High-Frequency Trading in Stock Market

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 09 Issue: 10 | Oct 2022 www.irjet.net p-ISSN: 2395-0072

High-Frequency Trading in Stock Market

Mayank

Gupta1

1 Jaypee Institute of Information Technology, mayankgupta0519@gmail.com ***

Abstract - Since its innovation, high-frequency trading has portrayed a significant role in the development of different financial markets across the globe. Artificial intelligence has opened new roads for researchers to enhance the performance of sophisticated decision-making by computers. This paper examines the high-frequency trading working and widely used strategies, as the role of artificial intelligence in shaping the future of HFT by surveying 24 already existing research articles. It also focuses on the impact caused by HFT in different aspects and the regulations around it.

Key Words: High-Frequency Trading, Stock Market, Machine Learning, Neural Networks, Impact, and Regulation

1. INTRODUCTION

High-Frequency Trading (HFT), a type of algorithmic trading, is defined as a method in which a very large number of ordersaretransactedwithinafractionof asecond.Thisisdonethroughdifferentcomplexalgorithm-basedprograms.In general, algorithmic trading saw its rise when the minimum tick size was changed in the US market, electronic communication networks and electronic execution of orders were introduced, and when SEC passed the Regulation Alternative Trading Systems and Regulation National Market System. The emergence of artificial intelligence and improvement in the speed of processing data are the reasons for HFT's origin in the financial markets. Artificial intelligence made our computer programs capable of performing tasks that require human intelligence and faster processing speed reduced the time taken by machines to execute and provide results. Despite being a recent innovation, HFT has influenced the related markets in multiple aspects and therefore has been under scrutiny from multiple organizationsandgovernments.

2. DEFINITION

The main objective of this paper was to study the relationship between HFT and artificial intelligence. Initially, HFT and the corresponding different strategies are examined in terms of the factors it is based upon. Then, the role played by artificial intelligence is scrutinized, as to how it will shape HFT’s future. Lastly, the impact of HFT in different financial marketsisobservedandtheregulationsimplementedandproposedsofarforproperacceptanceofHFT.

3. DISCUSSION

Thediscussionisbifurcatedintothreesections.ThefactorsbehindtheHFTworkinganddifferentstrategiesarediscussed in1st section.2nd sectionpresentstheroleofartificialintelligenceinHFT.Lastly,inthe3rd section,HFTimpactsindifferent financial markets are observed and the policies and regulations put forward by different government bodies are also discussed.

3.1 HFT Working and Strategies

Asperthestudiesconductedin[2,5,6,9],itisestablishedthatspeedandlatencyintroducedwhiletransferringinformation andprocessingcommandsareusedtoagoodadvantageinsuccessfulHFT.Latencyisdefinedasthelagbetweenwhenan orderissenttoanexchangeandwhenitisprocessedandexecuted.Itisobservedthatithasdecreasedfrom0.6to0.00113 (sec2)withinthelasttwodecadesbecauseoftheexponentialincreaseinthecomputationpowerofcomputers.Forseveral years,manytradingfirmshavebeentryingtoachievethelowestpossiblelatencyontheirsystems,toreceiveandprocess informationasquicklyas,ifnotfasterthan,theircompetitors. Latencyarbitrageisknownasthetechniqueintradingwhere the orders are transacted utilizing the minor price differences caused by acquiring the subsequent data from exchanges slightlyaheadofotherinvestors.Togenerateprofitsfromlatencyarbitrage,theusermustgetthepricesslightlyaheadof othermarketparticipants.Therefore,manydifferenttechnologicalbreakthroughsandstrategiesareutilizedtoachievethe lowest latency. Fiber optics are utilized to reduce transmission time as well as to push large amounts of data across the network withhightransferspeeds.FastProcessingGateArrays(FPGA)areusedtoperformrepeatedandrapidfunctions from the implemented algorithms and multi-core processors are employed for faster data processing. Also, many firms achievelowlatencybylocatingtheirserversasneartotheexchangesaspossibletogetraw,unfilteredfeed.Thereduced

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Volume: 09 Issue: 10 | Oct 2022 www.irjet.net p-ISSN: 2395-0072

physical distance between the trading server and exchanges guarantees low latency. In contrast, most other market participantsutilize onesingle channel encapsulatingthebest prices andothercorresponding data fromall the exchanges, ECNs,andAlternativeTradingSystems(ATS).Theprocessofcombiningintoonechannelintroducessomelatency,andthe dataisalreadyslightlyoldbythetimeitreachesthem.

AsHFTisatypeofalgorithmictrading, thereforeallthetradingdecisionsaretakenbytheimplementedalgorithmsinthe computer program. In [9], the author implemented a genetic programming and simulation-based computation model to address the trade-off between the speed and sophistication of algorithms. The algorithms are designed to implement the mentioneddifferentstrategiestobookprofits.

1) Market Making: Inthistechnique,theprofitisbookedonthebid-askspreads.Thealgorithmbetforbothsidesof the trade by putting a limit order to sell slightly above or purchase slightly below the current market price, and thereforebenefitingfromthedifferencebetweenthetwo.Thesystemcanentertradesautomaticallywhencertain conditionsaremetasperthealgorithm.

2) Liquidity Rebate: Multiple exchanges and ECNs employ the ‘maker-taker model’ through which they provide a rebate for providing liquidity in the market. The HFT programs look for large orders and provide a share of liquidityforthem.Thenthesesharesareofferedback tothe market byplacinga limitorder.Throughthis,some profitisgeneratedthroughaccumulatingrebatefeesforprovidingliquidity.

3) Statistical Arbitrage: Profits are reaped from market arbitrage by taking advantage of the variability in characteristicssuchasrates,prices,andotherconditionsthatexistbetweendifferentexchangesorassetclasses. Traders also take advantage of the difference between co-related securities like derivatives, Exchange Traded Funds(ETFs),andotherfundamentalsecuritiesandequities.

4) Momentum Ignition: This strategy comprises starting and canceling a series of transactions and orders for a particularassetina specificdirection,intendingto ignitea quick market pricemovement. Therapidsubmission and cancellation of these orders may be recognized by other traders' algorithms, resulting in them buying or sellingtheasset.Becausethefirmsubmittingtheseordersandtradeshasalreadybuiltaposition,itcanprofitby leveraging the ensuing price movement. As it raises multiple questions on the ethical ground, it has been identifiedasaprohibitedmarketmanipulationfromESMA,ICECanadaaswellasfromCMEGroup.

5) Employing Co-location Services: Tohaveanupperhandoverothermarketparticipants,multiplefirms placetheir servers as near to the exchange as possible to reduce the distance for transmission of the data feeds from the exchanges.Thisisachievedbyutilizingco-locationservicesandsometimesexploitingtherelationshipswithsome exchanges.

6) Quote-stuffing: In this strategy, an unusually large number of orders are submitted, which are then immediately canceled.Asaresult,itcausesordercongestion.Itsgoalistoslowdowntheexchange'soperationsandlowerthe entry barrier for other traders. A high volume of order submissions may also cause the exchange receiving the quotationstolagbehindotherexchanges,providingarbitrageopportunitiesforthemanipulator.Otherinvestors may be misled about which exchange has the most liquidity or the best pricing. Canceling orders in large quantitiesina short timemonopolizesresources. Other non-HF investorsareslowedby the trading profit made throughsuchmanipulation.

3.2 Role of Artificial Intelligence

Recentlyinthepastfewyears,itcanbeobservedartificialintelligencehasincreasedprofitsbydesigningbetteralgorithms andframeworksforimproveddecision-makingbyHFTprograms.Infinancialmarkets,anincreasingnumberofacademics useAItechniquessuchasmachinelearningandneuralnetworks.Someofthestudiesdonetopresenttheframeworksare analyzedbriefly.

In [20], an analytical framework for HFT based on order book information was proposed. In the designed algorithm, an optimizer based on Volume-Synchronised Probability of Informed Trading (VPIN) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) was used throughout the activation Support Vector Machine (SVM) layer. The effectiveness of the methodology was back-tested with observations of the CSI 300 futures which result in better results andreturnsascomparedtootheronlyGARCHandVPIN-basedmodels.

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In [15], to maximize the return on investment by forecasting the medium-short-term trend in FOREX an HFT algorithm was presented. The algorithm was derived from supervised deep learning and reinforcement learning algorithms. The framework consisted of an LSTM-based deep learning model, followed by an RL Correction block. A grid trading system was also implemented in the proposed strategy for HFT. The proposed framework reported an ROI of 98.23% and a marketdrawdownof15.97%.

Inanotherstudy[11],adeepreinforcementlearning-basedframeworkwasintroducedforactiveHFTwhichistrainedon limited orderbook data for theNASDAQ platform.The model istrained with the Proximal PolicyOptimisation algorithm andtuningofhyperparametersisdonebytheSequentialModel-BasedOptimisation(SMBO)method.

A very similar study [13], proposed the use of Very-Long Short-Term Memory Networks (VLSTMs) to handle the long sequences in the data points in HFT. The proposed model was tested by predicting the mid-price movement from limit orderbookdatafor10dayswithalmost400,000tickswith144featuresrepresentingeachtick. TheVLSTM-basedmodel reportedthebestF1scoreascomparedtootherLSTMmodels.

A novel architecture based upon Spiking Neural Networks and unsupervised learning to predict the spikes in price time seriesinsteadofthemovementdirectionswaspresentedin[16].Thepresentedarchitecturewasbacktestedonadataset of the crude oil and gold futures for 23 days of May 2017 from Chicago Mercantile Exchange. Spike accuracy and MomentumspikepercentagewereusedastheperformancemetricSpikebasednetworksreportedthebest-accumulated returnascomparedtoothermovementandLSTM-basedmodels.

Theauthorsin[22],aimedtodemonstrateavolatility-basedstudyinHFTbyusingbigdataanalytics.Theproposedmodel was intended to estimate volatility in HFT by exploiting the seasoned volatility on a dataset from NYSE with the help of Sparktocarryoutbigdataanalytics.

The work presented in [18], contributed an ensemble forecasting model of ARIMA-GARH-NN with LSTM for HFT and comparedthetechnicalindicators’impactontheforecastingvaluesastheevaluationmethodfortheproposedmodel.The model was trained on S&P 500 intraday dataset. The main concept was to test how near the model was to reality to determinetheamountofriskthattheuserwouldtakewhileguessingthevaluesxminutesahead.Asaresult,varioustrials were conducted to forecast 1-minute, 5-minute, and 10-minute ahead prices. Multiple technical indicator values are includedasfeaturesforadditionalstudy,andRMSEisemployedastheperformancemetric.

In [21], the authors presented architecture to examine the impact of AI learning and the trading speed of HFT in limitorder markets. They employed a genetic algorithm with a system classifier as their AI learning tool on the market conditionstolearnfromhistoricalprices,quotes,fundamentalvalue,bid-askspread,andothermarketinformation.They also observed both High and Low-frequency traders to examine their interplay and impact. The findings showed that trading speed has an inverse U-shaped non-linear influence on order submission, profit, and information efficiency because of the trade-off between the information advantage effect and competition effect for HFT. This provides an incentive for HF traders to avoid trading too quickly, which may help to increase pricing efficiency while also giving meaningfulknowledgeforinvestorsandregulatorstosuperviseAItrading.

To improve the overall operational performance in HFT, some new CNN-based architecture applied to order-based features was proposed to predict the direction of mid-price trends [12]. They introduced an order-encoding method to convert the order book information into appropriate input variables for the neural network. An average convolutional neuralnetwork(A-CNN),aconvolutionalneuralnetwork(CNN)extension,wasusedtoextractcharacteristicstoforecast pricesfromthegivenorderbookinformation.Inaddition,anupgradedversionofA-CNN(A-CNN+)wasdemonstratedto solvetheissuesassociatedwithA-CNN.Experimentsemployinglarge-scalehigh-frequencytradingdatarevealedthatthe proposed method outperforms other benchmark methods and that the learned model responds significantly to the ordering process in all tasks. Investment simulations were carried out to illustrate a practical application to buying and sellingbased onmodel predictionsbythesuggestedmethod,andpositiveresultswereobtained.Asa result,themodel's applicabilitywassuggested.

ToprovidetoolstoanalyzethesecurityandreliabilityofthewidelyusedartificialintelligencemodelsinHFT,astudywas done to analyze the robustness of the models from the perspective of adversarial machine learning [17]. To do so, they implementedalinearclassifier,amulti-layerperceptron(MLP),andanLSTManddevelopedabasicadversarialattackon these models. It was discovered that, while neural network models outperform classic linear valuation models in this situation, they are less robust. It was also discovered that the same adversarial patterns that deceived one model also deceived others and that these patterns are highly interpretable by humans. The portability of these attacks, combined

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with their potential to be effective with a low attack budget, suggests that they may be used by a malevolent actor with minimumknowledge.

3.3 HFT Impact and Regulations

High-FrequencyTradinghasindubitablychangedthecurrentfinancialsettingbyintroducinganon-conventionalmethod of trading. According to the most recent reports (2020), HFT contributes 50% of the trade volume in the US equities markets and around 24% to 43% in the European equity markets. Countries in the Asia-Pacific area, such as Japan and Singapore, have also welcomed HFT by enacting HFT-compliant regulations. Citadel LLC, Virtu Financial, IMC Financial Marketplaces, and Tower Research Capital are among the leading organizations involved in HFT across a wide range of financialassetclassesandmarkets.

Inaparticularstudy[14],thepresentscenarioandthescopeoffuturedevelopmentsinHFTwerediscussedfortheAsiaPacificregion.TheyinitiallyidentifiedthedifferentmarketregulatoryfactorsforHFTandgatheredinformationaboutthe financialmarketsofAustralia,China,HongKong,India,andJapan.ThentheycomparedtheempiricalfindingsfortheAsiaPacific region with other regions in terms of liquidity, information and price discovery, volatility and system risk, quote flickering, profitability, and the effects of anticipatory HFT. They reported that it is necessary to determine the optimal marketdesignsothatitcanfocusonmoremicrostructureeffects.Thelimitationsfacedwereintheformofvaguenessin thesourceofcomputerizedtraders’privatedataandthereforefocusontheimplicationsofHFT.

HFT has impacted the current financial setting in many positive aspects. According to reports, the entrance of HFT has resultedinariseinmarketliquidityduetoanincreaseinthenumberoftradesandthusportraysaveryprominentrolein supplying order flow and increasing liquidity level. Following the establishment of HFT, the bid-ask spreads shrank, resultinginmoreregularandaccuratepriceupdates.AccordingtoanNYSEEuronextanalysis,quotedspreadsfrom2007 to 2009 were lower than those from 2002 to 2006, when HFT was less widespread, suggesting that HFT led traders to offer the most competitive bid-ask pricing. Because HFT ensures accurate pricing at shorter time intervals and reduced trading costs, market efficiency has grown because prices are now more precisely and swiftly displaying market information. It has also enabled narrower spreads and cheaper trading costs, for individual investors who invest in the marketsindirectlythrough mutual andpension funds. AsHFThasraised tradingvolumeson exchangesand ECNs,many exchangesandECNshavereportedanincreaseinrevenueandtransactionfees.

While HFT is being widely accepted and appreciated for its positive impact across the globe, it has been under scrutiny from multiple governments, and organizations for the risks it brings with itself. Some HFT tactics are speculated as they look for recurring patterns in trading and keep the institution ahead by detecting an incoming order flow, after which it buys the security and then sells it to the institution at a slightly higher price. As a result, institutional investors' strategy andmarketimpactexpensesmaysuffer. Suddenpricechangesandshort-termvolatilityareoftencausedasHFTincludes rapid intraday trading with positions often held for minutes or even seconds. As HFT volumes are typically a rather substantialproportionoftotaltrading,priceswingsgeneratedbythisapproachcancontributetooverallmarketvolatility. Furthermore, the practice of initiating deals and immediately canceling them to trigger automated buying from other corporations is an ethical issue that many experts have questioned. Usage of co-location facilities and raw data feeds, which are often unavailable to smaller firms and retail investors, put them in a disadvantageous position. Furthermore, certainHFTbusinessesfrequentlyentertradesjustfortheliquidityrebate,whichprovidesnovaluetotheretail orlongterminvestor.

The negativeimpactof HFT cameunder thelimelight during the“FlashCrash” on May6,2010. During these 36minutes crashes, multiple US stock indices collapsed by a very significant value and rebounded rapidly. Over $1 trillion in equity wasreportedasalossbecauseofthisflashcrash.In[8],itismentionedthatmanyE-minifuturescontractsofatotalworth of $200 million were entered only to cancel later. These orders were replaced or modified for about 19,000 before cancellation and represented about 20% of the total orders. These orders were able to influence numerous genuine investors instantly. The DOW reported a drop of about 9% in the value within minutes and also recorded the largest intraday point drop. Surprisingly, the market recovered about 90% of the loss within 30 minutes. In another incident, Knight Capital Group was on the verge of the end when their trading software bought about 150 different stocks with a totalworthof$7billionwithoutanyfinancialsupport,onNYSEwithinthe1sthouroftradingonAugust1,2012.

To avoid such negative impacts of HFT and to regulate them, multiple frameworks and policies have been introduced. Mifid2andMARarethetwodifferentregulationsimplementedbytheEUforEuropeanmarketstoadaptandregulatethe obstructivesideofHFTandthefirmsemployingit[10].Someofthepoliciesincludedintheseregulationsarementioned below:

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Beforeaccessingmarkets,allHFTfirmsmustbeauthorizedbytheregulatorsaccordingtoMifid2,Article2. 

To monitor the algorithms, compliance is imposed by Article 17 (investment firms) and Article 48 (trading venues). Responsibility for disruptive occurrences can now be assigned to all relevant people at all levels of the organization, fromthedevelopertothetrader,thetradingsupervisor,compliance,andthefirm'smanagement.Tradingvenueswill be required to give historical data to testing platforms to ensure the resistance of trading and order-matching algorithmsunderstrainedmarketsituations. 

Article 12(2)(c) of the MAR prohibits sending orders that would damage the functioning of trading venues' systems andproduceorlikelycreatemisleadingsignalsaboutthesupplyanddemandforafinancialproduct. 

When investment firms or trading venues notice suspicious trading behavior, they must immediately notify their regulatorbysubmittingaSuspiciousOrderandTransactionReport.

3. CONCLUSIONS

Toconclude,HFThasdrasticallychangedthepresentfinancialmarketsituationbyintegratingartificialintelligence-based models. The firms deploying HFT, are always competing among themselves and other participants to book better and faster returns. Regardless of the benefits, HFT is advertised negatively among the public and individual investors due to therisk itcarries.Asitisarecentinnovation,thereare veryfewregulationsandpoliciesacross theglobeover HFT.The existing policies are often criticized for not being stringent enough. Therefore, further research should focus on introducing different Artificial intelligence-based frameworks for trading exchanges to monitor HFT-based activities and tokeeptheinterestsofindividualinvestorssafe.

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