Data Science For Economics And Finance

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Data Science for Economics and Finance

Author : Sergio Consoli
Publisher : Springer Nature
ISBN 13 : 3030668916
Total Pages : 357 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Data Science for Economics and Finance by : Sergio Consoli

Download or read book Data Science for Economics and Finance written by Sergio Consoli and published by Springer Nature. This book was released on 2021 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

Financial Data Analytics

Author : Sinem Derindere Köseoğlu
Publisher : Springer Nature
ISBN 13 : 3030837998
Total Pages : 393 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Financial Data Analytics by : Sinem Derindere Köseoğlu

Download or read book Financial Data Analytics written by Sinem Derindere Köseoğlu and published by Springer Nature. This book was released on 2022-04-25 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.

Data Analysis for Business, Economics, and Policy

Author : Gábor Békés
Publisher : Cambridge University Press
ISBN 13 : 1108483011
Total Pages : 741 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Data Analysis for Business, Economics, and Policy by : Gábor Békés

Download or read book Data Analysis for Business, Economics, and Policy written by Gábor Békés and published by Cambridge University Press. This book was released on 2021-05-06 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data (Second Edition)

Author : Graham L Giller
Publisher : World Scientific
ISBN 13 : 9811251827
Total Pages : 512 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data (Second Edition) by : Graham L Giller

Download or read book Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data (Second Edition) written by Graham L Giller and published by World Scientific. This book was released on 2022-06-27 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world.The book begins with entertaining tales from Graham Giller's career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went 'viral' before anybody knew what that meant, on being the person who forgot to hit 'enter' to run a hundred-million dollar statistical arbitrage system, what he decoded from his brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality.The majority of the content is a narrative of analytic work done on financial, economics, and alternative data, structured around both Dr Giller's professional career and some of the things that just interested him. The goal is to stimulate interest in predictive methods, to give accurate characterizations of the true properties of financial, economic and alternative data, and to share what Richard Feynman described as 'The Pleasure of Finding Things Out.'

Business Economics and Finance with MATLAB, GIS, and Simulation Models

Author : Patrick L. Anderson
Publisher : CRC Press
ISBN 13 : 0203494652
Total Pages : 499 pages
Book Rating : 4.2/5 (34 download)

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Book Synopsis Business Economics and Finance with MATLAB, GIS, and Simulation Models by : Patrick L. Anderson

Download or read book Business Economics and Finance with MATLAB, GIS, and Simulation Models written by Patrick L. Anderson and published by CRC Press. This book was released on 2004-07-27 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes recent theoretical advances in Finance and Economics and shows how they can be implemented in the real world. It presents tactics for using mathematical and simulation models to solve complex tasks of forecasting income, valuing businesses, predicting retail sales, and evaluating markets and tax and regulatory problems. Busine

Data Science and Multiple Criteria Decision Making Approaches in Finance

Author : Gökhan Silahtaroğlu
Publisher : Springer Nature
ISBN 13 : 3030741761
Total Pages : 183 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Data Science and Multiple Criteria Decision Making Approaches in Finance by : Gökhan Silahtaroğlu

Download or read book Data Science and Multiple Criteria Decision Making Approaches in Finance written by Gökhan Silahtaroğlu and published by Springer Nature. This book was released on 2021-05-29 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book considers and assesses essential financial issues by utilizing data science and fuzzy multiple criteria decision making (MCDM) methods. It introduces readers to a range of data science methods, and demonstrates their application in the fields of business, health, economics, finance and engineering. In addition, it provides suggestions based on the assessment results on each topic, which can help to enhance the efficiency of the financial system and the sustainability of economic development. Given its scope, the book will help readers broaden their perspective on the assessment and evaluation of financial issues using data science and MCDM approaches.

Big Data Science in Finance

Author : Irene Aldridge
Publisher : John Wiley & Sons
ISBN 13 : 1119602971
Total Pages : 336 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Big Data Science in Finance by : Irene Aldridge

Download or read book Big Data Science in Finance written by Irene Aldridge and published by John Wiley & Sons. This book was released on 2021-01-08 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.

Data Science for Financial Econometrics

Author : Nguyen Ngoc Thach
Publisher : Springer Nature
ISBN 13 : 3030488535
Total Pages : 633 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Data Science for Financial Econometrics by : Nguyen Ngoc Thach

Download or read book Data Science for Financial Econometrics written by Nguyen Ngoc Thach and published by Springer Nature. This book was released on 2020-11-13 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.

Machine Learning for Economics and Finance in TensorFlow 2

Author : Isaiah Hull
Publisher : Apress
ISBN 13 : 9781484263723
Total Pages : 368 pages
Book Rating : 4.2/5 (637 download)

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Book Synopsis Machine Learning for Economics and Finance in TensorFlow 2 by : Isaiah Hull

Download or read book Machine Learning for Economics and Finance in TensorFlow 2 written by Isaiah Hull and published by Apress. This book was released on 2020-11-26 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work on economic problems and solutions with tools from machine learning. ML has taken time to move into the space of academic economics. This is because empirical work in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for both students and professionals in the economics industry without a standard reference. This book focuses on economic problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, RNNs, LSTMs, the Transformer Model, etc.), generative machine learning models, random forests, gradient boosting, clustering, and feature extraction. You'll also learn about the intersection of empirical methods in economics and machine learning, including regression analysis, text analysis, and dimensionality reduction methods, such as principal components analysis. TensorFlow offers a toolset that can be used to setup and solve any mathematical model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. Otherwise complicated content is then distilled into accessible examples, so you can use TensorFlow to solve workhorse models in economics and finance. What You'll Learn Define, train, and evaluate machine learning models in TensorFlow 2 Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve workhorse models in economics and finance Who This Book Is For Students and data scientists working in the economics industry. Academic economists and social scientists who have an interest in machine learning are also likely to find this book useful.

Artificial Intelligence in Economics and Finance Theories

Author : Tankiso Moloi
Publisher : Springer Nature
ISBN 13 : 3030429628
Total Pages : 131 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Artificial Intelligence in Economics and Finance Theories by : Tankiso Moloi

Download or read book Artificial Intelligence in Economics and Finance Theories written by Tankiso Moloi and published by Springer Nature. This book was released on 2020-05-07 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.

The Economics of Artificial Intelligence

Author : Ajay Agrawal
Publisher : University of Chicago Press
ISBN 13 : 0226833127
Total Pages : 172 pages
Book Rating : 4.2/5 (268 download)

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Book Synopsis The Economics of Artificial Intelligence by : Ajay Agrawal

Download or read book The Economics of Artificial Intelligence written by Ajay Agrawal and published by University of Chicago Press. This book was released on 2024-03-05 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

Big Data for Twenty-First-Century Economic Statistics

Author : Katharine G. Abraham
Publisher : University of Chicago Press
ISBN 13 : 022680125X
Total Pages : 502 pages
Book Rating : 4.2/5 (268 download)

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Book Synopsis Big Data for Twenty-First-Century Economic Statistics by : Katharine G. Abraham

Download or read book Big Data for Twenty-First-Century Economic Statistics written by Katharine G. Abraham and published by University of Chicago Press. This book was released on 2022-03-11 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.

Applied Computational Economics and Finance

Author : Mario J. Miranda
Publisher : MIT Press
ISBN 13 : 0262291754
Total Pages : 529 pages
Book Rating : 4.2/5 (622 download)

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Book Synopsis Applied Computational Economics and Finance by : Mario J. Miranda

Download or read book Applied Computational Economics and Finance written by Mario J. Miranda and published by MIT Press. This book was released on 2004-08-20 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a variety of computational methods used to solve dynamic problems in economics and finance. It emphasizes practical numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economic analyses. The examples are drawn from a wide range of subspecialties of economics and finance, with particular emphasis on problems in agricultural and resource economics, macroeconomics, and finance. The book also provides an extensive Web-site library of computer utilities and demonstration programs. The book is divided into two parts. The first part develops basic numerical methods, including linear and nonlinear equation methods, complementarity methods, finite-dimensional optimization, numerical integration and differentiation, and function approximation. The second part presents methods for solving dynamic stochastic models in economics and finance, including dynamic programming, rational expectations, and arbitrage pricing models in discrete and continuous time. The book uses MATLAB to illustrate the algorithms and includes a utilities toolbox to help readers develop their own computational economics applications.

Advances in Econometrics, Operational Research, Data Science and Actuarial Studies

Author : M. Kenan Terzioğlu
Publisher : Springer Nature
ISBN 13 : 3030852547
Total Pages : 607 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Advances in Econometrics, Operational Research, Data Science and Actuarial Studies by : M. Kenan Terzioğlu

Download or read book Advances in Econometrics, Operational Research, Data Science and Actuarial Studies written by M. Kenan Terzioğlu and published by Springer Nature. This book was released on 2022-01-17 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents techniques and theories drawn from mathematics, statistics, computer science, and information science to analyze problems in business, economics, finance, insurance, and related fields. The authors present proposals for solutions to common problems in related fields. To this end, they are showing the use of mathematical, statistical, and actuarial modeling, and concepts from data science to construct and apply appropriate models with real-life data, and employ the design and implementation of computer algorithms to evaluate decision-making processes. This book is unique as it associates data science - data-scientists coming from different backgrounds - with some basic and advanced concepts and tools used in econometrics, operational research, and actuarial sciences. It, therefore, is a must-read for scholars, students, and practitioners interested in a better understanding of the techniques and theories of these fields.

The Essentials of Machine Learning in Finance and Accounting

Author : Mohammad Zoynul Abedin
Publisher : Routledge
ISBN 13 : 1000394123
Total Pages : 275 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis The Essentials of Machine Learning in Finance and Accounting by : Mohammad Zoynul Abedin

Download or read book The Essentials of Machine Learning in Finance and Accounting written by Mohammad Zoynul Abedin and published by Routledge. This book was released on 2021-06-20 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

Econometrics and Data Science

Author : Tshepo Chris Nokeri
Publisher : Apress
ISBN 13 : 9781484274330
Total Pages : 228 pages
Book Rating : 4.2/5 (743 download)

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Book Synopsis Econometrics and Data Science by : Tshepo Chris Nokeri

Download or read book Econometrics and Data Science written by Tshepo Chris Nokeri and published by Apress. This book was released on 2021-10-27 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models Who This Book Is For Beginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives

Stochastic Modeling in Economics and Finance

Author : Jitka Dupacova
Publisher : Springer Science & Business Media
ISBN 13 : 0306481677
Total Pages : 394 pages
Book Rating : 4.3/5 (64 download)

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Book Synopsis Stochastic Modeling in Economics and Finance by : Jitka Dupacova

Download or read book Stochastic Modeling in Economics and Finance written by Jitka Dupacova and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Part I, the fundamentals of financial thinking and elementary mathematical methods of finance are presented. The method of presentation is simple enough to bridge the elements of financial arithmetic and complex models of financial math developed in the later parts. It covers characteristics of cash flows, yield curves, and valuation of securities. Part II is devoted to the allocation of funds and risk management: classics (Markowitz theory of portfolio), capital asset pricing model, arbitrage pricing theory, asset & liability management, value at risk. The method explanation takes into account the computational aspects. Part III explains modeling aspects of multistage stochastic programming on a relatively accessible level. It includes a survey of existing software, links to parametric, multiobjective and dynamic programming, and to probability and statistics. It focuses on scenario-based problems with the problems of scenario generation and output analysis discussed in detail and illustrated within a case study.