Analyzing Financial Data And Implementing Financial Models Using R
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Analyzing Financial Data and Implementing Financial Models Using R by Clifford S. Ang Pdf
This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models. It demonstrates how to take publically available data and manipulate, implement models and generate outputs typical for particular analyses. A wide spectrum of timely and practical issues in financial modelling are covered including return and risk measurement, portfolio management, option pricing and fixed income analysis. This new edition updates and expands upon the existing material providing updated examples and new chapters on equities, simulation and trading strategies, including machine learnings techniques. Select data sets are available online.
Analyzing Financial and Economic Data with R by Marcelo S Perlin Pdf
book introduces the reader to the use of R and RStudio as a platform for analyzing financial and economic data. The book covers all necessary knowledge for using R, from its installation in your computer to the organization and development of scripts. For every chapter, the book presents practical and replicable examples of R code, providing context and facilitating the learning process. This is what you'll learn from this book: Using R and RStudio: In chapter 01 we will discuss the use of R as a programming platform designed to solve data-related problems in finance and economics. In chapter 02 we will explore basic commands and many functionalities of R and RStudio that will increase your productivity. Importing financial and economic data: In chapters 04 and 05 we will learn to import data from local files, such as an Excel spreadsheet, or the internet, using specialized packages that can download financial and economic data such as stock prices, economic indices, the US yield curve, corporate financial statements, and many others. Cleaning, structuring and analyzing the data with R: In chapters 06 and 07 we will concentrate our study on the ecosystem of basic and advanced classes of objects within R. We will learn to manipulate objects such as numeric vectors, dates and whole tables. In chapters 08 and 09 we'll study to use the programming tools to solve data-related problems such as cleaning and structuring messy data. In chapter 11 we will learn applications of the most common econometric models used in finance and economics including linear regression, generalized linear model, Arima model and others. Creating visual analysis of data: In chapter 10 we'll learn to use functions from package ggplot2 to create clever visualizations of our datasets, including the most popular applications in finance and economics, time series and statistical plots. Reporting your results: In chapter 12 we will see how to report our data analysis using specialized packages and the RMarkdown technology. Includes the topic of presenting and exporting tables, figure and models to a written report. Writing better and faster code: In the last chapter of the book we discuss best programming practices with R. We will look at how to profile code and search for bottlenecks, and improving execution time with caching strategies using package memoise, C++ code with Rcpp and parallel computing with furrr. All the material used in the book, including code examples separated by chapters, slides and exercises is publicly available on the Internet and distributed with a R package called afedR. It includes data files and several functions that can make it easier to run the examples of the book. If you plan to write some code as you read the book, this package will greatly help your journey. This book is recommended for researchers and students interested in learning how to use R. No prior knowledge of programming, finance or economics is required to take advantage of this book. After finishing, the reader will have enough knowledge to develop their own scripts autonomously, producing academic documents or data analysis for public and private institutions.
Processing and Analyzing Financial Data with R by Marcelo S. Perlin Pdf
This book introduces the reader to the use of R and RStudio as a platform for processing and analyzing financial data. The book covers all necessary knowledge for using R, from its installation in your computer to the organization and development of scripts. For every chapter, the book presents practical and replicable examples of R code, providing context and facilitating the learning process. Based on the material, the reader will learn how to download financial data from local files or the Internet, represent and process it using native objects in R, and create tables and figures to report the results in a technical document. The book is organized based on the author's practical experience in scientific research and includes instructions for using the best R packages for each purpose, such as xtable and texreg for reporting tables, dplyr in data processing, and ggplot2 in creating figures. After showing the capabilities of R in processing financial data, the last chapter presents three complete and reproducible examples of research in Finance. This book is recommended for researchers and students interested in learning how to use R. No prior knowledge of programming or finance is required to take advantage of this book. After finishing, the reader will have enough knowledge to develop their own scripts autonomously, producing academic documents or data analysis for public and private institutions.
Statistical Analysis of Financial Data in R by René Carmona Pdf
Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This textbook fills this gap by addressing some of the most challenging issues facing financial engineers. It shows how sophisticated mathematics and modern statistical techniques can be used in the solutions of concrete financial problems. Concerns of risk management are addressed by the study of extreme values, the fitting of distributions with heavy tails, the computation of values at risk (VaR), and other measures of risk. Principal component analysis (PCA), smoothing, and regression techniques are applied to the construction of yield and forward curves. Time series analysis is applied to the study of temperature options and nonparametric estimation. Nonlinear filtering is applied to Monte Carlo simulations, option pricing and earnings prediction. This textbook is intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. It is sprinkled with practical examples using market data, and each chapter ends with exercises. Practical examples are solved in the R computing environment. They illustrate problems occurring in the commodity, energy and weather markets, as well as the fixed income, equity and credit markets. The examples, experiments and problem sets are based on the library Rsafd developed for the purpose of the text. The book should help quantitative analysts learn and implement advanced statistical concepts. Also, it will be valuable for researchers wishing to gain experience with financial data, implement and test mathematical theories, and address practical issues that are often ignored or underestimated in academic curricula. This is the new, fully-revised edition to the book Statistical Analysis of Financial Data in S-Plus. René Carmona is the Paul M. Wythes '55 Professor of Engineering and Finance at Princeton University in the department of Operations Research and Financial Engineering, and Director of Graduate Studies of the Bendheim Center for Finance. His publications include over one hundred articles and eight books in probability and statistics. He was elected Fellow of the Institute of Mathematical Statistics in 1984, and of the Society for Industrial and Applied Mathematics in 2010. He is on the editorial board of several peer-reviewed journals and book series. Professor Carmona has developed computer programs for teaching statistics and research in signal analysis and financial engineering. He has worked for many years on energy, the commodity markets and more recently in environmental economics, and he is recognized as a leading researcher and expert in these areas.
This is a programming book written by a finance professor. This book will be an ideal textbook for many quantitative finance courses, such as (next generation) financial modeling, portfolio theory, empirical research in finance, computational finance, and risk management. The book has three unique characteristics: (1) use free software; (2) combine programming with various finance theories, such as ratio analysis, CAPM, Fama-French 5-factor model, portfolio theory, options and futures, credit analysis, VaR (Value at Risk), and Monte Carlo Simulation; and (3) download and process publicly available financial and economic data from various sources, such as Yahoo! Finance, Google Finance, FRED (Federal Reserve Bank's Economic Data Library), SEC, and Prof. French's Data Library
Financial Data Analytics with R by Jenny K. Chen Pdf
Financial Data Analysis with R: Monte-Carlo Validation is a comprehensive exploration of statistical methodologies and their applications in finance. Readers are taken on a journey in each chapter through practical explanations and examples, enabling them to develop a solid foundation of these methods in R and their applications in finance. This book serves as an indispensable resource for finance professionals, analysts, and enthusiasts seeking to harness the power of data-driven decision-making. The book goes beyond just teaching statistical methods in R and incorporates a unique section of informative Monte-Carlo simulations. These Monte-Carlo simulations are uniquely designed to showcase the reader the potential consequences and misleading conclusions that can arise when fundamental model assumptions are violated. Through step-by-step tutorials and realworld cases, readers will learn how and why model assumptions are important to follow. With a focus on practicality, Financial Data Analysis with R: Monte-Carlo Validation equips readers with the skills to construct and validate financial models using R. The Monte-Carlo simulation exercises provide a unique opportunity to understand the methods further, making this book an essential tool for anyone involved in financial analysis, investment strategy, or risk management. Whether you are a seasoned professional or a newcomer to the world of financial analytics, this book serves as a guiding light, empowering you to navigate the landscape of finance with precision and confidence. Key Features: An extensive compilation of commonly used financial data analytics methods from fundamental to advanced levels Learn how to model and analyze financial data with step-by-step illustrations in R and ready-to-use publicly available data Includes Monte-Carlo simulations uniquely designed to showcase the reader the potential consequences and misleading conclusions that arise when fundamental model assumptions are violated Data and computer programs are available for readers to replicate and implement the models and methods themselves
An Introduction to Analysis of Financial Data with R by Ruey S. Tsay Pdf
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.
Financial Analytics with R by Mark J. Bennett,Dirk L. Hugen Pdf
Financial Analytics with R sharpens readers' skills in time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.
Statistical Analysis of Financial Data by James Gentle Pdf
Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet. Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data. Features * Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions. * Describes both the basics of R and advanced techniques useful in financial data analysis. * Driven by real, current financial data, not just stale data deposited on some static website. * Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.
Reproducible Finance with R by Jonathan K. Regenstein, Jr. Pdf
Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.
Financial Analysis and Modeling Using Excel and VBA by Chandan Sengupta Pdf
An updated look at the theory and practice of financial analysis and modeling Financial Analysis and Modeling Using Excel and VBA, Second Edition presents a comprehensive approach to analyzing financial problems and developing simple to sophisticated financial models in all major areas of finance using Excel 2007 and VBA (as well as earlier versions of both). This expanded and fully updated guide reviews all the necessary financial theory and concepts, and walks you through a wide range of real-world financial problems and models that you can learn from, use for practice, and easily adapt for work and classroom use. A companion CD-ROM includes several useful modeling tools and fully working versions of all the models discussed in the book. Teaches financial analysis and modeling and illustrates advanced features of Excel and VBA, using a learn-by-doing approach Contains detailed coverage of the powerful features of Excel 2007 essential for financial analysis and modeling, such as the Ribbon interface, PivotTables, data analysis, and statistical analysis Other titles by Sengupta: Financial Modeling Using C++ and The Only Proven Road to Investment Success Designed for self-study, classroom use, and reference This comprehensive guide is an essential read for anyone who has to perform financial analysis or understand and implement financial models.
XV International Scientific Conference “INTERAGROMASH 2022” by Alexey Beskopylny,Mark Shamtsyan,Viktor Artiukh Pdf
The book contains proceedings of the XV International Scientific Conference INTERAGROMASH 2022, Rostov-on-Don, Russia. This conference is dedicated to the innovations in the field of precision agriculture, robotics and machines, as well as agriculture biotechnologies and soil management. It is a collection of original and fundamental research in such areas as follows: unmanned aerial systems, satellite-based applications, proximal and remote sensing of soil and crop, positioning systems, geostatistics, mapping and spatial data analysis, robotics, and automation. Potential and prospects for the use of hydrogen in agriculture, for example, in high-performance tractors with hybrid electric transmission, are disclosed in the research works of scientists from all over the world. It also includes such topics as precision horticulture, precision crop protection, differential harvest, precision livestock farming, controlling environment in animal husbandry, and other topics. One of the important issues raised in the book is to ensure the autonomy of local farms. The topic of the impact of the agro-industrial sector on the environment also received wide coverage. Ways to reduce the burden on the environment are proposed, and the use of alternative fuels and fertilizers is suggested. The research results presented in this book cover the experience and the latest studies on the sustainable functioning of agribusiness in several climatic zones. The tundra and taiga, forest-steppe, the steppe and semi-desert—all this is a unique and incredibly demanded bank of information, the main value of which is the real experience of the functioning of agribusiness in difficult climatic and geographic conditions. These materials are of interest for professionals and practitioners, for researchers, scholars, and producers. They are used in the educational process at specific agricultural universities or during vocational training at enterprises and also become an indispensable helper to farm managers in making the best agronomic decisions.
Financial Modeling, fifth edition by Simon Benninga,Tal Mofkadi Pdf
A substantially updated new edition of the essential text on financial modeling, with revised material, new data, and implementations shown in Excel, R, and Python. Financial Modeling has become the gold-standard text in its field, an essential guide for students, researchers, and practitioners that provides the computational tools needed for modeling finance fundamentals. This fifth edition has been substantially updated but maintains the straightforward, hands-on approach, with an optimal mix of explanation and implementation, that made the previous editions so popular. Using detailed Excel spreadsheets, it explains basic and advanced models in the areas of corporate finance, portfolio management, options, and bonds. This new edition offers revised material on valuation, second-order and third-order Greeks for options, value at risk (VaR), Monte Carlo methods, and implementation in R. The examples and implementation use up-to-date and relevant data. Parts I to V cover corporate finance topics, bond and yield curve models, portfolio theory, options and derivatives, and Monte Carlo methods and their implementation in finance. Parts VI and VII treat technical topics, with part VI covering Excel and R issues and part VII (now on the book’s auxiliary website) covering Excel’s programming language, Visual Basic for Applications (VBA), and Python implementations. Knowledge of technical chapters on VBA and R is not necessary for understanding the material in the first five parts. The book is suitable for use in advanced finance classes that emphasize the need to combine modeling skills with a deeper knowledge of the underlying financial models.
Applying Fuzzy Logic for the Digital Economy and Society by Andreas Meier,Edy Portmann,Luis Terán Pdf
This edited book presents the state-of-the-art of applying fuzzy logic to managerial decision-making processes in areas such as fuzzy-based portfolio management, recommender systems, performance assessment and risk analysis, among others. Presenting the latest research, with a strong focus on applications and case studies, it is a valuable resource for researchers, practitioners, project leaders and managers wanting to apply or improve their fuzzy-based skills.
Research Anthology on Strategies for Using Social Media as a Service and Tool in Business by Management Association, Information Resources Pdf
Social media has become an integral part of society as social networking has become a main form of communication and human interaction. To stay relevant, businesses have adopted social media tactics to interact with consumers, conduct business, and remain competitive. Social technologies have reached a vital point in the business world, being essential in strategic decision-making processes, building relationships with consumers, marketing and branding efforts, and other important areas. While social media continues to gain importance in modern society, it is essential to determine how it functions in contemporary business. The Research Anthology on Strategies for Using Social Media as a Service and Tool in Business provides updated information on how businesses are strategically using social media and explores the role of social media in keeping businesses competitive in the global economy. The chapters will discuss how social tools work, what services businesses are utilizing, both the benefits and challenges to how social media is changing the modern business atmosphere, and more. This book is essential for researchers, instructors, social media managers, business managers, students, executives, practitioners, industry professionals, social media analysts, and all audiences interested in how social media is being used in modern businesses as both a service and integral tool.
