On the afternoon of May 6, 2010, the Dow Jones Industrial Average (DJIA) plunged 800 points in less than 20 minutes before recovering most of its losses, creating a new term in the financial lexicon – the “flash crash.” The Dow’s intraday drop of 998.5 points or 9.2% was its largest points decline on record, while its intraday swing of 1,010 points was the second-largest in the history of the index, exceeded only by a 1,018-point swing on October 10, 2008. While the Dow’s temporary swoon during the flash crash was a harrowing reminder of the market collapse after the bankruptcy of Lehman Brothers in September 2008, the culprit this time was not overwhelmingly bearish sentiment but something altogether different – program trading.
What is Program Trading?
The NYSE has a rather dated definition of program trading, which it defines as (a) index arbitrage, or (b) the purchase or sale of a basket or group of 15 or more stocks as part of a coordinated trading strategy. Index arbitrage is one of the earliest program trading strategies, and essentially involves exploiting price discrepancies between, for example, futures on a stock index and the underlying stocks in that index.
More broadly, program trading can be defined as all aspects of computerized trading, including algorithmic trading, high-frequency trading (HFT) and quantitative trading. Program trading is generally undertaken by institutional traders at hedge funds, brokerages, and HFT firms, as well as institutions like mutual funds and pension funds. It typically involves the purchase or sale of various shares in very large quantities.
Not a New Concern, but a Growing One
Here’s an extract from a newspaper report on program trading: “It was a typical….day on Wall Street, a slow, nervous market, when suddenly the demand for big-name stocks jumped and started an apparent rally. Just as abruptly, prices tumbled and the Dow Jones Industrial Average ended the session with a loss.” The report goes on to say that at an investment firm, “executives knew the telephone calls would be coming in from irate clients demanding to know why the market had seesawed for no apparent reason. The executives also knew how they would answer: Computerized program trading done by a few big brokerages.” The report adds that program trading “has been widely blamed for injecting volatility into the stock market, frightening millions of ordinary investors and contributing to a malaise that has affected the securities business.” Sounds familiar? While the extract may appear to be from a 2013 or 2014 newspaper article, it is actually from the May 9, 1988 edition of the venerable Schenectady Gazette.
Program trading has come a long way in the quarter-century since then, thanks to the quantum leap in computing power and the proliferation of computerized trading strategies. These developments have also made disruptions caused by errant program trading more common. Apart from the flash crash, perhaps the best-known example of erroneous program trading is the one at market maker Knight Capital Group. On August 1, 2012, a technical glitch in Knight’s algorithmic trading systems caused misquotes in about 140 securities. The problem inflicted a loss of $440 million on Knight, taking it to the brink of bankruptcy and leading to its eventual acquisition by Getco.
Other prominent examples of problematic program trading include the cancelation of Bats Global Markets’ IPO in March 2012 on account of “technical issues,” and the May 2012 IPO for the Menlo Park, Ca.-based Facebook Inc. (Nasdaq:FB) that was dogged by technology problems and delayed trade confirmations. There have also been a number of stock-specific “flash crashes”, such as the one that affected the Woodlands, Texas-based Anadarko Petroleum Corp. (NYSE:APC) on May 20, 2013, when the stock plunged within seconds – for no apparent reason – from a price of about $90 to a penny, before recovering.
Like Anadarko, a number of stocks traded at absolutely crazy levels during the May 6, 2010 flash crash. For example, blue-chips like the Chicago, Il.-based energy company Exelon (NYSE:EXC), the Dublin, Ireland-based consulting firm Accenture PLC (NYSE:ACN), the Houston, Texas-based utility company CenterPoint Energy Inc. (NYSE:CNP) and the Boston-based brewing company Boston Beer Inc. (NYSE:SAM) briefly traded at zero on that day before rebounding, while the London, U.K.-based auction house Sotheby’s (NYSE:BID) briefly soared from the mid-$30s to $100,000 before closing at $33.
While the flash crash remains one of the most puzzling events in the history of the U.S. stock markets, a joint report by the Securities and Exchange Commission and Commodity Futures Trading Commission released in September 2010 shed some much-needed light on the subject. The report said that while volatility was unusually high and liquidity was thin on the morning of May 6, 2010 because of the European debt crisis, the flash crash itself was precipitated by a single $4.1 billion program trade generated by a trader at a mutual fund company.
What Caused the “Flash Crash”?
The trader used an automated execution algorithm to sell a total of 75,000 E-Mini contracts in the S&P 500 as a hedge to an existing equity position. The problem was that the algorithm was programmed to target an execution rate that was set to 9% of the trading volume calculated over the previous minute, but without regard to price or time. As a result, the sell program was executed in only 20 minutes, on a day when the markets were already under pressure. In contrast, a sell program by the same company on a previous occasion – which involved manual trading and several automated algorithms that took into account not just volume, but also price and time – required more than five hours to sell 75,000 contracts.
High-frequency traders and other intermediaries initially absorbed this selling pressure on May 6, 2010, building up temporary long positions. They then immediately and aggressively sold a number of E-Mini contracts to reduce their long positions. The report notes that during this time, HFTs traded nearly 140,000 E-Mini contracts or over one-third of the total trading volume, consistent with their strategy of trading a very large number of contracts but without accumulating an aggregate inventory of more than 3,000 to 4,000 contracts (long or short). The selling algorithm responded to this higher volume by increasing the rate at which it was feeding sell orders into the market. This adverse feedback loop created two liquidity crises – one at the E-Mini level, and the other in individual shares.
The biggest debacles are caused by a number of contributory factors, and the flash crash was no exception. As the E-Mini contract plunged 3% in just four minutes, arbitrageurs who had bought these E-Mini contracts simultaneously sold equivalent amounts in the equities markets, driving down the price of the S&P 500 SPDR ETF (or SPY) by a similar amount. The steep decline caused liquidity to dry up virtually instantaneously, with buy-side depth for the E-Mini plummeting to about 1% of its morning level.
This volatility caused a temporary halt in automated trading systems used by many liquidity providers. Because of the simultaneous plunge in many securities, these market participants were deeply concerned about the occurrence of a cataclysmic event of which they were not yet aware. As a result, market markets widened their bid-ask spreads, with many resorting to “stub quotes,” which are quotes generated at levels very far from the current level. This is done to dissuade trading, while fulfilling the market maker’s obligation to provide continuous two-way quotes.
But since liquidity had totally dried up in a number of shares and ETFs, some trades were executed at irrational prices of as low as one penny or as high as $100,000, as noted earlier. So for instance, if you had placed an order to sell a stock at the market price, and the only bid was at a penny, your sell order would have been executed at that price.
While the flash crash was caused by a combination of factors, a program trading error can be caused by something as mundane as faulty software. Knight’s glitch was caused by old software that was mistakenly reactivated when a new program was installed, resulting in numerous client buy orders being executed at the bid price and sell orders executed at the offer price, instead of the other way around.
What Can Investors Do?
The increasing use of program trading makes market glitches inevitable. While the exchanges do have the power to cancel clearly erroneous trades, investors may still be on the hook for losses that are a result of normal market action. For example, the vast majority of the nearly 2 billion shares traded between 2:40 p.m. and 3:00 p.m. (the exact timeframe of the flash crash) on May 6, 2010, were at prices that were within 10% of their 2:40 p.m. price. These trades were not reversed. But there were over 20,000 trades involving 5.5 million shares that were executed at prices more than 60% away from their 2:40 p.m. price. These trades were subsequently canceled (or “broken”) by the exchanges and the Financial Industry Regulatory Authority (FINRA) under their “clearly erroneous” rule, which confers on them the latitude to cancel trades that are executed at obviously unrealistic prices under severe market conditions.
In order to mitigate the risk of loses from faulty program trading, here are a few suggestions –
- Use limit orders rather than market orders: Limit orders are not infallible. But at least they may prevent you from experiencing the risk imposed by a market order of buying a stock at a ridiculously high price, or selling it at an absurdly low price, just because that’s where the “market” is temporarily.
- Use stop limit orders rather than plain stop (market) orders: This follows from the previous suggestion. A stop market order is one that becomes a market order when, for example, a level is breached on the downside for a long position. Rather than run the risk of getting “stopped out” at a really low price and incurring a hefty loss, use a stop limit order instead.
- Avoid getting whip-sawed: If you are tempted to use unusual market volatility to generate quick short-term trading, we suggest resisting the temptation in order to avoid getting whip-sawed, since you could well end up selling high and buying low.
The increasing use of program trading makes market glitches inevitable. In order to mitigate the risk of portfolio losses from such adverse events, consider using limit orders rather than market orders to initiate trades and cap losses on them.
Investing BasicsModern portfolio theory and behavioral finance represent differing schools of thought that attempt to explain investor behavior. Perhaps the easiest way to think about their arguments and positions ...
Active Trading FundamentalsThere are human tendencies that can block the road toward achieving our financial goals. Here's how to get around them.
Investing BasicsWe look at ways in which gambling creeps into trading, and what may drive an individual to trade - or gamble - in the first place.
Active TradingUnder and overtrading can lessen an investor's profits. Find out how to fix these issues with a trading plan.
Active TradingWhether you're a novice or an expert, these 10 rules should be the backbone of your trading career.
Mutual Funds & ETFsThe futures market is a lot less scary when these indicators are used to establish current trends.
Forex EducationPositive and negative trading experiences can affect the way you trade. Find out how.
Options & FuturesThese unique instruments take options trading to a whole new level.
Active Trading FundamentalsYou can't predict exactly how stocks will behave, but knowing what affects prices will put you ahead of the pack.
Chart AdvisorWeekly technical summary of the major U.S. indexes.
A series of domestic programs designed to help the United States ...
A trade where a stock or market appears to be making a move in ...
A trade on the short side with an overwhelmingly large number ...
Fintech is a portmanteau of financial technology that describes ...
Indicators are statistics used to measure current conditions ...
A technical indicator that combines aspects of candlestick analysis ...
The exhausted selling model is a pricing strategy used to identify and trade based off of the price floor of a security. ... Read Full Answer >>
Count analysis is a means of interpreting point and figure charts to measure vertical price movements. Technical analysts ... Read Full Answer >>
The U.S. Securities and Exchange Commission (SEC) has set forth disclosure requirements for private placements, including ... Read Full Answer >>
The common assumptions made when doing a t-test include those regarding the scale of measurement, random sampling, normality ... Read Full Answer >>
The inspector general of the U.S. Securities and Exchange Commission (SEC) oversees, audits and conducts investigations of ... Read Full Answer >>
Even the simplest merger and acquisition (M&A) deals are challenging. It takes a lot for two previously independent enterprises ... Read Full Answer >>