Algorithmic high-frequency trading 4 key risks
Amplification of systemic risk: the most dangerous risk, a look back in time
The risk algorithmic HFT poses to the financial system is one of the biggest. According to some distant reports by the Technical Committee of the International Organization of Securities Commissions (IOSCO), due to the close connections between financial markets, including those in the U.S., algorithms operating across markets can quickly transmit shocks from one market to the next, thereby escalating systemic risk. The Flash Crash in May 2010 was cited in the study as an excellent illustration of this risk.
The major U.S. market indices fell by 5% to 6% and then rose in a matter of minutes on the afternoon of May 6, 2010, which is referred to as the “Flash Crash.” At the time, the Dow Jones experienced its greatest point decline on record, falling by nearly 1,000 points in a single day.
As noted in the IOSCO reports, multiple equities and exchange-traded funds (ETFs) experienced a wild day, falling by 5% to 15% before recouping the majority of their losses (this also coincided with the growth of the ETF market in 2011 even though the benefits of ETFs were not so evident at that time). With prices as much as 60% off from their values just seconds earlier, over 20,000 deals in 300 securities were made, with some trades going for ridiculous prices of as little as a penny or as much as $100,000. Investors were alarmed by this uncharacteristically irregular trading behavior, which was all the more concerning given that it happened just over a year after the markets recovered from their greatest falls in more than six decades.
What role did “spoofing” have in the Flash Crash?
What led to this odd behavior? The SEC and the Commodities Futures Trading Commission placed the blame on a single $4.1 billion program trade made by a trader at a mutual fund company with headquarters in Kansas in a joint report published in September 2010.
Nevertheless, in April 2015, American officials accused day trader Navinder Singh Sarao, who is headquartered in London, of engaging in market manipulation that caused the disaster. Sarao admitted to the accusations after being extradited to the United States.
On the day of the Flash Crash, Sarao allegedly employed a strategy known as “spoofing,” which entails placing a significant number of fictitious orders in a financial instrument or derivative (Sarao utilized the E-mini S&P 500 contract). These orders are canceled before they are filled. When such sizable fake orders appear in the order book, they may cause other traders to believe that there is greater buying or selling interest than there actually is. This could affect how they decide to trade themselves.
As an illustration, a spoofer might offer to sell a significant portion of the shares of ABC stock for a price that is marginally over the existing price. The spoofer instantly cancels their sell orders in ABC and purchases the stock as a result of the price dropping when more sellers join the action. In order to raise the price of ABC, the spoofer then places a huge number of buy orders. Following this, the spoofer sells their ABC holdings for a healthy profit and cancels the fake buy orders. Repetition is key.
Several market observers have disputed the idea that one trader could have single-handedly produced a crash that instantly destroyed nearly a trillion dollars’ worth of market value for American stocks. Sarao’s action may have contributed to the Flash Crash, but that is a discussion for another day. Meanwhile, algorithmic HFT increases systemic risks for a number of good reasons. We should add that now spoofing is more applicable to crypto markets and traders now have all the best tools to detect it.
What causes systemic risk to increase in algorithmic high-frequency trading?
Secondly, given the prevalence of algorithmic HFT in markets, many algorithms are designed with the intention of continuously outwitting their rivals. Algorithms may respond instantly to changes in the market. Hence, during volatile markets, algorithms may dramatically widen their bid-ask spreads (to avoid being compelled to take trading positions) or temporarily stop trading entirely, which reduces liquidity and increases volatility.
With the growing degree of interdependence between markets and asset classes in the global economy, a major market or asset class’s collapse frequently has a cascading effect on other markets and asset classes.
Because European and other financial institutions held significant amounts of U.S. subprime paper in addition to U.S. banks, the U.S. housing market implosion resulted in a global recession and debt crisis. Another illustration of such cascading consequences is the harm caused to international equities between August 2015 and January 2016 by the drop in the crude oil price and the meltdown of the Chinese stock market.
Algorithmic HFT is a significant factor in exaggerated market volatility, which can harm consumer confidence over the long run and fuel investor concern in the short term. Investors are left perplexed by the causes of a market crash when it occurs unexpectedly. Large traders, especially HFT firms, will reduce their trading positions in order to reduce risk during the news vacuum that frequently prevails at such times, further depressing the markets.
A downward spiral is produced as a result of the markets falling further, which causes further stop-losses to be activated. The deterioration of stock market wealth and the recessionary signals coming from a significant market collapse, should a bear market arise as a result of such behavior, undermine consumer confidence.
Additional risk associated with algorithmic high-frequency trading
One flawed or errant algorithm can cause millions of dollars in losses in a very short amount of time due to the astounding speed at which most algorithmic HFT trading occurs. As a market maker who lost $440 million in a matter of 45 minutes on August 1, 2012, Knight Capital is a notorious illustration of the harm that a malfunctioning algorithm can inflict.
Millions of incorrect trades were conducted by a new trading algorithm at Knight, which bought 150 equities at the higher “ask” price and immediately sold them at the lower “bid” price. Unluckily, rival traders pounced and profited from Knight’s predicament as Knight staff desperately tried to identify the cause of the issue due to the hyper-efficiency of algorithmic HFT, which uses computers to monitor markets for just this kind of pricing mismatch continuously. By the time they did, Knight was on the verge of going bankrupt, which eventually resulted in Getco LLC buying it.
Serious investor losses
Investors may sustain significant losses as a result of volatility fluctuations made worse by algorithmic HFT. Many stockholders regularly put stop-loss orders at levels that are 5% below the stock’s current trading price. These stop-losses will be activated if the markets have an unexpected gap down (or even if there is a very excellent explanation).
To make matters worse, if equities quickly recovered in the future, investors would have unnecessarily lost money on trades and their investments. While a small number of trades were reversed or canceled during uncharacteristic market turbulence like the Flash Crash and the Knight debacle, most trades were not.
The majority of the almost two billion shares that were exchanged during the Flash Crash, for instance, were priced within 10% of their 2:40 p.m. close (the moment the crash began on May 6, 2010), and these trades were upheld. Only roughly 20,000 of the 5.5 million shares involved in the deals carried out at more than 60% over their 2:40 p.m. price were ultimately canceled.
So, a trader who set 5% stop-losses on their positions during the Flash Crash and had a $500,000 equity portfolio of American blue-chips would have lost at least $25,000 in that trade.
The NYSE rejected deals in six equities that took place on August 1, 2012, when the Knight algorithm was out of control because they were executed at prices that were 30% above or below the day’s opening price. The numerical standards for examining such trades are laid out in the NYSE’s “Clearly Erroneous Execution” regulation.
Reduced trust in market integrity
Because they have complete faith and confidence in their honesty, investors trade on the financial markets. Yet, frequent instances of exceptional market volatility, such as the Flash Crash, could undermine this certainty and cause some cautious investors to give up on the markets entirely.
When Facebook went public in May 2012, there were many technical problems that caused confirmation delays, and on August 22, 2013, a software failure caused Nasdaq to halt trading for three hours. A computer fault at the two U.S. options exchanges operated by Intercontinental Exchange Group in April 2014 forced the cancellation of nearly 20,000 incorrect trades. Another large explosion like the Flash Crash could severely shake investors’ faith in the reliability of markets.
Risk reduction for algorithmic high-frequency trading
Exchanges and authorities have been putting safety safeguards in place as a result of the Flash Crash and Knight Trading “Knightmare” showing the dangers of algorithmic HFT. For its member companies, the Nasdaq OMX Group created a “kill switch” in 2014 that would halt trading whenever a certain risk exposure level was reached. While many HFT firms already have “kill” switches that can halt all trading activity in specific situations, the Nasdaq switch had added an extra layer of security to guard against malicious algorithms.
After “Black Monday” in October 1987, circuit-breakers were developed and are now employed to calm the market when there is a significant sell-off. Circuit breakers may activate if the S&P 500 index declines 7% (from the previous day’s closing level) before 3:25 p.m. EST. This would suspend market-wide trade for 15 minutes. The SEC approved new rules that allowed this to happen in 2012. A 13% decline before 3:25 p.m. would result in a further 15-minute market suspension, and a 20% decline would close the stock market for the remainder of the day.
Regulations for businesses employing algorithmic trading in derivatives were put in place by the Commodities Futures Trading Commission in January 2021. These regulations would mandate pre-trade risk measures for these companies. A contentious requirement that businesses provide the government with access to their software’s source code was removed.
The largest danger associated with algorithmic HFT is its potential to increase systemic risk. Its tendency to make markets more volatile can have an impact on other markets and increase investor apprehension. Recurrent episodes of exceptional market volatility may eventually weaken the faith of many investors in the market’s integrity. Still, traders now have tools to secure their activity, they know how to detect spoofing trading, HFT trading software has improved immensely in the period of last 12 years and HFT software developers are creating a way better product for algorithmic trading.