High-frequency trading

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High-frequency trading (HFT) is a type of algorithmic trading in finance characterized by high speeds, high turnover rates, and high order-to-trade ratios that leverages high-frequency financial data and electronic trading tools. [1] [2] [3] While there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, co-location, and very short-term investment horizons in trading securities. [4] [5] [6] [7] HFT uses proprietary trading strategies carried out by computers to move in and out of positions in seconds or fractions of a second. [8]

Contents

In 2016, HFT on average initiated 10–40% of trading volume in equities, and 10–15% of volume in foreign exchange and commodities. [9] High-frequency traders move in and out of short-term positions at high volumes and high speeds aiming to capture sometimes a fraction of a cent in profit on every trade. [6] HFT firms do not consume significant amounts of capital, accumulate positions or hold their portfolios overnight. [10] As a result, HFT has a potential Sharpe ratio (a measure of reward to risk) tens of times higher than traditional buy-and-hold strategies. [11] High-frequency traders typically compete against other HFTs, rather than long-term investors. [10] [12] [13] HFT firms make up the low margins with incredibly high volumes of trades, frequently numbering in the millions.

A substantial body of research argues that HFT and electronic trading pose new types of challenges to the financial system. [5] [14] Algorithmic and high-frequency traders were both found to have contributed to volatility in the Flash Crash of May 6, 2010, when high-frequency liquidity providers rapidly withdrew from the market. [5] [13] [14] [15] [16] Several European countries have proposed curtailing or banning HFT due to concerns about volatility. [17] Other complaints against HFT include the argument that some HFT firms scrape profits from investors when index funds rebalance their portfolios. [18] [19] [20]

History

The rapid-fire computer-based HFT developed gradually since 1983 after NASDAQ introduced a purely electronic form of trading. [21] At the turn of the 21st century, HFT trades had an execution time of several seconds, whereas by 2010 this had decreased to milli- and even microseconds. [22] At that time, high-frequency trading was still a little-known topic outside the financial sector, with an article published by the New York Times in July 2009 being one of the first to bring the subject to the public's attention. [23]

On September 2, 2013, Italy became the world's first country to introduce a tax specifically targeted at HFT, charging a levy of 0.02% on equity transactions lasting less than 0.5 seconds. [24] [25]

Market growth

In the early 2000s, high-frequency trading still accounted for fewer than 10% of equity orders, but this proportion was soon to begin rapid growth. According to data from the NYSE, trading volume grew by about 164% between 2005 and 2009 for which high-frequency trading might be accounted. [23] As of the first quarter in 2009, total assets under management for hedge funds with high-frequency trading strategies were $141 billion, down about 21% from their peak before the worst of the crises, [26] although most of the largest HFTs are actually LLCs owned by a small number of investors. The high-frequency strategy was first made popular by Renaissance Technologies [27] who use both HFT and quantitative aspects in their trading. Many high-frequency firms are market makers and provide liquidity to the market which lowers volatility and helps narrow bid–offer spreads, making trading and investing cheaper for other market participants. [26]

Market share

In the United States in 2009, high-frequency trading firms represented 2% of the approximately 20,000 firms operating today, but accounted for 73% of all equity orders volume.[ citation needed ] [28] The major U.S. high-frequency trading firms include Virtu Financial, Tower Research Capital, IMC, Tradebot, Akuna Capital and Citadel LLC. [29] The Bank of England estimates similar percentages for the 2010 UK market share, also suggesting that in Europe HFT accounts for about 40% of equity orders volume and for Asia about 5–10%, with potential for rapid growth. [22] By value, HFT was estimated in 2010 by consultancy Tabb Group to make up 56% of equity trades in the US and 38% in Europe. [30]

As HFT strategies become more widely used, it can be more difficult to deploy them profitably. According to an estimate from Frederi Viens of Purdue University, profits from HFT in the U.S. has been declining from an estimated peak of $5bn in 2009, to about $1.25bn in 2012. [31]

Though the percentage of volume attributed to HFT has fallen in the equity markets, it has remained prevalent in the futures markets. According to a study in 2010 by Aite Group, about a quarter of major global futures volume came from professional high-frequency traders. [28] In 2012, according to a study by the TABB Group, HFT accounted for more than 60 percent of all futures market volume in 2012 on U.S. exchanges. [32]

Strategies

High-frequency trading is quantitative trading that is characterized by short portfolio holding periods. [33] All portfolio-allocation decisions are made by computerized quantitative models. The success of high-frequency trading strategies is largely driven by their ability to simultaneously process large volumes of information, something ordinary human traders cannot do. Specific algorithms are closely guarded by their owners. Many practical algorithms are in fact quite simple arbitrages which could previously have been performed at lower frequency—competition tends to occur through who can execute them the fastest rather than who can create new breakthrough algorithms.

The common types of high-frequency trading include several types of market-making, event arbitrage, statistical arbitrage, and latency arbitrage. Most high-frequency trading strategies are not fraudulent, but instead exploit minute deviations from market equilibrium. [33]

Market making

According to SEC: [34]

A "market maker" is a firm that stands ready to buy and sell a particular stock on a regular and continuous basis at a publicly quoted price. You'll most often hear about market makers in the context of the Nasdaq or other "over the counter" (OTC) markets. Market makers that stand ready to buy and sell stocks listed on an exchange, such as the New York Stock Exchange, are called "third market makers". Many OTC stocks have more than one market-maker. Market-makers generally must be ready to buy and sell at least 100 shares of a stock they make a market in. As a result, a large order from an investor may have to be filled by a number of market-makers at potentially different prices.

There can be a significant overlap between a "market maker" and "HFT firm". HFT firms characterize their business as "Market making" – a set of high-frequency trading strategies that involve placing a limit order to sell (or offer) or a buy limit order (or bid) in order to earn the bid-ask spread. By doing so, market makers provide a counterpart to incoming market orders. Although the role of market maker was traditionally fulfilled by specialist firms, this class of strategy is now implemented by a large range of investors, thanks to wide adoption of direct market access. As pointed out by empirical studies, [35] this renewed competition among liquidity providers causes reduced effective market spreads, and therefore reduced indirect costs for final investors." A crucial distinction is that true market makers don't exit the market at their discretion and are committed not to, where HFT firms are under no similar commitment.

Some high-frequency trading firms use market making as their primary strategy. [10] Automated Trading Desk (ATD), which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both the NASDAQ and the New York Stock Exchange. [36] In May 2016, Citadel LLC bought assets of ATD from Citigroup. Building up market making strategies typically involves precise modeling of the target market microstructure [37] [38] together with stochastic control techniques. [39] [40] [41] [42]

These strategies appear intimately related to the entry of new electronic venues. Academic study of Chi-X's entry into the European equity market reveals that its launch coincided with a large HFT that made markets using both the incumbent market, NYSE-Euronext, and the new market, Chi-X. The study shows that the new market provided ideal conditions for HFT market-making, low fees (i.e., rebates for quotes that led to execution) and a fast system, yet the HFT was equally active in the incumbent market to offload nonzero positions. New market entry and HFT arrival are further shown to coincide with a significant improvement in liquidity supply. [43]

Quote stuffing

Quote stuffing is a form of abusive market manipulation that has been employed by high-frequency traders (HFT) and is subject to disciplinary action. It involves quickly entering and withdrawing a large number of orders in an attempt to flood the market creating confusion in the market and trading opportunities for high-frequency traders. [44] [45] [46]

Ticker tape trading

Much information happens to be unwittingly embedded in market data, such as quotes and volumes. By observing a flow of quotes, computers are capable of extracting information that has not yet crossed the news screens. Since all quote and volume information is public, such strategies are fully compliant with all the applicable laws.

Filter trading is one of the more primitive high-frequency trading strategies that involves monitoring large amounts of stocks for significant or unusual price changes or volume activity. This includes trading on announcements, news, or other event criteria. Software would then generate a buy or sell order depending on the nature of the event being looked for. [47]

Tick trading often aims to recognize the beginnings of large orders being placed in the market. For example, a large order from a pension fund to buy will take place over several hours or even days, and will cause a rise in price due to increased demand. An arbitrageur can try to spot this happening, buy up the security, then profit from selling back to the pension fund. This strategy has become more difficult since the introduction of dedicated trade execution companies in the 2000s[ citation needed ] which provide optimal[ citation needed ] trading for pension and other funds, specifically designed to remove[ citation needed ] the arbitrage opportunity.

Statistical arbitrage

Another set of high-frequency trading strategies are strategies that exploit predictable temporary deviations from stable statistical relationships among securities. Statistical arbitrage at high frequencies is actively used in all liquid securities, including equities, bonds, futures, foreign exchange, etc. Such strategies may also involve classical arbitrage strategies, such as covered interest rate parity in the foreign exchange market, which gives a relationship between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency. High-frequency trading allows similar arbitrages using models of greater complexity involving many more than four securities.

The TABB Group estimates that annual aggregate profits of high-frequency arbitrage strategies exceeded US$21 billion in 2009, [48] although the Purdue study estimates the profits for all high frequency trading were US$5 billion in 2009. [31]

Index arbitrage

Index arbitrage exploits index tracker funds which are bound to buy and sell large volumes of securities in proportion to their changing weights in indices. If a HFT firm is able to access and process information which predicts these changes before the tracker funds do so, they can buy up securities in advance of the trackers and sell them on to them at a profit.

News-based trading

Company news in electronic text format is available from many sources including commercial providers like Bloomberg , public news websites, and Twitter feeds. Automated systems can identify company names, keywords and sometimes semantics to make news-based trades before human traders can process the news.

Low-latency strategies

A separate, "naïve" class of high-frequency trading strategies relies exclusively on ultra-low latency direct market access technology. In these strategies, computer scientists rely on speed to gain minuscule advantages in arbitraging price discrepancies in some particular security trading simultaneously on disparate markets. [49]

Another aspect of low latency strategy has been the switch from fiber optic to microwave and shortwave technology for long distance networking. The switch to microwave transmission was because microwaves traveling in air suffer a less than 1% speed reduction compared to light traveling in a vacuum, whereas with conventional fiber optics light travels over 30% slower. [31] Especially since 2011, companies involved in HFT have massively invested in microwaves infrastructure to transmit data across key connections such as the one between New York City and Chicago but also between London and Frankfurt, going through Belgium thanks to a network of former US army antennas. [50] [51] However, microwave transmission requires line-of-sight propagation, which is difficult over long distances, driving some HFT firms to use shortwave radio instead. [52] [53] Shortwave radio signals can be transmitted over a longer distance, but carry less information; in 2020, a hedge fund partner quoted in Bloomberg News said that shortwave bandwidth is insufficient for transmitting full order book feeds for low-latency strategies. [53] Firms have also looked into using satellites to transmit market data. [52]

Order properties strategies

High-frequency trading strategies may use properties derived from market data feeds to identify orders that are posted at sub-optimal prices. Such orders may offer a profit to their counterparties that high-frequency traders can try to obtain. Examples of these features include the age of an order [54] or the sizes of displayed orders. [55] Tracking important order properties may also allow trading strategies to have a more accurate prediction of the future price of a security.

Effects

The effects of algorithmic and high-frequency trading are the subject of ongoing research. High frequency trading causes regulatory concerns as a contributor to market fragility. [56] Regulators claim these practices contributed to volatility in the May 6, 2010, Flash Crash [62] and find that risk controls are much less stringent for faster trades. [14]

Members of the financial industry generally claim high-frequency trading substantially improves market liquidity, [10] narrows bid–offer spread, lowers volatility and makes trading and investing cheaper for other market participants. [65]

An academic study [35] found that, for large-cap stocks and in quiescent markets during periods of "generally rising stock prices", high-frequency trading lowers the cost of trading and increases the informativeness of quotes; [35] : 31  however, it found "no significant effects for smaller-cap stocks", [35] : 3  and "it remains an open question whether algorithmic trading and algorithmic liquidity supply are equally beneficial in more turbulent or declining markets. ...algorithmic liquidity suppliers may simply turn off their machines when markets spike downward." [35] : 31 

In September 2011, market data vendor Nanex LLC published a report stating the contrary. They looked at the amount of quote traffic compared to the value of trade transactions over 4 and half years and saw a 10-fold decrease in efficiency. [66] Nanex's owner is an outspoken detractor of high-frequency trading. [67] Many discussions about HFT focus solely on the frequency aspect of the algorithms and not on their decision-making logic (which is typically kept secret by the companies that develop them). This makes it difficult for observers to pre-identify market scenarios where HFT will dampen or amplify price fluctuations. The growing quote traffic compared to trade value could indicate that more firms are trying to profit from cross-market arbitrage techniques that do not add significant value through increased liquidity when measured globally.

More fully automated markets such as NASDAQ, Direct Edge, and BATS, in the US, gained market share from less automated markets such as the NYSE. Economies of scale in electronic trading contributed to lowering commissions and trade processing fees, and contributed to international mergers and consolidation of financial exchanges.

The speeds of computer connections, measured in milliseconds or microseconds, have become important. [68] [69] Competition is developing among exchanges for the fastest processing times for completing trades. For example, in 2009 the London Stock Exchange bought a technology firm called MillenniumIT and announced plans to implement its Millennium Exchange platform [70] which they claim has an average latency of 126 microseconds. [71] This allows sub-millisecond resolution timestamping of the order book. Off-the-shelf software currently allows for nanoseconds resolution of timestamps using a GPS clock with 100 nanoseconds precision. [72]

Spending on computers and software in the financial industry increased to $26.4 billion in 2005. [73]

May 6, 2010 Flash Crash

The brief but dramatic stock market crash of May 6, 2010, was initially thought to have been caused by high-frequency trading. [74] The Dow Jones Industrial Average plunged to its largest intraday point loss, but not percentage loss, [75] in history, only to recover much of those losses within minutes. [76]

In the aftermath of the crash, several organizations argued that high-frequency trading was not to blame, and may even have been a major factor in minimizing and partially reversing the Flash Crash. [77] CME Group, a large futures exchange, stated that, insofar as stock index futures traded on CME Group were concerned, its investigation had found no support for the notion that high-frequency trading was related to the crash, and actually stated it had a market stabilizing effect. [78]

However, after almost five months of investigations, the U.S. Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC) issued a joint report identifying the cause that set off the sequence of events leading to the Flash Crash [79] and concluding that the actions of high-frequency trading firms contributed to volatility during the crash.

The report found that the cause was a single sale of $4.1 billion in futures contracts by a mutual fund, identified as Waddell & Reed Financial, in an aggressive attempt to hedge its investment position. [80] [81] The joint report also found that "high-frequency traders quickly magnified the impact of the mutual fund's selling." [15] The joint report "portrayed a market so fragmented and fragile that a single large trade could send stocks into a sudden spiral", that a large mutual fund firm "chose to sell a big number of futures contracts using a computer program that essentially ended up wiping out available buyers in the market", that as a result high-frequency firms "were also aggressively selling the E-mini contracts", contributing to rapid price declines. [15] The joint report also noted "HFTs began to quickly buy and then resell contracts to each other – generating a 'hot-potato' volume effect as the same positions were passed rapidly back and forth." [15] The combined sales by Waddell and high-frequency firms quickly drove "the E-mini price down 3% in just four minutes". [15] As prices in the futures market fell, there was a spillover into the equities markets where "the liquidity in the market evaporated because the automated systems used by most firms to keep pace with the market paused" and scaled back their trading or withdrew from the markets altogether. [15] The joint report then noted that "Automatic computerized traders on the stock market shut down as they detected the sharp rise in buying and selling." [58] As computerized high-frequency traders exited the stock market, the resulting lack of liquidity "...caused shares of some prominent companies like Procter & Gamble and Accenture to trade down as low as a penny or as high as $100,000". [58] While some firms exited the market, high-frequency firms that remained in the market exacerbated price declines because they "'escalated their aggressive selling' during the downdraft". [13] In the years following the flash crash, academic researchers and experts from the CFTC pointed to high-frequency trading as just one component of the complex current U.S. market structure that led to the events of May 6, 2010. [82]

Granularity and accuracy

In 2015 the Paris-based regulator of the 28-nation European Union, the European Securities and Markets Authority, proposed time standards to span the EU, that would more accurately synchronize trading clocks "to within a nanosecond, or one-billionth of a second" to refine regulation of gateway-to-gateway latency time—"the speed at which trading venues acknowledge an order after receiving a trade request". Using these more detailed time-stamps, regulators would be better able to distinguish the order in which trade requests are received and executed, to identify market abuse and prevent potential manipulation of European securities markets by traders using advanced, powerful, fast computers and networks. The fastest technologies give traders an advantage over other "slower" investors as they can change prices of the securities they trade. [83]

Risks and controversy

According to author Walter Mattli, the ability of regulators to enforce the rules has greatly declined since 2005 with the passing of the Regulation National Market System (Reg NMS) by the SEC. As a result, the NYSE's quasi monopoly role as a stock rule maker was undermined and turned the stock exchange into one of many globally operating exchanges. The market then became more fractured and granular, as did the regulatory bodies, and since stock exchanges had turned into entities also seeking to maximize profits, the one with the most lenient regulators were rewarded, and oversight over traders' activities was lost. This fragmentation has greatly benefitted HFT. [84]

High-frequency trading comprises many different types of algorithms. [1] Various studies reported that certain types of market-making high-frequency trading reduces volatility and does not pose a systemic risk, [10] [63] [64] [78] and lowers transaction costs for retail investors, [13] [35] [63] [64] without impacting long term investors. [6] [10] [64] Other studies, summarized in Aldridge, Krawciw, 2017 [85] find that high-frequency trading strategies known as "aggressive" erode liquidity and cause volatility.

High-frequency trading has been the subject of intense public focus and debate since the May 6, 2010, Flash Crash. [89] At least one Nobel Prize–winning economist, Michael Spence, believes that HFT should be banned. [90] A working paper found "the presence of high frequency trading has significantly mitigated the frequency and severity of end-of-day price dislocation". [91]

In their joint report on the 2010 Flash Crash, the SEC and the CFTC stated that "market makers and other liquidity providers widened their quote spreads, others reduced offered liquidity, and a significant number withdrew completely from the markets" [79] during the flash crash.

Politicians, regulators, scholars, journalists and market participants have all raised concerns on both sides of the Atlantic. [30] [88] [92] This has led to discussion of whether high-frequency market makers should be subject to various kinds of regulations.

In a September 22, 2010, speech, SEC chairperson Mary Schapiro signaled that US authorities were considering the introduction of regulations targeted at HFT. She said, "high frequency trading firms have a tremendous capacity to affect the stability and integrity of the equity markets. Currently, however, high frequency trading firms are subject to very little in the way of obligations either to protect that stability by promoting reasonable price continuity in tough times, or to refrain from exacerbating price volatility." [93] She proposed regulation that would require high-frequency traders to stay active in volatile markets. [94] A later SEC chair Mary Jo White pushed back against claims that high-frequency traders have an inherent benefit in the markets. [95] SEC associate director Gregg Berman suggested that the current debate over HFT lacks perspective. In an April 2014 speech, Berman argued: "It's much more than just the automation of quotes and cancels, in spite of the seemingly exclusive fixation on this topic by much of the media and various outspoken market pundits. (...) I worry that it may be too narrowly focused and myopic." [96]

The Chicago Federal Reserve letter of October 2012, titled "How to keep markets safe in an era of high-speed trading", reports on the results of a survey of several dozen financial industry professionals including traders, brokers, and exchanges. [14] It found that

The CFA Institute, a global association of investment professionals, advocated for reforms regarding high-frequency trading, [97] including:

Flash trading

Exchanges offered a type of order called a "Flash" order (on NASDAQ, it was called "Bolt" on the Bats stock exchange) that allowed an order to lock the market (post at the same price as an order on the other side of the order book) for a small amount of time (5 milliseconds). This order type was available to all participants but since HFT's adapted to the changes in market structure more quickly than others, they were able to use it to "jump the queue" and place their orders before other order types were allowed to trade at the given price. Currently, the majority of exchanges do not offer flash trading, or have discontinued it. By March 2011, the NASDAQ, BATS, and Direct Edge exchanges had all ceased offering its Competition for Price Improvement functionality (widely referred to as "flash technology/trading"). [98] [99]

Violations and fines

Regulation and enforcement

In March 2012, regulators fined Octeg LLC, the equities market-making unit of high-frequency trading firm Getco LLC, for $450,000. Octeg violated Nasdaq rules and failed to maintain proper supervision over its stock trading activities. [100] The fine resulted from a request by Nasdaq OMX for regulators to investigate the activity at Octeg LLC from the day after the May 6, 2010, Flash Crash through the following December. Nasdaq determined the Getco subsidiary lacked reasonable oversight of its algo-driven high-frequency trading. [101]

In October 2013, regulators fined Knight Capital $12 million for the trading malfunction that led to its collapse. Knight was found to have violated the SEC's market access rule, in effect since 2010 to prevent such mistakes. Regulators stated the HFT firm ignored dozens of error messages before its computers sent millions of unintended orders to the market. Knight Capital eventually merged with Getco to form KCG Holdings. Knight lost over $460 million from its trading errors in August 2012 that caused disturbance in the U.S. stock market. [102]

In September 2014, HFT firm Latour Trading LLC agreed to pay a SEC penalty of $16 million. Latour is a subsidiary of New York-based high-frequency trader Tower Research Capital LLC. According to the SEC's order, for at least two years Latour underestimated the amount of risk it was taking on with its trading activities. By using faulty calculations, Latour managed to buy and sell stocks without holding enough capital. At times, the Tower Research Capital subsidiary accounted for 9% of all U.S. stock trading. The SEC noted the case is the largest penalty for a violation of the net capital rule. [103]

In response to increased regulation, such as by FINRA, [104] some [105] [106] have argued that instead of promoting government intervention, it would be more efficient to focus on a solution that mitigates information asymmetries among traders and their backers; others argue that regulation does not go far enough. [107] In 2018, the European Union introduced the MiFID II/MiFIR regulation. [108]

Order types

On January 12, 2015, the SEC announced a $14 million penalty against a subsidiary of BATS Global Markets, an exchange operator that was founded by high-frequency traders. The BATS subsidiary Direct Edge failed to properly disclose order types on its two exchanges EDGA and EDGX. These exchanges offered three variations of controversial "Hide Not Slide" [109] orders and failed to accurately describe their priority to other orders. The SEC found the exchanges disclosed complete and accurate information about the order types "only to some members, including certain high-frequency trading firms that provided input about how the orders would operate". [110] The complaint was made in 2011 by Haim Bodek. [109]

Reported in January 2015, UBS agreed to pay $14.4 million to settle charges of not disclosing an order type that allowed high-frequency traders to jump ahead of other participants. The SEC stated that UBS failed to properly disclose to all subscribers of its dark pool "the existence of an order type that it pitched almost exclusively to market makers and high-frequency trading firms". UBS broke the law by accepting and ranking hundreds of millions of orders [111] priced in increments of less than one cent, which is prohibited under Regulation NMS. The order type called PrimaryPegPlus enabled HFT firms "to place sub-penny-priced orders that jumped ahead of other orders submitted at legal, whole-penny prices". [112]

Quote stuffing

In June 2014, high-frequency trading firm Citadel LLC was fined $800,000 for violations that included quote stuffing. Nasdaq's disciplinary action stated that Citadel "failed to prevent the strategy from sending millions of orders to the exchanges with few or no executions". It was pointed out that Citadel "sent multiple, periodic bursts of order messages, at 10,000 orders per second, to the exchanges. This excessive messaging activity, which involved hundreds of thousands of orders for more than 19 million shares, occurred two to three times per day." [113] [114]

Spoofing and layering

In July 2013, it was reported that Panther Energy Trading LLC was ordered to pay $4.5 million to U.S. and U.K. regulators on charges that the firm's high-frequency trading activities manipulated commodity markets. Panther's computer algorithms placed and quickly canceled bids and offers in futures contracts including oil, metals, interest rates and foreign currencies, the U.S. Commodity Futures Trading Commission said. [115] In October 2014, Panther's sole owner Michael Coscia was charged with six counts of commodities fraud and six counts of "spoofing". The indictment stated that Coscia devised a high-frequency trading strategy to create a false impression of the available liquidity in the market, "and to fraudulently induce other market participants to react to the deceptive market information he created". [116]

In November 7, 2019, it was reported that Tower Research was ordered to pay $67.4 million in fines to the CFTC to settle allegations that three former traders at the firm engaged in spoofing from at least March 2012 through December 2013. The New York-based firm entered into a deferred prosecution agreement with the Justice Department. [117]

Market manipulation

In October 2014, Athena Capital Research LLC was fined $1 million on price manipulation charges. The high-speed trading firm used $40 million to rig prices of thousands of stocks, including eBay, according to U.S. regulators. The HFT firm Athena manipulated closing prices commonly used to track stock performance with "high-powered computers, complex algorithms and rapid-fire trades", the SEC said. The regulatory action is one of the first market manipulation cases against a firm engaged in high-frequency trading. Reporting by Bloomberg noted the HFT industry is "besieged by accusations that it cheats slower investors". [118]

In January 2023, Citadel Securities was fined $11.88 billion won ($9.66 million) by South Korea's financial regulator for distorting stock prices with the use of immediate-or-cancel orders and by filling gaps in bid prices. [119]

Frontrunning by a wholesaler

In July 2020, Citadel Securities was fined $700,000 by FINRA for trading ahead of customer orders. [120]

Advanced trading platforms

Advanced computerized trading platforms and market gateways are becoming standard tools of most types of traders, including high-frequency traders. Broker-dealers now compete on routing order flow directly, in the fastest and most efficient manner, to the line handler where it undergoes a strict set of risk filters before hitting the execution venue(s). Ultra-low latency direct market access (ULLDMA) is a hot topic amongst brokers and technology vendors such as Goldman Sachs, Credit Suisse, and UBS. [121] [122] [123] Typically, ULLDMA systems can currently handle high amounts of volume and boast round-trip order execution speeds (from hitting "transmit order" to receiving an acknowledgment) of 10 milliseconds or less.

Such performance is achieved with the use of hardware acceleration or even full-hardware processing of incoming market data, in association with high-speed communication protocols, such as 10 Gigabit Ethernet or PCI Express. More specifically, some companies provide full-hardware appliances based on FPGA technology to obtain sub-microsecond end-to-end market data processing.

Buy side traders made efforts to curb predatory HFT strategies. Brad Katsuyama, co-founder of the IEX, led a team that implemented THOR, a securities order-management system that splits large orders into smaller sub-orders that arrive at the same time to all the exchanges through the use of intentional delays. This largely prevents information leakage in the propagation of orders that high-speed traders can take advantage of. [124] In 2016, after having Intercontinental Exchange Inc. and others fail to prevent SEC approval of IEX's launch and having failed to sue as it had threatened to do over the SEC approval, Nasdaq launched a "speed bump" product of its own to compete with IEX. According to Nasdaq CEO Robert Greifeld "the regulator shouldn't have approved IEX without changing the rules that required quotes to be immediately visible". The IEX speed bump—or trading slowdown—is 350  microseconds, which the SEC ruled was within the "immediately visible" parameter. The slowdown promises to impede HST ability "often [to] cancel dozens of orders for every trade they make". [125]

Outside of US equities, several notable spot foreign exchange (FX) trading platforms—including ParFX, [126] EBS Market, [127] and Refinitiv FXall [128] —have implemented their own "speed bumps" to curb or otherwise limit HFT activity. Unlike the IEX fixed length delay that retains the temporal ordering of messages as they are received by the platform, the spot FX platforms' speed bumps reorder messages so the first message received is not necessarily that processed for matching first. In short, the spot FX platforms' speed bumps seek to reduce the benefit of a participant being faster than others, as has been described in various academic papers. [129] [130]

See also

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<span class="mw-page-title-main">Market data</span> Electronic financial trading price and related data

In finance, market data is price and other related data for a financial instrument reported by a trading venue such as a stock exchange. Market data allows traders and investors to know the latest price and see historical trends for instruments such as equities, fixed-income products, derivatives, and currencies.

Direct market access (DMA) is a term used in financial markets to describe electronic trading facilities that give investors wishing to trade in financial instruments a way to interact with the order book of an exchange. Normally, trading on the order book is restricted to broker-dealers and market making firms that are members of the exchange. Using DMA, investment companies and other private traders use the information technology infrastructure of sell side firms such as investment banks and the market access that those firms possess, but control the way a trading transaction is managed themselves rather than passing the order over to the broker's own in-house traders for execution. Today, DMA is often combined with algorithmic trading giving access to many different trading strategies. Certain forms of DMA, most notably "sponsored access", have raised substantial regulatory concerns because of the possibility of a malfunction by an investor to cause widespread market disruption.

An automated trading system (ATS), a subset of algorithmic trading, uses a computer program to create buy and sell orders and automatically submits the orders to a market center or exchange. The computer program will automatically generate orders based on predefined set of rules using a trading strategy which is based on technical analysis, advanced statistical and mathematical computations or input from other electronic sources.

In finance, a dark pool is a private forum for trading securities, derivatives, and other financial instruments. Liquidity on these markets is called dark pool liquidity. The bulk of dark pool trades represent large trades by financial institutions that are offered away from public exchanges like the New York Stock Exchange and the NASDAQ, so that such trades remain confidential and outside the purview of the general investing public. The fragmentation of electronic trading platforms has allowed dark pools to be created, and they are normally accessed through crossing networks or directly among market participants via private contractual arrangements. Generally, dark pools are not available to the public, but in some cases, they may be accessed indirectly by retail investors and traders via retail brokers.

<span class="mw-page-title-main">Electronic trading platform</span> Software for trading financial products

In finance, an electronic trading platform also known as an online trading platform, is a computer software program that can be used to place orders for financial products over a network with a financial intermediary. Various financial products can be traded by the trading platform, over a communication network with a financial intermediary or directly between the participants or members of the trading platform. This includes products such as stocks, bonds, currencies, commodities, derivatives and others, with a financial intermediary such as brokers, market makers, Investment banks or stock exchanges. Such platforms allow electronic trading to be carried out by users from any location and are in contrast to traditional floor trading using open outcry and telephone-based trading. Sometimes the term trading platform is also used in reference to the trading software alone.

Flash trading, otherwise known as a flash order, is a marketable order sent to a market center that is not quoting the industry's best price or that cannot fill that order in its entirety. The order is then flashed to recipients of the venue's proprietary data feed to see if any of those firms wants to take the other side of the order.

<span class="mw-page-title-main">2010 flash crash</span> U.S. stock market crash lasting 36 minutes in May 6, 2010

The May 6, 2010, flash crash, also known as the crash of 2:45 or simply the flash crash, was a United States trillion-dollar flash crash which started at 2:32 p.m. EDT and lasted for approximately 36 minutes.

In modern finance, a flash crash is a very rapid, deep, and volatile fall in security prices occurring within a very short time period followed by a quick recovery. Flash crashes are frequently blamed by media on trades executed by black-box trading, combined with high-frequency trading, whose speed and interconnectedness can result in the loss and recovery of billions of dollars in a matter of minutes and seconds, but in reality occur because almost all participants have pulled their liquidity and temporarily paused their trading in the face of a sudden increase in risk.

<span class="mw-page-title-main">Virtu Financial</span> Financial services company

Virtu Financial is an American company that provides financial services, trading products and market making services. Virtu provides product suite including offerings in execution, liquidity sourcing, analytics, broker-neutral, multi-dealer platforms in workflow technology and two-sided quotations and trades in equities, commodities, currencies, options, fixed income, and other securities on over 230 exchanges, markets, and dark pools. Virtu uses proprietary technology to trade large volumes of securities. The company went public on the Nasdaq in 2015.

<span class="mw-page-title-main">IEX</span> U.S.-based stock exchange

Investors Exchange (IEX) is a stock exchange in the United States. It was founded in 2012 in order to mitigate the effects of high-frequency trading. IEX was launched as a national securities exchange in September 2016. On October 24, 2017, it received regulatory approval from the U.S. Securities and Exchange Commission (SEC) to list companies. IEX listed its first public company, Interactive Brokers, on October 5, 2018. The exchange said that companies would be able to list for free for the first five years, before a flat annual rate of $50,000. On September 23, 2019, it announced it was leaving its listing business.

Bradley Toshio Katsuyama is a Canadian financial services executive. He is the CEO and co-founder of the IEX, the Investors Exchange. He left RBC in 2012 to co-found IEX under the premise that it would be a fairer stock trading venue than other exchanges.

In finance, quote stuffing refers to a form of market manipulation employed by high-frequency traders (HFT) that involves quickly entering and withdrawing a large number of orders in an attempt to flood the market. This can create confusion in the market and trading opportunities for high-speed algorithmic traders. The term is relatively new to the financial market lexicon and was coined by Nanex in studies on HFT behavior during the 2010 Flash Crash.

<span class="mw-page-title-main">Hudson River Trading</span> Quantitative trading firm based in NYC

Hudson River Trading is a quantitative trading firm headquartered in New York City and founded in 2002. In 2014, it accounted for about 5% of all trading in the United States. Hudson River Trading employs over 800 people in offices around the world, including New York, Chicago, Austin, Boulder, London, Singapore, Shanghai, Mumbai and Dublin. The firm focuses on research and development of automated trading algorithms using mathematical techniques, and trades on over 100 markets worldwide.

Spoofing is a disruptive algorithmic trading activity employed by traders to outpace other market participants and to manipulate markets. Spoofers feign interest in trading futures, stocks, and other products in financial markets creating an illusion of the demand and supply of the traded asset. In an order driven market, spoofers post a relatively large number of limit orders on one side of the limit order book to make other market participants believe that there is pressure to sell or to buy the asset.

<span class="mw-page-title-main">Jump Trading</span> American financial market trading firm

Jump Trading LLC is a proprietary trading firm with a focus on algorithmic and high-frequency trading strategies. The firm has over 700 employees in Chicago, New York, Austin, London, Tel Aviv, Singapore, Shanghai, Bristol, Gurgaon, Gandhinagar, Sydney, Amsterdam, Hong Kong, and Paris and is active in futures, options, cryptocurrency, and equities markets worldwide.

References

  1. 1 2 Aldridge, Irene (2013), High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems, 2nd edition, Wiley, ISBN   978-1-118-34350-0
  2. ↑ Lin, Tom C. W. "The New Financial Industry" (March 30, 2014). 65 Alabama Law Review 567 (2014); Temple University Legal Studies Research Paper No. 2014-11; SSRN   2417988.
  3. ↑
  4. ↑ Lemke and Lins, "Soft Dollars and Other Trading Activities," § 2:31 (Thomson West, 2016–2017 ed.).
  5. 1 2 3 "Regulatory Issues Raised by the Impact of Technological Changes on Market Integrity and Efficiency" (PDF), IOSCO Technical Committee, July 2011, retrieved 2011-07-12
  6. 1 2 3 Aldridge, Irene (July 8, 2010). "What is High Frequency Trading, After All?". Huffington Post . Retrieved August 15, 2010.
  7. ↑ "Advances in High Frequency Strategies", Complutense University Doctoral Thesis (published), December 2011, archived from the original on 2015-09-30, retrieved 2012-01-08
  8. ↑ "Stock Traders Find Speed Pays, in Milliseconds". The New York Times. 24 July 2009. Retrieved 27 June 2016.
  9. ↑ Aldridge, I., Krawciw, S., 2017. Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes. Hoboken: Wiley. ISBN   978-1-119-31896-5.
  10. 1 2 3 4 5 6 7 "Trade Worx / SEC letters" (PDF). April 21, 2010. Retrieved September 10, 2010.
  11. ↑ Aldridge, Irene (July 26, 2010). "How profitable is high frequency trading". Huffington Post.
  12. ↑ Easley, David; Marcos Lopez de Prado; Maureen O'Hara (October 2010), "The Microstructure of the 'Flash Crash': Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading", Journal of Portfolio Management , SSRN   1695041
  13. 1 2 3 4 Vuorenmaa, Tommi; Wang, Liang (October 2013), "An Agent-Based Model of the Flash Crash of May 6, 2010, with Policy Implications", VALO Research and University of Helsinki, SSRN   2336772
  14. 1 2 3 4 How to keep markets safe in the era of high-speed trading (PDF)
  15. 1 2 3 4 5 6 7 8 Lauricella, Tom (October 2, 2010). "How a Trading Algorithm Went Awry". The Wall Street Journal.
  16. ↑ Jones, Huw (July 7, 2011). "Ultra fast trading needs curbs -global regulators". Reuters . Retrieved July 12, 2011.
  17. ↑ Ross, Alice K; Will Fitzgibbon; Nick Mathiason (16 September 2012). "Britain opposes MEPs seeking ban on high-frequency trading. UK fighting efforts to curb high-risk, volatile system, with industry lobby dominating advice given to Treasury". The Guardian. Retrieved 2 January 2015.
  18. ↑ Amery, Paul (November 11, 2010). "Know Your Enemy". IndexUniverse.eu. Retrieved 26 March 2013.
  19. ↑ Petajisto, Antti (2011). "The index premium and its hidden cost for index funds" (PDF). Journal of Empirical Finance. 18 (2): 271–288. doi:10.1016/j.jempfin.2010.10.002 . Retrieved March 26, 2013.
  20. ↑ Rekenthaler, John (February–March 2011). "The Weighting Game, and Other Puzzles of Indexing" (PDF). Morningstar Advisor. pp. 52–56 [56]. Archived from the original (PDF) on July 29, 2013. Retrieved March 26, 2013.
  21. ↑ Aldridge, I., 2013. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems, 2nd edition. Hoboken: Wiley. ISBN   978-1-118-34350-0
  22. 1 2 "Patience and Finance" (PDF), Bank of England , Sep 2, 2010, retrieved Sep 10, 2010
  23. 1 2 Duhigg, Charles (July 23, 2009). "Stock Traders Find Speed Pays, in Milliseconds". New York Times . Retrieved Sep 10, 2010.
  24. ↑ AFP, Reuters (2013-09-02). "Italy first to slap tax on high speed stock trading". Deutsche Welle . Retrieved 2013-09-03.{{cite news}}: |author= has generic name (help)
  25. ↑ Stafford, Philip (2013-09-01). "Italy introduces tax on high-speed trade and equity derivatives" . The Financial Times . Retrieved 2013-09-03.
  26. 1 2 3 Rogow, Geoffrey, and Eric Ross Rise of the (Market) Machines, The Wall Street Journal, June 19, 2009
  27. ↑ "OlsenInvest – Scientific Investing" (PDF). Archived from the original (PDF) on 25 February 2012. Retrieved 27 June 2016.
  28. 1 2 Aite Group Survey[ dead link ]
  29. ↑ Hollis, James E. (Sep 2013). "HFT: Boon? Or Impending Disaster?". Cutter Associates . Retrieved June 29, 2015.
  30. 1 2 Grant, Justin (Sep 2, 2010). "High-frequency trading: Up against a bandsaw". Financial Times . Retrieved Sep 10, 2010.
  31. 1 2 3 Cookson, Clive (May 12, 2013). "Time is money when it comes to microwaves". Financial Times . Retrieved May 12, 2013.
  32. ↑ Polansek, Tom (23 August 2013). "CFTC finalizes plan to boost oversight of fast traders: official". Reuters. Retrieved 8 July 2014.
  33. 1 2 Aldridge, Irene (2009), High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems, Wiley, ISBN   978-0-470-56376-2
  34. ↑ "Fast Answers – Market Maker". U.S. Securities and Exchange Commission. Retrieved August 20, 2016.
  35. 1 2 3 4 5 6 Hendershott, Terrence; Jones, Charles M.; Menkveldf, Albert J. (February 2011). "Does Algorithmic Trading Improve Liquidity?" (PDF). Journal of Finance. LXVI (1): 1–33. CiteSeerX   10.1.1.105.7253 . doi:10.1111/j.1540-6261.2010.01624.x. S2CID   30441 . Retrieved January 30, 2015.
  36. ↑ "Citigroup to expand electronic trading capabilities by buying Automated Trading Desk", International Herald Tribune , The Associated Press, July 2, 2007, retrieved July 4, 2007
  37. ↑ Cartea, Á. and S. Jaimungal (2012) "Modeling Asset Prices for Algorithmic and High Frequency Trading". SSRN   1722202.
  38. ↑ Guilbaud, Fabien and Pham, Huyên, "Optimal High Frequency Trading with Limit and Market Orders" (2011). SSRN   1871969.
  39. ↑ Avellaneda, M.; Stoikov, S. (2008). "High frequency trading in a limit order book". Quantitative Finance. 8 (3): 217–224. doi:10.1080/14697680701381228. S2CID   6070889.
  40. ↑ Cartea, Á., S. Jaimungal and J. Ricci (2011) "Buy Low Sell High: A High Frequency Trading Perspective". SSRN   1964781.
  41. ↑ Cartea, Á. and S. Jaimungal (2012) "Risk Metrics and Fine Tuning of High Frequency Trading Strategies" SSRN   2010417.
  42. ↑ Guéant, O.; Lehalle, C.-A.; Fernandez-Tapia, J. (2013). "Dealing with the inventory risk: a solution to the market making problem". Mathematics and Financial Economics. 4 (7): 477–507. arXiv: 1105.3115 . doi:10.1007/s11579-012-0087-0. S2CID   154587956.
  43. ↑ The studies are available at: Jovanovic, Boyan and Albert J. Menkveld, "Middlemen in Limit Order Markets", SSRN   1624329, June 20, 2016; and Menkveld, Albert J. (2012). "High Frequency Trading and the New-Market Makers" (PDF). SSRN Electronic Journal. doi:10.2139/ssrn.1722924. S2CID   219393758.
  44. ↑ "Quote Stuffing Definition & Example". InvestingAnswers. Retrieved 2019-08-27. Quote stuffing occurs when traders place a lot of buy or sell orders on a security and then cancel them immediately afterward, thereby manipulating the market price of the security. Manipulating the price of shares in order to benefit from the distortions in price is illegal.
  45. ↑ "Quote Stuffing Definition". Investopedia. Retrieved 2014-08-22.
  46. ↑ "Quote Stuffing". nasdaq.com. NASDAQ. Retrieved 10 September 2014.
  47. ↑ "The World of High Frequency Trading: 6 Primary Strategies", www.T3Live.com, retrieved September 15, 2010
  48. ↑ Rob Iati, The Real Story of Trading Software Espionage Archived 2011-07-07 at the Wayback Machine , AdvancedTrading.com, July 10, 2009
  49. ↑ "Ultra-Low Latency OTN Technologies Boosting Brokerage Competitiveness". Lightwaveonline.com. 2022-09-28. Retrieved 2022-09-29.
  50. ↑ "Wall Street Buys NATO Towers in Trader Speed-of-Light Quest". Bloomberg.com. 2014-07-16. Retrieved 2022-06-05.
  51. ↑ MacKenzie, Donald A. (2021). Trading at the speed of light : how ultrafast algorithms are transforming financial markets. Princeton, New Jersey. ISBN   978-0-691-21779-6. OCLC   1221015294.{{cite book}}: CS1 maint: location missing publisher (link)
  52. 1 2 Osipovich, Alexander (1 April 2021). "High-Frequency Traders Eye Satellites for Ultimate Speed Boost". Wall Street Journal. Retrieved 7 July 2022.
  53. 1 2 "Companies Pitch Shortwave Radio to Shave Milliseconds Off Trades". Bloomberg.com. 17 June 2020. Retrieved 7 July 2022.
  54. ↑ Rogers, Kipp (20 January 2015). "Creating an HFT Strategy: Identifying Trader Type Pt. 2" . Retrieved 27 June 2016.
  55. ↑ Rogers, Kipp (9 February 2015). "Order Size in the HFT Era: Identifying Trader Type Pt. 3" . Retrieved 27 June 2016.
  56. ↑ Giovanni Cespa, Xavier Vives (February 2017). "High frequency trading and fragility" (PDF). Working Papers Series. European Central Bank (2020). This supports regulatory concerns about the potential drawbacks of automated trading due to operational and transmission risks and implies that fragility can arise in the absence of order flow toxicity.
  57. 1 2 Mehta, Nina (1 Oct 2010). "Automatic Futures Trade Drove May Stock Crash, Report Says". Bloomberg L.P.
  58. 1 2 3 4 Bowley, Graham (1 Oct 2010). "Lone $4.1 Billion Sale Led to 'Flash Crash' in May". The New York Times.
  59. 1 2 Spicer, Jonathan (1 Oct 2010). "Single U.S. trade helped spark May's flash crash". Reuters.
  60. 1 2 Goldfarb, Zachary (1 Oct 2010). "Report examines May's 'flash crash,' expresses concern over high-speed trading". Washington Post.
  61. 1 2 Spicer, Jonathan (15 Oct 2010). "Special report: Globally, the flash crash is no flash in the pan". Reuters.
  62. ↑ [15] [57] [58] [59] [60] [61]
  63. 1 2 3 "Commentary: How High Frequency Trading Benefits All Investors" . 17 March 2010. Retrieved 27 June 2016.
  64. 1 2 3 4 Lambert, Emily (20 January 2010). "High-Frequency Trading Good For Small Investors: CBOE – Forbes". Forbes . Retrieved 27 June 2016.
  65. ↑ [10] [26] [63] [64]
  66. ↑ "Nanex – Exhibit A" . Retrieved 27 June 2016.
  67. ↑ "Nanex's Hunsader Seeks To 'Save' Markets From High-Frequency Trading". Forbes. 6 February 2014. Retrieved 11 July 2014.
  68. ↑ "Business and finance". The Economist . Retrieved 27 June 2016.
  69. ↑ "InformationWeek Authors – InformationWeek". Archived from the original on 22 October 2007. Retrieved 27 June 2016.
  70. ↑ "London Stock Exchange Group to acquire MillenniumIT for US$30m (£18m)" (Press release). London Stock Exchange Group. 2009-09-16. Retrieved 2017-04-02.
  71. ↑ "Turquoise confirms it is the world's fastest trading platform" (PDF) (Press release). Turquoise. 2010-10-20. Archived from the original (PDF) on 2011-07-17.
  72. ↑ "Market Mechanics Timestamps".
  73. ↑ "Business and finance". The Economist . Archived from the original on 22 June 2008. Retrieved 27 June 2016.
  74. ↑ Braithwaite, Tom (2010-05-07). "Watchdogs under pressure on market swings". Financial Times . Retrieved 2010-05-08.
  75. ↑ Browning, E.S. (2007-10-15). "Exorcising Ghosts of Octobers Past". The Wall Street Journal. Dow Jones & Company. pp. C1–C2. Retrieved 2007-10-15.
  76. ↑ Lauricella, Tom, and McKay, Peter A. "Dow Takes a Harrowing 1,010.14-Point Trip," Online Wall Street Journal, May 7, 2010. Retrieved May 9, 2010
  77. ↑ Corkery, Michael, "High Frequency Traders Saved the Day", Wall Street Journal , September 13, 2010.
  78. 1 2 "What happened on May 6th?". CME Group. 2010-05-18.
  79. 1 2 "Findings Regarding the Market Events of May 6, 2010" (PDF). 2010-09-30.
  80. ↑ Scannell, Kara (2010-10-01). "Report: Algorithm Set Off 'Flash Crash' Amid Stressed Market". The Wall Street Journal . Retrieved 2010-10-01.
  81. ↑ Pritzke, Marc (2010-05-17). "Die Spur führt nach Kansas". Der Spiegel (in German). Retrieved 2010-10-01.
  82. ↑ Kirilenko, Andrei; Kyle, Albert S.; Samadi, Mehrdad; Tuzun, Tugkan (May 5, 2014), The Flash Crash: High Frequency Trading on an Electronic Market, doi:10.2139/ssrn.1686004, S2CID   169838937, SSRN   1686004
  83. ↑ Moshinsky, Ben (18 March 2015), Regulators Outpace Physicists in Race to Catch the 'Flash Boys', Bloomberg, retrieved 20 March 2015
  84. ↑ Mattli, Walter (2019). Darkness by Design: The Hidden Power in Global Capital Markets. Princeton University Press.
  85. ↑ Aldridge, I., Krawciw, S., 2017. Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes. Hoboken: Wiley. ISBN   1-119-31896-3
  86. ↑ Popper, Nathaniel (1 Oct 2010). "$4.1-billion trade set off Wall Street 'flash crash,' report finds". Los Angeles Times.
  87. ↑ Younglai, Rachelle (5 Oct 2010). "U.S. probes computer algorithms after "flash crash"". Reuters.
  88. 1 2 Tett, Gillian (Sep 9, 2010). "What can be done to slow high-frequency trading?". Financial Times . Retrieved Sep 10, 2010.
  89. ↑ [15] [57] [58] [59] [60] [61] [86] [87] [88]
  90. ↑ Philips, Matthew (28 March 2011). "Should High-Frequency Trading Be Banned? One Nobel Winner Thinks So" . Retrieved 27 June 2016.
  91. ↑ Cumming, Douglas; Zhan, Feng; Aitken, Michael (October 28, 2013), High-Frequency Trading and End-of-Day Price Dislocation, Social Science Research Network, SSRN   2145565
  92. ↑ Chilton, Bart (Sep 6, 2010). "Rein in the cyber cowboys". Financial Times . Retrieved Sep 10, 2010.
  93. ↑ Schapiro, Mary (September 22, 2010). "Remarks Before the Security Traders Association". U.S. Securities and Exchange Commission .
  94. ↑ Westbrook, Jesse (Oct 19, 2010). "NYSE's Niederauer Expects More Firms to Face Expanded Market-Maker Rules". Bloomberg.
  95. ↑ Bartash, Jeffry (April 29, 2014). "U.S. markets 'not rigged,' SEC boss says, White downplays 'flash boy' charges in new Michael Lewis book". MarketWatch. Dow Jones. Retrieved July 2, 2014.
  96. ↑ Murray, Timothy (April 16, 2014). "SEC's Berman: The Data Disputes HFT Narrative". WatersTechnology. waterstechnology.com. Retrieved July 2, 2014.
  97. ↑ High-Frequency Trading Investor Issues and Perspectives (PDF), CFA Institute, April 19, 2014
  98. ↑ Bunge, Jacob (February 25, 2011). "Direct Edge to Stop 'Flashing' Orders on Monday". The Wall Street Journal.
  99. ↑ Skjeltorp, Johannes A.; Sojli, Elvira; Tham, Wing Wah (February 1, 2016), "Flashes of trading intent at the NASDAQ", Journal of Financial and Quantitative Analysis, 51: 165–196, doi:10.1017/S0022109016000028, hdl: 1765/38217
  100. ↑ Mehta, Nina (March 22, 2012). "Getco Fined $450,000 for Failing to Supervise Equity Trading". Bloomberg.
  101. ↑ Grant, Justin (March 26, 2012). "Getco Slapped With $450k Fine For Weak HFT Oversight". Wall Street & Technology.
  102. ↑ Mamudi, Sam (October 16, 2013). "Knight Capital Agrees to $12 Million Settlement for 2012 Errors". Bloomberg.
  103. ↑ Patterson, Scott (September 17, 2014). "High-Frequency Trading Firm Latour to Pay $16 Million SEC Penalty". The Wall Street Journal.
  104. ↑ "Algorithmic Trading | FINRA.org". www.finra.org. Retrieved 2020-03-28.
  105. ↑ Bell, Holly (2015). "Beyond Regulation: A Cooperative Approach to High-Frequency Trading and Financial Market Monitoring" (PDF). Policy Analysis. Retrieved 3 November 2015.
  106. ↑ Shindler, Michael (29 October 2015). "High Frequency Trading Needs Information, Not Regulation". Economics21.org. Manhattan Institute. Retrieved 3 November 2015.
  107. ↑ October 8th; Comments, 2018|LSE alumni|0 (2018-10-08). "Is EU regulation of high frequency trading stringent enough?". LSE Business Review. Retrieved 2020-03-28.{{cite web}}: CS1 maint: numeric names: authors list (link)
  108. ↑ "MiFID II". www.esma.europa.eu. Retrieved 2020-03-28.
  109. 1 2 Levine, Matt (January 12, 2015). "'Hide Not Slide' Orders Were Slippery and Hidden". Bloomberg View.
  110. ↑ "SEC Charges Direct Edge Exchanges With Failing to Properly Describe Order Types". U.S. Securities and Exchange Commission. January 12, 2015.
  111. ↑ "In the Matter of UBS Securities LLC Respondent", sec.gov, January 15, 2015.
  112. ↑ "SEC Charges UBS Subsidiary With Disclosure Violations and Other Regulatory Failures in Operating Dark Pool". U.S. Securities and Exchange Commission. January 15, 2015.
  113. ↑ "Notice of acceptance ..." to Citadel Securities, NASDAQ Stock Market LLC, June 16, 2014.
  114. ↑ McCrank, John (5 August 2014). "Citadel fined $800,000 by U.S. regulators for trading violations". Reuters. Retrieved 22 April 2015.
  115. ↑ Fortado, Lindsay; Brush, Silla (July 22, 2013). "Panther, Coscia Fined Over High-Frequency Trading Algorithms". Bloomberg.
  116. ↑ "High-Frequency Trader Indicted for Manipulating Commodities Futures Markets in First Federal Prosecution for Spoofing". Federal Bureau of Investigation. October 2, 2014.
  117. ↑ "High-Frequency Trading Firm Pays $67.4 Million in Record Spoofing Penalty". www.bloomberg.com. 2019-11-08. Retrieved 2020-02-20.
  118. ↑ Geiger, Keri; Mamudi, Sam (October 16, 2014). "HFT Firm Fined $1 Million for Manipulating Nasdaq". Bloomberg.
  119. ↑ "S.Korea fines Citadel Securities for stock algorithm trading breaches".
  120. ↑ "US regulator fines Citadel Securities over trading breach".
  121. ↑ "Morgan Stanley's latest Goldman Sachs hire shows who's really in demand now". efinancialcareers.
  122. ↑ Addison, Andrew. "Low-Latency Trading in a Cloud Environment" (PDF). BJSS.
  123. ↑ "Credit Suisse launches ultra-low-latency DMA in Australia". The Trade. November 3, 2010.
  124. ↑ Lewis, Michael (31 March 2014). "The Wolf Hunters of Wall Street". New York Times.
  125. ↑ Michaels, Dave, "Nasdaq Tries to Appeal to Investors Lured by New Rival IEX" (possibly subscription-only), Wall Street Journal, August 14, 2016. Retrieved 2016-08-15.
  126. ↑ "Life in the slow lane | Algorithmic Trading Articles & Financial Insight". Automated Trader. Retrieved 2018-06-24.
  127. ↑ Zhou, Wanfeng. "Exclusive: EBS take new step to rein in high-frequency traders". U.S. Retrieved 2018-06-24.
  128. ↑ Melton, Hayden (2017-09-25). "Market mechanism refinement on a continuous limit order book venue: a case study". ACM SIGecom Exchanges. 16 (1): 72–77. doi:10.1145/3144722.3144729. S2CID   20655509.
  129. ↑ Harris, Larry (March 2013). "What to Do about High-Frequency Trading". Financial Analysts Journal. 69 (2): 6–9. doi:10.2469/faj.v69.n2.6. ISSN   0015-198X. S2CID   153935520.
  130. ↑ Budish, Eric; Cramton, Peter; Shim, John (2015-11-01). "The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response". The Quarterly Journal of Economics. 130 (4): 1547–1621. doi: 10.1093/qje/qjv027 . hdl: 1814/38326 . ISSN   0033-5533.
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