| Description |
1 online resource (299 pages) |
| Contents |
Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Time Series Modeling in the Financial Industry; Time series illustration; The importance of time series; Forecasting across industries; Characteristics of time series data; Seasonality; Trend; Outliers and rare events; Disruptions; Challenges in data; Influencer variables; Definition changes; Granularity required; Legacy issues; System differences; Source constraints; Vendor changes; Archiving policy; Good versus bad forecasts; Use of time series in the financial industry |
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Predicting stock prices and making portfolio decisionsAdhering to Basel norms; Demand planning; Inflation forecasting; Managing customer journeys and maintaining loyalty; Summary; References; Chapter 2: Forecasting Stock Prices and Portfolio Decisions using Time Series; Portfolio forecasting; A portfolio demands decisions; Forecasting process; Visualization of time series data; Business case study; Data collection and transformation; Model selection and fitting; Part A -- Fit statistics; Part B -- Diagnostic plots; Part C -- Residual plots; Dealing with multicollinearity; Role of autocorrelation |
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Scoring based on PROC REGARIMA; Validation of models; Model implementation; Recap of key terms; Summary; Chapter 3: Credit Risk Management; Risk types; Basel norms; Credit risk key metrics; Exposure at default; Probability of default; Loss given default; Expected loss; Aspects of credit risk management; Basel and regulatory authority guidelines; Governance; Validation; Data; PD model build; Genmod procedure; Proc logistic; Proc Genmod probit; Summary; Chapter 4: Budget and Demand Forecasting; The need for the Markov model; Business problem; Markovian model approach; ARIMA model approach |
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Markov method for imputationSummary; Chapter 5: Inflation Forecasting for Financial Planning; What is inflation?; Reasons for inflation; Inflation outcome and the Philips curve; Winners and losers; Business case for forecasting inflation; Data-gathering exercise; Modeling methodology; Multivariate regression model; Forward selection model; Backward selection; Maximize R; Univariate model; Summary; Chapter 6: Managing Customer Loyalty Using Time Series Data; Advantages of survival modeling; Key aspects of survival analysis; Data structure; Business problem; Data preparation and exploration |
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Non-parametric procedure analysisSurvival curve for groups; Survival curve and covariates; Parametric procedure analysis; Semi-parametric procedure analysis; Summary; Chapter 7 : Transforming Time Series -- Market Basket and Clustering; Market basket analysis; Segmentation and clustering; MBA business problem; Data preparation for MBA; Assumptions for MBA; Analysis of a set size of two; A segmentation business problem; Segmentation overview; Clustering methodologies; Segmentation suitability in the current scenario; Segmentation modeling; Summary; Other Books You May Enjoy; Index |
| Summary |
SAS is the ground-breaking tool for advanced, predictive, and statistical analytics. Right from refining your data using power of SAS analytics, you will be able to exploit the capabilities of high-powered package to create accurate financial models. You can easily assess the pros and cons of models to suit unique business needs |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| SUBJECT |
SAS (Computer file) http://id.loc.gov/authorities/names/n88028236
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SAS (Computer file) fast |
| Subject |
SAS
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Accounting software.
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Database design & theory.
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Information architecture.
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Data capture & analysis.
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Computers -- Desktop Applications -- Personal Finance Applications.
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Computers -- Data Modeling & Design.
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Computers -- Data Processing.
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| Form |
Electronic book
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| ISBN |
9781788622486 |
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1788622480 |
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1788624564 |
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9781788624565 |
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