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.

No description available.

No description available.

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.

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.

No description available.

[pt] As mudanças tecnológicas e regulatórias foram facilitadores para o surgimento dos investidores de alta frequência, HFTs, no mix de participantes do mercado financeiro. Como classe, estes investidores não constituem uma entidade coerente e seu impacto e contribuição para a formação do preço não é clara. Esse trabalho analisou 10 categorias de investidores, que se diferenciam por suas características de latência, a partir de uma base de dados composta por todas as ordens enviadas para o book de dólar futuro com vencimento em 1 de agosto de 2013, da Bolsa de Valores e Mercadorias e Futuros (BMFBovespa). Dentre toda as categorias de instidores testadas, a categoria HFT 1, a mais rápida de todas, foi a que apresentou o maior coeficiente de impacto no preço, 20 por cento, e a maior medida de contribuição relativa para a volatilidade fundamental, 10 por cento. / [en] Technological and regulatory changes were facilitators for the emergence of high frequency traders, HFTs, in the mix of financial market participants. As a class, these investors do not constitute a coherent entity and its impact and contribution to the price formation are not clear. This study analyzed 10 categories of investors, who are distinguished by their latency characteristics from a database comprised of all orders sent to the book of future dollar maturing on August 1, 2013, in the Brazilian Stock Exchange and Commodities and Futures Exchange (BMFBovespa). Among all the categories of investors tested, the HFT 1, the fastest of all, was the one that had the highest coefficient of impact on price, 20 per cent, and a larger measure of relative contribution to fundamental volatility, 10 per cent.

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.

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.

As science advances, computer technologies are developing rapidly in the past decades. The previous way of traders¡¦ yelling for orders in the house of exchange has been replaced by the Internet and computers. The trading modes of institutional investors are transforming gradually, particularly the radical changes in the US stock market for the past 5 years. The transaction volume from high frequency trading and algorithmic trading is growing dramatically per year, accounting for at least 70% in the U.S. market. And many researchers find these trading methods based on the computer programs good in increasing liquidity, reducing volatility and facilitating price discovery. By using intraday data of Taiwan stock market in 2008 to conduct empirical research, this study intends to analyze the effect of this trend on the TW stock market. Empirical results found that the greater the market capitalization, liquidity, stock volatility are, the higher the proportion of algorithmic trading will be, but which only exists in foreign institutional investors. On the other hand, the increase of the proportion of algorithmic trading can improve liquidity, meanwhile raise the volatility. The conclusion remains unchanged when applied to control the effect of financial tsunami. That means algorithmic trader¡¦s behaviors are not always positive. This result could be related to the special transaction mechanism or lower competition of algorithmic trading in Taiwan. As to trading strategy, the result found that foreign institutional investors focus on momentum strategies, whereas particular dealers act for the sake of index arbitrage or hedge. In summary, the algorithmic trader¡¦s transaction bears positive (liquidity) and negative (volatility) impact on the market at the same time. For individual investors, algorithmic trading¡¦s momentum strategy could appeal to them, but they may not make a profit from these trades, because this strategy could merely want to pull price higher and sell stock or the opposite. About regulators, algorithmic traders¡¦ behavior should be regulated partly; regulatory authorities might also consider adding the circuit mechanism similar to South Koreas¡¦, especially on the program trading. Keywords: algorithmic trading, high frequency trading, intraday, strategy, liquidity, volatility, market quality