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Garoosi, Shahab. "Trading algorithms for high-frequency currency trading." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-146315.
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This thesis uses modern portfolio theory together with machine learning techniques to generate stable portfolio returns over eleven currency pairs with spreads included. The backtests show that support vector machine predicted future returns better than neural network and linear regression. Principal component analysis and data smoothing combined with the local outlier factor further improved the performance of the trading algorithm. However, the ensemble of the top performed predictor performed below the individual predictors. Also, the use of different error estimates showed the criticality of mean arctangent absolute percentage error over mean absolute error and over mean squared error for profitability. For obtaining sensible results in a transaction costless setting, adopting risk adjusted leverage proved necessary. Otherwise, the profit-maximizing leverage surpassed the risk adjusted in a spread setting.
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Henrikson, Fredrik. "Characteristics of high-frequency trading." Thesis, KTH, Matematik (Inst.), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-35523.
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Infantino, Leandro Rafael, and Savion Itzhaki. "Developing high-frequency equities trading models." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59122.
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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 59).
The purpose of this paper is to show evidence that there are opportunities to generate alpha in the high frequency environment of the US equity market, using Principal Component Analysis (PCA hereafter) as a basis for short term valuation and market movements prediction. The time frame of trades and holding periods we are analyzing oscillate between one second to as high as 5 minutes approximately. We particularly believe that this time space offers opportunities to generate alpha, given that most of the known quantitative trading strategies are implemented in two different types of time frames: either on the statistical arbitrage typical type of time frames (with valuation horizons and trading periods in the order of days or weeks to maybe even months), or in the purely high frequency environment (with time frames on the order of the milliseconds). On the latter strategies, there is really not much intention to realize equity valuations, but rather to benefit from high frequency market making, which involves not only seeking to earn profit from receiving the bid/ask spread, but also from the transaction rebates offered by the numerous exchanges to those who provide liquidity. We believe that there are more opportunities to capture existing inefficiencies in this arena, and we show how with very simple mathematical and predictive tools, those inefficiencies can be identified and potentially exploited to generate excess returns. The paper describes our underlying intuition about the model we use, which is based on the results of short term PCA's on equity returns, and shows how these results can predict short term future cumulative returns. We randomly selected 50 of the most liquid equities in the S&P 500 index to test our results.
by Leandro Rafael Infantino [and] Savion Itzhaki.
M.B.A.
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Hanson, Thomas Alan. "Real Effects of High Frequency Trading." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1405290552.
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Mihailovs, Timurs. "Automated high-frequency foreign exchange trading." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/11488.
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Suvorin, Vadim, and Dmytro Sheludchenko. "Optimization importance in high-frequency algorithmic trading." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-14645.
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The thesis offers a framework for trading algorithm optimization and tests statistical and economical significance of its performance on American, Swedish and Russian futures markets. The results provide strong support for proposed method, as using the presented ideas one can build an intraday trading algorithm that outperforms the market in long term.
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Adamu, Adamu. "Evolutionary computation for high frequency trading systems." Thesis, University of Essex, 2011. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.537917.
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Sagade, Satchit. "Algorithmic and high-frequency trading in UK equities." Thesis, University of Reading, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590124.
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This thesis investigates the impact of technological and regulatory changes on UK equity market microstructure, and the implications of these changes for policy makers, regulators and market participants. In the first analysis, we model the execution performance of two popular volume participation algorithms. We compare the in-sample fit and out-of-sample predictive ability of two alternative models of execution costs, and find that the non-linear model provides a better fit than the linear model. We also examine the relative importance of different order-specific, stock-specific and market-specific variables in explaining the execution performance of these algorithms. We show that execution risk for volume participation algorithms comprises not just price risk, but also risk due to uncertain trading volumes. The growth in high·frequency trading has been one of the most significant developments in the equity trading landscape, and following a number of market mishaps; has also caught the attention of regulators. In the second analysis, we examine the intraday behavior of high-frequency traders and their impact on market quality. We first observe that high-frequency trading strategies differ significantly from each other in terms of the level of liquidity provision. We next explore the impact of different high-frequency trading strategies on price discovery and temporary- deviations from equilibrium values (noise). We find that all high-frequency traders have a larger contribution towards price discovery m iv ABSTRACT and noise than other traders in the market, thereby amplifying both the beneficial and detrimental components of price volatility. Finally, in the last analysis, we revisit issues related to the liquidity characteristics of limit order markets after Market in Financial Instruments Directive was operationalised in the European Union. We find that the top of the London Stock Exchange's limit order book is extremely thin, and the slope of the limit order book is steep near the top. We further observe that the limit order book contains significant information about future short-term price changes, especially for the less liquid stocks, and this information has economic value in an algorithmic trading environment.
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Xiao, Xiangguang. "High frequency trading system design and process management." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55249.
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Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2009.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 78-79).
Trading firms nowadays are highly reliant on data mining, computer modeling and software development. Financial analysts perform many similar tasks to those in software and manufacturing industries. However, the finance industry has not yet fully adopted high-standard systems engineering frameworks and process management approaches that have been successful in the software and manufacturing industries. Many of the traditional methodologies for product design, quality control, systematic innovation, and continuous improvement found in engineering disciplines can be applied to the finance field. This thesis shows how the knowledge acquired from engineering disciplines can improve the design and processes management of high frequency trading systems. High frequency trading systems are computation-based. These systems are automatic or semi-automatic software systems that are inherently complex and require a high degree of design precision. The design of a high frequency trading system links multiple fields, including quantitative finance, system design and software engineering. In the finance industry, where mathematical theories and trading models are relatively well researched, the ability to implement these designs in real trading practices is one of the key elements of an investment firm's competitiveness. The capability of converting investment ideas into high performance trading systems effectively and efficiently can give an investment firm a huge competitive advantage.
(cont.) This thesis provides a detailed study composed of high frequency trading system design, system modeling and principles, and processes management for system development. Particular emphasis is given to backtesting and optimization, which are considered the most important parts in building a trading system. This research builds system engineering models that guide the development process. It also uses experimental trading systems to verify and validate principles addressed in this thesis. Finally, this thesis concludes that systems engineering principles and frameworks can be the key to success for implementing high frequency trading or quantitative investment systems.
by Xiangguang Xiao.
S.M.
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Saliba, Pamela. "High-frequency trading : statistical analysis, modelling and regulation." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX044.
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Cette thèse est constituée de deux parties liées l’une à l’autre. Dans la première, nous étudions empiriquement le comportement des traders haute fréquence sur les marchés financiers européens. Nous utilisons les résultats obtenus afin de construire dans la seconde partie de nouveaux modèles multi-agents. L’objectif principal de ces modèles est de fournir aux régulateurs et plateformes de négociation des outils innovants leur permettant de mettre en place des règles pertinentes pour la microstructure et de quantifier l’impact des divers participants sur la qualité du marché.Dans la première partie, nous effectuons deux études empiriques sur des données uniques fournies par le régulateur français. Nous avons accès à l’ensemble des ordres et transactions des actifs du CAC 40, à l’échelle de la microseconde, avec par ailleurs les identités des acteurs impliqués. Nous commençons par comparer le comportement des traders haute fréquence à celui des autres intervenants, notamment pendant les périodes de stress, en termes de provision de liquidité et d’activité de négociation. Nous approfondissons ensuite notre analyse en nous focalisant sur les ordres consommant la liquidité. Nous étudions leur impact sur le processus de formation des prix et leur contenu informationnel selon les différentes catégories de flux : traders haute fréquence, participants agissant pour compte client et participants agissant pour compte propre.Dans la seconde partie, nous proposons trois modèles multi-agents. À l’aide d’une approche à la Glosten-Milgrom, nous parvenons avec notre premier modèle à construire l’ensemble du carnet d’ordres (spread et volume disponible à chaque prix) à partir des interactions entre trois types d’agents : un agent informé, un agent non informé et des teneurs de marché. Ce modèle nous permet par ailleurs de développer une méthodologie de prédiction du spread en cas de modification du pas de cotation et de quantifier la valeur de la priorité dans la file d’attente. Afin de se concentrer sur une échelle individuelle, nous proposons une deuxième approche où les dynamiques spécifiques des agents sont modélisées par des processus de type Hawkes non linéaires et dépendants de l’état du carnet d’ordres. Dans ce cadre, nous sommes en mesure de calculer en fonction des flux individuels plusieurs indicateurs pertinents relatifs à la microstructure. Il est notamment possible de classer les teneurs de marché selon leur contribution propre à la volatilité. Enfin, nous introduisons un modèle où les fournisseurs de liquidité optimisent leurs meilleurs prix à l’achat et à la vente en fonction du profit qu’ils peuvent générer et du risque d’inventaire auquel ils sont confrontés. Nous mettons alors en évidence théoriquement et empiriquement une nouvelle relation importante entre inventaire et volatilitéThis thesis is made of two related parts. In the first one, we study the empirical behaviour of high-frequency traders on European financial markets. We use the obtained results to build in the second part new agent-based models for market dynamics. The main purpose of these models is to provide innovative tools for regulators and exchanges allowing them to design suitable rules at the microstructure level and to assess the impact of the various participants on market quality.In the first part, we conduct two empirical studies on unique data sets provided by the French regulator. It covers the trades and orders of the CAC 40 securities, with microseconds accuracy and labelled by the market participants identities. We begin by investigating the behaviour of high-frequency traders compared to the rest of the market, notably during periods of stress, in terms of liquidity provision and trading activity. We work both at the day-to-day scale and at the intra-day level. We then deepen our analysis by focusing on liquidity consuming orders. We give some evidence concerning their impact on the price formation process and their information content according to the different order flow categories: high-frequency traders, agency participants and proprietary participants.In the second part, we propose three different agent-based models. Using a Glosten-Milgrom type approach, the first model enables us to deduce the whole limit order book (bid-ask spread and volume available at each price) from the interactions between three kinds of agents: an informed trader, a noise trader and several market makers. It also allows us to build a spread forecasting methodology in case of a tick size change and to quantify the queue priority value. To work at the individual agent level, we propose a second approach where market participants specific dynamics are modelled by non-linear and state dependent Hawkes type processes. In this setting, we are able to compute several relevant microstructural indicators in terms of the individual flows. It is notably possible to rank market makers according to their own contribution to volatility. Finally, we introduce a model where market makers optimise their best bid and ask according to the profit they can generate from them and the inventory risk they face. We then establish theoretically and empirically a new important relationship between inventory and volatility
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Hamza, Haval Rawf. "The impacts of high-frequency trading on the financial markets’ stability." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1428416050.
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Lin, Shinn-Juh. "Modelling high frequency financial time series with trading information." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ31160.pdf.
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Jones, C. M. "Automated technical foreign exchange trading with high frequency data." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343139.
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Hallstroem, Jonas. "Regulations and unhealthy market strategies in High Frequency Trading." Thesis, KTH, Fastigheter och byggande, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-152598.
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High frequency trading, a version of algorithmic trading, has in recent years increased, and now represents a significant part of the trading. It has occasionally upset the public, due to its connections to events that brought negative publicity in media. Because of the complicated structure that high frequency trading have, it makes it difficult for the common man to fully understand its nature. My purpose of this work is to give a clearer picture of the various less desirable market strategies that is currently being used, and what the situation is regarding regulations. The focus is thus not on high frequency trading as a whole, nor, consequently, on the potential benefits it can bring. The approach has been to use qualitative information in the form of interviews combined with literature studies to generate a clearer picture of the current situation. The conclusion is that the development of technology has led to a situation which is not optimal regarding high frequency trading. Market confidence has been affected and its competitive neutrality has been questioned. Regulations are unfortunately lagging, relative to the market situation. Despite this, my assessment, drawn from the interviews, is that it will get better.Högfrekvenshandel, som är en variant av algoritmhandel, har under de senaste åren ökat, och står numera för en betydande del av dagens handel. Med tiden har högfrekvenshandeln stundtals upprört allmänheten, i samband med händelser som givit negativt publicitet i media. På grund av högfrekvenshandelns komplicerade uppbyggnad har det dock varit svårt för mindre insatta att utläsa dess egentliga egenskaper. Mitt syfte med arbetet är att förmedla en tydligare bild av de olika mindre önskvärda marknadsstrategier som idag används, och hur situationen ser ut med regleringar. Fokus läggs således ej på högfrekvenshandel i sin helhet, det vill säga inte de potentiella fördelar den kan medföra. Tillvägagångssättet har varit att med kvalitativ information i form av intervjuer tillsammans med litteraturstudier skapa en tydligare bild av nuvarande situation. Slutsatsen som dras är att den tekniska utvecklingen som lett fram till dagens situation inte är optimal avseende högfrekvenshandeln. Marknadens förtroende har påverkats och dess konkurrensneutralitet ifrågasatts. Regleringarna är i förhållande till marknadssituation dessvärre eftersläpande. Trots detta är min bedömning utifrån gjorda intervjuer att det kommer att bli bättre.
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Romahi, Yazan Seif. "Computational learning techniques in high frequency foreign exchange trading." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615770.
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Frazier, Rosalie. "Market Sensitivity of a High Frequency Trading Firm Stock." Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/scripps_theses/775.
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The major purpose of this study is to explore the stock movements of a publicly traded high-frequency trading firm, Virtu Financial. Virtu Financial, as of November 2015, is the only publicly traded high frequency trading firm, offering a opportunity to study the market behavior of a new kind of stock. Since Virtu serves as a unique financial intermediary, my hypothesis is that Virtu should be a market-neutral company since it is able to profit equally in economic upswings and downturns. This study uses a regression based on the Fama and French three factor model, focusing on the influence of the market risk premium, small sized company vs. medium sized company returns, and growth stock vs. value stock returns in changes in inter-daily Virtu Financial returns, These results are then compared to the returns of Virtu’s brokerage competitors, as deemed so by analysts, and CBOE Holding, a company with . The results suggest that Virtu Financial has a market neutral stock, consistent with its means of generating revenue, while its traditional brokerage competitors do not. On the basis of this research, it is concluded that HFT brokerages may present an opportunity to invest in a non-cylcical segment of the finance industry.
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Virgilio, Gianluca. "Is high-frequency trading a threat to financial stability?" Thesis, University of Hertfordshire, 2017. http://hdl.handle.net/2299/18841.
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The purpose of this thesis is: (i) to produce an in-depth data analysis and computer-based simulations of the market environment to investigate whether financial stability is affected by the presence of High-Frequency investors; (ii) to verify how High-Frequency Trading and financial stability interact with each other under non-linear conditions; (iii) whether non-illicit behaviours can still lead to potentially destabilising effects; (iv) to provide quantitative support to the theses, either from the audit trail data or resulting from simulations. Simulations are provided to test whether High-Frequency Trading: (a) has an impact on market volatility, (b) leads to market splitting into two tiers; (c) takes the lion's share of arbitrage opportunities. Audit trail data is analysed to verify some hypotheses on the dynamics of the Flash Crash. The simulation on the impact of High-Frequency Trading on market volatility confirms that when markets are under stress, High-Frequency Trading may cause volatility to significantly increase. However, as the number of ultra-fast participants increases, this phenomenon tends to disappear and volatility realigns to its standard values. The market tiering simulation suggests that High-Frequency traders have some tendency to deal with each other, and that causes Low-Frequency traders also to deal with other slow traders, albeit at a lesser extent. This is also a kind of market instability. High-Frequency Trading potentially allows a few fast traders to grab all the arbitrage-led profits, so falsifying the Efficient Market Hypothesis. This phenomenon may disappear as more High-Frequency traders enter the competition, leading to declining profits. Yet, the whole matter seems a dispute for abnormal gains only between few sub-second traders. All simulations have been carefully designed to provide robust results: the behaviours simulated have been drawn from existing literature and the simplifying assumptions have been kept to a minimum. This maximises the reliability of the results and minimizes the potential of bias. Finally, from the data analysis, the impact of High-Frequency Trading on the Flash Crash seems significant; other sudden crashes occurred since, and more can be expected over the next future. Overall, it can be concluded that High-Frequency Trading shows some controversial aspects impacting on financial stability. The results are at a certain extent confirmed by the audit trail data analysis, although only indirectly, since the details allowing the match between High-Frequency traders and their behaviour are confidential and not publicly available Nevertheless, the findings about HFT-induced volatility, market segmentation and sub-optimal market efficiency, albeit not definitive, suggest that careful monitoring by regulators and policy-makers might be required.
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Kiselev, Ilya. "Can algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Economics, Finance and Statistics, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19495.
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The research at hand aims to define effectiveness of algorithmic trading, comparing with different benchmarks represented by several types of indexes. How big returns can be gotten by algorithmic trading, taking into account the costs of informational and trading infrastructure needed for robot trading implementation? To get the result, it’s necessary to compare two opposite trading strategies: 1) Algorithmic trading (implemented by high-frequency trading robot (based on statistic arbitrage strategy) and trend-following trading robot (based on the indicator Exponential Moving Average with the Variable Factor of Smoothing)) 2) Index investing strategy (classical index strategies “buy and hold”, implemented by four different types of indexes: Capitalization weight index, Fundamental indexing, Equal-weighted indexing, Risk-based indexation/minimal variance). According to the results, it was found that at the current phase of markets’ development, it is theoretically possible for algorithmic trading (and especially high-frequency strategies) to exceed the returns of index strategy, but we should note two important factors: 1) Taking into account all of the costs of organization of high-frequency trading (brokerage and stock exchanges commissions, trade-related infrastructure maintenance, etc.), the difference in returns (with superiority of high-frequency strategy) will be much less . 2) Given the fact that “markets’ efficiency” is growing every year (see more about it further in thesis), and the returns of high-frequency strategies tends to decrease with time (see more about it further in thesis), it is quite logical to assume that it will be necessary to invest more and more in trading infrastructure to “fix” the returns of high-frequency trading strategies on a higher level, than the results of index investing strategies.
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Nguenang, Kapnang Christian. "Essays in Financial Econometrics : Interlinked assets and High-Frequency Data." Thesis, Toulouse 1, 2018. http://www.theses.fr/2018TOU10023/document.
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Les changements institutionnels dans la régulation des marchés financiers ont amplifié la multiplication des marchés et la cotation simultanée des actifs sur plusieurs places. Les prix d'un titre sur ces places ou d’un titre et ses dérivés sont liés par des activités d'arbitrage. Dans ces cadres de marchés “informationnellement reliés”, il est intéressant pour le régulateur, les investisseurs et les chercheurs, de comprendre comment chaque marché contribue à la dynamique de la valeur fondamentale. Cette thèse développe de nouveaux outils pour mesurer la contribution, relativement à la fréquence, de chaque marché à la formation du prix et à la formation de la volatilité. Dans le premier chapitre, Je montre que les mesures existantes de la découverte des prix conduisent à des conclusions trompeuses lorsque l'on utilise des données à haute fréquence. En raison de bruits de microstructure, Ils créent une confusion entre la dimension « vitesse » et la dimension « bruit » dans le traitement de l’information. Je propose ensuite des mesures robustes au bruit qui détectent « quel marché est rapide » et produit des bornes très serrées. A l’aide de simulations Monte Carlo et des titres du Dow Jones vendues sur le NYSE et le NASDAQ, je montre que les données corroborent mes conclusions théoriques. Dans le deuxième chapitre, je propose une nouvelle définition de la découverte prix en construisant une fonction de réponse qui évalue l'impact permanent de l'innovation d’un marché, et je donne sa distribution asymptotique. Ce cadre innove en fournissant des résultats testables pour les métriques basées sur la variance d'innovation. Je présente ensuite un modèle d'équilibre des marchés à terme à différentes maturités, et montre qu'il soutient ma mesure : Conformément aux conclusions théoriques, la mesure sélectionne le marché avec le plus de participants comme dominant. Une application sur métaux de la LME montre que le contrat à terme de 3 mois domine à la fois le marché cash et le contrat à 15 mois. Le troisième chapitre introduit un cadre complet en temps continu pour l'analyse à haute fréquence, la littérature n'existant qu’en temps discret. Il présente aussi des avantages sur la littérature en traitant explicitement des bruits de microstructure et en intégrant une volatilité stochastique. Une application, faite sur les quatre actions du Dow Jones cotées au NASDAQ et négociées sur NYSE, montrent que le NASDAQ domine le processus continu de découverte des prix. Dans le quatrième chapitre, Alors que la littérature se concentre sur les prix, je développe un cadre pour étudier la volatilité de la volatilité. Ce qui permet de répondre à des questions telles que : La volatilité du marché futures contribue-t-elle plus que la volatilité du marché spot dans la formation de la volatilité du fondamental ? Je construis un VECM avec Volatilité Stochastique estimé avec les MCMC et inférence bayésienne. Je montre que les volatilités conditionnelles ont un facteur commun et propose des mesures de découverte de la volatilité. Je l'applique aux données journalières de Futures de métaux et de l'EuroStoxx50. Je trouve qu'alors que la formation des prix a lieu sur le marché au comptant, la découverte de la volatilité a lieu sur le marché Futures. Dans une seconde partie, je construis un cadre d'analyse qui exploite les données à Haute fréquence et évite la charge de calcul des MCMC. Je montre que les Volatilités Réalisées sont cointégrées et calcule la contribution du NYSE et NASDAQ à la volatilité permanente des titres du Dow Jones. J'obtiens que la volatilité des volumes est le meilleur déterminant de la découverte de la volatilité. Mais les chiffres faibles obtenues suggèrent l'existence d'autres facteursInstitutional changes in markets regulation in recent years have enhanced the multiplication of markets and the cross listing of assets simultaneously in many places. The prices for a security on those interrelated markets are strongly linked by arbitrage activities. This is also the case for one security and its derivatives: Cash and futures, CDS and Credit spread, spot and options. In those multiple markets settings, it is interesting for regulators, investors and academia to understand and measure how each market contributes to the dynamic of the common fundamental value. At the same time, improvement in ITC fueled trading activity and generated High frequency data. My thesis develops new frameworks, with respect to the data frequency, to measure the contribution of each market to the formation of prices (Price discovery) and to the formation of volatility (Volatility discovery). In the first chapter, I show that existing metrics of price discovery lead to misleading conclusions when using High-frequency data. Due to uninformative microstructure noises, they confuse speed and noise dimension of information processing. I then propose robust-to-noise metrics, that are good at detecting “which market is fast”, and produce tighten bounds. Using Monte Carlo simulations and Dow Jones stocks traded on NYSE and NASDAQ, I show that the data are in line with my theoretical conclusions. In the second chapter, I propose a new way to define price adjustment by building an Impulse Response measuring the permanent impact of market's innovation and I give its asymptotic distribution. The framework innovates in providing testable results for price discovery measures based on innovation variance. I later present an equilibrium model of different maturities futures markets and show that it supports my metric: As the theory suggests, the measure selects the market with the higher number of participants as dominating the price discovery. An application on some metals of the London Metal Exchange shows that 3-month futures contract dominates the spot and the 15-month in price formation. The third chapter builds a continuous time comprehensive framework for Price discovery measures with High Frequency data, as the literature exists only in a discrete time. It also has advantages on the literature in that it explicitly deals with non-informative microstructure noises and accommodates a stochastic volatility. We derive a measure of price discovery evaluating the permanent impact of a shock on a market’s innovation. Empirics show that it has good properties. In the fourth chapter, I develop a framework to study the contribution to the volatility of common volatility. This allows answering questions such as: Does volatility of futures markets dominate volatility of the Cash market in the formation of permanent volatility? I build a VECM with Autoregressive Stochastic Volatility estimated by MCMC method and Bayesian inference. I show that not only prices are cointegrated, their conditional volatilities also share a permanent factor at the daily and intraday level. I derive measures of market's contribution to Volatility discovery. In the application on metals and EuroStoxx50 futures, I find that for most of the securities, while price discovery happens on the cash market, the volatility discovery happens in the Futures market. Lastly, I build a framework that exploits High frequency data and avoid computational burden of MCMC. I show that Realized Volatilities are driven by a common component and I compute contribution of NYSE and NASDAQ to permanent volatility of some Dow Jones stocks. I obtain that volatility of the volume is the best determinant of volatility discovery, but low figures suggest others important factors
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Kablan, Abdalla. "The use of fuzzy logic applications for high frequency trading." Thesis, University of Essex, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542339.
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Park, Yonggi. "HJB Equation and Statistical Arbitrage applied to High Frequency Trading." Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5835.
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In this thesis we investigate some properties of market making and statistical arbitrage applied to High Frequency Trading (HFT). Using the Hamilton-Jacobi-Bellman(HJB) model developed by Guilbaud, Fabien and Pham, Huyen in 2012, we studied how market making works to obtain optimal strategy during limit order and market order. Also we develop the best investment strategy through Moving Average, Exponential Moving Average, Relative Strength Index, Sharpe Ratio.M.S.
Masters
Mathematics
Sciences
Mathematical Science; Industrial Mathematics
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Costa, Isac Silveira da. "High frequency trading (HFT) em câmera lenta: compreender para regular." reponame:Repositório Institucional do FGV, 2018. http://hdl.handle.net/10438/20720.
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High frequency trading (HFT) é uma técnica de negociação baseada em algoritmos que pode implementar estratégias variadas, das quais resultam um elevado número intradiário de mensagens enviadas aos sistemas de negociação das bolsas. High frequency traders (HFTs) são protagonistas no mercado secundário em termos de número de ofertas e negócios. Neste texto, procuramos definir os contornos deste já não tão novo fenômeno e os riscos a ele associados. Investigamos os objetivos pretendidos pela regulação, as regras aplicáveis e as dificuldades associadas a cada um delas. Assim, podemos avaliar se, no direito brasileiro, é necessário editar uma nova norma ou atualizar normas existentes e discutir qual poderia ser o seu conteúdo. A formulação de respostas regulatórias toma como ponto de partida os pressupostos teóricos do funcionamento do mercado de capitais, suas funções econômicas e o modo pelo qual o direito pode contribuir para que estas funções sejam desempenhadas adequadamente. Um estudo crítico dos HFTs nos permite examinar os riscos associados à sua atividade e à negociação algorítmica de um modo geral, bem como repensar o funcionamento do mercado, os objetivos da sua regulação e como estes podem ser alcançados. É imprescindível que seja realizado um esforço para a compreensão adequada de novas tecnologias que chegam ao mercado, avaliando seus riscos antes que seja disseminado um discurso de alarde ou medo. Este estudo também procura oferecer uma descrição atualizada do funcionamento do mercado secundário de capitais e como as tecnologias mais recentes influenciaram a dinâmica das negociações.
High frequency trading (HFT) is a kind of algorithmic trading which implements several strategies that result in a high number of intraday messages that are sent to exchanges and other trading venues. High frequency traders (HFTs) are key players in secondary markets given the number of orders and trades they generate. In this text, we explore the boundaries of this phenomenon and the associated risks. We investigate the regulation goals, the mechanisms to achieve such goals and the obstacles ahead. Then we evaluate whether it is necessary to create new rules or update the existing ones in Brazilian law – and what these new rules could be. The formulation of regulatory responses start with the analysis of a theoretical framework for the dynamics of capital markets, its economic functions and how Law can play a key part in this scenario. A critical study of HFTs enables us to assess its risks along with the risks of algorithmic trading in general, and, in addition, it is an invitation to rethinking how the market works, the goals that regulation can pursue and how they can be achieved. Understanding new technologies that emerge in capital markets is paramount before any risk assessment discussions in order to prevent hype and panic. This work also aims to provide an up to date description of the secondary market dynamics and how state-of-art trading technologies reshaped it.
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Berhane, Joel. "Zero-Inflated Hidden Markov Models and Optimal Trading Strategies in High-Frequency Foreign Exchange Trading." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-221098.
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The properties of high-frequency foreign exchange markets and how well they can be modeled using Hidden Markov Models will be studied in this thesis. Specifically, a Zero-inflated Poisson HMM will be implemented and evaluated for high-frequency price data for the EURSEK exchange rate. Furthermore, a trading strategy aimed at distributing large volumes optimally is developed and evaluated. The results show that the price model performs better than a random walk for some prediction horizons, both when used as a price predictor and as a classifier. The initial tests of the strategy indicate that it has good performance compared to the market benchmark. Both the price model and the strategy needs to undergo more testing before any final conclusions can be made.Egenskaperna hos högfrekventa valutamarknader och hur dessa kan modelleras med Dolda Markovmodeller behandlas i detta examensarbete. Noll-utökade Poisson distributioner, tillsammans med Dolda Markovmodeller, implementeras och utvärderas för högfrekvent växelkursdata för valutaparet EURSEK. Vidare, utvecklas och utvärderas en handelsstrategi med målet att distribuera stora volymer optimalt. Resultaten visar att prismodellen presterar bättre än en slumpvandring för en del prediktionshorisonter, både när den används för prisprediktion och för klassificering. Initiala tester av strategin indikerar att prestandan är bra jämfört med marknadens prestandamått. Både prismodellen och strategin behöver dock undersökas mer innan några definitiva slutsatser kan dras.
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Clark-Joseph, Adam Daniel. "Three Essays on Trading Behavior." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10783.
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This dissertation analyzes trading behavior in financial markets from multiple perspectives. In chapter 1, "Exploratory Trading," I investigate the mechanisms underlying high-frequency traders' capacity to profitably anticipate price movements. I develop a model of how a trader could gather valuable private information by using her own orders in an exploratory manner to learn about market conditions. The model's predictions are borne out empirically, and I find that this "exploratory trading" model helps to resolve several central open questions about high-frequency trading. Chapters 2 and 3 focus on the trading behavior of individuals. Chapter 2, "Foundations of the Disposition Effect: Experimental Evidence," (co-authored with Johanna Mollerstrom), presents and analyzes results from a laboratory experiment intended to examine if and how "regret aversion"--aversion to admitting mistakes--affects people's trading decisions. Although the experimental results resolve little about regret aversion specifically, they reveal some novel and unexpected effects, most importantly that subjects radically changed their trading decisions when they were compelled to devote a minimal amount of extra attention. In chapter 3, "Price Targets," I analyze how rational investors who privately observe information of indeterminate quality use prices to learn about whether or not their private information is valuable. I derive implications about trading behavior that not only help to explain a variety of empirical puzzles, but also generate several new testable predictions. Although these three essays differ considerably in methodology and focus, they all address the same basic issue of understanding the foundations of trading behavior.Economics
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Alshami, Abdullah. "Essays in high frequency trading, portfolio selection and oil futures markets." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12807/.
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High frequency trading (HFT) requires a detailed analysis of the quote structure of the continuous limit order book in order to correctly de- rive viable arbitrage strategies. Traders can manipulate order books by submitting and retracting ‘spoof’ orders at various levels of the order book by introducing, quote volume at or above (below) the best ask(bid). However, the limit order book data for heavily traded finan- cial instruments presents an almost unique problem to the econome- trician interested in constructing high frequency measures of liquidity impact over and above the inside spread. A single month of data for an individual maturity of an activity traded futures contract, in our example light crude, can easily exceed 10 Billion bytes of data, even when stored using the single precision floating point format. In this thesis we conduct a large scale analysis of the West Texas Inter- mediate (WTI) futures contract across the 120 simultaneously traded maturities for five levels of the order book from 2008 to 2016 sample at the continuous limit. Using this very-large data-set we estimate a new form of realized vector autoregression and derive the impulse re- sponse functions useful in building a HFT strategy. we show that for WTI futures a speed of execution of the order of 100s of milliseconds is needed to fully exploit a false quoting strategy designed to system- atically unbalance the order flow. Furthermore, we demonstrate that viable strategies can be built by spoofing up to three levels above the inside spread. A second part of the thesis involves creating new bootstrap routines to extract meaningful composition data to generate factor pricing mod- els from high frequency data. The key element of this analysis is in understanding the eigendecomposition and subsequent principal com- ponent analysis to extract factors from the data. our bootstrap is new and we provide an analysis of power and consistency in correct- ing bias in the estimation of the eigenstructure and hence evaluating the optimal number of principal components within the data.
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Nolte, Ingmar. "Three Essays on High Frequency Financial Econometrics and Individual Trading Behavior." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-57583.
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Hassan, Abdallah. "Column-based storage for analysis of high-frequency stock trading data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255019.
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This study investigated the efficiency of the available open-source columnbased storage formats with support for semi-flexible data in combination with query engines that support querying these formats. Two different formats were identified, Parquet and ORC, and both were tested in two different modes, uncompressed and compressed with the compression algorithm Snappy. They were tested by running two queries on the host company’s data converted to the appropriate formats, one simple averaging query and one more complicated with counts and filtering. The queries were run with two different query engines, Spark and Drill. They were also run on two dataset with different sizes to test scalability. The query execution time was recorded for each tested alternative. The results show that Snappy compressed formats always outperformed their non-compressed counterparts, and that Parquet was always faster than ORC. Drill performed faster on the simple query while Spark performed faster on the complex query. Drill also had the least increase in query execution time when the size of the dataset increased on both queries. The conclusion is that Parquet with Snappy is the storage format which gives the fastest execution times. However, both Spark and Drill have their own advantages as query engines.Denna studie undersökte effektiviteten av de i öppen källkod tillgängliga kolumnbaserade lagringsformaten med stöd för semistrukturerad data i kombination med frågemotorer som stödjer dessa format. Två olika format identifierades, Parquet och ORC, och båda testades i på olika sätt, okomprimerade och komprimerade med kompressionsalgoritmen Snappy. De testades genom att köra två frågor på uppdragsgivarens data som konverterades till de testade formaten, en enkel som räknar genomsnitt och en mer komplicerad med radräkning och filtrering. Båda frågorna kördes med två olika frågemotorer, Spark and Drill. De kördes på två datamängder med olika storlekar för att testa skalbarhet. Exekveringstiden mättes för varje testat alternativ. Resultaten visar att Snappy-komprimerade format alltid exekverade snabbare än de ickekomprimerade formaten, och att Parquet alltid körde snabbare än ORC. Drill var snabbare på den enkla frågan medan Spark var snabbare på den komplexa. Drill hade också den minsta ökningen i exekveringstiden när storleken på datamängden ökade på båda frågorna. Slutsatsen är att Parquet med Snappy är det lagringsformat som ger den snabbaste exekveringstiden, och att både Spark och Drill har sina egna fördelar.
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Tong, Lin. "Essays on mutual fund performance, ambiguity aversion, and high frequency trading." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/4773.
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In this dissertation, I address a range of topics in the context of mutual fund performance and high frequency trading.
The first chapter provides novel evidence on the role of ambiguity aversion in determining the response of mutual fund investors to historical fund performance information. It presents a model of ambiguity averse investors who receive multiple performance-based signals of uncertain precision about manager skill. A key implication of the model is that when investors receive multiple signals of uncertain quality, they place a greater weight on the worst signal. There is strong empirical support for this prediction in the data. Fund flows display significantly higher sensitivity to the worst performance measure even after controlling for fund performance at multiple horizons, performance volatility, flow-performance convexity, and a host of other relevant explanatory variables. This effect is particularly pronounced in the case of retail funds in contrast to institutional funds. The results suggest that fund investor behavior is best characterized as reflecting both Bayesian learning and ambiguity aversion.
The second chapter combines data on high frequency trading (HFT) activities of a randomly selected sample of 120 stocks and data on institutional trades, I find that HFT increases the trading costs of traditional institutional investors. An increase of one standard deviation in the intensity of HFT activities increases institutional execution shortfall costs by a third. Further analysis suggests that HFT represents an opportunistic and extra-expensive source of liquidity when demand and supply among institutional investors are imbalanced. Moreover, the impact on institutional trading costs is most pronounced when high frequency (HF) traders engage in directional strategies (e.g., momentum ignition and order anticipation). I perform various analyses to rule out an alternative explanation that HF traders are attracted to stocks that have high trading costs. First, HFT is most prevalent in liquid stocks. Second, the results are robust to controls for stable stock liquidity characteristics and events that might jointly affect HFT and trading costs. Third, an analysis of the HFT behavior around the temporary short selling ban in September 2008 highlights the opportunistic nature of liquidity provision by HF traders. Finally, Granger causality tests show that intensive HFT activity significantly contributes to institutional trading costs, but not vice versa.
The third chapter analyzes the implications of the tournament-like competition in the mutual fund industry using a framework that addresses the risk-taking incentives facing fund managers. The theoretical model presented in this chapter suggests that the increase in the \emph{activeness} of the interim loser manager's portfolio is directly related to the magnitude of the performance gap at the interim stage, and to the strength of the investor (cash flow) response to the relative performance rankings of the funds (i.e., the strength of the tournament effect). The empirical evidence based on quarterly Active Share data for a sample of domestic stock funds, is consistent with the key predictions of the model.
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Zhang, Shuo Sarah [Verfasser], and C. [Akademischer Betreuer] Weinhardt. "High Frequency Trading in Financial Markets / Shuo Sarah Zhang. Betreuer: C. Weinhardt." Karlsruhe : KIT-Bibliothek, 2013. http://d-nb.info/1045663735/34.
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Papalexiou, Vasilios. "An analysis of the impact of high frequency trading on equity markets." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205752/1/Vasilios_Papalexiou_Thesis.pdf.
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This thesis investigated the impacts of high frequency traders on the equity markets. At a base level, high frequency traders improve market liquidity and efficiency because they both compete with and undercut each other, thereby benefitting slower traders. However, high frequency traders have increased both systematic and systemic risk within the equity markets and have also increased liquidity resilience commonality during unfavourable market conditions. Overall, the results suggest that high frequency traders contribute both positively and negatively to the market, thus making it difficult to draw a clear conclusion about their overall value to the market.
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Johansson, Henrik. "High Frequency Trading : Market abuse and how to reestablish confidence in the market?" Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Nationalekonomi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-21442.
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In today’s highly technologic advanced trading environment traditional investors are no longer competing at same levels as companies using automatic trading strategies. Advanced technology is of significant importance in today’s trading strategies and has forced the trading process away from humans. Instead, using programed computers packed with algorithmic formulas, these robots can spot trends before an ordinary investor can blink, changing strategies and execute orders within milliseconds. Given this technological advantage firms perhaps have crossed the line when trying to earn abnormal return by using market manipulating trading strategy without any respect to traditional investors and business ethics. My research at hand will bring clarity to what extent this problem are related to Swedish markets and discussion around upcoming market regulations and firms social ethics and responsibility will be made.
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Romar, Daniel. "On the moral agency for high frequency trading systems and their role in distributed morality." Thesis, Umeå universitet, Institutionen för idé- och samhällsstudier, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-105641.
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Lopes, Rodrigo Soares. "Aplicação de estratégias de high frequency trading no mercado brasileiro de dólar futuro." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/12/12136/tde-21082018-142155/.
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A pesquisa tem por finalidade avaliar dois modelos econométricos de mudanças de preços, que podem ser utilizados em estratégias de arbitragem estatística, o probit ordenado e o de decomposição, estimando seus parâmetros em quatro pregões de mini contratos de dólar futuro negociados na bolsa de valores brasileira. O estudo da negociação em alta frequência com a utilização de dados de transação a transação revela informações relativas à microestrutura de mercado que o ferramental mais tradicional não é capaz de desvendar. Uma das razões é que modelos tradicionais trabalham com variações de preço como variáveis contínuas, enquanto que ao considerar as variações de preço uma variável contínua e não uma variável discreta, como nos modelos aqui avaliados. Este trabalho acrescenta à literatura sobre microestrutura de mercado ao aplicar os modelos estudados em um ativo distinto daqueles avaliados nos papers originais, voltados ao exame do mercado de ações. Esta pesquisa concluiu que os modelos probit ordenado e de decomposição podem ser utilizados para previsão de mini contratos de dólar futuro e que o modelo de decomposição apresenta parâmetros mais significantes. Também concluiu-se que, no modelo probit ordenado, as variáveis de volume e time duration não se apresentaram relevantes na determinação do preço desse contrato e que a quantidade de defasagens utilizadas nos parâmetros estimados pode variar dentre os pregões.The research aims to evaluate two econometric models of price change, which can be used in strategies of statistical arbitrage, the ordered probit model and the decomposition model, estimating its parameters in four trading sessions of mini US dollar futures contracts traded on the Brazilian Stock Exchange. The study of high frequency trading with the use of trade-by-trade price movements reveals information related to the market microstructure that the more traditional econometric tools are not able to solve when considering the price changes as a continuous variable and not a discrete one, like in the models evaluated here. This work adds to the literature on market microstructure by applying the models studied in an asset different from those evaluated in the original papers, aimed at examining the stock market. This research concluded that the ordered probit and decomposition models can be used to predict mini US dollar futures price changes and that the decomposition model presents more significant parameters. It was also concluded that, in the ordered probit model, the volume and time duration variables were not relevant in determining the price of this contract and that the number of lags used to estimate parameters can vary among the trading sessions.
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Kremer, Marcel [Verfasser], and Rüdiger [Akademischer Betreuer] Kiesel. "High-frequency electricity trading : empirics, fundamentals, and stochastics / Marcel Kremer ; Betreuer: Rüdiger Kiesel." Duisburg, 2021. http://d-nb.info/1236501764/34.
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Goosen, Kelly. "Calibrating high frequency trading data to agent based models using approximate Bayesian computation." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33699.
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We consider Sequential Monte Carlo Approximate Bayesian Computation (SMC ABC) as a method of calibration for the use of agent based models in market micro-structure. To date, there are no successful calibrations of agent based models to high frequency trading data. Here we test whether a more sophisticated calibration technique, SMC ABC, will achieve this feat on one of the leading agent based models in high frequency trading literature (the Preis-Golke-Paul-Schneider Agent Based Model (Preis et al., 2006)). We find that, although SMC ABC's naive approach of updating distributions can successfully calibrate simple toy models, such as autoregressive moving average models, it fails to calibrate this agent based model for high frequency trading. This may be for two key reasons, either the parameters of the model are not uniquely identifiable given the model output or the SMC ABC rejection mechanism results in information loss rendering parameters unidentifiable given insucient summary statistics.
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Vella, Vincent. "Improving risk-adjusted performance in high-frequency trading : the role of fuzzy logic systems." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/18928/.
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In recent years, algorithmic and high-frequency trading have been the subject of increasing risk concerns. A general theme that we adopt in this thesis is that trading practitioners are predominantly interested in risk-adjusted performance. Likewise, regulators are demanding stricter risk controls. First, we scrutinise conventional AI model design approaches with the aim to increase the risk-adjusted trading performance of the proposed fuzzy logic models. We show that applying risk-return objective functions and accounting for transaction costs improve out-of-sample results. Our experiments identify that neuro-fuzzy models exhibit superior performance stability across multiple risk regimes when compared to popular neural network models identified in AI literature. Moreover, we propose an innovative ensemble model approach which combines multiple risk-adjusted objective functions and dynamically adapts risk-tolerance according to time-varying risk. Next, we extend our findings to the money management aspects of trading algorithms. We introduce an effective fuzzy logic approach which dynamically discriminates across different regions in the trend and volatility space. The model prioritises higher performing regions at an intraday level and adapts capital allocation policies with the objective to maximise global risk-adjusted performance. Finally, we explore trading improvements that can be attained by advancing our type-1 fuzzy logic ideas to higher order fuzzy systems in view of the increased noise (uncertainty) that is inherent in high-frequency data. We propose an innovative approach to design type-2 models with minimal increase in design and computational complexity. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of trading frequencies. In conclusion, this thesis sets a framework for practitioners, researchers and regulators in the design of fuzzy logic systems for better management of risk in the field of algorithmic and high-frequency trading.
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Breuer, Arne [Verfasser]. "An Empirical Analysis of Order Dynamics in a High Frequency Trading Environment. / Arne Breuer." Berlin : Duncker & Humblot GmbH, 2020. http://d-nb.info/1238496415/34.
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Cremaschi, Andrea. "Comparing computational approaches to the analysis of high-frequency trading data using Bayesian methods." Thesis, University of Kent, 2017. https://kar.kent.ac.uk/60839/.
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Financial prices are usually modelled as continuous, often involving geometric Brownian motion with drift, leverage, and possibly jump components. An alternative modelling approach allows financial observations to take discrete values when they are interpreted as integer multiples of a fixed quantity, the ticksize, the monetary value associated with a single change in the asset evolution. These samples are usually collected at very high frequency, exhibiting diverse trading operations per seconds. In this context, the observables are modelled in two different ways: on one hand, via the Skellam process, defined as the difference between two independent Poisson processes; on the other, using a stochastic process whose conditional law is that of a mixture of Geometric distributions. The parameters of the two stochastic processes modelled as functions of a stochastic volatility process, which is in turn described by a discretised Gaussian Ornstein-Uhlenbeck AR(1) process. The work will present, at first, a parametric model for independent and identically distributed data, in order to motivate the algorithmic choices used as a basis for the next Chapters. These include adaptive Metropolis-Hastings algorithms, and Interweaving Strategy. The central Chapters of the work are devoted to the illustration of Particle Filtering methods for MCMC posterior computations (or PMCMC methods). The discussion starts by presenting the existing Particle Gibbs and the Particle Marginal Metropolis-Hastings samplers. Additionally, we propose two extensions to the existing methods. Posterior inference and out-of-sample prediction obtained with the different methodologies is discussed, and compared to the methodologies existing in the literature. To allow for more flexibility in the modelling choices, the work continues with a presentation of a semi-parametric version of the original model. Comparative inference obtained via the previously discussed methodologies is presented. The work concludes with a summary and an account of topics for further research.
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Nunes, Gustavo de Faro Colen. "Modelo da dinâmica de um livro de ordens para aplicações em high-frequency trading." reponame:Repositório Institucional do FGV, 2013. http://hdl.handle.net/10438/10570.
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Submitted by Gustavo de Faro Colen Nunes (gustavocolennunes@gmail.com) on 2013-02-28T19:45:35Z
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MODELO DA DINÂMICA DE UM LIVRO DE ORDENS PARA APLICAÇÕES EM HIGH-FREQUENCY TRADING.pdf: 1769569 bytes, checksum: fcb41165f230caf02656cf7b8a709951 (MD5)Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2013-02-28T21:30:40Z (GMT) No. of bitstreams: 1 MODELO DA DINÂMICA DE UM LIVRO DE ORDENS PARA APLICAÇÕES EM HIGH-FREQUENCY TRADING.pdf: 1769569 bytes, checksum: fcb41165f230caf02656cf7b8a709951 (MD5)
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As operações de alta frequência (High-Frequency Trading - HFT) estão crescendo cada vez mais na BOVESPA (Bolsa de Valores de São Paulo), porém seu volume ainda se encontra muito atrás do volume de operações similares realizadas em outras bolsas de relevância internacional. Este trabalho pretende criar oportunidades para futuras aplicações e pesquisas nesta área. Visando aplicações práticas, este trabalho foca na aplicação de um modelo que rege a dinâmica do livro de ordens a dados do mercado brasileiro. Tal modelo é construído com base em informações do próprio livro de ordens, apenas. Depois de construído o modelo, o mesmo é utilizado em uma simulação de uma estratégia de arbitragem estatística de alta frequência. A base de dados utilizada para a realização deste trabalho é constituída pelas ordens lançadas na BOVESPA para a ação PETR4.
High-frequency trading (HFT) are increasingly growing on BOVESPA (São Paulo Stock Exchange), but their volume is still far behind the volume of similar operations performed on other internationally relevant exchange markets. The main objective of this work is to create opportunities for future research and applications in this area. Aiming at practical applications, this work focuses on applying a model that governs the dynamics of the order book to the Brazilian market. This model is built based in the information of the order book alone. After building the model, a high frequency statistical arbitrage strategy is simulated to validate the model. The database used for this work consists on the orders posted on the equity PETR4 in BOVESPA.
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Elofsson, Bjesse Mimmi, and Emma Eriksson. "Algoritmisk handel - en kartläggning av risk, volatilitet, likviditet och övervakning." Thesis, Södertörns högskola, Företagsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-35459.
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As technological changes have revolutionized the way financials assets are traded today, algorithmic trading has grown to become a major part of the world's stock markets. This study aims to explore algorithmic trading through the eyes of different market operators. The market operators have, partly based on the stakeholder theory, been categorized into six categories, namely private investors, day traders, banks, the stock market, algorithmic developers and regulators. In this study we used a qualitative research design and 11 semistructured interviews have been conducted with the market operators about the main categories risks, volatility, liquidity and monitoring. The results contributed a broader view of algorithmic trading. Respondents saw a lot of risks with the business, but the majority did not express any serious concerns about this. Volatility and liquidity were considered to be affected in both directions, depending on context. Regarding monitoring of algorithmic trading, respondents considered it necessary, but the answers differ if the current monitoringis sufficient or not. The empirical results are partly in line with previous research.
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Björkman, Jonas, and Johan Durling. "The impact of high-frequency trading on the Swedish stock market – based on liquidity and volatility." Thesis, Linköpings universitet, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148882.
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This paper studies how high-frequency trading (HFT) affects the Swedish stock market quality based on volatility and liquidity measures. Previous studies show ambiguous results where a few propose that HFT deteriorates market quality by increasing volatility and decreasing liquidity while some studies point in the opposite direction.By setting up a simultaneous equations system with instrumental variables and estimating the parameters with Generalized Methods of Moments (GMM), this paper finds that in the majority of the investigated stocks high-frequency trading activity reduces bid ask spreads and therefore increases liquidity, i.e. enhancing market quality. Additionally, the results also show that the volatility decreases through high-frequency trading activity. Hence, both measures are indicating that the market quality is positively affected by high-frequency trading.However, interesting is the analysis and discussion on whether high-frequency trading strategies such as spoofing and layering potentially can contribute to false liquidity. This would mean that the market quality is impaired due to HFT. This paper also examines the reversed relationship, how the liquidity and volatility affect HFT activity and conclude that HFT is not affected by how liquid or volatile the market is.
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Rechenthin, Michael David. "Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/4732.
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This thesis explores predictability in the market and then designs a decision support framework that can be used by traders to provide suggested indications of future stock price direction along with an associated probability of making that move. Markets do not remain stable and approaches that are highly predictive at one moment may cease to be so as more traders spot the patterns and adjust their trading techniques. Ideally, if these "concept drifts" could be anticipated, then the trader could store models to use with each specific market condition (or concept) and later apply those models to incoming data. The assumption however is that the future is uncertain, therefore future concepts are still undecided.
Maintaining a model with only the most up-to-date price data is not necessarily the most ideal choice since the market may stabilize and old knowledge may become useful again. Additionally, decreasing training times to enable modified classifiers to work with streaming high-frequency stock data may result in decreases in performance (e.g. accuracy or AUC) due to insufficient learning times. Our framework takes a different approach to learning with drifting concepts, which is to assume that concept drift occurs and builds this into the model. The framework adapts to these market changes by building thousands of traditional base classifiers (SVMs, Decision Trees, and Neural Networks), using random subsets of past data, and covering similar (sector) stocks and heuristically combining the best of these base classifiers. This "ensemble", or pool of multiple models selected to achieve better predictive performance, is then used to predict future market direction. As the market moves, the base classifiers in the ensemble adapt to stay relevant and keep a high level of model performance. Our approach outperforms existing published algorithms.
This thesis also addresses problems specific to learning with stock data streams, specifically class imbalance, attribute creation (e.g. technical and sentiment analysis), dimensionality reduction, and model performance due to release of news and time of day. Popular methods for dealing with each are discussed.
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Kalimullina, Lina [Verfasser], and Rainer [Akademischer Betreuer] Schöbel. "High-Frequency Trading : Insights from Analytical Models and Simulated Agent-Based Models / Lina Kalimullina ; Betreuer: Rainer Schöbel." Tübingen : Universitätsbibliothek Tübingen, 2019. http://d-nb.info/1204929955/34.
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Vieira, Paulo Sérgio Coelho. "Industry analysis of the high frequency trading industry: an assessment of the industry boundaries, environment and strategic options." reponame:Repositório Institucional do FGV, 2015. http://hdl.handle.net/10438/13638.
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U. S. Equity Trading Industry has evolved quickly over the last decade. The U.S. equity market became an open architecture in which entrants with innovative technology can compete effectively. Several regulatory changes and technological innovations have enabled profound changes in market structure. These changes, along with improving high-speed technology, have acted as a catalyst, giving rise to a new approach to trading, named High Frequency Trading, hereafter referred as HFT. HFT Firms emerged and took over in large extent the market making business in providing liquidity. Although HFT has been growing massively, over the past four years, HFT firms have been far less profitable, since more firms entered the industry eroding the margins. Within this context, therefore, this thesis sought to provide a brief review of HFT business, followed by the analysis of its industry boundaries and the characteristics of the HFT environment. To this end, the thesis conducted an extensive literature review of previous research, qualitative public documents, such as, newspapers, meeting minutes and official reports. The thesis employed a series of frameworks, Entry Barriers and Mobility Barriers (Porter, 1980); Models of Industry Evolution (McGahan, 2004); Information-Intensive Industry Structure (Sampler, 1998), to analyze the boundaries of the HFT industry. Additionally, it employed Models of Industry Evolution (McGahan, 2004) and PESTEL (JOHNSON, SCHOLES, and WHITTINGTON, 2011) frameworks to analyze the industry and the environment surrounding HFT business. The analysis concluded that the firms employing HFT to compete in the Securities Trading industry compose an independent industry.
O Mercado Acionário Americano evoluiu rapidamente na última década. Este tornou-se uma arquitetura aberta em que participantes com tecnologia inovadora podem competir de forma eficaz. Várias mudanças regulatórias e inovações tecnológicas permitiram mudanças profundas na estrutura do mercado. Essas mudanças, junto com o desenvolvimento tecnológico de redes de alta velocidade, agiu como um catalisador, dando origem a uma nova forma de negociação, denominada Negociação em Alta Frequência (HFT). As empresas de HFT surgiram e se apropriaram em larga escala do negócio de formação de mercado, no fornecimento de liquidez. Embora HFT tem crescido massivamente, ao longo dos últimos quatro anos, HFT perdeu rentabilidade significativamente, uma vez que mais empresas aderiram ao setor reduzindo as margens. Portanto, diante deste contexto, esta tese buscou apresentar uma breve revisão sobre a atividade de HFT, seguida de uma análise dos limites deste setor, bem como, das características do macroambiente do HFT. Para tanto, a tese realizou uma extensa revisão do histórico literário, documentos públicos qualitativos, tais como, jornais, atas de reunião e relatórios oficiais. A tese empregou um ferramental de análise, Barreiras de Entrada e Mobilidade (Porter, 1980); Modelos de Evolução Setorial (McGahan, 2004); Estrutura do Setor de Informação Intensiva (Sampler, 1998), para analisar os limites do setor de HFT. Adicionalmente, empregou as ferramentas de análise, Modelos de Evolução Setorial (McGahan, 2004) e PESTEL (JOHNSON, SCHOLES, and WHITTINGTON, 2011), para analisar o setor e o contexto que envolve o negócio de HFT. A análise concluiu que as empresas que empregam HFT para atuar e competir no mercado acionário, compoem um setor independente.
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Palmborg, Adam, and Max Malm. "Högfrekvenshandel : En kvalitativ studie." Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-27857.
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Syfte: Högfrekvenshandel har på senare år varit ett omdiskuterat och kontroversiellt ämne. Fenomenet har genomgått omfattande granskning och åsikterna kring dess påverkan på marknaden och dess aktörer går isär. Då tidigare forskning främst genomförts på den amerikanska marknaden är syftet med den här studien att bistå med en djupare insikt kring denna typ av handel och dess avtryck på den svenska finansmarknaden. Metod: För att behandla syftet har en kvalitativ studie av högfrekvenshandel med en deduktiv ansats genomförts. Teori: Studien utgår från Rational Choice Theory, Effektiva marknadshypotesen och tidigare forskning inom ämnet. Med hjälp av det teoretiska ramverket har studien analyserat det empiriska underlaget. Relevanta aspekter har identifierats som kan förklara varför studiens respondenter har ett specifikt förhållningssätt gentemot högfrekvenshandel. Empiri: Studien består av en dokumentstudie och fyra semistrukturerade intervjuer med intressenter på den svenska finansmarknaden. Intervjuerna ämnar identifiera de olika intressenternas förhållningssätt gentemot högfrekvenshandel och dess bakomliggande orsaker. Slutsats: Studien har kommit fram till att förhållningssättet gentemot högfrekvenshandel står i relation till vilken typ av verksamhet som intressenten bedriver. Vidare kan det konstateras att tidigare forskning till stor del går att applicera på den svenska marknaden.Purpose: In recent years, High Frequency Trading has been a widely debated and controversial topic. The phenomenon has been subject to extensive examination and the opinions regarding its effect on the financial markets are inconsistent. Previous research has foremost been conducted on the American financial market. Thus the purpose of this thesis is to contribute with deeper insight regarding this kind of trading and its impact on the Swedish financial market. Method: To address the purpose of this thesis, a qualitative study with a deductive approach has been conducted. Theory: The thesis emanates from Rational Choice Theory, The Efficient Market Hypothesis and previous research within the field. Using the theoretical framework, the thesis has analyzed the empirical data. Relevant aspects has been identified which can explain why the thesis’ respondents has a specific approach towards High Frequency Trading. Empirics: The thesis consists of a document study and four semi structured interviews with stakeholders on the Swedish financial market. Through these interviews, the thesis aims to identify the stakeholders’ different approaches towards High Frequency Trading and what might cause this particular point of view. Conclusion: The thesis can conclude that the approach towards High Frequency Trading is correlated to the type of operation conducted by the respondent. Furthermore, it can be concluded that previous research in general is applicable on the Swedish financial market.
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Dayri, Khalil Antoine. "Microsturcture des marchés et modelistion des flux de trading." Phd thesis, Ecole Polytechnique X, 2012. http://pastel.archives-ouvertes.fr/pastel-00689127.
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On propose une perspective originale d'analyser les différents flux hautes fréquences d'information provenant des marchés financiers et fournit des modèles simples et intuitives qui reflètent étroitement la réalité. On observe les données empiriques et note certains faits stylisés et propose des modèles pour capturer ces faits. Dans le chapitre 1, on passe en revue les définitions et propriétés de base des marchés électroniques. En particulier, on revoit les travaux de microstructure et de modélisation du marché, et leurs relations à ce travail. On introduit la taille du "tick", qu'on utilise pour classifier les actifs et interpréter les différents résultats. Dans le chapitre 2, on montre empiriquement que l'impact d'une seule transaction dépend de la durée inter-transactions. En effet, lorsque le taux des échanges devient plus rapide, la variance des rendements des transactions augmente fortement et que ce comportement persiste à des échelles de temps plus grossières. On montre également que la valeur du spread augmente avec l'activité et on en déduit que les carnets d'ordres sont plus vide lorsque le taux des échanges est élevé. Dans le chapitre 3, on présente un modèle pour capturer le bruit de microstructure. Les prix des actifs sont représentés par la somme des rendements "tick" arrivant à des temps de Poisson aléatoires. Le modèle se compose d'une martingale diffusive contaminée par un bruit autocorrélé mais disparaissant aux échelles grossières. On est capable de capturer la signature de la variance et l'autocorrélation faible mais significative des rendements "tick". Dans le chapitre 4, on utilise les processus ponctuels de Hawkes pour modéliser l'arrivée aléatoire des transactions. On modélise la transformation échelle fine - échelle grossière des prix et en particulier l'effet sur les moments des rendements des prix. On propose une technique simple d'estimation non paramétrique de la structure de dépendance des processus de Hawkes dans le cas unidimensionnel et dans quelques cas particuliers multidimensionnels. On applique la méthode à des actifs de Future et trouve des noyaux de dépendance en loi de puissance.
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Zhou, Long. "Irregularly-Spaced Financial High-Frequency Data Simulation Using Multi-Dimensional Hawkes Processes: Estimation, Prediction And Corresponding Trading Strategy." Thesis, Uppsala universitet, Analys och tillämpad matematik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175715.
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Tadiello, Guilherme. "High-frequency trading e eficiência informacional: uma análise empírica do mercado de capitais brasileiro no período 2007-2015." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-12012017-161053/.
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Operações de alta frequência ganharam destaque nos últimos anos, tanto no mercado nacional quanto internacional, e têm atraído a atenção de reguladores, pesquisadores e da mídia. Assim, surgiu a necessidade de estudar o mercado de capitais brasileiro no contexto dos dados em alta frequência. Este estudo preocupa-se em analisar os efeitos dos avanços tecnológicos e novas formas de negociação na qualidade do mercado. Tais pontos são caracterizados pelo HFT. Gomber e Haferkorn (2013) explicam que HFT é um subgrupo das negociações com algoritmos. Os investidores HFTs são caracterizados por negociarem com seu próprio capital, manterem posições por espaços curtos de tempo, pelo alto volume de negociação e por atualizarem as ordens com frequência. A revisão da literatura permitiu delinear o termo e identificar as estratégias adotadas, os impactos positivos e negativos na qualidade de mercado, os riscos advindos da prática e medidas adotadas ou propostas para mitigar esses riscos. A contribuição decorrente das negociações em alta frequência foi analisada empiricamente com ênfase na questão da eficiência informacional do mercado nacional. Para isso, foram utilizados dados intradiários do índice Bovespa, com frequências de observação a partir de 1 minuto. Aplicações do teste de sequência para aleatoriedade e teste de razão de variância de Lo e Mackinlay (1988) evidenciaram um aumento na eficiência do mercado ao longo do período analisado, entre 2007 e 2015, para a frequência de observações de 1 minuto. Foi encontrada relação entre esse ganho em eficiência e o aumento da participação do HFT no mercado. Também foi constatado que o mercado se mostra menos eficiente quando a frequência de observação aumenta e que os ganhos em eficiência são mais acentuados para frequências maiores. Os últimos resultados fortalecem a percepção de que a melhora na eficiência está relacionada diretamente à atuação dos HFTs no mercado, haja vista a característica destes de explorarem ineficiências de preço em frações de segundos. Descreveu-se assim o mercado de capitais nessa era de alta frequência e os impactos do HFT na eficiência de mercado. Tais pontos podem ser colocados como contribuições práticas deste estudo.High-frequency trading has gained notoriety in recent years and attracted incresing attention among policymakers, researchers and media. This brought about the need for research of high frequency data on brazilian capital market. This study aims to investigate the effects of technological advancements and new forms of trading, specially HFT, on market quality. Gomber and Haferkorn (2013, p. 97) define HFT as a subset of algorithmic trading \"characterized by short holding periods of trading positions, high trading volume, frequent order updates and proprietary trading\". The literature review made it possible to define the term and identify strategies, positive and negative impacts on market quality, risks and ways to mitigate these risks. The contribution arising from HFT was analyzed empirically with an emphasis on price efficiency in the domestic market, using intraday Bovespa index data in different frequencies. Run tests and Lo and Mackinlay (1988) variance ratio tests showed increasing efficiency over the period, between 2007 and 2015, for observations in 1 minute frequency. Relationship between this gain in price efficieny and the growth of HFT market share was found. It was found that the market is less eficiente when higher frequencies are analyzed, and that the efficiency gains are more pronounced for higher frequencies. The last results strengthen the perception that the efficiency gains are directly related to high-frequency trading, given its characteristc of exploring price inefficiencies that last fractions of seconds. The capital market in this high frequency era and the impacts of HFT on market efficiency were described in this study
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Sapkota, N. (Niranjan). "Evaluating performance capacity of high frequency trading strategies, based on comparative ratios and market inefficiency at Helsinki Stock Exchange." Master's thesis, University of Oulu, 2014. http://urn.fi/URN:NBN:fi:oulu-201412042083.
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High frequency trading is not only about the speed but also about the effective trading strategies it uses to perform the trade. Performance capacity evaluation of high frequency trading strategies is done using different comparative ratios. Studies find, due to tight spread, it is difficult for high frequency traders to generate significant alpha by trading the highly liquid stocks using market making strategy. But they can still generate positive return with Sharpe ratio almost equal to market. They act more like market makers following this strategy. The capacity of other high frequency trading strategies lies in between (58–75) %. Statistical arbitrage strategy is the best among all the high frequency trading strategies. Sharpe ratio as a main tool of comparison between high frequency and non-high frequency traders, shows multiple times higher Sharpe for high frequency traders in comparison to non-high frequency traders. Value at Risk (VaR) suggests the probability of generating positive return for all the strategies having long and short positions.
This thesis takes one month high frequency limit order and tick data from NASDAQ OMX Nordic and select six mostly traded Finnish stocks based on their limit order book activities. Basic limit order book activities of all the selected stocks is analyzed including and excluding non-high frequency activates to make sure all the selected stocks are influenced by high frequency activities, so that the result is more accurate. This thesis follows the high frequency trading strategies and respective holding periods suggested by Aldridge (2009). Sometimes strategies work not because strategies are efficient but due to market inefficiency. This thesis crosschecks the market inefficiency with auto-regressive based test. Due to tick data and a very short time interval between the two observations, it finds strong influence of past returns and past price movements in the current return suggesting inefficiency in the market.
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Anane, Marouane. "Une approche mathématique de l'investissement boursier." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2015. http://www.theses.fr/2015ECAP0017/document.
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Le but de cette thèse est de répondre au vrai besoin de prédire les fluctuations futures des prix d'actions. En effet, l'aléatoire régissant ces fluctuations constitue pour des acteurs de la finance, tels que les Market Maker, une des plus grandes sources de risque. Tout au long de cette étude, nous mettons en évidence la possibilité de réduire l'incertitude sur les prix futurs par l'usage des modèles mathématiques appropriés. Cette étude est rendue possible grâce à une grande base de données financières et une puissante grille de calcul mises à notre disposition par l'équipe Automatic Market Making de BNP Paribas. Dans ce document, nous présentons uniquement les résultats de la recherche concernant le trading haute fréquence. Les résultats concernant la partie basse fréquence présentent un intérêt scientifique moindre pour le monde académique et rentrent par ailleurs dans le cadre des résultats confidentiels. Ces résultats seront donc volontairement omis.Dans le premier chapitre, nous présentons le contexte et les objectifs de cette étude. Nous présentons, également, les différentes méthodes utilisées, ainsi que les principaux résultats obtenus. Dans le chapitre 2, nous nous intéressons à l'apport de la supériorité technologique en trading haute fréquence. Dans ce but, nous simulons un trader ultra rapide, omniscient, et agressif, puis nous calculons son gain total sur 3 ans. Les gains obtenus sont très modestes et reflètent l'apport limité de la technologie en trading haute fréquence. Ce résultat souligne l'intérêt primordial de la recherche et de la modélisation dans ce domaine.Dans le chapitre 3, nous étudions la prédictibilité des prix à partir des indicateurs de carnet d'ordre. Nous présentons, à l'aide des espérances conditionnelles, des preuves empiriques de dépendances statistiques entre les prix et les différents indicateurs. L'importance de ces dépendances résulte de la simplicité de la méthode, éliminant tout risque de surapprentissage des données. Nous nous intéressons, ensuite, à la combinaison des différents indicateurs par une régression linéaire et nous analysons les différents problèmes numériques et statistiques liés à cette méthode. Enfin, nous concluons que les prix sont prédictibles pour un horizon de quelques minutes et nous mettons en question l'hypothèse de l'efficience du marché.Dans le chapitre 4, nous nous intéressons au mécanisme de formation du prix à partir des arrivés des évènements dans le carnet d'ordre. Nous classifions les ordres en douze types dont nous analysons les propriétés statistiques. Nous étudions par la suite les dépendances entre ces différents types d'ordres et nous proposons un modèle de carnet d'ordre en ligne avec les observations empiriques. Enfin, nous utilisons ce modèle pour prédire les prix et nous appuyons l'hypothèse de la non-efficience des marchés, suggérée au chapitre 3The aim of this thesis is to address the real need of predicting the prices of stocks. In fact, the randomness governing the evolution of prices is, for financial players like market makers, one of the largest sources of risk. In this context, we highlight the possibility of reducing the uncertainty of the future prices using appropriate mathematical models. This study was made possible by a large base of high frequency data and a powerful computational grid provided by the Automatic Market Making team at BNP Paribas. In this paper, we present only the results of high frequency tests. Tests are of less scientific interest in the academic world and are confidential. Therefore, these results will be deliberately omitted.In the first chapter, the background and the objectives of this study are presented along with the different methods used and the main results obtained.The focus of chapter 2 is on the contribution of technological superiority in high frequency trading. In order to do this, an omniscient trader is simulated and the total gain over three years is calculated. The obtained gain is very modest and reflects the limited contribution of technology in high frequency trading. This result underlines the primary role of research and modeling in this field.In Chapter 3, the predictability of prices using some order book indicators is studied. Using conditional expectations, the empirical evidence of the statistical dependencies between the prices and indicators is presented. The importance of these dependencies results from the simplicity of the method, eliminating any risk of over fitting the data. Then the combination of the various indicators is tested using a linear regression and the various numerical and statistical problems associated with this method are analyzed. Finally, it can be concluded that the prices are predictable for a period of a few minutes and the assumption of market efficiency is questioned.In Chapter 4, the mechanism of price formation from the arrival of events in the order book is investigated. The orders are classified in twelve types and their statistical properties are analyzed. The dependencies between these different types of orders are studied and a model of order book in line with the empirical observations is proposed. Finally, this model is used to predict prices and confirm the assumption of market inefficiency suggested in Chapter 3