Analyzing Financial Data and Implementing Financial Models Using R
โ Scribed by Clifford S. Ang (auth.)
- Publisher
- Springer International Publishing
- Year
- 2015
- Tongue
- English
- Leaves
- 360
- Series
- Springer Texts in Business and Economics
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement financial models. This text will show students how to obtain publicly available data, manipulate such data, implement the models, and generate typical output expected for a particular analysis.
This text aims to overcome several common obstacles in teaching financial modeling. First, most texts do not provide students with enough information to allow them to implement models from start to finish. In this book, we walk through each step in relatively more detail and show intermediate R output to help students make sure they are implementing the analyses correctly. Second, most books deal with sanitized or clean data that have been organized to suit a particular analysis. Consequently, many students do not know how to deal with real-world data or know how to apply simple data manipulation techniques to get the real-world data into a usable form. This book will expose students to the notion of data checking and make them aware of problems that exist when using real-world data. Third, most classes or texts use expensive commercial software or toolboxes. In this text, we use R to analyze financial data and implement models. R and the accompanying packages used in the text are freely available; therefore, any code or models we implement do not require any additional expenditure on the part of the student.
Demonstrating rigorous techniques applied to real-world data, this text covers a wide spectrum of timely and practical issues in financial modeling, including return and risk measurement, portfolio management, options pricing, and fixed income analysis.
โฆ Table of Contents
Front Matter....Pages i-xvi
Prices....Pages 1-53
Individual Security Returns....Pages 55-78
Portfolio Returns....Pages 79-113
Risk....Pages 115-159
Factor Models....Pages 161-191
Risk-Adjusted Portfolio Performance Measures....Pages 193-208
Markowitz Mean-Variance Optimization....Pages 209-240
Fixed Income....Pages 241-302
Options....Pages 303-331
Back Matter....Pages 333-351
โฆ Subjects
Finance/Investment/Banking; Econometrics; Financial Economics; Statistics and Computing/Statistics Programs
๐ SIMILAR VOLUMES
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.
I must agree with the other reviewers of Sengupta's very weak effort. This book is overly simplistic and of very little use to any true financial professional. For an excellent overview of financial modeling in general, look at Ragsdale 5e or The Art of Modeling with Spreadsheets by Powell of the
<div><p>The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guid
<div><p>The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guid
<div><p>The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guid