1,720,957 research outputs found
Automated algorithmic trading: machine learning and agent-based modelling in complex adaptive financial markets
No full textOver the last three decades, most of the world's stock exchanges have transitioned to electronic trading through limit order books, creating a need for a new set of models for understanding these markets. In this thesis, a number of models are described which provide insight into the dynamics of modern financial markets as well as providing a platform for optimising trading and regulatory decisions.The first part of this thesis proposes an autonomous system that uses novel machine learning techniques to predict the price return over well documented seasonal events and uses these predictions to develop a profitable trading strategy. The DAX, FTSE 100 and S&P 500 are explored for the presence of seasonality events before an automated trading system based on performance weighted ensembles of random forests is introduced and shown to improve the profitability and stability of trading such events. The performance of the models is analysed using a large sample of stocks and the results show that the system described in this section produces superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques.The second part of this thesis explores price impact. For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This section of the thesis proposes an adaptation of the system introduced in the first part for predicting the price impact of order book events. The system's performance is benchmarked using ensembles of other popular regression algorithms including: linear regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks. Finally, a novel procedure for extracting the directional effects of features is proposed and used to explore the features most dominant in the price formation process.The final part of this thesis addresses the requirement for testing algorithmic trading strategies laid out in the Markets in Financial Instruments Directive (MiFID) II by describing an agent-based simulation. Five types of agent operate in a limit order market producing a model that is able to reproduce a number of stylised market properties including: clustered volatility, autocorrelation of returns, long memory in order flow, concave price impact and the presence of extreme price events. The model is found to be insensitive to reasonable parameter variations. Finally, the model is used to explore how trading strategy affects the implementation shortfall of trading a large order. A number of execution strategies with various order types, are evolved and evaluated in the agent-based market. It is shown that the evolved strategies outperform the simple, well known strategies significantly, suggesting that execution strategy plays an important role in determining the implementation shortfall of trading large orders
Automated trading with performance weighted random forests and seasonality
No full textSeasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades. This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy. While simple approaches to trading these regularities can prove profitable, such trading leads to potential large drawdowns (peak-to-trough decline of an investment measured as a percentage between the peak and the trough) in profit. In this paper, we introduce an automated trading system based on performance weighted ensembles of random forests that improves the profitability and stability of trading seasonality events. An analysis of various regression techniques is performed as well as an exploration of the merits of various techniques for expert weighting. The performance of the models is analysed using a large sample of stocks from the DAX. The results show that recency-weighted ensembles of random forests produce superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. It is also found that using seasonality effects produces superior results than not having them modelled explicitly
Predicting equity market price impact with performance weighted ensembles of random forests
Get PDFFor many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events. Specifically, we introduce a prediction system based on performance weighted ensembles of random forests. The system’s performance is benchmarked using ensembles of other popular regression algorithms including: liner regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency-weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmark
High frequency trading strategies, market fragility and price spikes: an agent based model perspective
Get PDFGiven recent requirements for ensuring the robustness of algorithmic trading strategies laid out in the Markets in Financial Instruments Directive II (MiFID II), this paper proposes a novel agent-based simulation for exploring algorithmic trading strategies. Five different types of agents are present in the market. The statistical properties of the simulated market are compared with equity market depth data from the Chi-X exchange and found to be significantly similar. The model is able to reproduce a number of stylised market properties including: clustered volatility, autocorrelation of returns, long memory in order flow, concave price impact and the presence of extreme price events. The results are found to be insensitive to reasonable parameter variations
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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