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| Author | : Coulibaly Oumar Baba |
| Publisher | : |
| Total Pages | : 300 |
| Release | : 2007 |
| Genre | : Investment analysis |
| ISBN | : |
| Author | : Richard J. Bauer |
| Publisher | : John Wiley & Sons |
| Total Pages | : 324 |
| Release | : 1994-03-31 |
| Genre | : Business & Economics |
| ISBN | : 9780471576792 |
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.
| Author | : Fan Zhang |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2013 |
| Genre | : Genetic algorithms |
| ISBN | : |
In this project, a genetic algorithm (GA) is used in the development of investment strategies to decide the optimum asset allocations that back up a portfolio of term insurance contracts and the re-balancing strategy to respond to the changing financial markets, such as change in interest rates and mortality experience. The objective function used as the target to be maximized in GA allows us to accommodate three objectives that should be of interest to the management in insurance companies. The three objectives under consideration are maximizing the total value of wealth at the end of the period, minimizing the variance of the total value of the wealth across the simulated interest rate scenarios and achieving consistent returns on the portfolio from year to year. One objective may be in conflict with another, and GA tries to find a solution, among the large searching space of all the solutions, that favors a particular objective as specified by the user while not worsening other objectives too much. Duration matching, a popular approach to manage risks underlying the traditional life insurance portfolios, is used as a benchmark to examine the effectiveness of the strategies obtained through the use of genetic algorithms. Experiments are conducted to compare the performance of the investment strategy proposed by the genetic algorithm to the duration matching strategy in terms of the different objectives under the testing scenarios. The results from the experiments successfully illustrate that with the help of GA, we are able to find a strategy very similar to the strategy from duration matching. We are also able to find other strategies that could outperform duration matching in terms of some of the desired objectives and are robust in the tested changing environment of interest rate and mortality.
| Author | : Kapoor, Vivek |
| Publisher | : IGI Global |
| Total Pages | : 262 |
| Release | : 2021-06-25 |
| Genre | : Computers |
| ISBN | : 1799841065 |
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.
| Author | : Tiago Martins |
| Publisher | : Springer Nature |
| Total Pages | : 68 |
| Release | : 2021-07-08 |
| Genre | : Technology & Engineering |
| ISBN | : 3030766802 |
This book presents a genetic algorithm that optimizes a grid template pattern detector to find the best point to trade in the SP 500. The pattern detector is based on a template using a grid of weights with a fixed size. The template takes in consideration not only the closing price but also the open, high, and low values of the price during the period under testing in contrast to the traditional methods of analysing only the closing price. Each cell of the grid encompasses a score, and these are optimized by an evolutionary genetic algorithm that takes genetic diversity into consideration through a speciation routine, giving time for each individual of the population to be optimized within its own niche. With this method, the system is able to present better results and improves the results compared with other template approaches. The tests considered real data from the stock market and against state-of-the-art solutions, namely the ones using a grid of weights which does not have a fixed size and non-speciated approaches. During the testing period, the presented solution had a return of 21.3% compared to 10.9% of the existing approaches. The use of speciation was able to increase the returns of some results as genetic diversity was taken into consideration.
| Author | : Madeha Shahid |
| Publisher | : |
| Total Pages | : 146 |
| Release | : 2008 |
| Genre | : |
| ISBN | : |
| Author | : Adrian E. Drake |
| Publisher | : |
| Total Pages | : 32 |
| Release | : 1998 |
| Genre | : Genetic algorithms |
| ISBN | : 9781862743342 |
| Author | : Rick Riolo |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 322 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1441989838 |
Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international group of genetic programming theorists and practitioners met to examine how GP theory informs practice and how GP practice impacts GP theory. The contributions cover the full spectrum of this relationship and are written by leading GP theorists from major universities, as well as active practitioners from leading industries and businesses. Chapters include such topics as John Koza's development of human-competitive electronic circuit designs; David Goldberg's application of "competent GA" methodology to GP; Jason Daida's discovery of a new set of factors underlying the dynamics of GP starting from applied research; and Stephen Freeland's essay on the lessons of biology for GP and the potential impact of GP on evolutionary theory.
| Author | : Jyotsna Kumar Mandal |
| Publisher | : Springer Nature |
| Total Pages | : 246 |
| Release | : 2021-10-11 |
| Genre | : Technology & Engineering |
| ISBN | : 9811643016 |
This book gathers a collection of high-quality peer-reviewed research papers presented at First International Conference on Innovations in Software Architecture and Computational Systems (ISACS 2021), held at Guru Nanak Institute of Technology, Kolkata, India, during 2 – 3 April 2021. The book primarily focuses on developing artificial intelligence-based algorithms and methodologies for enabling intelligent hardware and software systems. This book brings together the latest findings on efficient technological solutions for developing intelligent and hybrid systems, intelligent software architecture, machine intelligence-based analytical tools and also smart sensors and networks. The prime focus is on solving technological problems using state-of-the-art research finding like fuzzy computing, evolutionary and hybrid frameworks, neuro-computing, etc., along with other AI-based computation platforms. The book offers a valuable resource for all undergraduate, postgraduate students and researchers interested in exploring solution frameworks for social good problems using artificial intelligence.
| Author | : Hamido Fujita |
| Publisher | : Springer Nature |
| Total Pages | : 644 |
| Release | : 2021-07-19 |
| Genre | : Computers |
| ISBN | : 3030794636 |
This two-volume set of LNAI 12798 and 12799 constitutes the thoroughly refereed proceedings of the 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, held virtually and in Kuala Lumpur, Malaysia, in July 2021. The 87 full papers and 19 short papers presented were carefully reviewed and selected from 145 submissions. The IEA/AIE 2021 conference will continue the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas. These areas include the following: Part I, Artificial Intelligence Practices: Knowledge discovery and pattern mining; artificial intelligence and machine learning; sematic, topology, and ontology models; medical and health-related applications; graphic and social network analysis; signal and bioinformatics processing; evolutionary computation; attack security; natural language and text processing; fuzzy inference and theory; and sensor and communication networks Part II, From Theory to Practice: Prediction and recommendation; data management, clustering and classification; robotics; knowledge based and decision support systems; multimedia applications; innovative applications of intelligent systems; CPS and industrial applications; defect, anomaly and intrusion detection; financial and supply chain applications; Bayesian networks; BigData and time series processing; and information retrieval and relation extraction
