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SAS for Finance : Forecasting and data analysis techniques with real-world examples to build powerful financial models.
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.