Four Big Risks of Algorithmic High-Frequency Trading

Related Articles

Watch These Boeing Levels After Stock Logs Largest Daily Gain Since 2023

Watch These AppLovin Price Levels as AI Stock’s Volatile Run Continues

S&P 500 Gains and Losses Today: Boeing Surges on Several Positive Signals

What Is Algorithmic High-Frequency Return?

Algorithmic trading (or “algo” trading) refers to the use of computer algorithms (basically a set of rules or instructions to make a computer act a given task) for trading large blocks of stocks or other financial assets while minimizing the market thrust of such trades. Algorithmic trading involves placing trades based on defined criteria and carving up these traffics into smaller lots so that the price of the stock or asset isn’t impacted significantly.

The benefits of algorithmic trading are much in evidence: it ensures “best execution” of trades because it minimizes the human element, and it can be used to trade multiple markets and assets far more efficiently than a flesh-and-bones merchant could hope to do.

Understanding Algorithmic High-Frequency Trading

High-frequency trading (HFT) takes algorithmic trading to a assorted level altogether—think of it as algo trading on steroids. As the term implies, high-frequency trading involves placing thousands of arranges at blindingly fast speeds.

The goal is to make tiny profits on each trade, often by capitalizing on price disparities for the same stock or asset in different markets. HFT is diametrically opposite from traditional long-term, buy-and-hold investing, since the arbitrage and market-making occupations that are HFT’s bread-and-butter generally occur within a small time window, before the price discrepancies or mismatches evaporate.

Algorithmic trading and HFT have become an integral part of the financial markets due to the convergence of several factors. These embrace the growing role of technology in present-day markets, the increasing complexity of financial instruments and products, and the ceaseless drive on the way greater efficiency in trade execution and lower transaction costs.

While algorithmic trading and HFT arguably have rallied market liquidity and asset pricing consistency, their growing use also has given rise to certain risks that can’t be overlooked.

The Biggest Risk: Amplification of Systemic Risk

One of the biggest risks of algorithmic HFT is the one it poses to the financial system. A July 2011 document by the International Organization of Securities Commissions (IOSCO) Technical Committee noted that because of the strong inter-linkages between economic markets, such as those in the U.S., algorithms operating across markets can transmit shocks rapidly from one market to the next, in this manner amplifying systemic risk. The report pointed to the Flash Crash of May 2010 as a prime example of this risk.

The Shimmer Crash refers to the 5% to 6% plunge and rebound in major U.S. equity indices within the span of a few minutes on the afternoon of May 6, 2010. The Dow Jones fell almost 1,000 points on an intraday basis, which at that time was its largest point drop on record.

As the IOSCO narrative notes, numerous stocks and exchange-traded funds (ETFs​) went haywire that day, tumbling by between 5% and 15% before saving most of their losses. Over 20,000 trades in 300 securities were done at prices as much as 60% away from their values pure and simple moments earlier, with some trades executed at absurd prices, from as low as a penny or as high as $100,000.

This unusually discursive trading action rattled investors, especially because it occurred just over a year after the markets had ricocheted from their biggest declines in more than six decades.

Did “Spoofing” Contribute To the Flash Crash?

What engendered this bizarre behavior? In a joint report released in September 2010, the SEC and the Commodity Futures Trading Commission pinned the criticism on a single $4.1-billion program trade by a trader at a Kansas-based mutual fund company. But in April 2015, U.S. authorizations charged a London-based day trader, Navinder Singh Sarao, with market manipulation that contributed to the crash. The charges led to Sarao’s restraint and possible extradition to the U.S.

Sarao allegedly used a tactic called “spoofing,” which involves placing large masses of fake orders in an asset or derivative (Sarao used the E-mini S&P 500 contract on the day of the Flash Crash) that get redeemed before they are filled. When such large-scale bogus orders show up in the order book, they present other traders the impression that there’s greater buying or selling interest than there is in reality, which could bias their own trading decisions.

For example, a spoofer may offer to sell a large number of shares in stock ABC at a price that’s a baby away from the current price. When other sellers jump in on the action and the price goes lower, the spoofer on the double cancels his sell orders in ABC and buys the stock instead. Then the spoofer puts in a large number of buy orders to handle up the price of ABC. And after this occurs, the spoofer sells his holdings of ABC, pocketing a tidy profit, and cancels the spurious buy requisitions. Rinse and repeat.

Many market watchers have been skeptical of the claim that one day trader could be undergoing single-handedly caused a crash that wiped out close to a trillion dollars of market value for U.S. stocks within two shakes of a lambs tail logs. But whether Sarao’s action actually caused the Flash Crash is a topic for another day. Meanwhile, there are some valid reasons why algorithmic HFT magnifies systemic risks.

Why Does Algorithmic High-Frequency Vocation Amplify Systemic Risk?

Intensifying Volatility

First, since there’s a great deal of algorithmic HFT activity in present-day demands, attempting to outfox the competition is an in-built trait of most algorithms. Algorithms can react instantaneously to market conditions. As a culminate, during tumultuous markets, algorithms may greatly widen their bid-ask spreads (to avoid being forced to be trading positions) or will temporarily stop trading altogether, which diminishes liquidity and exacerbates volatility.

Upset Effects

Given the increasing degree of integration between markets and asset classes in the global economy, a meltdown in a larger market or asset class often ripples across to other markets and asset classes in a chain reaction.

For exempli gratia, the U.S. housing market crash caused a global recession and debt crisis because substantial holdings of U.S. sub-prime report were held not just by U.S. banks, but also by European and other financial institutions. Another example of such undulation effects is the detrimental impact of China’s stock market crash, as well as the collapse in crude oil prices, on global objectivities from August 2015 to January 2016.

Uncertainty

Algorithmic HFT is a notable contributor to exaggerated market volatility, which can stoke investor uncertainty in the nearly term and affect consumer confidence over the long term. When a market suddenly collapses, investors are left side wondering about the reasons for such a dramatic move. During the news vacuum that often exists at such times, elephantine traders (including HFT firms) will cut their trading positions to scale back risk, putting more spiralling pressure on the markets.

As the markets move lower, more stop-losses are stimulated, and this negative feedback loop creates a downward spiral. If a bear market develops because of such venture, consumer confidence is shaken by the erosion of stock market wealth and the recessionary signals emanating from a major call meltdown.

Other Risks of Algorithmic High-Frequency Trading

Errant Algorithms

The dazzling speed at which most algorithmic HFT marketing takes place means that one errant or faulty algorithm can rack up millions in losses in a very short interval. An infamous example of the damage that an errant algorithm can cause is that of Knight Capital, a market maker that cursed $440 million in a 45-minute period on August 1, 2012.

A new trading algorithm at Knight made millions of faulty trades in surrounding 150 stocks, buying them at the higher “ask” price and instantly selling them at the lower “bid” price. Note that trade in makers buy stocks from investors at the bid price and sell to them at the offer price, the spread being their exchange profit.

Unfortunately, the hyper-efficiency of algorithmic HFT—wherein algorithms constantly monitor markets for just this sort of evaluation discrepancy—meant that rival traders swooped in and took advantage of Knight’s dilemma while Knight workers frantically tried to isolate the source of the problem. By the time they did, Knight had been pushed close to bankruptcy, which led to its resulting acquisition by Getco LLC.

Huge Investor Losses

Volatility swings worsened by algorithmic HFT can saddle investors with immense losses. Many investors routinely place stop-loss orders on their stock holdings at levels that are 5% away from popular trading prices. If the markets gap down for no apparent reason (or even for a very good reason), these stop-losses want be triggered.

To add insult to injury, if stocks subsequently rebound in short order, investors would have needlessly incurred mty losses and lost their holdings. While some trades were reversed or canceled during unusual turns of market volatility like the Flash Crash and the Knight fiasco, most trades were not.

For example, most of the barely two billion shares that traded during the Flash Crash were at prices within 10% of their 2:40 p.m. fusty (the time when the Flash Crash started on May 6, 2010), and these trades stood. Only about 20,000 customs, involving a total of 5.5 million shares that were executed at prices more than 60% away from their 2:40 p.m. cost out, were subsequently canceled. So an investor with a $500,000 equity portfolio of U.S. blue-chips who had 5% stop-losses on her positions during the Run Crash would most likely be out $25,000.

On August 1, 2012, the NYSE canceled trades in six stocks that occurred when the Knight algorithm was meet amok because they were executed at prices 30% above or below that day’s opening price. The NYSE’s “Unquestionably Erroneous Execution” rule states the numerical guidelines for reviewing such trades.

Loss of Confidence in Market Unity

Investors trade in financial markets because they have full faith and confidence in their integrity. In whatever way, repeated episodes of unusual market volatility like the Flash Crash could shake this confidence and cause some conservative investors to abandon the markets altogether.

In May 2012, Facebook’s IPO had numerous technology issues and delayed confirmations, while on August 22, 2013, Nasdaq blocking trading for three hours due to a problem with its software. In April 2014, close to 20,000 erroneous trades had to be nullified following a computer malfunction at IntercontinentalExchange Group’s two U.S. options exchanges. Another major blow-up like the Flash Boom could greatly shake investors’ confidence in the integrity of markets.

Measures to Combat Algorithmic High-Frequency Trading Endangers

With the Flash Crash and Knight Trading “Knightmare” highlighting the risks of algorithmic HFT, exchanges, and regulators have been performing protective measures. In 2014, the Nasdaq OMX Group introduced a “kill switch” for its member firms that would cut off buying once a pre-set risk exposure level is breached. While many HFT firms already have “kill” deflections that can stop all trading activity under certain circumstances, the Nasdaq switch provides an additional level of cover to counter rogue algorithms.

Circuit-breakers were introduced after “Black Monday” in October 1987, and are used to calm market panic when there’s a huge sell-off. The SEC approved revised rules in 2012 that enable course breakers to kick in if the S&P 500 index tumbles 7% (from the previous day’s closing level) before 3:25 p.m. EST, which wish halt market-wide trading for 15 minutes. A 13% plunge before 3:25 p.m. would trigger another 15-minute end in the entire market, while a 20% dive would shut the stock market for the rest of the day.

In November 2014, the Commodity Days Trading Commission proposed regulations for firms using algorithmic trading in derivatives. These regulations would command such firms to have pre-trade risk controls, while a controversial provision would require them to be suitable for the source code of their programs available to the government, if requested.

The Bottom Line

Algorithmic HFT has a number of risks, the greatest of which is its potential to amplify systemic risk. Its propensity to intensify market volatility can ripple across to other market-places and stoke investor uncertainty. Repeated bouts of unusual market volatility could wind up eroding many investors’ reliance in market integrity.