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Performing Financial Studies A Methodological Cookbook

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ISBN-10: 0130479810

ISBN-13: 9780130479815

Edition: 2004

Authors: Michael J. Seiler

List price: $106.20
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Description:

Ideal for both reference and self-study, this unique volume goes beyond simply explaining how statistical procedures should be done, to showing in "no-detail-left-out" fashion what should be done at each step-much like following a recipe in a cookbook. Additionally, raw financial data sets are used in the examples so each method is related directly to finance and to the specific problems financial data presents. Statistical procedures related exclusively to finance (and often only found tersely described in academic journals) are also covered. Features screen captures in various computer programs (Excel, SPSS, or EViews). Understanding Your Data. Preparing Your Data for Analysis.…    
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Book details

List price: $106.20
Copyright year: 2004
Publisher: Prentice Hall PTR
Publication date: 1/24/2003
Binding: Paperback
Pages: 448
Size: 8.25" wide x 10.25" long x 0.75" tall
Weight: 1.738
Language: English

Preface
About the Author
Getting Started
Understanding Your Data
Nonmetric Data
Nominal Data
Ordinal Data
Metric Data
Interval Data
Continuous (Ratio) Data
Preparing Your Data for Analysis
Getting Data Off the Internet and into Excel
Preparing Data in Excel
Getting Data into EViews
Getting Data into SPSS
Screening the Data for Typos and Outliers
Transforming/Computing Financial Variables
Calculating Returns
Calculating Excess Returns
Tying Up Loose Ends: Labels and Decimal Points
Basic Financial Statistics/Methodologies
Correlation
Purpose of a Correlation Test
Performing a Correlation with Metric Variables
Performing a Correlation with Nonmetric Variables
Autocorrelation
Purpose of an Autocorrelation Test
Performing an Autocorrelation Test
Partial Autocorrelation
Purpose of a Partial Autocorrelation Test
Performing a Partial Autocorrelation Test
Autocorrelation for Nonparametric Data (Wald-Wolfowitz Runs Test)
Purpose of a Wald-Wolfowitz Runs Test
Performing a Wald-Wolfowitz Runs Test
T-Test
Purpose of a T-Test
Performing a One-Sample T-Test
Performing an Independent-Samples T-Test
Performing a Paired-Samples T-Test
Analysis of Variance
Purpose of an Analysis of Variance
Performing an ANOVA
Post Hoc Tests
Equal Variances Assumed vs. Unequal Variances Assumed
Regression
Purpose of a Regression
Performing a Linear Regression
Testing for Multicollinearity
Performing a Two-Stage Least Squares Regression
Performing a 2SLS Regression in SPSS
Performing a 2SLS Regression in EViews
Factor Analysis
Purpose of a Factor Analysis
Performing a Factor Analysis
Using Factor Scores in a Regression
Using a Summated Scale in a Regression
Calculating a Stock's Beta
Purpose of Calculating a Stock's Beta
Calculating Beta
Interpreting Beta
Predictive Ability
Purpose of Measuring Predictive Ability
Measuring Predictive Ability Using Ordinal Rankings
Measuring Predictive Ability Using Raw Returns
Advanced Financial Techniques/Methodologies
Event Studies
Purpose of an Event Study
Background
Identify the Event Date
Define the Event Window
Define the Estimation Period
Select the Sample of Firms
Calculate Normal (or Nonevent) Returns
Mean Return
Market Return
Proxy (or Control) Portfolio Return
Risk-Adjusted Return
Calculate ARs
Calculate CARs
Determine the Statistical Significance of the ARs and CARs
Performing an Event Study
Identifying the Event Date
Defining the Event Window
Defining the Estimation Period
Selecting the Sample of Firms
Calculating Normal (or Nonevent) Returns
Calculating ARs, CARs, and Their Significance
Setting up the Event Study in Excel
Performing Intermediate Calculations
Calculating Total SAR
Calculating the Cumulative TSAR
Unit Root Test
Purpose of a Unit Root Test
Performing a Unit Root Test
Granger Causality
Purpose of Granger Causality
Performing Granger Causality
Number of Lags
Limitations of Granger Causality
Cointegration
Purpose of Cointegration
Performing a Cointegration Test
Verifying That the Series Are Integrated
Continuing with the Cointegration Test
Using Centered (Orthogonalized) Seasonal Dummy Variables
Vector Autoregression
Purpose of Vector Autoregression
Performing a VAR
Vector Error Correction
Purpose of Vector Error Correction
Performing a VEC
ARCH/GARCH
Purpose of ARCH/GARCH
Performing ARCH/GARCH
Verifying the Correct Model Specification
The Mean Equation
The Variance Equation
Testing for ARCH Effects
Creating an Adjusted Series Free from ARCH/GARCH
Variations of ARCH/GARCH
Threshold ARCH
Exponential GARCH
ARCH-in-Mean
Component ARCH
Asymmetric Component
Programming a Binomial Option Pricing Model
Purpose of a Binomial Option Model
Programming a Binomial European Call Option
Programming a Binomial European Put Option
Programming a Binomial American Call Option
Programming a Binomial American Put Option
Programming a Black-Scholes Option Pricing Model
Programming a Call Option without Dividends Using Black-Scholes
Programming a Put Option without Dividends Using Black-Scholes
Programming a Call Option with Dividends Using Black-Scholes
Continuous Dividends
Discrete Dividends
Programming a Put Option with Dividends Using Black-Scholes
Continuous Dividends
Discrete Dividends
Understanding the Effects of Inputs on Call and Put Options
Stock Price
Exercise (or Strike) Price
Volatility
Time to Maturity
Risk-Free Rate
Dividends
Writing a Financial Study
Sections in a Financial Study
Cover Page and Abstract
Introduction
Literature Review
Data
Methodology
Results
Summary and Conclusions
References
Tables
Bringing Output into Microsoft Word
Bringing SPSS Output into Microsoft Word
Bringing Excel Output into Microsoft Word
Bringing EViews Output into Microsoft Word
Dataset Descriptions
Glossary
Index