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Author: Ramaprasad Bhar
Publisher: Springer Science & Business Media
ISBN: 1402079400
Category : Business & Economics
Languages : en
Pages : 167
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Book Description
Markov chains have increasingly become useful way of capturing stochastic nature of many economic and financial variables. Although the hidden Markov processes have been widely employed for some time in many engineering applications e.g. speech recognition, its effectiveness has now been recognized in areas of social science research as well. The main aim of Hidden Markov Models: Applications to Financial Economics is to make such techniques available to more researchers in financial economics. As such we only cover the necessary theoretical aspects in each chapter while focusing on real life applications using contemporary data mainly from OECD group of countries. The underlying assumption here is that the researchers in financial economics would be familiar with such application although empirical techniques would be more traditional econometrics. Keeping the application level in a more familiar level, we focus on the methodology based on hidden Markov processes. This will, we believe, help the reader to develop more in-depth understanding of the modeling issues thereby benefiting their future research.
Author: Ramaprasad Bhar
Publisher: Springer Science & Business Media
ISBN: 1402079400
Category : Business & Economics
Languages : en
Pages : 167
Get Book Here
Book Description
Markov chains have increasingly become useful way of capturing stochastic nature of many economic and financial variables. Although the hidden Markov processes have been widely employed for some time in many engineering applications e.g. speech recognition, its effectiveness has now been recognized in areas of social science research as well. The main aim of Hidden Markov Models: Applications to Financial Economics is to make such techniques available to more researchers in financial economics. As such we only cover the necessary theoretical aspects in each chapter while focusing on real life applications using contemporary data mainly from OECD group of countries. The underlying assumption here is that the researchers in financial economics would be familiar with such application although empirical techniques would be more traditional econometrics. Keeping the application level in a more familiar level, we focus on the methodology based on hidden Markov processes. This will, we believe, help the reader to develop more in-depth understanding of the modeling issues thereby benefiting their future research.
Author: Johannes Fürnkranz
Publisher: Springer
ISBN: 354046056X
Category : Computers
Languages : en
Pages : 873
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Book Description
This book constitutes the refereed proceedings of the 17th European Conference on Machine Learning, ECML 2006, held, jointly with PKDD 2006. The book presents 46 revised full papers and 36 revised short papers together with abstracts of 5 invited talks, carefully reviewed and selected from 564 papers submitted. The papers present a wealth of new results in the area and address all current issues in machine learning.
Author: Rabinarayan Satpathy
Publisher: John Wiley & Sons
ISBN: 111978560X
Category : Computers
Languages : en
Pages : 433
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Book Description
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Author: Ali Selamat
Publisher: Springer
ISBN: 3642365434
Category : Computers
Languages : en
Pages : 584
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Book Description
The two-volume set LNAI 7802 and LNAI 7803 constitutes the refereed proceedings of the 5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013, held in Kuala Lumpur, Malaysia in March 2013. The 108 revised papers presented were carefully reviewed and selected from numerous submissions. The papers included are grouped into topical sections on: innovations in intelligent computation and applications; intelligent database systems; intelligent information systems; tools and applications; intelligent recommender systems; multiple modal approach to machine learning; engineering knowledge and semantic systems; computational biology and bioinformatics; computational intelligence; modeling and optimization techniques in information systems, database systems and industrial systems; intelligent supply chains; applied data mining for semantic Web; semantic Web and ontology; integration of information systems; and conceptual modeling in advanced database systems.
Author: Irene Aldridge
Publisher: John Wiley & Sons
ISBN: 111960298X
Category : Computers
Languages : en
Pages : 336
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Book Description
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.
Author: Franklin J. Parker
Publisher: John Wiley & Sons
ISBN: 1119906121
Category : Business & Economics
Languages : en
Pages : 262
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Book Description
An in-depth overview of investing in the real world In Goals-Based Portfolio Theory, award-winning Chartered Financial Analyst® Franklin J. Parker delivers an insightful and eye-opening discussion of how real people can navigate the financial jungle and achieve their financial goals. The book accepts the reality that the typical investor has specific funding requirements within specified periods of time and a limited amount of wealth to dedicate to those objectives. It then works within those limits to show you how to build an investment portfolio that maximizes the possibility you’ll achieve your goals, as well as how to manage the tradeoffs between your goals. In the book, you’ll find: Strategies for incorporating taxation and rebalancing into a goals-based portfolio A discussion of the major non-financial risks faced by people engaged in private wealth management An incisive prediction of what the future of wealth management and investment management may look like An indispensable exploration of investing as it actually works in the real world for real people, Goals-Based Portfolio Theory belongs in the library of all investors and their advisors who want to maximize the chances of meeting financial goals.
Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 0471746185
Category : Business & Economics
Languages : en
Pages : 576
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Book Description
Provides statistical tools and techniques needed to understandtoday's financial markets The Second Edition of this critically acclaimed text provides acomprehensive and systematic introduction to financial econometricmodels and their applications in modeling and predicting financialtime series data. This latest edition continues to emphasizeempirical financial data and focuses on real-world examples.Following this approach, readers will master key aspects offinancial time series, including volatility modeling, neuralnetwork applications, market microstructure and high-frequencyfinancial data, continuous-time models and Ito's Lemma, Value atRisk, multiple returns analysis, financial factor models, andeconometric modeling via computation-intensive methods. The author begins with the basic characteristics of financialtime series data, setting the foundation for the three maintopics: Analysis and application of univariate financial timeseries Return series of multiple assets Bayesian inference in finance methods This new edition is a thoroughly revised and updated text,including the addition of S-Plus® commands and illustrations.Exercises have been thoroughly updated and expanded and include themost current data, providing readers with more opportunities to putthe models and methods into practice. Among the new material addedto the text, readers will find: Consistent covariance estimation under heteroscedasticity andserial correlation Alternative approaches to volatility modeling Financial factor models State-space models Kalman filtering Estimation of stochastic diffusion models The tools provided in this text aid readers in developing adeeper understanding of financial markets through firsthandexperience in working with financial data. This is an idealtextbook for MBA students as well as a reference for researchersand professionals in business and finance.
Author: Boris Kovalerchuk
Publisher: Springer Science & Business Media
ISBN: 0306470187
Category : Computers
Languages : en
Pages : 323
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Book Description
Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.
Author: Lingjie Ma
Publisher: Springer Nature
ISBN: 3030472027
Category : Business & Economics
Languages : en
Pages : 462
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Book Description
This book provides readers with a systematic approach to quantitative investments and bridges the gap between theory and practice, equipping students to more seamlessly enter the world of industry. A successful quantitative investment strategy requires an individual to possess a deep understanding of the financial markets, investment theories and econometric modelings, as well as the ability to program and analyze real-world data sets. In order to connect finance theories and practical industry experience, each chapter begins with a real-world finance case study. The rest of the chapter introduces fundamental insights and theories, and teaches readers to use statistical models and R programming to analyze real-world data, therefore grounding the learning process in application. Additionally, each chapter profiles significant figures in investment and quantitative studies, so that readers can more fully understand the history of the discipline. This volume will be particularly useful to advanced students and practitioners in finance and investments.
Author: Frank J. Fabozzi
Publisher: MIT Press
ISBN: 0262331594
Category : Business & Economics
Languages : en
Pages : 1088
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Book Description
The substantially revised fifth edition of a textbook covering the wide range of instruments available in financial markets, with a new emphasis on risk management. Over the last fifty years, an extensive array of instruments for financing, investing, and controlling risk has become available in financial markets, with demand for these innovations driven by the needs of investors and borrowers. The recent financial crisis offered painful lessons on the consequences of ignoring the risks associated with new financial products and strategies. This substantially revised fifth edition of a widely used text covers financial product innovation with a new emphasis on risk management and regulatory reform. Chapters from the previous edition have been updated, and new chapters cover material that reflects recent developments in financial markets. The book begins with an introduction to financial markets, offering a new chapter that provides an overview of risk—including the key elements of financial risk management and the identification and quantification of risk. The book then covers market participants, including a new chapter on collective investment products managed by asset management firms; the basics of cash and derivatives markets, with new coverage of financial derivatives and securitization; theories of risk and return, with a new chapter on return distributions and risk measures; the structure of interest rates and the pricing of debt obligations; equity markets; debt markets, including chapters on money market instruments, municipal securities, and credit sensitive securitized products; and advanced coverage of derivative markets. Each chapter ends with a review of key points and questions based on the material covered.
