Computational Intelligence Techniques For Trading And Investment

Author : Christian Dunis
Publisher : Routledge
ISBN 13 : 1136195114
Total Pages : 239 pages
Book Rating : 4.12/5 ( download)

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Download or read book Computational Intelligence Techniques for Trading and Investment written by Christian Dunis and published by Routledge. This book was released on 2014-03-26 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational intelligence, a sub-branch of artificial intelligence, is a field which draws on the natural world and adaptive mechanisms in order to study behaviour in changing complex environments. This book provides an interdisciplinary view of current technological advances and challenges concerning the application of computational intelligence techniques to financial time-series forecasting, trading and investment. The book is divided into five parts. The first part introduces the most important computational intelligence and financial trading concepts, while also presenting the most important methodologies from these different domains. The second part is devoted to the application of traditional computational intelligence techniques to the fields of financial forecasting and trading, and the third part explores the applications of artificial neural networks in these domains. The fourth part delves into novel evolutionary-based hybrid methodologies for trading and portfolio management, while the fifth part presents the applications of advanced computational intelligence modelling techniques in financial forecasting and trading. This volume will be useful for graduate and postgraduate students of finance, computational finance, financial engineering and computer science. Practitioners, traders and financial analysts will also benefit from this book.

Author : Cris Doloc
Publisher : John Wiley & Sons
ISBN 13 : 1119550521
Total Pages : 304 pages
Book Rating : 4.25/5 ( download)

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Download or read book Applications of Computational Intelligence in Data-Driven Trading written by Cris Doloc and published by John Wiley & Sons. This book was released on 2019-10-31 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.

Author : Christian L. Dunis
Publisher : Springer
ISBN 13 : 1137488808
Total Pages : 349 pages
Book Rating : 4.00/5 ( download)

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Download or read book Artificial Intelligence in Financial Markets written by Christian L. Dunis and published by Springer. This book was released on 2016-11-21 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.

Author : Simão Moraes Sarmento
Publisher : Springer Nature
ISBN 13 : 3030472515
Total Pages : 108 pages
Book Rating : 4.11/5 ( download)

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Download or read book A Machine Learning based Pairs Trading Investment Strategy written by Simão Moraes Sarmento and published by Springer Nature. This book was released on 2020-07-13 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.

Author : Cris Doloc
Publisher : John Wiley & Sons
ISBN 13 : 1119550505
Total Pages : 304 pages
Book Rating : 4.01/5 ( download)

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Download or read book Applications of Computational Intelligence in Data-Driven Trading written by Cris Doloc and published by John Wiley & Sons. This book was released on 2019-10-29 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.

Author : Ondrej Martinsky
Publisher : Harriman House Limited
ISBN 13 : 1906659532
Total Pages : 212 pages
Book Rating : 4.30/5 ( download)

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Download or read book Intelligent Trading Systems written by Ondrej Martinsky and published by Harriman House Limited. This book was released on 2010-02-15 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work deals with the issue of problematic market price prediction in the context of crowd behavior. "Intelligent Trading Systems" describes technical analysis methods used to predict price movements.

Author : Qingquan Tony Zhang
Publisher : Springer Nature
ISBN 13 : 3031116127
Total Pages : 340 pages
Book Rating : 4.24/5 ( download)

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Download or read book Alternative Data and Artificial Intelligence Techniques written by Qingquan Tony Zhang and published by Springer Nature. This book was released on 2022-10-31 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.

Author : Christian L. Dunis
Publisher : John Wiley & Sons
ISBN 13 : 0470871342
Total Pages : 426 pages
Book Rating : 4.48/5 ( download)

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Download or read book Applied Quantitative Methods for Trading and Investment written by Christian L. Dunis and published by John Wiley & Sons. This book was released on 2004-01-09 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a manual on quantitative financial analysis. Focusing on advanced methods for modelling financial markets in the context of practical financial applications, it will cover data, software and techniques that will enable the reader to implement and interpret quantitative methodologies, specifically for trading and investment. Includes contributions from an international team of academics and quantitative asset managers from Morgan Stanley, Barclays Global Investors, ABN AMRO and Credit Suisse First Boston. Fills the gap for a book on applied quantitative investment & trading models Provides details of how to combine various models to manage and trade a portfolio

Author : Söhnke M. Bartram
Publisher : CFA Institute Research Foundation
ISBN 13 : 195292703X
Total Pages : 95 pages
Book Rating : 4.34/5 ( download)

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Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Author : Paul P. Wang
Publisher : Springer Science & Business Media
ISBN 13 : 354072821X
Total Pages : 232 pages
Book Rating : 4.14/5 ( download)

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Download or read book Computational Intelligence in Economics and Finance written by Paul P. Wang and published by Springer Science & Business Media. This book was released on 2007-07-11 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Readers will find, in this highly relevant and groundbreaking book, research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results.