› › Dynamic Bayesian Learning And Optimization In Portfolio Choice Models

High Performance Optimization

Author : Hans Frenk

Publisher : Springer Science & Business Media

Page : 506 pages

File Size : 31,39 MB

Release : 2000

Category : Language Arts & Disciplines

ISBN : 9780792360131

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Book Description

For a long time the techniques of solving linear optimization (LP) problems improved only marginally. Fifteen years ago, however, a revolutionary discovery changed everything. A new `golden age' for optimization started, which is continuing up to the current time. What is the cause of the excitement? Techniques of linear programming formed previously an isolated body of knowledge. Then suddenly a tunnel was built linking it with a rich and promising land, part of which was already cultivated, part of which was completely unexplored. These revolutionary new techniques are now applied to solve conic linear problems. This makes it possible to model and solve large classes of essentially nonlinear optimization problems as efficiently as LP problems. This volume gives an overview of the latest developments of such `High Performance Optimization Techniques'. The first part is a thorough treatment of interior point methods for semidefinite programming problems. The second part reviews today's most exciting research topics and results in the area of convex optimization. Audience: This volume is for graduate students and researchers who are interested in modern optimization techniques.



Portfolio Choice Problems

Author : Nicolas Chapados

Publisher : Springer Science & Business Media

Page : 107 pages

File Size : 43,2 MB

Release : 2011-07-12

Category : Computers

ISBN : 1461405777

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Book Description

This brief offers a broad, yet concise, coverage of portfolio choice, containing both application-oriented and academic results, along with abundant pointers to the literature for further study. It cuts through many strands of the subject, presenting not only the classical results from financial economics but also approaches originating from information theory, machine learning and operations research. This compact treatment of the topic will be valuable to students entering the field, as well as practitioners looking for a broad coverage of the topic.



Discrete Choice Methods with Simulation

Author : Kenneth Train

Publisher : Cambridge University Press

Page : 399 pages

File Size : 17,54 MB

Release : 2009-07-06

Category : Business & Economics

ISBN : 0521766559

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Book Description

This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.



Empirical Asset Pricing

Author : Wayne Ferson

Publisher : MIT Press

Page : 497 pages

File Size : 33,86 MB

Release : 2019-03-12

Category : Business & Economics

ISBN : 0262039370

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Book Description

An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.



Financial Decision Making Using Computational Intelligence

Author : Michael Doumpos

Publisher : Springer Science & Business Media

Page : 336 pages

File Size : 44,34 MB

Release : 2012-07-23

Category : Business & Economics

ISBN : 1461437733

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Book Description

The increasing complexity of financial problems and the enormous volume of financial data often make it difficult to apply traditional modeling and algorithmic procedures. In this context, the field of computational intelligence provides an arsenal of particularly useful techniques. These techniques include new modeling tools for decision making under risk and uncertainty, data mining techniques for analyzing complex data bases, and powerful algorithms for complex optimization problems. Computational intelligence has also evolved rapidly over the past few years and it is now one of the most active fields in operations research and computer science. This volume presents the recent advances of the use of computation intelligence in financial decision making. The book covers all the major areas of computational intelligence and a wide range of problems in finance, such as portfolio optimization, credit risk analysis, asset valuation, financial forecasting, and trading.



Bayesian Statistics 9

Author : José M. Bernardo

Publisher : Oxford University Press

Page : 717 pages

File Size : 42,8 MB

Release : 2011-10-06

Category : Mathematics

ISBN : 0199694583

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Book Description

Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.



Online Portfolio Selection

Author : Bin Li

Publisher : CRC Press

Page : 227 pages

File Size : 19,51 MB

Release : 2018-10-30

Category : Business & Economics

ISBN : 1482249642

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Book Description

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.



Stochastic Analysis, Filtering, and Stochastic Optimization

Author : George Yin

Publisher : Springer Nature

Page : 466 pages

File Size : 21,16 MB

Release : 2022-04-22

Category : Mathematics

ISBN : 3030985199

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Book Description

This volume is a collection of research works to honor the late Professor Mark H.A. Davis, whose pioneering work in the areas of Stochastic Processes, Filtering, and Stochastic Optimization spans more than five decades. Invited authors include his dissertation advisor, past collaborators, colleagues, mentees, and graduate students of Professor Davis, as well as scholars who have worked in the above areas. Their contributions may expand upon topics in piecewise deterministic processes, pathwise stochastic calculus, martingale methods in stochastic optimization, filtering, mean-field games, time-inconsistency, as well as impulse, singular, risk-sensitive and robust stochastic control.



Data Analysis, Machine Learning and Applications

Author : Christine Preisach

Publisher : Springer Science & Business Media

Page : 714 pages

File Size : 30,15 MB

Release : 2008-04-13

Category : Computers

ISBN : 354078246X

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Book Description

Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.



Financial Modeling Excellence

Author : Azhar ul Haque Sario

Publisher : Azhar Sario Authorship and Publishing

Page : 145 pages

File Size : 23,91 MB

Release : 2024-09-09

Category : Business & Economics

ISBN : 3759250378

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Book Description

Financial Modeling Excellence: Innovative Approaches to Stock Predictions (Third Edition) offers a comprehensive and advanced exploration of probabilistic models used in stock price predictions. The book begins with an in-depth analysis of time series data, covering essential topics such as stationarity, trend and seasonality analysis, and time series decomposition. It then delves into autoregressive (AR) models, moving average (MA) models, and their combinations, including ARMA and ARIMA models. Each chapter provides detailed explanations of model selection, parameter estimation, diagnostics, and validation, along with practical applications in financial forecasting. The book further explores state space models and the Kalman filter, offering insights into their implementation and applications in stock price predictions. Hidden Markov models (HMM), Bayesian models, and stochastic processes are also thoroughly examined, with a focus on their mathematical formulations, parameter estimation techniques, and real-world applications. Case studies and practical examples are provided throughout the book to illustrate the effectiveness of these models in financial analysis. This edition also introduces advanced techniques and future directions for each model, ensuring that readers are equipped with the latest tools and knowledge in the field. This third edition follows the first edition titled Stock Price Predictions: An Introduction to Probabilistic Models and the second edition titled Forecasting Stock Prices: Mathematics of Probabilistic Models. It continues to build on the foundation laid by its predecessors, offering new insights and innovations in financial modeling. As the first series of this edition, readers can look forward to the next series, which will be released soon, providing even more advanced techniques and applications in stock price predictions. This edition aims to equip readers with the latest tools and knowledge in the field, ensuring they are well-prepared to tackle the challenges of financial forecasting and stock price predictions.