Biologically Inspired Algorithms For Financial Modelling
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| Author | : Anthony Brabazon |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 276 |
| Release | : 2006-03-28 |
| Genre | : Computers |
| ISBN | : 3540313079 |
Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling. In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies – neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures. The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain.
| Author | : Anthony Brabazon |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 220 |
| Release | : 2010-06-09 |
| Genre | : Computers |
| ISBN | : 3642139493 |
The chapters in this book illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. The eleven chapters were selected following a rigorous, peer-reviewed, selection process.
| Author | : Alam, Shafiq |
| Publisher | : IGI Global |
| Total Pages | : 397 |
| Release | : 2014-05-31 |
| Genre | : Computers |
| ISBN | : 1466660791 |
Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. Over the past decade, research materials in this area have dramatically increased, providing clear evidence of the popularity of these techniques. Biologically-Inspired Techniques for Knowledge Discovery and Data Mining exemplifies prestigious research and shares the practices that have allowed these areas to grow and flourish. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems. Providing quality work from established researchers, this publication serves to extend existing knowledge within the research communities of data mining and knowledge discovery, as well as for academicians and students in the field.
| Author | : Marco Tomassini |
| Publisher | : Springer |
| Total Pages | : 518 |
| Release | : 2013-04-12 |
| Genre | : Computers |
| ISBN | : 3642372139 |
The book constitutes the refereed proceedings of the 11th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2013, held in Lausanne, Switzerland, in April 2013. The 51 revised full papers presented were carefully reviewed and selected from a total of 91 submissions. The papers are organized in topical sections on neural networks, evolutionary computation, soft computing, bioinformatics and computational biology, advanced computing, and applications.
| Author | : Vasant, Pandian |
| Publisher | : IGI Global |
| Total Pages | : 913 |
| Release | : 2014-11-30 |
| Genre | : Computers |
| ISBN | : 1466672595 |
For decades, optimization methods such as Fuzzy Logic, Artificial Neural Networks, Firefly, Simulated annealing, and Tabu search, have been capable of handling and tackling a wide range of real-world application problems in society and nature. Analysts have turned to these problem-solving techniques in the event during natural disasters and chaotic systems research. The Handbook of Research on Artificial Intelligence Techniques and Algorithms highlights the cutting edge developments in this promising research area. This premier reference work applies Meta-heuristics Optimization (MO) Techniques to real world problems in a variety of fields including business, logistics, computer science, engineering, and government. This work is particularly relevant to researchers, scientists, decision-makers, managers, and practitioners.
| Author | : Leandro N. de Castro |
| Publisher | : Springer |
| Total Pages | : 449 |
| Release | : 2007-08-28 |
| Genre | : Computers |
| ISBN | : 354073922X |
This book constitutes the refereed proceedings of the 6th International Conference on Artificial Immune Systems, ICARIS 2007, held in Santos, Brazil, August 2007. The papers are organized in topical sections on search and optimization, classification and clustering, anomaly detection and negative selection, robotics, control and electronics. Modeling papers, conceptual papers, and technical papers and general applications are also included.
| Author | : Mario R. Guarracino |
| Publisher | : Springer |
| Total Pages | : 684 |
| Release | : 2011-06-24 |
| Genre | : Computers |
| ISBN | : 3642218784 |
This book constitutes thoroughly refereed post-conference proceedings of the workshops of the 16th International Conference on Parallel Computing, Euro-Par 2010, held in Ischia, Italy, in August/September 2010. The papers of these 9 workshops HeteroPar, HPCC, HiBB, CoreGrid, UCHPC, HPCF, PROPER, CCPI, and VHPC focus on promotion and advancement of all aspects of parallel and distributed computing.
| Author | : De-Shuang Huang |
| Publisher | : Springer |
| Total Pages | : 882 |
| Release | : 2016-07-11 |
| Genre | : Computers |
| ISBN | : 331942291X |
This two-volume set LNCS 9771 and LNCS 9772 constitutes - in conjunction with the volume LNAI 9773 - the refereed proceedings of the 12th International Conference on Intelligent Computing, ICIC 2016, held in Lanzhou, China, in August 2016. The 221 full papers and 15 short papers of the three proceedings volumes were carefully reviewed and selected from 639 submissions. The papers are organized in topical sections such as signal processing and image processing; information security, knowledge discovery, and data mining; systems biology and intelligent computing in computational biology; intelligent computing in scheduling; information security; advances in swarm intelligence: algorithms and applications; machine learning and data analysis for medical and engineering applications; evolutionary computation and learning; independent component analysis; compressed sensing, sparse coding; social computing; neural networks; nature inspired computing and optimization; genetic algorithms; signal processing; pattern recognition; biometrics recognition; image processing; information security; virtual reality and human-computer interaction; healthcare informatics theory and methods; artificial bee colony algorithms; differential evolution; memetic algorithms; swarm intelligence and optimization; soft computing; protein structure and function prediction; advances in swarm intelligence: algorithms and applications; optimization, neural network, and signal processing; biomedical informatics and image processing; machine learning; knowledge discovery and natural language processing; nature inspired computing and optimization; intelligent control and automation; intelligent data analysis and prediction; computer vision; knowledge representation and expert system; bioinformatics.
| Author | : Erik Cuevas |
| Publisher | : Springer |
| Total Pages | : 236 |
| Release | : 2016-12-28 |
| Genre | : Technology & Engineering |
| ISBN | : 3319511092 |
This book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods.
| Author | : Argimiro Arratia |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 305 |
| Release | : 2014-05-08 |
| Genre | : Computers |
| ISBN | : 9462390703 |
The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R. Thus, the first chapter gives an introduction to the Principles of Corporate Finance: the markets of stock and options, valuation and economic theory, framed within Computation and Information Theory (e.g. the famous Efficient Market Hypothesis is stated in terms of computational complexity, a new perspective). Chapters 2 and 3 give the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. Chapters 4 and 5 review the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. Chapter 6 covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This chapter is suitable for people outside academics and into the world of financial investments, as a primer in the methods of charting and analysis of value for stocks, as it is done in the financial industry. Moreover, a mathematical foundation to the seemly ad-hoc methods of TA is given, and this is new in a presentation of TA. Chapter 7 reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence) which is material to feed the computer savvy readers. Chapter 8 gives the basic principles of portfolio management, through the mean-variance model, and optimization under different constraints which is a topic of current research in computation, due to its complexity. One important aspect of this chapter is that it teaches how to use the powerful tools for portfolio analysis from the RMetrics R-package. Chapter 9 is a natural continuation of chapter 8 into the new area of research of online portfolio selection. The basic model of the universal portfolio of Cover and approximate methods to compute are also described.
