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When you combine nature's efficiency and the computer's speed, thefinancial possibilities are almost limitless. Today's traders andinvestment analysts require faster, sleeker weaponry in today'sruthless financial marketplace. Battles are now waged at computerspeed, with skirmishes lasting not days or weeks, but mere hours.In his series of influential articles, Richard Bauer has shown whythese professionals must add new computerized decision-making toolsto their arsenal if they are to succeed. In Genetic Algorithms andInvestment Strategies, he uniquely focuses on the most powerfulweapon of all, revealing how the speed, power, and flexibility ofGAs can help them consistently devise winning investmentstrategies. The only book to demonstrate how GAs can workeffectively in the world of finance, it first describes thebiological and historical bases of GAs as well as othercomputerized approaches such as neural networks and chaos theory.It goes on to compare their uses, advantages, and overallsuperiority of GAs. In subsequently presenting a basic optimizationproblem, Genetic Algorithms and Investment Strategies outlines theessential steps involved in using a GA and shows how it mimicsnature's evolutionary process by moving quickly toward anear-optimal solution. Introduced to advanced variations ofessential GA procedures, readers soon learn how GAs can be usedto: * Solve large, complex problems and smaller sets of problems * Serve the needs of traders with widely different investmentphilosophies * Develop sound market timing trading rules in the stock and bondmarkets * Select profitable individual stocks and bonds * Devise powerful portfolio management systems Complete with information on relevant software programs, a glossaryof GA terminology, and an extensive bibliography coveringcomputerized approaches and market timing, Genetic Algorithms andInvestment Strategies unveils in clear, nontechnical language aremarkably efficient strategic decision-making process that, whenimaginatively used, enables traders and investment analysts to reapsignificant financial rewards.
After a decade's development, evolutionary computation (EC) proves to be a powerful tool kit for economic analysis. While the demand for this equipment is increasing, there is no volume exclusively written for economists. This volume for the first time helps economists to get a quick grasp on how EC may support their research. A comprehensive coverage of the subject is given, that includes the following three areas: game theory, agent-based economic modelling and financial engineering. Twenty leading scholars from each of these areas contribute a chapter to the volume. The reader will find himself treading the path of the history of this research area, from the fledgling stage to the burgeoning era. The results on games, labour markets, pollution control, institution and productivity, financial markets, trading systems design and derivative pricing, are new and interesting for different target groups. The book also includes informations on web sites, conferences, and computer software.
Genetic algorithms (GAs) are based on Darwin’s theory of natural selection and survival of the fittest. They are designed to competently look for solutions to big and multifaceted problems. Genetic algorithms are wide groups of interrelated events with divided steps. Each step has dissimilarities, which leads to a broad range of connected actions. Genetic algorithms are used to improve trading systems, such as to optimize a trading rule or parameters of a predefined multiple indicator market trading system. Genetic Algorithms and Applications for Stock Trading Optimization is a complete reference source to genetic algorithms that explains how they might be used to find trading strategies, as well as their use in search and optimization. It covers the functions of genetic algorithms internally, computer implementation of pseudo-code of genetic algorithms in C++, technical analysis for stock market forecasting, and research outcomes that apply in the stock trading system. This book is ideal for computer scientists, IT specialists, data scientists, managers, executives, professionals, academicians, researchers, graduate-level programs, research programs, and post-graduate students of engineering and science.
After a decade of development, genetic algorithms and genetic programming have become a widely accepted toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering volume devoted entirely to a systematic and comprehensive review of this subject. Chapters cover various areas of computational finance, including financial forecasting, trading strategies development, cash flow management, option pricing, portfolio management, volatility modeling, arbitraging, and agent-based simulations of artificial stock markets. Two tutorial chapters are also included to help readers quickly grasp the essence of these tools. Finally, a menu-driven software program, Simple GP, accompanies the volume, which will enable readers without a strong programming background to gain hands-on experience in dealing with much of the technical material introduced in this work.
This book presents a new computational finance approach combining a Symbolic Aggregate approximation (SAX) technique with an optimization kernel based on genetic algorithms (GA). While the SAX representation is used to describe the financial time series, the evolutionary optimization kernel is used in order to identify the most relevant patterns and generate investment rules. The proposed approach considers several different chromosomes structures in order to achieve better results on the trading platform The methodology presented in this book has great potential on investment markets.
This book is a collection of papers presented at the International Conference on Intelligent Computing, Information and Control Systems (ICICCS 2020). It encompasses various research works that help to develop and advance the next-generation intelligent computing and control systems. The book integrates the computational intelligence and intelligent control systems to provide a powerful methodology for a wide range of data analytics issues in industries and societal applications. The book also presents the new algorithms and methodologies for promoting advances in common intelligent computing and control methodologies including evolutionary computation, artificial life, virtual infrastructures, fuzzy logic, artificial immune systems, neural networks and various neuro-hybrid methodologies. This book is pragmatic for researchers, academicians and students dealing with mathematically intransigent problems.
Computationally-intensive tools play an increasingly important role in financial decisions. Many financial problems-ranging from asset allocation to risk management and from option pricing to model calibration-can be efficiently handled using modern computational techniques. Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics. This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested empirically. This revised edition includes two new chapters, a self-contained tutorial on implementing and using heuristics, and an explanation of software used for testing portfolio-selection models. Postgraduate students, researchers in programs on quantitative and computational finance, and practitioners in banks and other financial companies can benefit from this second edition of Numerical Methods and Optimization in Finance.
Control of an impartial balance between risks and returns has become important for investors, and having a combination of financial instruments within a portfolio is an advantage. Portfolio management has thus become very important for reaching a resolution in high-risk investment opportunities and addressing the risk-reward tradeoff by maximizing returns and minimizing risks within a given investment period for a variety of assets. Metaheuristic Approaches to Portfolio Optimization is an essential reference source that examines the proper selection of financial instruments in a financial portfolio management scenario in terms of metaheuristic approaches. It also explores common measures used for the evaluation of risks/returns of portfolios in real-life situations. Featuring research on topics such as closed-end funds, asset allocation, and risk-return paradigm, this book is ideally designed for investors, financial professionals, money managers, accountants, students, professionals, and researchers.
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.