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Got some time before I have to go tend the fantasy bball team mired in the middle of the dogpile. This a generalized two exchange trade crossing model. The generalization to more than two exchanges is straightforward.
Here, in the DynaPie Latency Model Figure (below), we have represented two colo installations. The first colo contains Exchange EX1, Gateway GW1, and Client Server CL1. The second colo similarly contains an exchange, gateway, and Client server (EX2, GW2, CL2). The Client Servers run the relevant exchange protocols to perform order execution via respective broker dealer gateways and solicit current market data from each exchange locally. To simplify the analysis we assume all latency is accounted for in the links, and for example, the portion of the gateway latency not accounted for in the link latency is negligible. The market data and client to gateway Link latency, represented by the variable L, is equal to a constant C. We assume the latency for any link within the colo is a constant. If you worry about this, you can assume C is the longest latency measured on average over a sufficiently large sampling period of all links within a single colo installation. The client, via the colo gateways, has two order entry options. CL1 can send a new order (wlg any other message) to EX1 via GW1 or send a new order to EX2 via GW1. CL2 has symmetric corresponding order options via GW2. The latency to submit a new order within the colo is C as previously discussed. The latency cost to submit a new order from GW1 to EX2 is ax+b, where a and b are constants and x is proportional to the minimal transmission line latency connecting GW1 and EX2. Similarly the latency between GW2 and EX1 is ax+b (we assume the same transmission lines, nics, and switch connecting the gateways and exchanges). Finally, the latency between the two client servers is represented as dy+e where d and e are constants and y is proportional to the minimal transmission line latency connecting CL1 and CL2.
Figure: DynaPie Latency Model
This model is useful because it represents the crossing opportunities between LN and NY for vanilla interest rate swaps, NY and Chicago for basis trades, and wlg Weehawken and Carteret for Equity and ETF. The internal colo latencies are independent (we are representing them as constants) of the distance between the colos.
We know from previous DynaPie posts that it is optimal for the client server to move between colo installations for both market data collection and order execution. In this case we are assuming no movement and all market data collection and trade execution happens within some colo installation.
With this model framework there are several interesting questions:
1. if L= ax+b = dy+e is it ever profitable to allow the gateway to route an order to a non-colo exchange? I think the answer is obviously no, but that is just a guess.
2. If ax+b >> dy+e how much more profitable is it for the client to route their own orders? Don’t know but this is a practical question as the client servers can be connected by 40GB links w DMA versus the 10GB gated links to the exchanges.
3. Is there even any reason for the Gateway to route outside the colo? Pretty sure the answer is no, maybe HA ?
4. Given the nature of the analytics/computation executed at the Client Servers how does one best hide latency l=dy+e? Presumably the latency overlap techniques change with the magnitude of dy+e. The client computation crossing equity between London and NY is very different than crossing Carteret and Weehawken.
Kenneth Rogoff, Business Insider,These 3 Things Keep Pushing Interest Rates Lower And Lower, here. Long UST until Euro sorts itself out and a bunch of Germans and Japanese start to spend to check off their bucket list items.
How long can today’s record-low, major-currency interest rates persist? Ten-year interest rates in the United States, the United Kingdom, and Germany have all been hovering around the once unthinkable 1.5% mark. In Japan, the ten-year rate has drifted to below 0.8%. Global investors are apparently willing to accept these extraordinarily low rates, even though they do not appear to compensate for expected inflation. Indeed, the rate on inflation-adjusted US Treasury bills (so-called “TIPS”) is now negative up to 15 years.
Is this extraordinary situation stable? In the very near term, certainly; indeed, interest rates could still fall further. Over the longer term, however, this situation is definitely not stable.
Cardiff Garcia, alphaville, The decline of “real money” trading, here. HFT, EMM, an ETFs grow market volumes in past 10 years. Accounting for these structural growth factors real money trading at decade low levels. The CS Trading strategy links look interesting. The July 2012 CS Chartbook has interesting data, for example: global bid ask spreads, US Equity market share, and intraday vol for S&P500.
So US equity trading volumes are now at four-year lows. Or five-year lows. Whatever, they’re low, but it’s also August.
Something to keep in mind as the global risk asset rally — which appears driven by some combination of better-than-expected-but-not-that-awesome US macro data and… talking from European policymakers — continues.
Martin Sibileau, Zerohedge, An Austrian View On High Frequency Trading, here. And you might have thought for a moment that the every order’s sacred ditty was wide of the mark.
This is not a healthy way to price assets, because just like the parents had never thought of trading her daughter for money, market participants not challenged by economic developments but by millions of fake orders, were forced to do so. A trade actually took place in an otherwise illiquid market but…what will happen next time? Neither the daughter will be left alone by the parents nor our market participants will be there for the criminals to profit from them, which is why retail money will keep flowing out of the stock exchanges (the system) as long as the status-quo is not changed.
Salmon, Why Cash WinFall is a model for future lottery games, here.
If I were running the Massachusetts lottery, I’d look at what happened in Cash WinFall, and create a game designed to be as attractive as possible to consortiums and the rich. Make it really easy to buy hundreds of thousands of dollars’ worth of tickets at a time; maybe even have e-tickets which can have hundreds of thousands of lines, and which can be redeemed individually. Tell the rich that if they get their timing and strategy right, this is a way they can make real money. While, of course, appealing to regular weekly punters as well. You’d essentially be broadening the lottery tax base, and increasing revenues, by appealing to the people who can most afford to play.
But that’s not going to happen: the likes of the Boston Globe editorial board would inevitably call such a game “fatally flawed” since the strategic element would appeal to the rich more than to the poor. But the rich like games combining luck and strategy. Why not give them what they want?
Salmon, Dennis Kelleher, Libor, and high-frequency trading, here.
Reminds me of a musical high frequency Python number went something like this:
Every order’s sacred
Every order’s great
If you go and waste them
He gets quite irate
…to demonstrate my anti-HFT bona fides. In the very post that Kelleher’s responding to, I write this:
I do think that the amount of HFT we’re seeing today is excessive, and I do think that we’ve created a large-scale, highly-complex system which is out of anybody’s control and therefore extremely dangerous.
On second reading this might be more of “if you want to be in the PFJ you have to really hate the Romans (read HFT)” kind of thing, not sure yet.
Salmon, Chart of the day, HFT edition, here. Folks at MCCR know the value of a penny, but they do not tolerate fighting and wouldn’t recognize an algobrot if they ran into one on the street. Mr. Salmon sure seems worked up about this graph; reminds me of that tee vee character what was his name… Barney! …. Barney Fife. Take a look here and you can see things got a little calmer. We’re always for taking the long view: fundamental data analysis, pick good deals, do your homework, and you could make money.
The stock market today is a war zone, where algobots fight each other over pennies, millions of times a second. Sometimes, the casualties are merely companies like Knight, and few people have much sympathy for them. But inevitably, at some point in the future, significant losses will end up being borne by investors with no direct connection to the HFT world, which is so complex that its potential systemic repercussions are literally unknowable. The potential cost is huge; the short-term benefits are minuscule. Let’s give HFT the funeral it deserves.
Bloomberg. Knight Market-Making Unit Says It Had ‘Technical Issue’, here. You know, I have an idea how to do these pre-trade risk checks so this doesn’t happen. I don’t think this is all that difficult. You can program a machine to beat Kasparov at chess, you can debug the Ivy Bridge silicon fab line, but you cannot keep your automated trading program from losing massively in one day?
Computers sent more than 100 stocks into trading spasms just after U.S. markets opened, whipsawing investors and pushing shares of Knight Capital Group Inc. (KCG) down by the most ever on speculation it was to blame.
Business Insider, GASPARINO: A $5 Billion Trade That Was Supposed To Happen Over 5 Weeks Is What Doomed Knight Capital, here.
This is actually very similar to what Doug Kass’ “Gnome” said in a Real Money post yesterday:
High above the Alps, my Gnome is saying that an algo program that was supposed to be executed over a five-day period was mistakenly executed over a five-minute period this morning and caused the unusual price behavior in certain equities.
It’s worth noting that this kind of massive volume trading is a big part of what “algos” do. Large institutions want to sell huge blocks of stock, which, if done at one time, or if done without subtlety, would cause huge market distortions.
So algos (and algorithmic traders) find the best way to execute the trade while making the fewest ripples possible.
That did not happen yesterday.
Salmon, When large-scale complex IT systems break, here.
It’s rogue algo day in the markets today, which sounds rather as though the plot to The Fear Index has just become real, especially since the firm at the center of it all is called The Dark Knight, or something like that. At heart, however, is something entirely unsurprising: weird things happen when you get deep into the weeds of high-frequency trading, a highly-complex system which breaks in entirely unpredictable ways.
Joe Nocera, NYT, Financial Scandal Scorecard, here.
Meanwhile, it has become clear since the scandal broke that Libor is a problematic benchmark in any case, because a lot of the unsecured interbank lending it is supposed to represent doesn’t even occur anymore. “It is clear that the Libor system is structurally flawed,” Ben Bernanke, the chairman of the Federal Reserve, told the Senate this week.Now he tells us.
So, Nocera did not know of the structural flaws in Libor prior to Bernanke’s recent testimony? This is Joe Nocera the business columnist for the New York Times, right? You don’t really have to be John Meriwether to follow along with what is happening in Finance, if that is your job. I’m not saying you should be able to forecast how the BBA Libor process will be perceived and interpreted in 2012. Similarly, anticipating the release of the celebratory BarCap bromails is hard, although their existence should not be that surprising if you have ever met a Wall Street broham. Perhaps part of the problem is the disconnect between the reporters covering Wall Street at the major outlets and Wall Street since BBA defined LIBOR. NYT finance reporting folks, there’s this guy Blodget, runs a blog Fustercluck or something on the interweb, he seems to kind of follow along with what is happening in the money/business department, call him maybe?
Wall Street & Technology, Floodgates on U.S. Derivative Reforms Set to Open, here. Buncha folks just waiting for the IR swap market to open up.
The U.S. swaps regulator is set to finalize this week a critical reform that will trigger banks and traders having to comply with costly new derivatives rules.
Direct Edge to Offer Free Trades at the Close in Challenge to NYSE and Nasdaq, here.
Direct Edge, the No. 4 U.S. stock exchange operator, said on Tuesday it plans to offer free trading at the close of markets, in a direct pricing challenge to the New York Stock Exchange and the Nasdaq.
Noahpinion, Something Big happened in the early 70s, here. Seems similar to the question of when did finance become disproportionally large.
This leaves me with two candidate explanations for the possible early-70s trend break. These are the end of Bretton Woods in 1971, and the Oil Crisis in 1973.
The end of Bretton Woods seems like a big deal. It ushered in the era of floating exchange rates and ended the de facto gold standard that had prevailed since WW2. Why would this have held down wages in the U.S.? Well, it might have allowed the start of globalization, which began to add labor-rich, capital-poor countries to the rich-country trading system, thus holding down wages via factor price equalization. The catch-up of Europe and Japan in the 70s and 80s, and then of China et al. in the 2000s, might have held down U.S. wages as these countries’ catch-up productivity gains outpaced their wages. Alternatively, exchange rate risk must have spiked after the end of Bretton Woods; this could have reduced investment as a percent of GDP, raising the return on capital relative to labor, while simultaneously decreasing nondurables TFP via endogenous growth effects. I’m not quite sure if either of these mechanisms holds up under close scrutiny, however.
The Oil Crisis of ’73 seems like a big deal. It represented the start of an era of highly variable energy prices. Since energy is an input for basically everything, lots of people have speculated that higher (and more variable) energy costs have caused a general productivity stagnation.
Advanced Trading, Buy-Side Traders Find Liquidity in Dark Pools, here.
Despite any heightened concern, however, in the current low-volume environment, buy-side traders appear to be using dark pools more than ever. With U.S. equity volumes averaging around 6.8 billion shares per day, down 14 percent from Q1 2011, traders at asset management firms and hedge funds are seeking liquidity in dark pools. “With volatility where it is, volume in dark pools picks up,” explains Kellner. “The lower the volatility, the more passive people are going to be, and the more likely there will be more trading in dark pools.”
Dark pools accounted for 13 percent of U.S. equity volume in March, according to estimates by Tabb Group. Typically, dark pool market share has been 10 percent to 12 percent of equity volume, but in January this year, it hit a high of 14 percent, according to Cheyenne Morgan, a senior analyst at Tabb Group who tracks equity volume for dark and lit markets. “People continue to use them and are more comfortable trading in the dark,” she says. “That’s the reason for the higher market share.”
But coupled with the growing volume of internalized trades — which occur when brokers match orders on sales trading desks or via algorithms before routing them to trading venues — the growth of dark pools is stoking a long-simmering controversy over transparency into executions. Nearly one-third of U.S. equity flow was traded away from exchanges during the first quarter of 2012, estimates Tabb Group. And Tabb Group founder and CEO Larry Tabb says buy-side traders are turning to internalizers — big brokers that match trades internally — and dark pools much earlier in the trading process.
Wall Street & Technology, Dangers of the Dark? here.
Yesterday, Tabb Group released some statistics on U.S. equity volumes that should raise some eyebrows as to why order flow is going underground. According to the market research and advisory firm, 32.96 percent of US equities trading volume — or nearly one third— is traded away from the primary exchanges. Due to an error, the firm originally reported that 37 percent of US equities trading volume was traded off-exchange, suggesting that internalization had reached a new high.
But today, Tabb Group corrected the error. “This did not represent a historic high. Nevertheless, the error does not invalidate the concerns expressed in our comments about the rise in off-exchange trading,” noted co-founder and CEO Larry Tabb in a statement.
But the surprising part is that dark pools only make up about 13 percent of U.S. equity volume. The rest (19. 3 percent) is being executed through internalization — a practice whereby brokers match the orders internally on their own trading desks — before the orders are sent to dark pools or exchanges, according to Tabb.
In February, off-exchange trading was 34 percent of U.S. equity volume (including both internalization and dark pools) , so that has dropped to about 33 percent in March, By comparison, in 2008 internalization and dark pools totaled 15 percent, according to the market research and advisory firm.
We already knew that for the past few years, buy-side institutions have preferred to execute in dark pools first before going to the public markets. Dark pools are private, electronic networks that match buy and sell orders — to avoid market impact, preserve anonymity and execute at the midpoint of the bid-ask spread. But what’s behind the increase in internalization?
Petrella and Anoli, Internalization in European Equity Markets Following the Adoption of the EU Mifid Directive, 2007, here.
This paper uses order flow and limit order book data in order to estimate the internalization rate (i.e., the portion of the total order flow that could be internalized), to estimate the internalization expected revenues, and to investigate the main factors affecting both the internalization rate and the magnitude of internalization revenues. To simulate the systematic internalization activity we collected detailed order flow data for 57 liquid stocks traded on the Italian Stock Exchange, which is a currently concentrated market. To be internalized, an order should jointly satisfy the following two requirements (expressly requested by the Level 1 law text). First, the quantity of the order should not be greater than the estimated standard market size (SMS). Second, the price limit of the order should be compatible with immediate execution by a systematic internalizer in respect of the best execution principle. Based on this procedure we identify internalized orders and compute estimates of internalization rate, gross trading revenues, spread revenues and positioning revenues on a per stock per day basis and on a per internalizer firm per day basis.
Our main findings relate to (i.) the relationship between internalization rates and stocks turnover; (ii.) the size and variability of internalization trading revenues; (iii.) the value of the inventories for an internalizer firm.
Salmon, Wall Street’s preference for low-priced stocks, here. Plotting what you already kind of knew. Why do folks like BAC so much? Look at the graph across the link.
Three weeks ago, Alex Tabarrok found an intriguing post by high-frequency trader Chris Stuccio. The idea is very elegant: if you want to stop high-frequency traders extracting rents from the market, there’s an easy way to do so — you just allow stocks to trade in increments of much less than a penny. Matt Levine puts it well: right now, he says, “because you can’t be outbid by another bidder within the same penny increment, you get free money by just getting there first”. If high-frequency traders could compete on price rather than just on speed, then a lot of the silly arms-race stuff would be replaced by better prices for investors.
Interesting how much play Stuccio gets.
Kid Dynamite, On UBS, Facebook, Nasdaq, and Erroneous Orders: A Story From The Good Ol’ Days, here.
So my boss’s trading desk goes to sell a basket of stocks while they are simultaneously buying futures on the phone with a broker in Chicago, trying to capture the spread. They hit the SELL button for the basket. No confirm. So the trader hits SELL again. Nothing. SELL. SELL. SELL. “Why the f*ck isn’t this order going through?”
Then just as the market begins to drop (under the weight of these waves of sell orders that were in fact going through), someone yells out “THE PRINTER IS OUT OF PAPER!“ It wasn’t that the orders weren’t going through, it was that the acknowledgements weren’t coming back! *gulp*.
Pogue, NYT, MacBook, a Point Shy of Perfect, here.
Superfast. Superthin. Superlight. Superlong battery life. Immense storage. Enough memory to keep lots of programs open at once. Stunning screen, comfortable keyboard, terrific sound. Fast start-up, rugged body, gorgeous looks.
Carlin had a bit that sounded like this, here.
Ron Coleman, Likelihood of Confusion, Jonathan Rogers: Is YouTube “Monetizing Piracy”? here. Pointers from Ron Coleman.
As for me, I’m Ron Coleman, a commercial litigator, business attorney and, some say, “IP maven” with a special interest in copyright and trademark infringement involving the Internet–including advising clients how to avoid them. I am also a writer and general counsel of thenotionalMedia Bloggers Association.
ETF Industry Association, here. Odd that I have not bumped into more ETF stories recently. Must be missing good information somewhere.
Some of the key highlights from the May 2012 ETF Data report include:
- Assets in US listed Exchange Traded Funds (ETF) and Exchange Traded Notes (ETN) totaled approximately $1.14 trillion at May 2012 month-end, an increase of 2% over May 2011 month-end, when assets totaled $1.11 trillion.
- ETF/ETN net cash inflows totaled approximately $4.2 billion for the month of May 2012, bringing year-to-date 2012 net cash inflows to $63.1 billion.
- At May 2012 month-end, there were 1,465 U.S. listed products, an increase of 17% compared to 1,254 U.S. listed products at the same time last year.
- Fixed Income led all categories for May with $8.9 billion in net inflows, bringing the YTD total to over $30.2 billion.
Zerohedge, Did The SEC Hint At A 7% Market Plunge? here.
This is how the market-wide circuit breaker language will look going forward:
- Reducing the market decline percentage thresholds needed to trigger a circuit breaker to 7, 13, and 20percent from the prior day’s closing price, rather than declines of 10, 20, or 30 percent.
- Shortening the duration of trading halts that do not close the market for the day to 15 minutes, from 30, 60, or 120 minutes.
- Simplifying the structure of the circuit breakers so that there are only two relevant trigger time periods, those that occur before 3:25 p.m. and those that occur on or after 3:25 p.m. The two periods replace the current six-period structure.
- Using the broader S&P 500 Index, rather than the Dow Jones Industrial Average, as the pricing reference to measure a market decline.
- Requiring the trigger thresholds to be recalculated daily rather than quarterly.
Additional, the SEC also adopted less relevant single-stock trading halts.
Zerohedge, Why A Grexit Would Make Lehman Look Like Childs Play, here. Tchir via Durden.
Again, I fail to see the optimistic case of a Grexit. Every time I try and play through scenarios where the IMF and ECB come to the rescue, it seems like it will be far too little and far too late. Maybe the powers that be are smarter and have figured out a plan, but given their track record, that is hard to believe. The more they look at the situation, the more I am convinced they will see not only how potentially awful the situation becomes, but that the cost to avoiding it right now are relatively small, and with proper preparation a Grexit can be managed down the road.
I still think we should have had more Lehman moments. In fact, not letting the AIG moment occur was probably a bigger mistake, but most politicians have taken the lesson never to let a “Lehman” happen again, so once they see that Greece is Lehman on steroids, they will back down and figure out enough to give Europe and the markets a solid kick.
Levine, DealBreaker, The CDS Market For Lemons, here. Although, this asymmetric information apparently did not work out super well for the London Whale so far. Leverage and falling correlation did it?
A stylized picture of a credit default swap is that it’s a way for a bank to offload to the market the credit risk of loans that it makes, while still funding those loans and making a profit on them. If you start from that stylized picture, you must at some point get comfortable with the stylized fact that this market is probably rife with insider trading. Turns out it is!Part of the reason for that is that it’s maybe legal,* part of it is just the general run of market-participantscumminess,** but there’s also the fact that the basic model sort of requires it. Here is the basic model:
- private side bank employees evaluate a company for a loan, using lender materials that contain nonpublic information and banker relationships that are all about nonpublic information,***
- private side bank employees negotiate and fund that loan with a company,
- [magic happens], and
- public side bank employees buy CDS on some but not all of the companies that the bank lends to in sizes that vary among companies.
