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Algorithmic fundamental trading
Pižl, Vojtěch ; Krištoufek, Ladislav (advisor) ; Bubák, Vít (referee)
This thesis aims to apply methods of value investing into developing field of algorithmic trading. Firstly, we investigate the effect of several fundamental variables on stock returns using the fixed effects model and portfolio approach. The results confirm that size and book- to-market ratio explain some variation in stock returns that market alone do not capture. Moreover, we observe a significant positive effect of book-to-market ratio and negative effect of size on future stock returns. Secondly, we try to utilize those variables in a trading algorithm. Using the common performance evaluation tools we test several fundamentally based strategies and discover that investing into small stocks with high book-to-market ratio beats the market in the tested period between 2009 and 2015. Although we have to be careful with conclusions as our dataset has some limitations, we believe that there is a market anomaly in the testing period which may be caused by preference of technical strategies over value investing by market participants.
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Comparison of double auction bidding strategies for automated trading agents
Vach, Daniel ; Maršál, Aleš (advisor) ; Burda, Martin (referee)
Comparison of double auction bidding strategies for automated trading agents Bc. Daniel Vach Absctract In this work, ZIP, GDX, and AA automated bidding strategies are compared in symmetric agent-agent experiments with a variable composition of agent population. ZIPOJA, a novel strategy based on ZIP with Oja's rule extension for updating its optimal price, is introduced. Then it is showed that ZIPOJA underperforms in competition against other strategies and that it underperforms even against the original ZIP. Dominance of AA over GDX and ZIP is questioned and it is showed that it is not robust to composition changes of agent population and that the experimental setup strongly affects the results. GDX is a dominant strategy over AA in many experiments in this work in contrast to the previous literature. Some mixed strategy Nash equilibria are found and their basins of attraction are shown by dynamic analysis.
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AI techniques in algorhitmic trading
Šmejkal, Oldřich ; Pavlíčková, Jarmila (advisor) ; Berka, Petr (referee)
Diploma thesis is focused on research and description of current state of machine learning field, focusing on methods that can be used for prediction and classification of time series, which could be then applied in the algorithmic trading field. Reading of theoretical section should explain basic principles of financial markets, algorithmic trading and machine learning also to reader, which was previously familiar with the subject only very thoroughly. Main objective of application part is to choose appropriate methods and procedures, which match current state of art techniques in machine learning field. Next step is to apply it to historical price data. Result of application of selected methods is determination of their success at out of sample data that was not used during model calibration. Success of prediction was evaluated by accuracy metric along with Sharpe ratio of basic trading strategy that is based on model predictions. Secondary outcome of this work is to explore possibilities and test usability of technologies used in application part. Specifically is tested and used SciPy environment, that combines Python with packages and tools designed for data analysis, statistics and machine learning.
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Algorithmic Trading Using Artificial Neural Networks
Červíček, Karel ; Glembek, Ondřej (referee) ; Szőke, Igor (advisor)
Forex is the biggest foreign exchange market. Thanks to high liquidity it is a good candidate for intraday trading with certain trading strategies based on technical and fundamental analysis.Trading strategies can be proposed for automatic algorithmic trading.Strategy in this article is designed with a neural network that holds positions as approximator of time series data based on the exchange rate, which can predict the future.
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Algorithmic Trading Using Artificial Neural Networks
Chlud, Michal ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
This diploma thesis delas with algoritmic trading using neural networks. In the first part, some basic information about stock trading, algorithmic trading and neural networks are given. In the second part, data sets of historical market data are used in trading simulation and also as training input of neural networks. Neural networks are used by automated strategy for predicting future stock price. Couple of automated strategies with different variants of neural networks are evaluated in the last part of this work.
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Low-Latency Architecture for Order Book Building
Závodník, Tomáš ; Kořenek, Jan (referee) ; Dvořák, Milan (advisor)
Information technology forms an important part of the world and algorithmic trading has already become a common concept among traders. The High Frequency Trading (HFT) requires use of special hardware accelerators which are able to provide input response with sufficiently low latency. This master's thesis is focused on design and implementation of an architecture for order book building, which represents an essential part of HFT solutions targeted on financial exchanges. The goal is to use the FPGA technology to process information about an exchange's state with latency so low that the resulting solution is effectively usable in practice. The resulting architecture combines hardware and software in conjunction with fast lookup algorithms to achieve maximum performance without affecting the function or integrity of the order book.
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Hadoop and Business Intelligence
Kerner, Josef ; Šperková, Lucie (advisor) ; Augustín, Jakub (referee)
The main purpose of this thesis is to describe how an integration of a Hadoop platform into currently existing Business Intelligence technologies and processes can augment its data processing and analysis capabilities while encountering Big Data. Furthermore, it describes reasons why the whole Hadoop application ecosystem was founded and informs the reader about the functionality of its primary components. It continues with provision of overview about Hadoop higher-level components architecture and their use in existing Business Intelligence processes such as data ingestion, transformation and analysis. In the last theoretical chapter it focuses itself on describing specific areas of utilization of the Hadoop platform and Big Data in data warehousing, text mining and predictive analytics. From the practical point of view, a particular use case is provided, an implementation of Big Data ETL process in the field of financial markets and trading with a detailed explanation of the corresponding necessities such as data model, ETL code and proposed metrics, which can be further implemented for achieving increased return on investments.
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Algorithmic trading
Uherek, Jiří ; Stádník, Bohumil (advisor) ; Fičura, Milan (referee)
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.
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Currency Trading Strategies
Krpálek, Jan ; Bašta, Milan (advisor) ; Žváčková, Lenka (referee)
My bachelor thesis is concerned with algorithmic trading on foreign exchange markets. Motivation is to create practical work which will cover basics for my ongoing work. From my perspective it is very important to describe assumptions which are necessary in order to properly understand functionality of automated trading. The initial theoretical part includes essential description of methods and tools for Technical Analysis and Money Management which are used afterwards. Further my aim is to show optimization process that is used by professional traders to achieve better and consistent trading results. Finally all calculations will be processed by the TradeStation trading platform and written in EasyLanguage. The end of my thesis includes complete algorithm, ready for immediate use.
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Analysis of Morgan Stanley during the financial crisis
Holiš, Jakub ; Dvořák, Petr (advisor) ; Jablonský, Petr (referee)
The main task of the diploma thesis is an analysis of financial performance and position of Morgan Stanley during several successive periods before and during the subprime financial crisis. Through the analysis of trends in key items, it also demonstrates strong cyclicality of financial performance and position of the investment bank. The first chapter deals with history and key divisions of the Company. The following chapter generally discusses selected phenomena, which, as per the author's view, significantly influenced industry-wide record-breaking performance during the period before the subprime crisis, and which substantially determined Morgan Stanley's risk profile and performance's corrections later during the Crisis. The core part of the Thesis is conceived as an analysis of financial performance and position of Morgan Stanley during the selected periods. The analysis of pre-crisis period until 2006 in the third chapter demonstrates growth of activities lying behind the unprecedented profitability of the Institution. The following fourth chapter analyzes deteriorating financial performance during the subprime crisis and indicates crucial strategy changes, implemented by the Company at the end of 2008. Effects of the strategic changes and challenges of the future development of the Institution are discussed in the last chapter. Additionally, the Thesis includes annexes, which further deal with selected topics and their general relations to investments banks and two annexes which compare Morgan Stanley with its nearest peers during specific periods.
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