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Online Algorithms For The Portfolio Selection Problem
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Book Synopsis Online Algorithms for the Portfolio Selection Problem by : Robert Dochow
Download or read book Online Algorithms for the Portfolio Selection Problem written by Robert Dochow and published by Springer. This book was released on 2016-05-24 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robert Dochow mathematically derives a simplified classification structure of selected types of the portfolio selection problem. He proposes two new competitive online algorithms with risk management, which he evaluates analytically. The author empirically evaluates online algorithms by a comprehensive statistical analysis. Concrete results are that follow-the-loser algorithms show the most promising performance when the objective is the maximization of return on investment and risk-adjusted performance. In addition, when the objective is the minimization of risk, the two new algorithms with risk management show excellent performance. A prototype of a software tool for automated evaluation of algorithms for portfolio selection is given.
Book Synopsis Online Portfolio Selection by : Bin Li
Download or read book Online Portfolio Selection written by Bin Li and published by CRC Press. This book was released on 2018-10-30 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.
Book Synopsis Online Computational Algorithms for Portfolio-selection Problems by : Raphael Ndem Nkomo
Download or read book Online Computational Algorithms for Portfolio-selection Problems written by Raphael Ndem Nkomo and published by . This book was released on 2015 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling by : Kyle Robert Harrison
Download or read book Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling written by Kyle Robert Harrison and published by Springer Nature. This book was released on 2021-11-13 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book consists of eight chapters, authored by distinguished researchers and practitioners, that highlight the state of the art and recent trends in addressing the project portfolio selection and scheduling problem (PPSSP) across a variety of domains, particularly defense, social programs, supply chains, and finance. Many organizations face the challenge of selecting and scheduling a subset of available projects subject to various resource and operational constraints. In the simplest scenario, the primary objective for an organization is to maximize the value added through funding and implementing a portfolio of projects, subject to the available budget. However, there are other major difficulties that are often associated with this problem such as qualitative project benefits, multiple conflicting objectives, complex project interdependencies, workforce and manufacturing constraints, and deep uncertainty regarding project costs, benefits, and completion times. It is well known that the PPSSP is an NP-hard problem and, thus, there is no known polynomial-time algorithm for this problem. Despite the complexity associated with solving the PPSSP, many traditional approaches to this problem make use of exact solvers. While exact solvers provide definitive optimal solutions, they quickly become prohibitively expensive in terms of computation time when the problem size is increased. In contrast, evolutionary and memetic computing afford the capability for autonomous heuristic approaches and expert knowledge to be combined and thereby provide an efficient means for high-quality approximation solutions to be attained. As such, these approaches can provide near real-time decision support information for portfolio design that can be used to augment and improve existing human-centric strategic decision-making processes. This edited book provides the reader with a broad overview of the PPSSP, its associated challenges, and approaches to addressing the problem using evolutionary and memetic computing.
Book Synopsis Online Dynamic Algorithm Portfolios: Minimizing the Computational Cost of Problem Solving by :
Download or read book Online Dynamic Algorithm Portfolios: Minimizing the Computational Cost of Problem Solving written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents methods for minimizing the computational effort of problem solving. Rather than looking at a particular algorithm, we consider the issue of computational complexity at a higher level, and propose techniques that, given a set of candidate algorithms, of unknown performance, learn to use these algorithms while solving a sequence of problem instances, with the aim of solving all instances in a minimum time. An analogous meta-level approach to problem solving has been adopted in many different fields, with different aims and terminology. A widely accepted term to describe it is algorithm selection. Algorithm portfolios represent a more general framework, in which computation time is allocated to a set of algorithms running on one or more processors. Automating algorithm selection is an old dream of the AI community, which has been brought closer to reality in the last decade. Most available selection techniques are based on a model of algorithm performance, assumed to be available, or learned during a separate offline training sequence, which is often prohibitively expensive. The model is used to perform a static allocation of resources, with no feedback from the actual execution of the algorithms. There is a trade-off between the performance of model-based selection, and the cost of learning the model. In this thesis, we formulate this trade-off as a bandit problem. We propose GambleTA, a fully dynamic and online algorithm portfolio selection technique, with no separate training phase: all candidate algorithms are run in parallel, while a model incrementally learns their runtime distributions. A redundant set of time allocators uses the partially trained model to optimize machine time shares for the algorithms, in order to minimize runtime. A bandit problem solver picks the allocator to use on each instance, gradually increasing the impact of the best time allocators as the model improves. A similar approach is adopted for learning restart strategi.
Book Synopsis Online Computation and Competitive Analysis by : Allan Borodin
Download or read book Online Computation and Competitive Analysis written by Allan Borodin and published by Cambridge University Press. This book was released on 2005-02-17 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains theoretical foundations, applications, and examples of competitive analysis for online algorithms.
Book Synopsis Artificial Intelligence Research by : Aurona Gerber
Download or read book Artificial Intelligence Research written by Aurona Gerber and published by Springer Nature. This book was released on 2020-12-21 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First Southern African Conference on Artificial Intelligence Research, SACAIR 2020, held in Muldersdrift, South Africa, in February 2021. Due to the COVID-19 pandemic the SACAIR 2020 has been postponed to February 2021. The 19 papers presented were thoroughly reviewed and selected from 53 submissions. They are organized on the topical sections on AI for ethics and society; AI in information systems, AI for development and social good; applications of AI; knowledge representation and reasoning; machine learning theory.
Book Synopsis An Effective and Efficient Hybrid Algorithm for the Constrained Portfolio Selection Problem by : Jianhua Zheng
Download or read book An Effective and Efficient Hybrid Algorithm for the Constrained Portfolio Selection Problem written by Jianhua Zheng and published by . This book was released on 2013 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning and Intelligent Optimization by : Clarisse Dhaenens
Download or read book Learning and Intelligent Optimization written by Clarisse Dhaenens and published by Springer. This book was released on 2015-06-18 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on Learning and Optimization, LION 9, which was held in Lille, France, in January 2015. The 31 contributions presented were carefully reviewed and selected for inclusion in this book. The papers address all fields between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Special focus is given to algorithm selection and configuration, learning, fitness landscape, applications, dynamic optimization, multi-objective, max-clique problems, bayesian optimization and global optimization, data mining and - in a special session - also on dynamic optimization.
Book Synopsis Advances in Neural Information Processing Systems 16 by : Sebastian Thrun
Download or read book Advances in Neural Information Processing Systems 16 written by Sebastian Thrun and published by MIT Press. This book was released on 2004 with total page 1694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.
Book Synopsis LATIN 2000: Theoretical Informatics by : Gaston H. Gonnet
Download or read book LATIN 2000: Theoretical Informatics written by Gaston H. Gonnet and published by Springer Science & Business Media. This book was released on 2000-03-23 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Conference, Latin American Theoretical Informatics, LATIN 2000, held in Punta del Est, Uruguay, in April 2000. The 42 revised papers presented were carefully reviewed and selected from a total of 87 submissions from 26 countries. Also included are abstracts or full papers of several invited talks. The papers are organized in topical sections on random structures and algorithms, complexity, computational number theory and cryptography, algebraic algorithms, computability, automata and formal languages, and logic and programming theory.
Book Synopsis Machine Learning for Asset Management by : Emmanuel Jurczenko
Download or read book Machine Learning for Asset Management written by Emmanuel Jurczenko and published by John Wiley & Sons. This book was released on 2020-10-06 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
Book Synopsis Towards Extensible and Adaptable Methods in Computing by : Shampa Chakraverty
Download or read book Towards Extensible and Adaptable Methods in Computing written by Shampa Chakraverty and published by Springer. This book was released on 2018-11-04 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses extensible and adaptable computing, a broad range of methods and techniques used to systematically tackle the future growth of systems and respond proactively and seamlessly to change. The book is divided into five main sections: Agile Software Development, Data Management, Web Intelligence, Machine Learning and Computing in Education. These sub-domains of computing work together in mutually complementary ways to build systems and applications that scale well, and which can successfully meet the demands of changing times and contexts. The topics under each track have been carefully selected to highlight certain qualitative aspects of applications and systems, such as scalability, flexibility, integration, efficiency and context awareness. The first section (Agile Software Development) includes six contributions that address related issues, including risk management, test case prioritization and tools, open source software reliability and predicting the change proneness of software. The second section (Data Management) includes discussions on myriad issues, such as extending database caches using solid-state devices, efficient data transmission, healthcare applications and data security. In turn, the third section (Machine Learning) gathers papers that investigate ML algorithms and present their specific applications such as portfolio optimization, disruption classification and outlier detection. The fourth section (Web Intelligence) covers emerging applications such as metaphor detection, language identification and sentiment analysis, and brings to the fore web security issues such as fraud detection and trust/reputation systems. In closing, the fifth section (Computing in Education) focuses on various aspects of computer-aided pedagogical methods.
Book Synopsis Advances in Optimization and Applications by : Nicholas N. Olenev
Download or read book Advances in Optimization and Applications written by Nicholas N. Olenev and published by Springer Nature. This book was released on 2021-12-08 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Conference on Optimization and Applications, OPTIMA 2021, held in Petrovac, Montenegro, in September - October 2021. Due to the COVID-19 pandemic the conference was partially held online. The 19 revised full papers presented were carefully reviewed and selected from 38 submissions. The papers are organized in topical sections on mathematical programming; global optimization; stochastic optimization; optimal control; mathematical economics; optimization in data analysis; applications.
Book Synopsis Introduction to Online Convex Optimization, second edition by : Elad Hazan
Download or read book Introduction to Online Convex Optimization, second edition written by Elad Hazan and published by MIT Press. This book was released on 2022-09-06 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process. In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features: Thoroughly updated material throughout New chapters on boosting, adaptive regret, and approachability and expanded exposition on optimization Examples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training, offered throughout Exercises that guide students in completing parts of proofs
Book Synopsis Approximation and Online Algorithms by : Klaus Jansen
Download or read book Approximation and Online Algorithms written by Klaus Jansen and published by Springer. This book was released on 2017-01-06 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-workshop proceedings of the 14th International Workshop on Approximation and Online Algorithms, WAOA 2016, held in Aarhus, Denmark, in August 2016 as part of ALGO 2016. The 16 revised full papers presented together with 2 invited lectures were carefully reviewed and selected from 33 submissions. Topics of interest for WAOA 2016 were: coloring and partitioning, competitive analysis, network design, packing and covering, paradigms for design and analysis of approximation and online algorithms, randomization techniques, real world applications, and scheduling problems.
Book Synopsis Linear and Mixed Integer Programming for Portfolio Optimization by : Renata Mansini
Download or read book Linear and Mixed Integer Programming for Portfolio Optimization written by Renata Mansini and published by Springer. This book was released on 2015-06-10 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.
