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.

<p> This dissertation consists of two topics. In chapter 1, we develop a discrete disaggregated model in which, the market maker can observe individual order flow instead of a batch order in Kyle (1985). The model suggests that the behavior of the uninformed traders play an important role in how the informed make the optimal trading strategy : when the uninformed is more likely to use large order, the informed will also trade large, no matter what size of signal he receives, and when the uninformed tend to trade with small size order, the informed will have to trade small quantity to maximize his expected profit, even if he receives the large value signal. When the uninformed does not prefer size of order, the informed will trade smaller (larger) quantities when receiving small(large) value signals. The result is consistent with the behavior of the informed in Kyle (1985). We further investigate order flow disaggregation on market liquidity by comparing aggregated order flow structure, in which market maker observes aggregated order flow. When the model setup is symmetric, the aggregated structure can provide more liquidity, while the disaggregated structure is more liquid under the asymmetric model setup. In chapter 2, we employ the type 2 joint power law distribution in Mardia (1962) to study the joint effect of the return and trading volume. The parameter estimate for marginal distribution in joint power-law exhibits the same pattern as in univariate power law literature for return and volume, but the value are smaller due to the joint effect of return and trading volume. However, we find the joint power law shows higher predictability than the univariate power law by employing the measure MSE (Means squared error). Additionally, the type 2 joint power law indicates the linear relationship between log absolute value of return and log trading volume , which suggests the none linear impact of trading volume on price. We also find that, as sampling interval shrinks from day to 15 seconds, the price impact will increase. And also as the waiting time for two consecutive transactions shrinks, the price impact will increase, which is in line with the result of Dufour and Engle (2000). </p>

Submitted by Gustavo de Faro Colen Nunes (gustavocolennunes@gmail.com) on 2013-02-28T19:45:35Z No. of bitstreams: 1 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) / Made available in DSpace on 2013-03-01T11:06:28Z (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) Previous issue date: 2013-02-01 / 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.

This dissertation addresses the fundamental question of what factors drive equity prices and investigates the mechanisms through which the drivers influence the price dynamics. The studies are based on the two different frequency levels of financial data. The first part aims to identify what systematic risk factors affect the expected return of stocks based on historical data with frequency being daily or monthly. The second part aims to explain how the hidden supply-demand of a stock affects the stock price dynamics based on market data observed at frequency levels generally between a millisecond and a second. With more and more financial market data becoming available, it greatly facilitates quantitative approaches for analyzing asset price dynamics and market microstructure problems. In the first part, we propose an econometric measure, terms as modularity, for characterizing the cluster structure in a universe of stocks. A high level of modularity implies that the cluster structure of the universe of stocks is highly evident, and low modularity implies a blurred cluster structure. The modularity measure is shown to be related to the cycle of the economy. In addition, individual stock's sensitivity to the modularity measure is shown to be related to its expected return. From 1992 to 2011, the average annual return of stocks with the lowest sensitivity exceeds that of the stocks with highest sensitivities by approximately 7.6%. Considerations of modularity as an asset pricing factor expand the investment opportunity set to passive investors. In the second part, we analyze the effect of hidden demands/supplies in equity trading market on the stock price dynamics. We propose a statistical estimation model for average hidden liquidity based on the limit orderbook data. Not only the estimated hidden liquidity explains the probabilistic property in market microstructure better, it also refines the existing price impact model and achieves higher explanation powers. Our enhanced price impact model offers a base for devising optimal order execution strategies. After we develop an optimal execution strategy based on the price impact function, the advantage of this strategy over benchmark strategies is tested on a simulated stock trading model calibrated by historical data. Simulation tests indicate that our strategy yields significant savings in transaction cost over the benchmark strategies.

This thesis examines an impact of high frequency trading on equity market qualities. As an indicator of market quality, stock prices realized volatility is used. To estimate the high frequency trading activity, we implement a special method of identification of high frequency orders from quote data. Study of relation between high frequency trading and market qualities is incited by growing concerns about the welfare impacts of high frequency trading and connected activities. In order to test the dependence and causality between high frequency trading activity and volatility, we implement time-scale estimation techniques. Wavelet coherence is used to study localized dependence. The analysis is amended by a robustness check, using wavelet correlation. Results show inconsistent dependence at short trading horizons and regions of significant continuous dependence at trading horizons within hours. Powered by TCPDF (www.tcpdf.org)