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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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Algorithmization for decision support
Strečková, Nikola ; Budík, Jan (referee) ; Dostál, Petr (advisor)
This thesis is focused on understanding investment strategies on cryptocurrency markets and thanks to the own algorithm create an automated program to support the decision making. To deploy and develop the algorithm is used MetaTrader5 platform, which uses the MQL5 programming language. The strategy was backtested on historical data of BTCUSD and BTCEUR to validate the efficiency of the strategy.
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Algorithmic and high-frequency trading on capital market
Kádě, Lukáš ; Kohajda, Michael (advisor) ; Kotáb, Petr (referee)
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...
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Neural Networks for Machine Learning in Algorithmic Trading
Koubek, David ; Krištoufek, Ladislav (advisor) ; Debatz, Laure (referee)
This thesis investigates the forecasting ability of the artificial neural network (ANN) models on five major currency pairs and compares the accuracy of several ANN ar- chitectures to the difficult to outperform random walk (RW) benchmark. The ANNs mostly stand ground against the RW, yet fail to attain significantly different results for most of the currencies in out-of-sample testing. A good predictive accuracy of a few ANN models was shown only for the Japanese yen in our results. Less complex neural network architectures supported the notion of having better generalisation capabilities for most of our datasets. JEL Classification C01, C32, C45, C51, C52, C53, C87 Keywords artificial neural networks, machine learning, finan- cial markets, Forex, day trading, algorithmic trad- ing, pattern recognition, computational learning the- ory, backtesting, forecasting Author's e-mail 56374598@fsv.cuni.cz, mrkoubek@gmail.com Supervisor's e-mail ladislav.kristoufek@fsv.cuni.cz Abstrakt Tato práce zkoumá schopnost modelů na bázi neuronových sítí (ANN) předpovídat budoucí cenu pěti hlavních měnových párů a porovnává přesnost předpovědí s těžce překonatelným modelem random walk (RW), který vždy hádá následující cenu jako totožnou se současnou cenou. ANN modely převážně obstály oproti RW, ale pro většinu měn...
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Pairs trading at CEE markets
Šedivý, Jakub ; Maršál, Aleš (advisor) ; Kraicová, Lucie (referee)
We investigate the use of investment strategy called pairs trading on small-sized equity markets located in Central Eastern Europe. Pairs trading is self-financing trading strategy that identifies two stocks based on their historical relationship, and makes profit on their short-term relative mispricing, since the strategy relies on their convergence into the long- term equilibrium. The objective of this thesis is to compare two different methods of pairs trading, distance method based on minimizing the sum of squared deviations between nor- malized historical prices and cointegration method using daily data from June 2008 to March 2017. We examine whether any of those method is profitable on Prague Stock Exchange, Bucharest Stock Exchange and Budapest Stock Exchange and can be used on such markets with high industry diversity. Our findings were not stastically different from zero in all but one case and majority of average returns was negative. In comparison to US and Finnish equity markets the strategy falls behind. Even though we identified some cointegrated pairs, their profitability was more than questionable and further investiga- tion showed that small equity markets such as the ones we have studied are not a good fit for pairs trading strategy.
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Algorithmic Trading Using Artificial Neural Networks
Radoš, Daniel ; Plchot, Oldřich (referee) ; Szőke, Igor (advisor)
This master's thesis is focused on algorithmic trading on the forex market using artificial neural networks. In the introduction, there are generally described terms concerning the trading. Subsequently, in the following chapters, the thesis describes the theory of neural networks and their possible use. The practical part contains designed business strategies with neural networks. Inputs used in the network are indicators of technical analysis or directly price level. Business strategies have been implemented and tested. In the conclusion, there are summarized findings of individual business models.
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Algoritmické obchodování párů
Razumňak, Michal ; Stádník, Bohumil (advisor) ; Fučík, Vojtěch (referee)
Pair trading is a well-known strategy based on statistical arbitrage. This strategy uses a short-term deviation from the mean value of the price ratio of two highly correlated stocks from the same sector as the opportunity to open a position. When ratio returns to its mean value again, the position closes. This strategy has been used for many years and the main outcome of this thesis was to test whether this strategy can be profitable even in current market conditions. For that purpose, data ranging from 2010 to April 2017 on all stocks included in the S&P 500 index were used. It was subsequently found that a pair trading strategy generated 25x higher absolute profit in comparison to random agent. Thus, it can still be considered as a profitable strategy.
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High frequency trading and its impact on the financial market stability
Haushalterová, Gabriela ; Brůna, Karel (advisor) ; Pour, Jiří (referee)
The thesis analyses high frequency trading, specifically its main characteristics, which make it different from algorithmic trading. Furthermore, the thesis looks closer into major risks, which are new to market, and their impact on market quality and other investors. The next chapter is dedicated to trading strategies, which are typical for high frequency trading. In conclusion, there is discussed the impact on the market quality caused by high frequency trading, namely in terms of liquidity, volatility and price discovery.
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Statistická arbitráž při algoritmickém obchodování amerických dluhopisů
Juhászová, Jana ; Stádník, Bohumil (advisor) ; Janda, Karel (referee)
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.
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