There’s been a lot of attention focused on trading battlebots recently. It’s important to keep in mind that this is part of a long-standing, broad and arguably inexorable trend that is now spreading rapidly away from its successful base in industrial manufacturing to every other conceivable field from scheduling and logistics, to CAD and on to more aggressive pursuits like trading and battlefield operations. Perhaps looking at the state of the art in related fields can inform us about the direction of our algo bots.
This article in Foreign Policy illustrates an area where automation is making great strides into historically human undertakings. The use of so-called drone aircraft for recon and tactical missile strikes has reached a remarkable milestone: this year, the US Air Force will train more “pilots” for unmanned aircraft than for real fighters or bombers. Evidently there’s good reason for this change:
By 2013, software and communications improvements will allow the Air Force’s unmanned-aircraft pilots to simultaneously fly three drones at one time, and four in an emergency. Another factor supporting the likely proliferation of drones such as the Predator, Reaper, and Global Hawk is their low cost compared with new manned aircraft such as the F-35 Joint Strike Fighter.
According to the Government Accountability Office, $24.5 million will purchase a set of four MQ-9 Reaper hunter-killer drones plus a ground station and satellite relay. (See page 117 of this report.) The latest guess of the price for a single F-35 fighter-bomber is $100 million. (See page 93.) This gap in cost led Defense Secretary Robert Gates to demand the cancellation of the manned F-22 Raptor program in order to fund the purchase of more drones for service in Afghanistan and Iraq.
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dereferenced, technology
Steal a little and they call you “thief”… Steal a lot and they call you “King” – Bob Dylan
I try to avoid the news during the trading day. I never trade manually and as I’ve mentioned before, I’ve never yet had much success trading the news and none of our models presently use news feeds for decision-making. So I really try to avoid keeping excessively abreast of the news as it’s just a distraction from real work.
would-be king
That said, this morning I noted a pretty good jump in our pre-market p&l and wanted to see what splendid news had prompted the spike. So I scanned some headlines.
On Bloomberg I saw:
•U.S. Payroll Cuts Slow, Jobless Rate Unexpectedly Falls as Recession Eases
•AIG Reports First Profit in Seven Quarters After Investment Losses Shrink
•Dollar Advances as U.S. Employers Cut Fewer Jobs Than Economists Estimated
Unemployment down? AIG profitable? Dollar rumbling to strength? Splendid, splendid and splendid.
I also saw the bit about Hank Greenberg paying the SEC $15M as he “thought it would be good to get it behind us.” Indeed, good thinking.
And anyone looking at the news this week knows that regulators are likely going to put the kabosh on flash orders and that Goldman trades profitably. (And they improve!)
my kind of fat tail
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dereferenced, our managed markets
I’ve been saving the above image in a stubbed-out blog post I’ve wanted to write since a conversation I’d had in Jerusalem last fall. The recent attention to high frequency trading and all of its attendant evils has reminded me that the topic is relevant and so I relate various thoughts at the risk of jumping on a cacophonous bandwagon of rumbling misinformation.
First of all, the conversation. It was with a talented guy who acted as the CFO for a variety of companies including a small startup hedge fund which traded US equities at a high frequency. Although he was a part-time cfo, he seemed pretty plugged-into their trading operations and noted that they use an agency-only brokerage service for automated traders I’m familiar with and that they were “looking at full data for many” hundred stocks concurrently. He remarked that their trading was going well but that their hit rate was something like 4% and dropping. By hit rate, he meant that they were placing limits frequently and generally pulling the orders if they didn’t get hit immediately. He didn’t specify, but I imagine that “immediately” might range from milliseconds out to a second or twenty. If the market is composed of makers and takers, then these guys were definitely makers of liquidity in the strict sense that they were placing limits and making markets.
At the time I thought it was interesting because it seemed that so many people were focused on the very, very short term trade that the frequency was becoming saturated. It looked like a reminder that trading frequencies populate a spectrum; in this case, this part of the spectrum was becoming so saturated that returns were becoming increasingly difficult to obtain as more players crowded into it. I’m not sure how this hedge fund has fared, but at the time I remember thinking that they were going to have a tough time competing if they were only geared for high-frequency trading as the space becomes increasingly expensive to play in as the inevitable talent and technology arms race marches on.
Lo and Khandani provide the below image illustrating this phenomenon happening to a class of contrarian strategies Lo & MacKinlay had described in 1990. The strategies stop working as people squeeze out the alpha.
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hedge funds, our managed markets, startup, strategy development, technology
as suspected, the seat of evil can be found in NJ
There’s a whole bunch of interesting things about this story of how a programmer has allegedly stolen some of the code at the place he’d worked. One is the remarkable reverb it’s created amongst bloggers. The house pictured left is evidently the diabolical mastermind’s home according to the NJ Real Estate Report. Another is the fact that a programmer stealing some code is news. Funny what becomes news (apparently a fêted pedophile died) and what doesn’t (we are creating millions of refugees in Pakistan).
One angle that I haven’t seen highlighted in all of the commentary is Mr Aleynikov’s choice of weapon. Seems that he was an erlang guy with an interest in ocaml. Choosing functional programming for algo trading systems is an interesting but not unique choice.
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our managed markets, strategy development, technology
This weekend I read Jason Zweig’s “Will leveraged ETFs Put Cracks in Market Close?” which references a paper by Minder Cheng and Ananth Madhaven at Barclay’s. I tried, but couldn’t find their original paper over the weekend. As luck would have it from across the internets Paul Kedrosky came to the rescue with a post referencing that paper, “The Dynamics of Leveraged and Inverse Exchange-Traded Funds“.
If you have any interest in ETFs, then you should read this paper carefully as it provides a very nice and accessible mathematical treatment of leveraged and inverse ETFs.
I’ve had success using ETFs in portfolio-oriented strategies to conveniently provide specific exposures, eg, to emerging markets. I’ve also explored strategies that pit ETFs against futures and similar arbs that take advantage of contract rolls or other anomalous behaviors across the markets. But I’ve never looked at ETFs the way they really should be understood: as structured products that should have well-defined (if not necessarily obvious) properties.
Like many structured products, some of these characteristics are not obvious and may be quite unintuitive but are always important to understand. For instance, the hedging required to implement these funds is both non-linear and asymmetric.
Specifically, leveraged ETFs must re-balance their exposures on a daily basis to produce the promised leveraged returns. What may seem counterintuitive is that irrespective of whether the ETFs are leveraged, inverse or leveraged inverse, their re-balancing activity is always in the same direction as the underlying index’s daily performance. The hedging flows from equivalent long and short leveraged ETFs thus do not “offset” each other. [...]
The impact is particularly significant for inverse ETFs. For example, a double-inverse ETF promising -2X the index return requires a hedge equal to 6X the day’s change in the fund’s Net Asset Value (NAV), whereas a double-leveraged ETF requires only 2X the day’s change. This daily re-leveraging has profound microstructure e ffects, exacerbating the volatility of the underlying index and the securities comprising the index.
Hence Mr Zweig’s concern that these ETFs feed the volatility we’ve seen for the last 8 months or so near the market close. If the day has been up then both “bull” and “bear” levered ETFs will need to buy in order to stay hedged – reinforcing the trend and effectively supporting serial correlation of returns.
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dereferenced, portfolio management, strategy development
Competition has forever been fierce and at times may not be entirely fair. Thus, a student of the market must be ever aware of the trends around them so they can promptly identify growing areas of opportunity which haven’t yet been revealed to the majority. Mr Madoff made a very good living (while it lasted) offering clients a steady ~10% return on their investment. Bush league stuff, it turns out. The real maestros of money are doing rather better.
Accounting and legal researchers at the University of Kansas have identified a bull market in influence-peddling: returns on the order of 22,000% for firms who “invested” in lobbying efforts to favorably modify the tax code. These people obviously learned that it’s important to play by the rules.
I’ve written some decent strategies and have been blessed with moments of great luck, but I’m ashamed to note that I’ve never gotten remotely close to these kinds of returns. Can you imagine the sharpe ratio these guys can claim? And it’s a repeatable process. Although the Kansas researchers don’t mention it, there are many other cases of such legal arbitrage as pointed out in an AP piece on the subject:
The nonpartisan group recently released a study comparing the amount spent by bailed-out banks on political contributions and lobbying with the amount of money they got from the Wall Street rescue fund, known as the Troubled Asset Relief Program. The results produced eye-popping rates of return, an overall 258,449 percent for the $114 million they spent on campaign donations and lobbying.
Now this number – ~260,000% ROI – is clearly a bit inflated as $114M barely covers what Citi paid out to Mr Rubin for his services over the relevant period, but we’re probably in the ballpark. Perhaps the banks only made 100,000% on their investment, but we can still see why they’re “the pros.”
I’m wracking my brains trying to figure out how to shoe-horn this marvelous alpha-generator into my trading algorithms. I confess that I haven’t yet figured it out. But I take solace in the knowledge that, as an American, I have the best government money can buy!
dereferenced, our managed markets
A reader of this blog (hey – I’m as surprised as you are!) sent me an email recently detailing a strategy they’d developed. While the details of that strategy aren’t relevant here, they sounded good and they got me to thinking about the process of selling a trading strategy. This is an activity that I’ve spent some time on and have decided just isn’t for me.
There are a lot of difficulties with selling a trading strategy. One of them is a consequence of the foundational problem of back-testing about which I first started posting on this blog. For any given period of time (that has already elapsed!), it’s not difficult to generate a good number of pretty impressive strategies. All you have to do is try a good enough number of random strategies and some of them will prove to be too good to be true.
Presumably, any credible person who might be listening to your pitch will be at least intuitively aware of this fact and will thus be highly suspicious of any back-tested results you might present. For this reason, it’s impossible to sell a strategy on the basis of back-tested results. Only auditable, real-world returns will be considered valid by any serious person. Of course, you might find someone who’s less particular, but then you’re flirting with fraud rather than a legitimate sale.
So let’s say you have impressive, verifiable results. You still have to answer the question:
If this strategy is so good, why are you selling it? Why not just trade it yourself?
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hedge funds, startup, strategy development
rebranding opportunity?
A friend of mine pointed out an article he came across on his bloomberg terminal today which reminded him of a strategy I’d described to him sometime back and which we’ve been trading over the past year or so with good results.
To the great chagrin of some of my partners, I even wrote a few posts about the phenomenon underlying our strategy and its evolution as we capitalized on it. Eventually, they persuaded me to shut up already, but the outline was there for all – including Goldman! – to see.
My first post on the topic, “unsung virtues of a dynamic hedge” published June 4th of last year, was pretty coy and didn’t mention the source of alpha itself but talked about enhancing it with a dynamic hedge.
My next post on the topic, “to dream” was published July 14th of last year and laid out the exploitable discrepancy of the market’s behavior. Interestingly, the data I provided in that posting went back the same amount of time as in Goldman’s piece.
I explicitly wrote one last time about the strategy in “evolution of a strategy” wherein I detailed the process by which we’d been evolving the strategy.
Now, one of the more entertaining things about having a blog is that you get to see who is viewing your content. I’m happy to note that all of the major IBs are represented including a variety of distinct IPs within Goldman.
Now, I’m not accusing them of stealing my ideas or anything untoward like that… but I’ll admit that I am wondering how long it’s going to take them to make similar observations across markets beyond US Equities…
Read on for the Bloomberg article…
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back-testing, dereferenced, strategy development
During some recent travels, I read William Poundstone’s ramblingly entertaining Fortune’s Formula. It had been sitting on my shelf after I’d originally gotten it, perused it and offhandedly discarded it as yet another of these science-is-fun-and-full-of-wacky-characters books for the butch humanities student. My initial impression was a bit harsh as the book proved entertaining and covered a lot of ground including significant coverage of Ed Thorp and his stat arb alchemy (see here for his own papers on the topic).
One of the more compelling segments of the book relates Claude Shannon’s demon which is a nice thought-experiment / trading-strategy which illustrates the tractability of the problem of trading on a random walk market with fixed properties. I wrote the above applet to explore the impacts of applying friction and otherwise modifying the behaviors of the market and the demon.
The original demon posited a world with no friction in which the market contains one instrument which doubled or halved in value each day. Shannon’s demon looks to take advantage of this volatility by maintaining a portfolio which was rebalanced each day to ensure a 50/50 split between cash and the market. The applet implements a very simple monte-carlo test-bed for Shannon’s Demon. You can configure the demon and the marketplace along a variety of parameters, and then run many instances of the demon, each on its own self-contained random-walk market.
Although Shannon’s demon is a highly “stylized” case in the sense that it operates on a very synthetic, unrealistic and favorable formulation of a random-walk marketplace, it has spawned a great deal of interest and serious research.
Most of all, it’s a revealing illustration of the kind of reasoning one must embrace in order to address stat arb strategy development. Enjoy.
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Updated: March 4th – made price axis logarithmic to better reveal mc paths.
books, dereferenced, monte-carlo methods, strategy development
one view on the important stuff
There seem to be two kinds of economists in today’s world.
Keynesians and Austrians? Freshwater and saltwater? Macros and micros? Voodoos and uh well-adjusted?
No. These may be valid distinctions ordinarily, but in today’s debate on how to solve the great self-inflicted wound known as the credit crisis the only two that matter are those who’ve worked for prop trading outfits (or perhaps more broadly, those who would someday like to once their time of public service is up) and those who just practice economics, typically academically.
Among the former, the solution is uniformly, as Mish so memorably put it, “to patch the busted dam with water” and to do it now or the consequences could be incomprehensibly bad.
Among the latter, the views are many and divergent, but they at least agree that throwing trillions of dollars about is a serious bit of work and should be undertaken with deliberation, transparency and a long view.
I’m not qualified to opine on which type of economist has a better chance of saving us from ourselves. But I can observe that the only kind sitting at the decision-making side of our president, pre- or post- January 20th 2009, is the prop trader.
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dereferenced, our managed markets
