An edition of Analysis of financial data (2006)

Analysis of financial data

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December 19, 2023 | History
An edition of Analysis of financial data (2006)

Analysis of financial data

  • 0 Ratings
  • 1 Want to read
  • 0 Currently reading
  • 0 Have read

Analysis of Financial Data teaches the basic methods and techniques of data analysis to finance students, by showing them how to apply such techniques in the context of real-world empirical problems. Adopting a largely non-mathematical approach Analysis of Financial Data relies more on verbal intuition and graphical methods for understanding. Key features include: Coverage of many of the major tools used by the financial economist e.g. correlation, regression, time series analysis and methods for analyzing financial volatility. Extensive use of real data examples, which involves readers in hands-on computer work. Mathematical techniques at a level suited to MBA students and undergraduates taking a first course in the topic. Supplementary material for readers and lecturers provided on an accompanying website.

Publish Date
Language
English
Pages
252

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Previews available in: English

Edition Availability
Cover of: Analysis of Financial Data
Analysis of Financial Data
2012, Wiley & Sons, Incorporated, John
in English
Cover of: Analysis of Financial Data
Analysis of Financial Data
2012, Wiley & Sons, Incorporated, John
in English
Cover of: Analysis of Financial Data
Analysis of Financial Data
2010, Wiley & Sons, Incorporated, John
in English
Cover of: Analysis of Financial Data
Analysis of Financial Data
2007, Wiley & Sons, Incorporated, John
in English
Cover of: Analysis of Financial Data
Analysis of Financial Data
2006, John Wiley & Sons, Ltd.
Electronic resource in English
Cover of: Analysis of financial data
Analysis of financial data
2006, John Wiley & Sons Inc.
in English
Cover of: Analysis of Financial Data
Analysis of Financial Data
2006, Wiley & Sons, Incorporated, John
in English

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Book Details


Table of Contents

Introduction
Organization of the book
Useful background
Appendix 1.1: Concepts in mathematics used in this book
Basic data handling
Types of financial data
Obtaining data
Working with data: graphical methods
Working with data: descriptive statistics
Expected values and variances
Chapter summary
Appendix 2.1: Index numbers
Appendix 2.2: Advanced descriptive statistics
Correlation
Understanding correlation
Understanding why variables are correlated
Understanding correlation through xy-plots
Correlation between several variables
Covariances and population correlations
Chapter summary
Appendix 3.1: Mathematical details
An introduction to simple regression
Regression as a best fitting line
Interpreting OLS estimates
Fitted values and r2: Measuring the fit of a regression model
Nonlinearity in regression
Chapter summary
Appendix 4.1: Mathematical details
Statistical aspects of regression
Which factors affect the accuracy of the estimate ??
Calculating a confidence interval for ?
Testing whether ?????
Hypothesis testing involving r2: The f-statistic
Chapter summary
Appendix 5.1: Using statistical tables for testing whether ?????
Multiple regression
Regression as a best fitting line
Ordinary least squares estimation of the multiple regression model
Statistical aspects of multiple regression
Interpreting OLS estimates
Pitfalls of using simple regression in a multiple regression context
Omitted variables bias
Multicollinearity
Chapter summary
Appendix 6.1: Mathematical interpretation of regression coefficients
Regression with dummy variables
Simple regression with a dummy variable
Multiple regression with dummy variables
Multiple regression with both dummy and non-dummy explanatory variables
Interacting dummy and non-dummy variables
What if the dependent variable is a dummy?
Chapter summary
Regression with lagged explanatory variables
Aside on lagged variables
Aside on notation
Selection of lag order
Chapter summary
Univariate time series analysis
The autocorrelation function
The autoregressive model for univariate time series
Nonstationary versus stationary time series
Extensions of the AR(1) model
Testing in the AR(p) with deterministic trend model
Chapter summary
Appendix 9.1: Mathematical intuition for the AR(1) model
Regression with time series variables
Time series regression when x and y are stationary
Time series regression when y and x have unit roots: spurious regression
Time series regression when y and x have unit roots: cointegration
Time series regression when y and x are cointegrated: the error correction model
Time series regression when y and x have unit roots but are not cointegrated
Chapter summary
Regression with times series variables with several equations
Granger causality
Vector autoregressions
Chapter summary
Appendix 11.1: Hypothesis tests involving more than one coefficient
Appendix 11.2: Variance decompositions
Financial volatility
Volatility in asset prices: Introduction
Autoregressive conditional heteroskedasticity (arch)
Chapter summary
Appendix A Writing an empirical project
Description of a typical empirical project
General considerations.

Edition Notes

Includes bibliographical references and index.

Published in
Hoboken, NJ

Classifications

Dewey Decimal Class
332/.01/5195
Library of Congress
HG106 .K67 2006, HG106.K67 2006

The Physical Object

Pagination
p. cm.
Number of pages
252

ID Numbers

Open Library
OL3402366M
Internet Archive
analysisfinancia00koop
ISBN 10
0470013214
LCCN
2005017245
OCLC/WorldCat
60742000
Goodreads
1310159

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December 19, 2023 Edited by ImportBot import existing book
December 29, 2022 Edited by MARC Bot import existing book
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September 16, 2021 Edited by ImportBot import existing book
April 1, 2008 Created by an anonymous user Imported from Scriblio MARC record