Financial Data Analytics

Financial Data Analytics PDF Author: Sinem Derindere Köseoğlu
Publisher: Springer Nature
ISBN: 3030837998
Category : Business & Economics
Languages : en
Pages : 393

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Book Description
​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.

Finance Analytics in Business

Finance Analytics in Business PDF Author: Sanjay Taneja
Publisher: Emerald Group Publishing
ISBN: 1837535728
Category : Business & Economics
Languages : en
Pages : 361

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Book Description
Finance Analytics in Business brings together specialists around the world working in various disciplines to reflect on finance analytics in business. This crucial field gives different views of a company’s financial data, and helps it gain knowledge to take action to improve financial performance.

Financial Analytics with R

Financial Analytics with R PDF Author: Mark J. Bennett
Publisher: Cambridge University Press
ISBN: 1107150752
Category : Business & Economics
Languages : en
Pages : 397

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Book Description
Financial Analytics with R sharpens readers' skills in time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.

Financial Data Analytics with Machine Learning, Optimization and Statistics

Financial Data Analytics with Machine Learning, Optimization and Statistics PDF Author: Yongzhao Chen
Publisher: John Wiley & Sons
ISBN: 1119863376
Category : Business & Economics
Languages : en
Pages : 823

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Book Description
An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.

Global Financial Analytics and Business Forecasting

Global Financial Analytics and Business Forecasting PDF Author: Sanjay Taneja
Publisher:
ISBN:
Category : Finance
Languages : en
Pages : 0

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Book Description
"Global Financial Analytics and Business Forecasting is a comprehensive guide that delves into the intricacies of financial analytics and forecasting in the modern global business landscape. Divided into 15 chapters, this book provides a holistic understanding of various aspects of financial analytics and their application in forecasting. In the first chapter, the book explores the dynamic world of Fintech in India, discussing the opportunities and challenges it presents. Readers gain insight into the rapid growth of Fintech in India, and the role of leading technology systems. The chapter also highlights the pivotal role of artificial intelligence in shaping financial markets and examines a case study on consumer preferences and satisfaction levels in the banking sector in the Republic of Moldova. Subsequent chapters explore the range of Fintech tools used in finance and unlock the predictive power of ARMA models on Algoquant Fintech's daily returns. The book further delves into the vast potential of big data in academic organizations, examining its opportunities and challenges. Readers gain valuable insights into the barriers and challenges faced by the Fintech industry and the applications of Fintech in banking. Machine learning algorithms take center stage in a dedicated chapter, showcasing their role in accelerating the development of business analytics. The book also emphasizes the significance of business intelligence in the financial sector, providing valuable strategies for effective decision-making. Examining the dynamic linkages between stock market indices and exchange rates for BRICS nations, the book sheds light on the complex interplay between these variables. Moreover, it introduces a new leadership pattern that advocates for an analytical approach to business decision-making. The book also explores how artificial intelligence can enable a granular finance approach tailored to the needs of less advantaged countries, businesses, and individuals. Global Financial Analytics and Business Forecasting is an essential resource for finance professionals, researchers, academicians, and students seeking a comprehensive understanding of financial analytics and its application in forecasting. Through its diverse range of topics, this book offers valuable insights, practical techniques, and emerging trends that equip readers with the knowledge necessary to thrive in the ever-evolving financial landscape"--

Financial Statistics and Data Analytics

Financial Statistics and Data Analytics PDF Author: Shuangzhe Li
Publisher: MDPI
ISBN: 3039439758
Category : Business & Economics
Languages : en
Pages : 232

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Book Description
Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

Quantitative Financial Analytics: The Path To Investment Profits

Quantitative Financial Analytics: The Path To Investment Profits PDF Author: Edward E Williams
Publisher: World Scientific Publishing Company
ISBN: 9813224274
Category : Business & Economics
Languages : en
Pages : 621

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Book Description
This book provides a comprehensive treatment of the important aspects of investment theory, security analysis, and portfolio selection, with a quantitative emphasis not to be found in most other investment texts.The statistical analysis framework of markets and institutions in the book meets the need for advanced undergraduates and graduate students in quantitative disciplines, who wish to apply their craft to the world of investments. In addition, entrepreneurs will find the volume to be especially useful. It also contains a clearly detailed explanation of many recent developments in portfolio and capital market theory as well as a thorough procedural discussion of security analysis. Professionals preparing for the CPA, CFA, and or CFP examinations will also benefit from a close scrutiny of the many problems following each chapter.The level of difficulty progresses through the textbook with more advanced treatment appearing in the latter sections of each chapter, and the last chapters of the volume.

An Introduction to Analysis of Financial Data with R

An Introduction to Analysis of Financial Data with R PDF Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 1119013461
Category : Business & Economics
Languages : en
Pages : 388

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Book Description
A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.

Analytics for Insurance

Analytics for Insurance PDF Author: Tony Boobier
Publisher: John Wiley & Sons
ISBN: 1119141079
Category : Business & Economics
Languages : en
Pages : 296

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Book Description
The business guide to Big Data in insurance, with practical application insight Big Data and Analytics for Insurers is the industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. Written from a non-IT perspective, this book focusses less on the architecture and technical details, instead providing practical guidance on translating analytics into target delivery. The discussion examines implementation, interpretation, and application to show you what Big Data can do for your business, with insights and examples targeted specifically to the insurance industry. From fraud analytics in claims management, to customer analytics, to risk analytics in Solvency 2, comprehensive coverage presented in accessible language makes this guide an invaluable resource for any insurance professional. The insurance industry is heavily dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential – yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. Understand what Big Data is and what it can do Delve into Big Data's specific impact on the insurance industry Learn how advanced analytics can revolutionise the industry Bring Big Data out of IT and into strategy, management, marketing, and more Big Data and analytics is changing business – but how? The majority of Big Data guides discuss data collection, database administration, advanced analytics, and the power of Big Data – but what do you actually do with it? Big Data and Analytics for Insurers answers your questions in real, everyday business terms, tailored specifically to the insurance industry's unique needs, challenges, and targets.

Key Business Analytics

Key Business Analytics PDF Author: Bernard Marr
Publisher: Pearson UK
ISBN: 1292017465
Category : Business & Economics
Languages : en
Pages : 312

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Book Description
Key Business Analytics will help managers apply tools to turn data into insights that help them better understand their customers, optimize their internal processes and identify cost savings and growth opportunities. It includes analysis techniques within the following categories: Financial analytics – cashflow, profitability, sales forecasts Market analytics – market size, market trends, marketing channels Customer analytics – customer lifetime values, social media, customer needs Employee analytics – capacity, performance, leadership Operational analytics – supply chains, competencies, environmental impact Bare business analytics – sentiments, text, correlations Each tool will follow the bestselling Key format of being 5-6 pages long, broken into short sharp advice on the essentials: What is it? When should I use it? How do I use it? Tips and pitfalls Further reading This essential toolkit also provides an invaluable section on how to gather original data yourself through surveys, interviews, focus groups, etc.