Silicon Valley Hedge Fund Takes on Wall Street with AI Trader
bloomberg.com
bloomberg.com
> We have too many executives for a company this size - most don't add much value except for fighting with each other and politicizing issues. The CEO lives in HongKong and remotely manages this crew of distrusting, non-supportive and close-minded execs. The HR function is practically a joke. You don't ask questions, challenge their decisions or speak up - they'll threaten to fire you if you did. They have not been able to productize their technology, their trading business has not picked up and their sales pipeline is pretty dry for the other businesses. They have laid off a large number of people recently and financial trouble seems to be brewing - lot of marketing smoke in here!
> Focus on one product and give it more time.
> Productize, productize, productize.
What you do is make money, and if you're making money, well, you don't need to ask anybody else for money. This tells me that its a bunch of executives who either don't know anything about trading and/or don't have any coherent strategy, and are really just trying to throw enough buzzwords out there to get investor capitol.
Sharpe Ratio, Marketing department
≤ 0: Runs the firm
0.25: Very important; involved in all investment decisions; major focus on asset gathering
0.5–1.0: Secondary
1.0–2.0: Almost superfluous
≥ 2.0: What marketing department?
[1] https://faculty.fuqua.duke.edu/~charvey/Teaching/BA453_2004/...
Having spent some time in the industry, I disagree, you're always trying to maintain or increase AUM. Funds constantly sell to existing and future limited partners (investors). Internal prop trading desks sell to management in order to keep their allocated VaR and to risk for similar reasons. Traders themselves sell to their boss and peers. VCs sell their top quartile-ness. All of the above sell to their junior staff to keep them around.
The thing is you don't know if you are good or lucky until many years into your career and someone has to put up the capital until then. Even the top funds with great track records can one day stop producing results, at which point people abandon ship.
You can usually make a lot more money with Other People's Money than with in-house money.
Trading yes but a hedge fund or any fund really implies customers no?
If a hedge fund is consistently highly profitable, they stop taking customers. For example, see RenTec's Medallion Fund, which is only open to its own employees.
That said, the track record for Bay Area hedge funds trying to do things "the Silicon Valley way" without experienced founders (experienced in trading) is pretty grim. The ones who succeeded had a track record of doing it before.
Interesting do you have any data to look at regarding that? How do they justify their 2 and 20 then?
Yes, but knowing how to trade is obviously very important, it's not just picking stocks, it's risk management, regulatory issues, and tons of industry specific knowledge.
That said - if they have 'tech that works' they should be able to partner with a few financial people and wrap a fund around it without too much trouble.
That so much negativity is on Glassdoor is really a bad sign.
It's certainly more truthful that marketing fluff pieces like this.
People still make up fiction and post it on there all. the. time. Keep that in mind.
Yes there will always be people that were fired writing things up as well as others just lying (competitors). Like you said, at some point when there is enough smoke you should expect a fire.
Interestingly, Peter Thiel starts his book Zero To One with a reference to the Anna Karenina principle by showing a corollary in business: "All happy companies are different, all unhappy companies are alike."
Hope it fails
Most real electronic trading firms are putting those algorithms in hardware.
Source: Have worked in HFT the past 10 years.
His point was trying to bet against a momentum trade or some other scenario was foolish. He watched time and again. There were opportunities to get with the crowd, or be on the wrong side.
A friend who works in quant finance said something along the lines of "if a trader can identify a mom-and-pop investor, it's like taking candy from a baby" in terms of making an easy profit.
Sure HFT guys are faster than you to get short term signals and make money on the micro scale - but even ignoring that way too many people are thinking they're beating the market and they really really aren't.
But that's a great point that any kind of active trading strategy needs to be compared against the right index. I think this holds true for pension/sovereign funds which invest in multiple hedge funds as well - comparing to the overall market performance may be less relevant than comparing to sector-weighted market performance, for example.
OT question: how would I get into acquiring market data to try my own backtesting strategies ? Is there anyway to "get in" without putting up $5k + for the data ? I'm not green to this, I worked at a prop shop for a year writing market feed handlers. I know the dangers of overfitting data and how hard it is to make money (so I'm not deluded by a fantasy). I want to start out just for fun doing backtesting and see if I can scrape out a (paper) profit, but accessing historical data seems to be the hard part. I dont think online services that offer this would work for me because I want to use intraday data and replay many times with different parameters. I want access to "the whole firehose" if you will.
Some exchanges offer free historical data, for instance BM&F BOVESPA[2], trading in some markets (such as Brasil and China) is often difficult if you are a foreigner.
[2] http://www.bmfbovespa.com.br/en_us/services/market-data/hist...
SHUT UP AND TAKE MY MONEY!
This is not about High Frequency Trading, but about machine learning applied to the stock market.
What do you think their 100k cpu jobs are doing? Machine learning and quantative analysis, what else?
This is not about exploiting a privileged access to market conditions (as in the sub millisecond access HFT firms have) in order to gain an advantage.
This is about using machine learning to predict future (as in hours/days) moves on the market and take advantage of them.
Speed is nice, but smarts are better. "Machine Learning" is the new buzzword for a way to mathematically deducing future outcomes based on data science and quantitative analysis. That is a grotesque simplifications and there are specific ways of approaching this (Tensorflow/Keras from Google, Torch from Facebook, etc). This could be nanoseconds in the futures, it could be hours, or even weeks / months. And this is the part you're failing to understand by trying to assume (incorrectly) that all HFT is made up of is bid sniping and latency arbitrage (often referred to as front running). It isn't really that privileged access if you yourself can pay any exchange, such as NYSE, for a feed to your house.
TL;DNR: Your understanding of electronic / HFT trading is wrong. If you try to understand it first, you'll realize your statements bear absolutely no basis whatsoever in facts. Facts are that Electronic Trading firms have been using machine learning literally since the invention of machine learning and quantitative analysis to make money. A new firm trying to do this can simply join the club.
Electronic trading as opposed to what?
And even if I call my broker, and he clicks somewhere to enter the order, it's still executed by some HFT agent somewhere. And the same applies to low frequency strategies, I guess (why bother with the execution details, just use a broker).
TL;DNR: You keep ignoring both the point of the comments and the point of the TFA. This is about market prediction, not technical exploitation of a privileged access to market.
anyone can buy co-located rack space.
If you buy a co-located rack space you are using a PRIVILEGED CONNECTION to the market that and taking advantage of that using some algorithm: That's High Frequency Trading.
The all point of TFA and the comments was the OPPOSITE: To be in a NON PRIVILEGED position (as in, the same access time to market transactions as the median of the access time to market of EVERYONE ELSE) and to use machine learning to predict market moves in a long enough term so that the market access time was irrelevant.
Do you understand the complete difference between the two scenarios now?
But yes, hedge funds and prop shops have been doing this for over a decade.
That said, anyone actually doing it would be smart enough to shut the hell up about it.
Finally - one might argue that the big money is all made in 'soft insider' information anyhow, and that with so much tech, analysts already there ... there's just no way to win without clear leverage i.e. relationships, servers on premises of the trading facility, or some other non-market advantage.
I don't know how true that is, he's a washed up piece of shit that wall street chewed out but has the eyes and ears of gordon gekko wanna be finance grads.
but I'm writing this because this is like the 3rd time I've heard this. Just throwing it up there if "soft insider" information is what they were referring to.
They'll sit down for an interview. Technically speaking, everything that the CEO will say has to be public information. It has to be above bar.
But sitting in the room, being right there, one might easily be able to glean more information than is actually public. Ergo - and edge. And it's not quite illegal.
So that is a form of fairly above board 'soft inside' information that nobody will ever go to jail for.
As far as more obvious 'insider' - maybe so, maybe not - I don't know - but I do know that you don't even need to do that.
But generally I believe there is basically no reasonable way to beat the market: all of the quant stuff is done by very smart people, fast computers, the value investing done by massive players like Buffet, and the regular investing done by people with 'extra info'.
I really do think that small retail investors are the losers in the casino.
Oh - and also 'big dumb money', i.e. low-performing people at big banks, sitting on huge sums that the hedges get little bites out of.
I actually started to treat trading very much like the casino. I just do it to make news reading interesting. Of course the mental pictures I form in my head are influenced by what I read. I use my gut....
Therefore retail trading is a social acceptable gambling.
brb shorting TSLA.
"We are doing some shit but we have nothing great to show but you should probably read the rest of our PR article."
> The CEO lives in HongKong and remotely manages this crew of distrusting, non-supportive and close-minded execs.
"We are a highly disruptive people working on disruptive technology in a disruptive way."
I find the Renaissance Technologies story more compelling and worthy than a press release drafted by the VCs funding/cheering Sentient (Kleiner Perkins, Tata, Horizon, etc.)
>Sentient's system is inspired by evolution. According to patents, Sentient has thousands of machines running simultaneously around the world, algorithmically creating what are essentially trillions of virtual traders that it calls "genes." These genes are tested by giving them hypothetical sums of money to trade in simulated situations created from historical data. The genes that are unsuccessful die off, while those that make money are spliced together with others to create the next generation. Thanks to increases in computing power, Sentient can squeeze 1,800 simulated trading days into a few minutes
> It shares little about the data used for the AI's decision-making and isn't profitable
https://scholar.google.com.hk/scholar?q=Babak+Hodjat+genetic...
I somehow doubt how 'disruptive' this will be.
SALIENCY bias. People remember memorable things. The guy who made $100 million by investing in a Romanian immigrant will invest in you if you're a Romanian immigrant, but not if you're one country over even if their education and politics are the same. Computers don't care.
AVAILABILITY bias. People analyze data that is available. If you have worldwide sales figures your market seems huge. If you no data investors will be strongly prejudiced against it.
CONFIRMATION bias. Investors who have a bubble mentality will see the positive and reinforce their theory, however non-rooted in facts (for example the theory that silicon valley teams will succeed and, for example, foreign teams will fail)
There are a bunch more, too.
http://rationalwiki.org/wiki/List_of_cognitive_biases
https://en.wikipedia.org/wiki/List_of_cognitive_biases
An AI would not suffer from any of these. However, due to the skills required to evaluate a pitch, the AI would have to be much, much smarter than any expert system today. Today you can't even tell a robot how to boil a pot of water (no matter how explicit your verbal description is) or anything else, and have it even come close to succeeding.
We're far away from robots (AI) evaluating pitches and business plans. But how cool would that be!
It's actually a trivial problem. Just need to ask a few questions to determine the investor risk profile and goals, then pick the appropriate Vanguard fund.
Another generic, contentless article using the standard outline: (name) takes on Wall Street with (scheme).
Percentage of traders who beat the market average: 50%.
Traders to the left of the average who assign the outcome of bad luck: 100%.
Traders to the right of the average who assign the outcome to a secret method and/or genius: 100%.
An unscupulous broker can "prove" to you that he is a stock picking genius, by mailing you correct predictions of the market in advance of the outcomes for, say, six months, then ask you to assign your assets over to him -- but it's a scam, a trick. The explanation: http://arachnoid.com/equities_myths/#Miracle_Man
The lure of money is too strong.
The lure of money is too strong.
How is it possible to generate seven years of convincing sample data from historical trading data without overfitting your models?
From the article:
> These genes are tested by giving them hypothetical sums of money to trade in simulated situations created from historical data.
So they simply use historical data (going 7 years back). This is fed into a simulator (a trading day where you have access to the data of all days leading up to the simulated trading day).
> without overfitting your models?
Traditionally backtesting is used: http://www.investopedia.com/terms/b/backtesting.asp
Care should be taken to run the tests over significant periods of time (to reflect changing market conditions) and to have a final out-of-time holdout set to lessen the effects of picking "winners" that were winning purely due to chance (introduced by cherry-picking winners from thousands of models).
Simple -- create a large set of models, each using different selected parameters, run them against the market, and pick the outcomes that make the scheme look good. It's called "data mining."
A famous "psychic," who shall go nameless, made a career of appearing with a sealed, dated, registered letter, opening it, and proving that she had correctly predicted the outcome of an election / horse race / other event in advance. She was always right, and the registered letters were real and were mailed before the event to be predicted. How did she do it? She mailed herself more than one registered letter for each event. People are sooo ... credulous.
In my opinion, the whole idea of investing to try and maximize profits is myopic. The real reason to invest should be to gain a measure of control over the corporation being invested in. This suggests that the board should play a more active role in the governance of corporations.
People who just want to make a buck off a corporation should be limited to providing debt financing.
The Western world is capitalist, not a Platonic ideal. Even if you strongly feel that people should have different reasons to invest, you will not be able to sway them (unless you can show that your alternative makes them even more money).
If you claim that long-term outperforming of the market is highly unlikely, you contribute outperforming to short-time flukes: The stock market is essentially unpredictable or in perfect equilibrium.
Unpredictability implies all these hedge funds would do better consulting random number generators, instead of well-paid quants.
Equilibrium implies the current market is operating at maximum efficiency, yet one currently makes money by exploiting non-equilibrium and erroneous evaluations.
Sure, the world is less than perfect in a lot of ways. In most cases we try and fix it. Why is capitalism the one way where we say "oh well, that's just how things are" ??
Research has demonstrated that barring insider information, random stock picking often outperforms "experts".
I believe the market is fairly efficient, and people who make money are either lucky or are leveraging short term information differentials, which in today's hyper-connected society are going to become increasingly rare (barring insider information, again).
As trading goes, past performance does not equate future performance.