Data Mining In Finance
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Data Mining In Finance
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Author : Boris Kovalerchuk
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-18
Data Mining In Finance written by Boris Kovalerchuk and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-18 with Computers categories.
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.
Applications Of Data Mining In E Business And Finance
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Author : Carlos A. Mota Soares
language : en
Publisher: IOS Press
Release Date : 2008
Applications Of Data Mining In E Business And Finance written by Carlos A. Mota Soares and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Contains extended versions of a selection of papers presented at the workshop Data mining for business, held in 2007 together with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Nanjing China--Preface.
Data Mining To Business Analytics Finance Budgeting And Investments
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Author : Jagdish Chandra Patni
language : en
Publisher:
Release Date : 2017-09-12
Data Mining To Business Analytics Finance Budgeting And Investments written by Jagdish Chandra Patni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-12 with categories.
Academic Paper from the year 2017 in the subject Computer Science - General, grade: 5, University of Petroleum and Energy Studies, language: English, abstract: This paper utilizes the distinctive mining techniques as an answer for business needs. It presents Finance, Budgeting and Investments as the principle working ground for the data mining algorithms actualized. With the increment of monetary globalization and development of information technology, financial data are being produced and gathered at an extraordinary pace. Thus, there has been a basic requirement for automated ways to deal with compelling and proficient usage of gigantic measure of data to support companies and people in doing the Business. Data mining is turning out to be strategically imperative region for some business associations including financial sector. Data mining helps the companies to search for hidden example in a gathering and find obscure relationship in the data. Financial Analysis alludes to the assessment of a business to manage the arranging, budgeting, observing, forecasting, and enhancing of every financial point of interest inside of an association. The task concentrates on comprehension the association's financial health as a major part of reacting to today's inexorably stringent financial reporting prerequisites. It exhibits the capacity of the data mining to robotize the procedure of looking the boundless customer's connected data to discover patterns that are great indicators of the practices of the customer. This will cover the analysis of: Profit arranging, Cash flow analysis, Investment decisions and risk analysis, Dividend Policies and Portfolio Analysis through algorithms like Apriori, Naivebayes, Prediction algorithm and so forth. Along these lines this Data mining arrangement actualizes advanced data analysis techniques utilized by companies for discovering startling patterns extricated from tremendous measures of data, patterns that offer applicable knowledge for
Applications Of Data Mining In E Business And Finance
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Author :
language : en
Publisher:
Release Date : 2008
Applications Of Data Mining In E Business And Finance written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Data mining categories.
In spite of the close relationship between research and practice in Data Mining, it is not easy to find information on some of the important issues involved in real world application of DM technology. This book address some of these issues. It is suitable for Data Mining researchers and practitioners.
Ordinary Shares Exotic Methods
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Author : Francis Eng-Hock Tay
language : en
Publisher: World Scientific
Release Date : 2003-01-29
Ordinary Shares Exotic Methods written by Francis Eng-Hock Tay and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-29 with Business & Economics categories.
Exotic methods refer to specific functions within general soft computing methods such as genetic algorithms, neural networks and rough sets theory. They are applied to ordinary shares for a variety of financial purposes, such as portfolio selection and optimization, classification of market states, forecasting of market states and data mining. This is in contrast to the wide spectrum of work done on exotic financial instruments, wherein advanced mathematics is used to construct financial instruments for hedging risks and for investment. In this book, particular aspects of the general method are used to create interesting applications. For instance, genetic niching produces a family of portfolios for the trader to choose from. Support vector machines, a special form of neural networks, forecast the financial markets; such a forecast is on market states, of which there are three — uptrending, mean reverting and downtrending. A self-organizing map displays in a vivid manner the states of the market. Rough sets with a new discretization method extract information from stock prices. Contents: Financial Forecasting Problem and Data Mining TechniquesGenetic Algorithms and Genetic NichingPortfolio Selection and Optimization Using Genetic OperatorsThe Rough Sets Theory Basics and Its Applications in Economic and Financial ForecastingTime Series Forecasting Using Rough Sets TheoryA Review of Support Vector Machines in Regression EstimationApplication of Support Vector Machines in Financial Time Series ForecastingOther Methods and Their Applications Readership: Researchers and practitioners in soft computing and artificial intelligence, as well as graduate students in related areas. Keywords:
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes
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Author : Cheng Few Lee
language : en
Publisher: World Scientific
Release Date : 2020-07-30
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng Few Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-30 with Business & Economics categories.
This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
Applications Of Data Mining In E Business And Finance
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Author : Carlos Soares
language : en
Publisher:
Release Date : 2008
Applications Of Data Mining In E Business And Finance written by Carlos Soares and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Contains extended versions of a selection of papers presented at the workshop Data mining for business, held in 2007 together with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Nanjing China--Preface.
Data Science For Economics And Finance
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Author : Sergio Consoli
language : en
Publisher: Springer Nature
Release Date : 2021
Data Science For Economics And Finance written by Sergio Consoli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Application software categories.
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.
From Opinion Mining To Financial Argument Mining
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Author : Chung-Chi Chen
language : en
Publisher: Springer Nature
Release Date : 2021
From Opinion Mining To Financial Argument Mining written by Chung-Chi Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Application software categories.
Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. These works indicate the trend toward fine-grained opinion mining in the financial domain. When expressing opinions in finance, terms like bullish/bearish often spring to mind. However, the market sentiment of the financial instrument is just one type of opinion in the financial industry. Like other industries such as manufacturing and textiles, the financial industry also has a large number of products. Financial services are also a major business for many financial companies, especially in the context of the recent FinTech trend. For instance, many commercial banks focus on loans and credit cards. Although there are a variety of issues that could be explored in the financial domain, most researchers in the AI and NLP communities only focus on the market sentiment of the stock or foreign exchange. This open access book addresses several research issues that can broaden the research topics in the AI community. It also provides an overview of the status quo in fine-grained financial opinion mining to offer insights into the futures goals. For a better understanding of the past and the current research, it also discusses the components of financial opinions one-by-one with the related works and highlights some possible research avenues, providing a research agenda with both micro- and macro-views toward financial opinions.
Machine Learning And Its Applications
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Author : Georgios Paliouras
language : en
Publisher: Springer
Release Date : 2003-06-29
Machine Learning And Its Applications written by Georgios Paliouras and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.