market microstructure and financial trading - sfu.ca · market microstructure and financial trading...
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Market Microstructure and Financial Trading
Ramo GencaySimon Fraser University, Vancouver, Canada
Arctic Fibre Project to Link Japan and U.K.
• Light will transmit data from one end to the other in just 154 milliseconds—24 ms less than today’s speediest digital connection between Japan and the United Kingdom.
Earning a fraction‐of‐a‐second advantage over competitors that the US $850 million price tag for the approximately 15,600‐kilometer cable may well be worth it.
• Barges will lay it along most of the route. But to prevent a 1,800‐km detour by sea, there is a 51‐km section that must cross the Boothia Peninsula, a roadless scrap of tundra in northern Canada.
• Laying this stretch will require stuffing four large reels of cable through the door of a Hercules aircraft, flying onto a remote airstrip, packing the cable onto sleds, and pulling it across a frozen lake.
• There was a big difference between the trading speed that was available between exchanges and the theoretical trading speed.
• Given the speed of light in fiber, it should have been possible for a trader who needed to trade in both places at once to send his order from Chicago to New York and back in roughly 12 milliseconds, or roughly a tenth of the time it takes you to blink your eyes.
• Telecom carriers were slower and inconsistent. One instance, it was 17 milliseconds, the next, 16 milliseconds.
• As late as 2008, major telecom carriers were unaware that the financial markets had changed, radically, the value of a millisecond.
Value of Microsecond
Value of Microsecond
• Light in a vacuum travels at 186,000 miles per second, or, 186 miles a millisecond.
• Light inside of fiber bounces off the walls and travels at only about two‐thirds of its theoretical speed.
• The biggest enemy of the speed of a signal is the distance the signal needed to travel.
Value of Microsecond
• How much money may be made from it, from the spread trade between New York and Chicago—the simple arbitrage between cash and futures.
• Exploiting the countless tiny discrepancies in price would make several billions profit a year.
• This led to construction of new fiber optic lines between Chicago and New York.
• Cutting more than a hundred miles off the distance traveled by the telecom carriers.
• An extra road‐crossing added an extra nanosecond delay.
• 13.33 milliseconds round trip for customers using the Spread Networks® Chicago‐NY Dark Fiber to run their own private optical networks from Chicago to New York.
• ‐ 14.75 milliseconds round trip for customers using the Spread Networks® Ultra Low Latency Wave Services over our shared private optical network from Chicago to New York.
• Spread Networks forged a new path to reduce the distance from New York to Chicago to 825 fiber miles ‐ the straightest and shortest route possible.
Why such higher speed needed?• The market on screens was no longer the market. • Hit a button to buy or sell a stock and the market would move
away from you.• E.g. Markets showed 10,000 shares of Exxon offered at $86 a
share, a bunch of smaller sell orders together. • Is it that some at the back of the line had the ability to jump
out of the queue ‐‐‐when the people in the front of the line sold their shares.
• Or something else?• Trading reports did not separate out the exchanges. If a partial
fill, you weren’t informed which exchanges missing shares had vanished from.
• Broker‐dealer‐owned dark pools• JPMorgan Chase Bank ‐ JPMX• Barclays Capital ‐ LX Liquidity Cross• BNP Paribas ‐ BNP Paribas Internal eXchange (BIX)• BNY ConvergEx Group (an affiliate of Bank of New York Mellon)• Cantor Fitzgerald ‐ Aqua Securities• Citi ‐ Citi Match, Citi Cross• Credit Agricole Cheuvreux ‐ BLINK• Credit Suisse ‐ CrossFinder• Deutsche Bank Global Markets ‐ DBA (Europe), SuperX ATS (U.S.)• Fidelity Capital Markets• GETCO ‐ GETMatched• Goldman Sachs SIGMA X• Knight Capital Group ‐ Knight Link, Knight Match• Merrill Lynch ‐ Instinct‐X• Morgan Stanley ‐ MSPOOL• Nomura ‐ Nomura NX• UBS Investment Bank ‐ UBS ATS, UBS MTF, UBS PIN• Societe Generale ‐ ALPHA Y• Daiwa ‐ DRECT• Wells Fargo Securities LLC ‐ WELX ‐ has since closed
• Consortium‐owned dark pools• BIDS Trading ‐ BIDS ATS• LeveL ATS• Luminex (Buyside Only)
• Exchange‐owned dark pools• ASX Centre Point• International Securities Exchange• NYSE Euronext• BATS Trading• Turquoise• Swiss Block• Nordic@Mid
• Other dark pools• Chi‐X• IEX
• Dark pool aggregators• Fidessa ‐ Spotlight• Bloomberg Tradebook• Liquidnet LN Dark• Credit Suisse Crossfinder Plus• SuperX+ – Deutsche Bank• Quod Financial ASOR• Progress Apama• ONEPIPE – Weeden & Co. & Pragma Financial• Xasax Corporation• Crossfire – Credit Agricole Cheuvreux• Instinet ‐ Nighthawk• Bernstein ‐ Shadow
•Dark pool aggregator is a market of markets. You send your order off to the aggregator.
•First, they try and cross themselves (match opposing orders) between their customers and then to pass on unmatched orders to various dark pools in an attempt to find a match.
•This helps if you are a smaller player as you are more likely find somewhere that is liquid without having full connectivity yourself.
• The market on screens is no longer the market if sent to multiple exchanges.
• When send an order to a single exchange, the market as it appears on your screen is, once again, market.
• Why would the market on your screen be real if you sent your order only to one exchange but not real when you sent your order to all the exchanges at once?
• It may be because orders are not arriving at the same time to all exchanges.
• My time to travel between first and last exchange ~2 milliseconds, depending on network traffic and equipment between any two points.
• What if someone faster than me, say 500 microseconds, 4 times faster than me?
• Is someone out there was using market orders arrived at different times at different exchanges to front‐run orders from one market to another?
Leveling the playing field
• This box kept at the facility in Secaucus, N.J., contains a 38‐mile coil of fiber‐optic cable that creates a slight delay in the processing of orders, which levels the playing field among traders.
Fragmented Markets
• Stock market is longer a single market. It is a collection of small markets scattered across some distance from each other.
• If orders arrive even a millisecond apart, the market may vanish, and all bets may be off.
• Being front‐run—some other trader noticing my demand for stock on one exchange and buying it on others in anticipation of selling it to me at a higher price.
• This requires being faster than others, automation and precision.
Payment for Order Flow
• The firms are paid for sending orders to some exchanges and charged for sending orders to others.
• All online brokers effectively auctioned their customers’ stock market orders.
• Brokers earning hundreds of millions of dollars each year to send their orders to a high‐frequency trading firms which execute orders on their behalf.
Latency• Latency is the time between a signal is sent and when it is received. • Several factors determine the latency of a stock market trading system:
hardware (computer servers, signal amplifiers), software and the fiber optic cables.
• The single biggest determinant of speed is the length of the fiber. • Light in a vacuum travels at 186,000 miles per second, or 186 miles a
millisecond. • Light inside of fiber bounces off the walls and so travels at only about two‐
thirds of its theoretical speed. The biggest enemy of the speed of a signal is the distance the signal needs to travel.
• Free‐market competition leads the creation of a new exchange. The new exchange is always located at some distance from the original exchange. Each new exchange give rise to the need for high‐speed routes between the exchanges.
•In the microseconds it takes a high‐frequency trader —depicted in blue — to reach the various stock exchanges housed in these New Jersey towns.
• Conventional trader’s order, theoretically, makes it only as far as the red line.
•Time can be financially advantageous in a number of ways.
• The war for space within the exchanges, the tens of millions being spent by high‐frequency traders for tiny increments of speed.
• The haves paid for nanoseconds.• The have‐nots had no idea that a nanosecond had value.
• The haves enjoyed a perfect view of the market; the have‐nots never saw the market at all.
• HFTs use elaborate time tables, in microseconds, for an order to travel from any given brokerage house to each of the exchanges.
• “Latency tables”. Times are different for every brokerage house. They depend upon where the brokerage house physically was located and which fiber networks it leases.
• Latency tables enable HFTs to identify brokers by the time their orders took to travel from one exchange to the other.
• The HFTs guys don’t need perfect information to make riskless profits; they only needed to skew the odds systematically in their favor.
Latency and cancellations
Stuffing• Stuffing is the clogging up of trading systems by HFTs, who submit and cancel multiple orders within a second.
• This leads to orders piling up in the buffer and slowing down the trading system.
• Stuffing leads to phantom orders, which are orders that are valid for less time than it takes for information about these orders to reach “normal” traders.
Smoking (Layering)• In a stock, the current best bid price is 1,000 and the best ask price
is 1,000.25. Let us say that the best ask price is posted by an HFT. • However, no buyer in the market is willing to trade against the HFT’s
sell order. The HFT submits sells orders at various prices like |1,000.05,|1,000.10, |1,000.15 and |1,000.20.
• These orders appear long enough in the book to be publicly disseminated. A trader sees these lower prices on the sell side and decides to trade against them by placing a buy order.
• By the time this buy order reaches the market, the HFT has already canceled the sell orders at |1,000.05,|1,000.10, |1,000.15 and |1,000.20.
• The buyer’s order ends up executing at the original best ask price of 1,000.25 and may be higher. By “smoking” the HFT briefly makes prices appear attractive to traders on the opposite side but withdraws those orders before their traders reach the market.
Spoofing• HFT manipulates the market from the side opposite to which
she wants to trade. The best ask price of |1,000.25 is by an HFT.
• However, again, no one willing to buy from the HFT at this price. In order to “force” buyers to trade against her best ask price, the HFT places a large limit buy order at |999.90.
• Potential buyers infer that this large buy order is informed and prices are going to increase soon.
• They buy by trading against the HFT’s best ask price. • The HFT cancels the large limit buy order once her order at
the best ask executes.
Domino Effects• HFTs can generate several types of negative externalities:
• Congestion externalities• Adverse selection• Crowding out fundamental liquidity suppliers• Systemic risk due to default and contagion
Smart Routers• Broker gets paid to send an order to buy 10,000 shares of Intel
to BATS but was charged to send the same order to the New York Stock Exchange.
• Along with the trading algorithms, the routers are critical piece of technology in automated markets. The algorithm decides how to slice up any given order. The router determines where the order is sent.
• A router might instruct the order to go first to a firm’s dark pool before going to the exchanges. Or it might instruct the order to go first to any exchange that will pay the broker to trade, and only then to exchanges on which the broker will be required to pay to trade, “sequential cost‐effective router”.
Smart Routers
• Suppose you instructed your broker—to whom you are paying a commission—that you wish to buy 100,000 shares of Intel at $25.
• There are 100,000 shares for sale at $25, 10,000 on each of ten different exchanges, all of which will charge the broker to trade on your behalf (though far less than your commission).
• There are, however, another 100 shares for sale, also at $25, on the BATS exchange—which will pay the broker for the trade.
• The sequential cost‐effective router will go first to BATS and buy the 100 shares—and may cause the other 100,000 shares to vanish into the hands of high‐frequency traders.
• In the bargain relieving the broker of the obligation to pay to trade.
Flash Crash• At 2:45 on May 6, 2010, for no obvious reason, the market fell six hundred
points in a few minutes. A few minutes later, it bounced right back up to where it was before.
• Stock market regulators did not possess sufficient information. The unit of trading is microsecond, but the records kept by the exchanges were by the second. (One million microseconds in a second.)
Best Prices in Fragmented Markets• Reg NMS required brokers to find the best market prices for the investors.
• Reg NMS relied on the concept of the National Best Bid and Offer, known as the NBBO.
• If an investor wished to buy 10,000 shares of Microsoft, and 100 shares were offered on the BATS exchange at $30 a share, while the full 10,000 listed on the other twelve exchanges were offered at $30.01, his broker was required to purchase the 100 shares on Bats at $30 before moving on to the other exchanges.
Securities Information Processor
• The new law required creating the National Best Bid and Offer—by compiling all the bids and offers for all U.S. stocks in one place.
• The Securities Information Processor, became known as the SIP. The thirteen stock markets piped their prices into the SIP, and the SIP calculated the NBBO. The SIP was the picture of the U.S. stock market most investors saw.
• It failed to specify the speed of the SIP. View of the market and that of ordinary investors could be twenty‐five milliseconds delayed, or twice the time it now took to travel from New York to Chicago and back again.
Securities Information Processor• This is why volatility is so valuable to high‐frequency traders: It creates new prices for fast traders to see first and to exploit.
• The infinitesimal period of time between the moment the order is submitted and the moment it is executed is value to the traders with faster connections.
• How much value depends on two variables: a) the gap in time between the public SIP and the private ones and b) how much intraday volatility.
Securities Information Processor• SIP price of a stock and the price seen by traders with faster channels of market information may differ 55,000 times in a single day.
• There may be 55,000 times a day, HFT could exploit the SIP‐generated discrepancy of the wider market.
• Fifty‐five thousand times a day, he might buy shares at an outdated price, then turn around and sell them at the new, higher price, exploiting slow investor on either end of his trades.
Volatility• One might argue that intermediaries have always profited from market volatility, but not necessarily true.
• The old specialists on the New York Stock exchange were somewhat obliged to buy in a falling market and to sell in a rising one, often found that their worst days were the most volatile days.
• Volatility is valuable to high‐frequency traders. It creates new prices for fast traders to see first and to exploit.
Volatility• Since the implementation of Reg NMS, the average trade size has gone down, markets are fragmented, and time gap between the public view of the markets and the view of high‐frequency traders is there.
• The price volatility within each trading day in the U.S. stock market between 2010 and 2013 was nearly 40 percent higher than the volatility between 2004 and 2006.
Dark Pools• High‐frequency traders make their money by digesting
publicly available information faster than others.• Dark pools conceal order information. The dark pools are not
required to report their trades in real time.• Why would anyone pay for access to the customers’ orders
inside a dark pool? • The order is large and slow, because of the time it is forced to
spend inside the dark pool before accessing the wider market. • High‐frequency trader with a special connection to the pool
would ping the pool with tiny buy and sell orders in every listed stock, searching for activity.
• Once discovered liquidity, wait for the moment when stock ticked lower on the public exchanges and sell it to the pension fund in the dark pool at the stale, higher “best” price.
Dark Pools• Banks tend to send their customers orders first to their
own dark pools before routing them out to the wider market.
• Inside the dark pool, the bank could trade against the orders themselves; or they could sell special access to the dark pool to HFTs.
• Either way, the value of the customers’ orders is monetized.
• If the bank was unable to execute a stock market order in its own dark pool, the bank directed that order first to the exchange that paid the biggest rebate for it—when the rebate may be the bait for some flash trap.
Value of Microseconds• When stock markets moved from a floor with human traders into a single black box, the building that housed the exchange did not shrink.
• The old New York Stock Exchange building on the corner of Wall and Broad streets was 46,000 square feet.
• The NYSE data center in Mahwah is 400,000 square feet.
• Because the value of the space around the black box is so precious, the exchanges expanded to enclose greater amounts of that space so that they sell the proximity.
Arbitrage opportunities in different time scales
• In Panel A (days), Panel B (hours), Panel C (minutes) nearly perfectly correlation.
• In Panel D, the correlation breaks down. • In 2011, the median return correlation :0.1016 at 10 milliseconds, 0.0080 at 1 millisecond.
• This correlation breakdown leads to obvious mechanical arbitrage opportunities, available to whoever is fastest.
Correlations at different time intervals•This figure displays the median, min, and max daily return correlation between ES (E‐mini S&P500 future) and SPY (SPDR S&P 500 ETF ) for time intervals ranging from 1 millisecond to 60 seconds, for 2011 data.•Correlation between ES and SPY is nearly 1 at long enough intervals, but breaks down at high‐frequency time intervals.
Correlations at different time intervals•This figure depicts the correlations between the return of the E‐mini S&P 500 future (ES) and the SPDR S&P 500 ETF (SPY) bid‐ask midpoints as a function of the return time interval in 2011. • Correlations break at high frequency intervals. •10‐millisecond correlation is 0.1016. •1‐millisecond correlation is 0.0080.
Evolution of Correlations •This figure displays the ES‐SPY correlation versus time interval curve but separately for each year in the time period 2005–2011.•Market has gotten faster over time in the sense that economically meaningful correlations emerge morequickly in the later years.•For instance, in 2011 the ES‐SPY correlation reaches 0.50 at a142‐millisecond interval, whereas in 2005 the ES‐SPY correlationonly reaches 0.50 at a 2.6‐second interval.•In all years, correlations are essentially zero at high enough frequency.
Duration of ES‐SPY Arbitrage Opportunities• Duration of ES‐SPY arbitrage opportunities declines dramatically, from a median of 97 milliseconds in 2005 to a median of 7 milliseconds in 2011. •This reflects the substantial investments by HFT firms in speed during this time period.
Distributions of Arbitrage Duration • Distributions of arbitrage durations over time, asking what proportion of arbitrage opportunities last at least a certain amount of time, for each year in our data.• The figure conveys how the speed race has steadily raised the bar for how fast one must be to capture arbitrage opportunities.•For instance, in 2005 nearly all arbitrage opportunities lasted at least 10 milliseconds and most lasted at least 50 milliseconds, whereas by 2011 essentially none lasted 50 milliseconds and very few lasted even 10 milliseconds.
Per‐arbitrage Profits• In contrast to arbitrage durations, arbitrage profits have remained remarkablyconstant over time.•Median profits per contract traded have remained steady at around 0.08 index points, with the exception of the 2008 financial crisis when they were a bit larger.
Distribution of Per‐arbitrage profits •In contrast to arbitrage durations, arbitrage profits have remained remarkably constant over time.•This figure shows that the distribution of profits has also remained relatively stable over time, again with the exceptionof the 2008 financial crisis where the right tail of profitopportunities is noticeably larger.
Recap ‐ HFTs• What is high‐frequency trading?• High‐frequency trading implies speed: Firms make trades in a matter of microseconds, or one‐millionth of a second.
• Goals vary. Some trading firms try to catch tiny discrepancies in everything from stocks to currencies to commodities.
• Other firms are "market makers," providing securities on each side of a buy and sell order.
Recap ‐ HFTs• Many high‐frequency traders collect tiny gains, often measured in pennies, on short‐term market fluctuations.
• They hunt for temporary "inefficiencies" in the market.
• Trade such that make them money before the brief distortions go away.
• Market making, arbitrage, directional trading, structural, manipulation.
Recap ‐ HFTs• Market‐making, high‐frequency firms hope to make money on the difference between how much investors are willing to buy and sell a stock, or the "bid‐ask spread."
• They do this by selling and buying on both sides of the trade.
• Many exchanges offer "rebates" of about one‐third of a penny a share to outfits that are willing to step up and provide shares when needed.