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.

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.

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.

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.

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.

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.

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

This MSc Thesis was performed during a study stay at the Hochschule Furtwangen University, Furtwangen, Germany. This Master Project provides a theoretical background for understanding financial market principles. It focuses on foreign exchange market, where it gives a description of fundamentals and price analysis. Further, it covers principles of high-frequency trading including strategy, development and cost. FIX protocol is the financial market communication protocol and is discussed in detail. The core part of Master Project are sorting algorithms, these are covered on theoretical and practical level. Aggregator design includes implementation environment, specification and individual parts of aggregator application represented as objects. Implementation overview can be found in last Chapter.

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...

Dynamic Huffman compression algorithms operate on data-streams with a bounded symbol list. With these algorithms, the complete list of symbols must be contained in main memory or secondary storage. A horizontal format transaction database that is streaming can have a very large item list. Many nodes tax both the processing hardware primary memory size, and the processing time to dynamically maintain the tree. This research investigated Huffman compression of a transaction-streaming database with a very large symbol list, where each item in the transaction database schema’s item list is a symbol to compress. The constraint of a large symbol list is, in this research, equivalent to the constraint of a memory-limited machine. A large symbol set will result if each item in a large database item list is a symbol to compress in a database stream. In addition, database streams may have some temporal component spanning months or years. Finally, the horizontal format is the format most suited to a streaming transaction database because the transaction IDs are not known beforehand This research prototypes an algorithm that will compresses a transaction database stream. There are several advantages to the memory limited dynamic Huffman algorithm. Dynamic Huffman algorithms are single pass algorithms. In many instances a second pass over the data is not possible, such as with streaming databases. Previous dynamic Huffman algorithms are not memory limited, they are asymptotic to O(n), where n is the number of distinct item IDs. Memory is required to grow to fit the n items. The improvement of the new memory limited Dynamic Huffman algorithm is that it would have an O(k) asymptotic memory requirement; where k is the maximum number of nodes in the Huffman tree, k < n, and k is a user chosen constant. The new memory limited Dynamic Huffman algorithm compresses horizontally encoded transaction databases that do not contain long runs of 0’s or 1’s.