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SAS for Finance

Book Description

Leverage the analytical power of SAS to perform financial analysis efficiently

About This Book
  • Leverage the power of SAS to analyze financial data with ease
  • Find hidden patterns in your data, predict future trends, and optimize risk management
  • Learn why leading banks and financial institutions rely on SAS for financial analysis
Who This Book Is For

Financial data analysts and data scientists who want to use SAS to process and analyze financial data and find hidden patterns and trends from it will find this book useful. Prior exposure to SAS will be helpful but is not mandatory. Some basic understanding of the financial concepts is required.

What You Will Learn
  • Understand time series data and its relevance in the financial industry
  • Build a time series forecasting model in SAS using advanced modeling theories
  • Develop models in SAS and infer using regression and Markov chains
  • Forecast in?ation by building an econometric model in SAS for your financial planning
  • Manage customer loyalty by creating a survival model in SAS using various groupings
  • Understand similarity analysis and clustering in SAS using time series data
In Detail

SAS is a groundbreaking tool for advanced predictive and statistical analytics used by top banks and financial corporations to establish insights from their financial data.

SAS for Finance offers you the opportunity to leverage the power of SAS analytics in redefining your data. Packed with real-world examples from leading financial institutions, the author discusses statistical models using time series data to resolve business issues.

This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate financial models. You can easily assess the pros and cons of models to suit your unique business needs.

By the end of this book, you will be able to leverage the true power of SAS to design and develop accurate analytical models to gain deeper insights into your financial data.

Style and approach

A comprehensive guide filled with use-cases will ensure that you have a very good conceptual and practical understanding of using SAS in the finance domain.

Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Title Page
  2. Copyright and Credits
    1. SAS for Finance
  3. Packt Upsell
    1. Why subscribe?
    2. PacktPub.com
  4. Contributors
    1. About the author
    2. About the reviewer
    3. Packt is searching for authors like you
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
      1. Download the example code files
      2. Download the color images
      3. Conventions used
    4. Get in touch
      1. Reviews
    5. Disclaimer
  6. Time Series Modeling in the Financial Industry
    1. Time series illustration
    2. The importance of time series
    3. Forecasting across industries
    4. Characteristics of time series data
      1. Seasonality
      2. Trend
      3. Outliers and rare events
      4. Disruptions
    5. Challenges in data
      1. Influencer variables
      2. Definition changes
      3. Granularity required
      4. Legacy issues
      5. System differences
      6. Source constraints
      7. Vendor changes
      8. Archiving policy
    6. Good versus bad forecasts
    7. Use of time series in the financial industry
      1. Predicting stock prices and making portfolio decisions
      2. Adhering to Basel norms
      3. Demand planning
      4. Inflation forecasting
      5. Managing customer journeys and maintaining loyalty
    8. Summary
    9. References
  7. Forecasting Stock Prices and Portfolio Decisions using Time Series
    1. Portfolio forecasting
    2. A portfolio demands decisions
    3. Forecasting process
    4. Visualization of time series data
      1. Business case study
      2. Data collection and transformation
      3. Model selection and fitting
        1. Part A – Fit statistics
        2. Part B - Diagnostic plots
        3. Part C - Residual plots
    5. Dealing with multicollinearity
    6. Role of autocorrelation
    7. Scoring based on PROC REG
      1. ARIMA
      2. Validation of models
      3. Model implementation
    8. Recap of key terms
    9. Summary
  8. Credit Risk Management
    1. Risk types
    2. Basel norms
    3. Credit risk key metrics
      1. Exposure at default
      2. Probability of default
      3. Loss given default
      4. Expected loss
    4. Aspects of credit risk management
      1. Basel and regulatory authority guidelines
      2. Governance
      3. Validation
      4. Data
    5. PD model build
      1. Genmod procedure
      2. Proc logistic
      3. Proc Genmod probit
    6. Summary
  9. Budget and Demand Forecasting
    1. The need for the Markov model
    2. Business problem
    3. Markovian model approach
    4. ARIMA model approach
    5. Markov method for imputation
    6. Summary
  10. Inflation Forecasting for Financial Planning
    1. What is inflation?
      1. Reasons for inflation
      2. Inflation outcome and the Philips curve
        1. Winners and losers
    2. Business case for forecasting inflation
      1. Data-gathering exercise
    3. Modeling methodology
      1. Multivariate regression model
        1. Forward selection model
        2. Backward selection
      2. Maximize R
      3. Univariate model
    4. Summary
  11. Managing Customer Loyalty Using Time Series Data
    1. Advantages of survival modeling
    2. Key aspects of survival analysis
      1. Data structure
    3. Business problem
      1. Data preparation and exploration
      2. Non-parametric procedure analysis
      3. Survival curve for groups
      4. Survival curve and covariates
      5. Parametric procedure analysis
      6. Semi-parametric procedure analysis
    4. Summary
  12. Transforming Time Series – Market Basket and Clustering
    1. Market basket analysis
    2. Segmentation and clustering
    3. MBA business problem
    4. Data preparation for MBA
    5. Assumptions for MBA
    6. Analysis of a set size of two
    7. A segmentation business problem
    8. Segmentation overview
    9. Clustering methodologies
    10. Segmentation suitability in the current scenario
    11. Segmentation modeling
    12. Summary
  13. Other Books You May Enjoy
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