This thesis deals with statistical arbitrage as a strategy applied in algorithmic trading of US Treasury bonds in the selected timeframe from 1980 until 2017. Our aim is to prove that a specific event on the treasury market, namely reopening of the bonds, constitutes an arbitrage opportunity that enables the investor to systematically yield extraordinary profits on the market. This thesis includes a theoretical introduction to algorithmic trading and statistical arbitrage. Based on this introduction we formulate hypotheses, which are then tested in the application part by constructing an algorithm that simulates a trading strategy on historical data. Comparing three strategies we determined that this strategy is meaningful, or performs better than a random walk and that it is profitable.

In this paper, we utilize White's Reality Check, White (2000), and Hansen's SPA test, Hansen (2004), to evaluate technical trading rules while quantifying the data-snooping bias. Secondly, we discuss the result with Probability of Backtest Overfitting framework, introduced by Bailey et al. (2015). Hence, the study presents a comprehensive test of momentum trading across the US futures markets from 2004 to 2016. The evidence indicates that technical trading rules have not been pro?table in the US futures markets after correcting for the data snooping bias.

The diploma thesis is focused on algorithmic trading. In the first part the theoretical background is summarized. This part is particularly focused on definition of algorithmic trading, execution mechanisms, quantitative strategies, including problems regarding backtesting, and also on benefits and threats of algorithmic trading in market's point of view. The thesis also offers an introduction to genetic algorithms. In the practical part the strategy using genetic algorithm to find optimal combination of particular strategies is developed. The results showed that using genetic algorithms was beneficial for given data series. They also showed that the size of transaction costs is crucial for strategy performance same as dividing data series into testing sample and validation sample.

The activity of algorithmic trading is increasing steadily across capital markets due to technological developments. This thesis analyses the common algorithmic intraday trading strategies of momentum, mean reversion, and statistical arbitrage. Conclusions were drawn from a literature review of prior and current research. Algorithmic arbitrage was found to be the most profitable of the three evaluated strategies, because it typically takes place in high frequency trading. Furthermore, this thesis analyses the impact of algorithmic trading on market liquidity and volatility. While the literature mainly agrees that algorithmic trading has a positive effect on liquidity, its impact on volatility is subject to discussion. Algorithmic and high-frequency trading carry risks that will likely lead to new future regulations.:1 INTRODUCTION 1.1 Background 1.2 Problem description and goal of the research 1.3 Structure of the thesis and research questions 2 THEORETICAL FUNDAMENTALS 2.1 Intraday trading 2.1.1 Definition 2.1.2 Characteristics of intraday trading markets 2.1.3 Financial instruments of intraday trading 2.1.4 Goals and profit chances of individual intraday traders 2.2 Algorithmic trading 2.2.1 Algorithm definitions 2.2.2 Algorithmic trading definitions 2.2.3 High-frequency trading 2.2.4 Characteristics of algorithmic trading and high-frequency trading 2.2.5 Trading algorithm characteristics 3 METHODOLOGY 3.1 Data collection 3.2 Data analysis 4 ALGORITHM-BASED INTRADAY TRADING STRATEGIES AND THEIR PROFIT POTENTIAL 4.1 Momentum strategy 4.1.1 Definition and basic principle of the strategy 4.1.2 Underlying theories of the momentum strategy 4.1.3 Selected studies of an algorithmic intraday momentum strategy 4.2 Mean reversion strategy 4.2.1 Definition and basic principle of the strategy 4.2.2 Underlying theories of the mean reversion strategy 4.2.3 Relation of mean reversion and momentum 4.2.4 Selected studies of an algorithmic intraday mean reversion strategy 4.3 Arbitrage strategy 4.3.1 Definition and basic principle of the strategy 4.3.2 Types of Arbitrage 4.3.3 Underlying theories of the arbitrage strategy 4.3.4 Selected studies of an algorithmic intraday statistical arbitrage strategy 4.4 Further trading algorithms and strategy components 4.4.1 Speed Advantage algorithms 4.4.2 Accuracy Advantage Algorithms 5 IMPACT OF ALGORITHMIC TRADING ON MARKET LIQUIDITY AND VOLATILITY 5.1 Market liquidity 5.1.1 Definition 5.1.2 Bid-Ask Spread 5.1.3 Dimensions of liquidity 5.1.4 The impact of algorithmic trading on market liquidity 5.2 Market volatility 5.2.1 Definition and characteristics of volatility 5.2.2 The impact of algorithmic trading on market volatility 6 CONCLUSION AND FUTURE DEVELOPMENTS OF ALGORITHMIC TRADING PUBLICATION BIBLIOGRAPHY DECLARATION OF HONOR

Inovácia na finančných trhoch poskytuje nové príležitosti. Algoritmické obchodovanie je vhodný spôsob využitia týchto príležitostí. Táto práca sa zaoberá návrhom a implementáciou systému, ktorý by dovoľoval svojím uživateľom vytvárať vlastné obchodovacie stratégie, a pomocou nich obchodovať na burzách. Práca kladie dôraz na návrh distribuovaného systému, ktorý bude škálovatelný, pomocou technológií cloud computingu.

Algorithmic and high-frequency trading on capital market Abstract The subject of this diploma thesis is legal regulation and development of regulation of algorithmic and high-frequency trading on capital market within Community Law but also within several European countries, USA and Japan. The aim of this diploma thesis is to define terms of algorithmic and high-frequency trading, which were not thoroughly regulated until lately, to outline development of legal regulation, to compare different approaches to their regulation in different countries and to assess the phenomenon of algorithmic and high- frequency trading. The diploma theses uses descriptive method to define the fundamental terms and discuss positive legal framework. It also uses deduction for assessment and comparative method to examine different approaches to legal regulation in different countries. The first chapter characterizes capital market as a place in which algorithmic and high- frequency trading takes place, including its historical development, participants and supervisory authorities. The second chapter defines terms of algorithmic and high-frequency trading considering their historical development and both mutual similarities their differences and their characteristics. It also includes an analysis of their key aspects and related...

In this thesis, the aim is to investigate whether a pairs trading strategy on Swedish stocks can generate a higher risk-adjusted return compared to a buy-and-hold strategy on a benchmark index. The benchmark index is the OMX Stockholm Benchmark-index (OMXSBPI), which is an index that should reflect the Swedish market in general. With a statistical focus, a trading algorithm is built which is then evaluated on data between the years 2018 to 2021. The statistical concepts this thesis is based on are stationarity and cointegration and it is the Augmented Dickey-Fuller test that forms the basis for being able to test these concepts. The risk-adjusted return for the strategy is evaluated using the popular measure Sharpe ratio, which is then compared to the Sharpe ratio for the OMXSBPI-index. The results obtained in this study can not confirm that the pairs trading strategy is better than a buy-and-hold strategy on the OMXSBPI-index in terms of risk-adjusted return. One indication, however, is that the strategy seems to perform better in conditions when the market is declining. In 2018, the index went down by 7.7060 while the strategy went up by 7.5100 percent. As it is data for only one year, it is not possible to determine whether it is due to chance or a potential edge of the strategy.

In this master's thesis, hidden Markov models (HMM) are evaluated as a tool for forecasting movements in a currency cross. With an ever increasing electronic market, making way for more automated trading, or so called algorithmic trading, there is constantly a need for new trading strategies trying to find alpha, the excess return, in the market. HMMs are based on the well-known theories of Markov chains, but where the states are assumed hidden, governing some observable output. HMMs have mainly been used for speech recognition and communication systems, but have lately also been utilized on financial time series with encouraging results. Both discrete and continuous versions of the model will be tested, as well as single- and multivariate input data. In addition to the basic framework, two extensions are implemented in the belief that they will further improve the prediction capabilities of the HMM. The first is a Gaussian mixture model (GMM), where one for each state assign a set of single Gaussians that are weighted together to replicate the density function of the stochastic process. This opens up for modeling non-normal distributions, which is often assumed for foreign exchange data. The second is an exponentially weighted expectation maximization (EWEM) algorithm, which takes time attenuation in consideration when re-estimating the parameters of the model. This allows for keeping old trends in mind while more recent patterns at the same time are given more attention. Empirical results shows that the HMM using continuous emission probabilities can, for some model settings, generate acceptable returns with Sharpe ratios well over one, whilst the discrete in general performs poorly. The GMM therefore seems to be an highly needed complement to the HMM for functionality. The EWEM however does not improve results as one might have expected. Our general impression is that the predictor using HMMs that we have developed and tested is too unstable to be taken in as a trading tool on foreign exchange data, with too many factors influencing the results. More research and development is called for.

<p>In this master's thesis, hidden Markov models (HMM) are evaluated as a tool for forecasting movements in a currency cross. With an ever increasing electronic market, making way for more automated trading, or so called algorithmic trading, there is constantly a need for new trading strategies trying to find alpha, the excess return, in the market.</p><p>HMMs are based on the well-known theories of Markov chains, but where the states are assumed hidden, governing some observable output. HMMs have mainly been used for speech recognition and communication systems, but have lately also been utilized on financial time series with encouraging results. Both discrete and continuous versions of the model will be tested, as well as single- and multivariate input data.</p><p>In addition to the basic framework, two extensions are implemented in the belief that they will further improve the prediction capabilities of the HMM. The first is a Gaussian mixture model (GMM), where one for each state assign a set of single Gaussians that are weighted together to replicate the density function of the stochastic process. This opens up for modeling non-normal distributions, which is often assumed for foreign exchange data. The second is an exponentially weighted expectation maximization (EWEM) algorithm, which takes time attenuation in consideration when re-estimating the parameters of the model. This allows for keeping old trends in mind while more recent patterns at the same time are given more attention.</p><p>Empirical results shows that the HMM using continuous emission probabilities can, for some model settings, generate acceptable returns with Sharpe ratios well over one, whilst the discrete in general performs poorly. The GMM therefore seems to be an highly needed complement to the HMM for functionality. The EWEM however does not improve results as one might have expected. Our general impression is that the predictor using HMMs that we have developed and tested is too unstable to be taken in as a trading tool on foreign exchange data, with too many factors influencing the results. More research and development is called for.</p>

Titel:  Flash-krascher – ett allvarligt problem på Stockholmsbörsen? Författare:  Madelene Söderström &amp; Sebastian Roth Handledare: Bo Sjö Ämne:  Nationalekonomi – Kandidatuppsats inom finans Syfte:  Syftet med arbetet är att fördjupa förståelsen kring flash-krascher och vilken påverkan dessa har på handeln av värdepapper som sker på Stockholmsbörsen. Vi hoppas också att studien ger en klarare bild av hur flash-krascher påverkar olika aktörer med koppling till aktiehandeln i Sverige. Metod:  Uppsatsen är baserad på en kvalitativ studie utförd med intervjurespondenter med varierande koppling till Stockholmsbörsen och den svenska finansmarknaden. Teori:  Uppsatsen utgår främst från tidigare forskning inom ämnet bestående av studier baserade på händelser och data från USA. Annan ekonomisk teori som presenteras i studien är adverse selection. Empiri:  Uppsatsen är bestående av sju semistrukturerade intervjuer med aktörer på finansmarknaden. Intervjuerna jämförs med tidigare inträffade händelser i USA för att diskutera möjliga slutsatser om flash-krascher på Stockholmsbörsen. Slutsats:  Studien kommer fram till att det är osannolikt att flash-krascher av den magnituden som inträffat i USA 6 maj 2010 inträffar på Stockholmsbörsen idag. Vidare så verkar flash-krascher inte ha särskilt stor påverkan på aktörer på Stockholmsbörsen, däremot kan det finnas en viss oros- och förtroendeproblematik kopplad till flash-krascher som bör tas på allvar. I studien av tidigare forskning finner vi intressanta teorier för hur flash-krascher kan förutses. Vi kan däremot inte dra några slutsatser kring dessa teorier kopplat till Stockholmsbörsen. / Title:  Flash crashes – a severe problem at Nasdaq OMX Stockholm? Authors:  Madelene Söderström &amp; Sebastian Roth Advisor:  Bo Sjö Subject:  Bachelor thesis in finance Purpose:  The purpose of this study is to understand and critically examine the impact flash crashes might have on the market for securities at Nasdaq OMX Stockholm. Our goal is to provide a clearer view on how flash crashes affect the trade and the market participants. Method:  This thesis is a qualitative study based on interviews with respondents with different approach to both Nasdaq OMX Stockholm and the financial market in Sweden. Theory:  The thesis is based on earlier studies within the subject made from data and events from United States of America. Other economic theories that the thesis involve is adverse selection. Empirics:  The study is predicated around seven semi structured interviews with participants on the financial market in Sweden. The interviews are compared with the earlier events from USA to make for conclusions about flash crashes on Nasdaq OMX Stockholm. Conclusion:  We find that it is unlikely that a flash crash of the same magnitude as the May 6, 2010 flash crash will occur on the Nasdaq OMX Stockholm exchange today. Furthermore, flash crashes appear to have little impact on the market participants at Nasdaq OMX Stockholm, though there may be concerns about trust issues following flash crashes that should be considered. While studying some of the earlier research we find interesting theories about ways to predict flash crashes before they have occurred, we can’t make any conclusions about these theories connected to Nasdaq OMX Stockholm though.