
models is going to produce superior rates of returns.
Its also possible to arbitrage volatility and other statistical properties of the market and
many of these statistical properties of the market time series are still very inefficient. If you
want to see how inefficient the market is, you should do independent component analysis
on the market time series for the daily closing-closing price rate of return and decompose
the market series over the extracted factors.
Renaissance Technologies' performance is not surprising at all. They do not have any
finance people; they are a mathematician and physicist shop. Most of these people do not
even have enough finance background to construct a believable ponzi scheme.
The human fund managers I have talked to have told me that they prefer simple algorithms
that they can understand and which they can explain to their investors, over algorithms that
more accurately model the market and which produce higher rates of return!
They prefer 'simplicity' to achieving high rates of return at lower variances! The question
should not be "How likely is it that Renaissance Technologies is a Ponzi scheme?", but
rather "Why are we trusting our retirement fund investments to fund managers using 30 year
old commodity quantitative methods?".
I think Renaissance Technologies is just one example of a trend in finance towards
automated markets where trading and pricing are performed by quantitative statistical
models instead of ad-hoc human reasoning and intuition.
Laurent Bernut, ex-Fidelity short seller, ex-Hedge Funds analyst, ex-CPA, algo trader
Answered 4 years ago · Author has 584 answers and 5.1M answer views
How can Renaissance Technologies make so much money from financial markets by
hiring scientists and/or mathematicians with no domain knowledge of finance?
Originally Answered: How can Renaissance Technologies make so much money from financial markets
by hiring scientists/mathematicians with no domain knowledge of finance?
I have never worked at Renaissance, so please take my answer with a grain of salt, but here
is a first hand story that could shed some light.
On June 22nd in NYC, my colleague, who is also ex-US department of Defense consultant
and myself, met with one of the foremost US experts on sonar detection (good luck finding
him on Facebook, LinkedIn). He is a physicist with multiple PHDs, geeky funny. His expertise
is signal processing.
It was one of the most refreshing experiences ever. He explained his world. I explained
mine. Cotes de Provence Rose, beer and wild berry Zinfandel helping, we tumbled down the
rabbit hole talking even about epistemology, the philosophy behind math.
His world, signal processing, bears uncanny resemblances with ours. We explored Bayesian
probabilistic determinism, which models (Gauss, Poisson etc) to apply to distributions, the
cost of false positives (think trading edge), arbitrage between time and action with sparse
data (confirmation). We spoke the same language. We were talking real problems: how do
distinguish signal from the noise ? How fast ? What is the cost of being wrong ? What is the
cost of being right ? Which statistical law applies to randomness ?
We entered a massive time distortion. We started around 2 pm and a couple of bottles
down the road, but then after what seemed like 5 minutes, we were hungry. It was 10 pm.
We could have gone on forever (*)
Compare this with glorified journalists, otherwise referred to as fundamental analysts.
●●●●●●“This is fairly valued”... life is unfair darling, so do you really think markets
are fair ?
“On a sum of the parts valuation”... Frank N. Stein zombie valuation
“Fundamentals are strong”... Make fundamentals great again...
“Long term story is still intact”... Some HF reality TV celeb says that about
Valeant by the way...
“On a DCF basis, our target price is +10% above current market valuation” ...
stop tinkering the terminal value to rationalise your subjective views
“top quality management” ... was also said about Enron, Bear Sterns, Kodak,
GM, Chrysler,
Too much B/S bingo, too much theory,
Bottom line:
Physicists approach the markets as a statistical problem. This is practical.
MBAs have too much untested theories in their head. It is costly and time consuming to
unlearn all that junk.
“In theory, theory and practice are the same. In practice, they are not”. Yogi Berra
(*) There is no way i could ever afford someone of that caliber; he charges something the size
of Liberia’s national deficit per hour. But, he wants to send his granddaughter to Mars and he
thinks our algo could be the right fuel, so i invited him to have fun with us. Maybe good guys
do not always finish last
https://www.quora.com/What-are-the-top-high-frequency-trading-firms :
Christina Qi, Partner, Domeyard LP
Updated 8 months ago · Upvoted by Marc Bodnick, Co-Founder, Elevation Partners
[In wake of the 2020 crisis, this list may change substantially. Will keep you guys posted.]
[Updated again February 2019. Original post April 2014.]
[If you’re a reporter looking to reference this post, please give credit where due and don’t
just copy me word-for-word.]
I was originally compelled to respond because many of the answers were outdated
(Infinium blew up, Knight had just become KCG Holdings, etc), and wanted to compile a list
of current HFT firms for everyone’s reference. These firms are not necessarily the largest by
head count or operating capital, but are generally known to trade the most volume (by
notional value) and possess the fastest and most deterministic response times.
I also thought it would be helpful to list whether they’re a prop shop or hedge fund. Note
that some hedge funds have a proprietary arm, and that most HFT strategies fall under prop
due to capacity constraints. For those who aren’t familiar, proprietary trading firms (or prop
shops) are internal and smaller in AUM compared to your “Top 10 Biggest Hedge Funds”,
and you’re probably trading the CEO and Partners’ funds. It’s hard to compare a prop shop
to a hedge fund due to strategy and AUM differences. Thus, there’s a natural tendency for
HFT strategies to be proprietary. However, we’ve seen a few HFT hedge funds sprout up
over the years, though they all reach capacity quickly. We’ve also seen more hedge funds
raise venture capital and PE funding recently, as well as a record number of M&A activity in
HFT, but I digress.
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●Virtu Financial (acquired KCG) - prop shop
Two Sigma Investments - prop shop that launched a hedge fund
Citadel - hedge fund with prop arm
Hudson River Trading (acquired Sun Trading) - prop shop
Optiver - prop shop
Quantlab Financial (acquired the prop trading arm of Teza Technologies) -
prop shop
Tower Research Capital (Spire Europe) - prop shop
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●Jump Trading - prop shop
Tradebot Systems - prop shop
Flow Traders - prop shop
DRW Holdings - prop shop
RSJ Algorithmic Trading - hedge fund with prop arm
IMC - prop shop
RIP since last update: Spot Trading
If you’d like a larger list, this article by Grainestone Lee is pretty comprehensive. Below is a
screenshot (sorry for the highlight over my company - I was very happy to have barely made
the list):
I confirmed with Jim Simons: Renaissance Technologies doesn’t do HFT. He got pretty
annoyed when I asked him. Perhaps it’s a common misconception about their firm.
(my note: Simons invested in her Domeyard HFT firm, so makes sense that it is a
diversification play for Simons, rather than a competitor; and that she would be able to ask
him directly).
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"Learning, n. The kind of ignorance distinguishing the studious." |
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| lol - i honestly thought it would be wayyy more complicated it sounds just like an expert system or like Watson which (Peter Brown & Robert Mercer did the ground work for) used multiple machine learning models and had a scoring system to pick the best answer. |
You going to carry that wieght? |
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James Simons, Robert Mercer, Others at Renaissance to Pay Up to $7 Billion to Settle Tax Probe
Tax settlement, which current and former executives will personally pay, may be the largest in history
[https://www.wsj.com/articles/james-simons-robert-mercer-others-at-renaissance-to-pay-7-billion-to-settle-tax-probe-11630617328] |
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Hey Guys/Gals,
I wanted to give some research I've done on what I think RenTech could be doing in broad strokes but a much better level of detail than all the public sources I've seen.
1. It seems they're clearly doing the classic quant fund "least squares regression over a factor model" to hedge out their risk of the entire portfolio. This will make you neutral to all factors in your model, as well as almost dollar/beta neutral. ( depending on your definition of Beta... )
2. They may be using, or did use a PCA model for some/all of their risk model. It's nice and clean because your risk model factors are orthogonal and you can choose how many factors to hedge since you naturally get any percentage of the variance you want explained by using up to N eigenvectors where N is the number of securities. ( They mention PCA being the breakthrough in their equities model in the book "The Man Who Solved the Market". ) Now there's many problems with covariance drift that they had to solve but I assume some sort of shrinkage or exponential weighting on the covariance matrix used to calculate the PCA?
3. Their signals are "auto-generated" or mined from the data and then put through a barrage of tests for "out of sample performance", "statistical significance", "correlation to prior signals", "model complexity" etc... to see if they should be added to the portfolio. The evidence for this is all hearsay but here's a nice breakdown in Hacker News:
Quote: "Renaissance Technologies has completely automated the process of signal discovery.[1]"
https://news.ycombinator.com/item?id=16649002
Also many firms do this already such as WorldQuant, Trexquant, etc.. They use Genetic Programming to generate and test millions of generated factor models and combine them together.
4. They combine these signals together with optimization algorithms, examples being the ones from scipy.optimize ( constraint optimization library ). This is so you can optimize around trading costs. A lot of your signals will have high turnover so you won't want to weight them too heavily as the signal is not strong enough to overcome the transaction fees but may be useful when it's blended with thousands of other signals.
5. I know this works because I've done it all and traded it on Binance embedded in a Market Maker and had a Sharpe of 15 ( no down days ).. until they booted me for VPN violations. ;)
Anyone have any other ideas? or email to chat? I have a bunch more stuff. |
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