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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.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Gulati, Harish. | |
| 245 | 1 | 0 | |a SAS for Finance : |b Forecasting and data analysis techniques with real-world examples to build powerful financial models. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2018. | ||
| 300 | |a 1 online resource (299 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 504 | |a Includes bibliographical references. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 630 | 0 | 0 | |a SAS (Computer file) |
| 630 | 0 | 7 | |a SAS (Computer file) |2 fast |
| 650 | 0 | |a SAS. | |
| 650 | 7 | |a Accounting software. |2 bicssc | |
| 650 | 7 | |a Database design & theory. |2 bicssc | |
| 650 | 7 | |a Information architecture. |2 bicssc | |
| 650 | 7 | |a Data capture & analysis. |2 bicssc | |
| 650 | 7 | |a Computers |x Desktop Applications |x Personal Finance Applications. |2 bisacsh | |
| 650 | 7 | |a Computers |x Data Modeling & Design. |2 bisacsh | |
| 650 | 7 | |a Computers |x Data Processing. |2 bisacsh | |
| 758 | |i has work: |a SAS for finance (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGGfpMGFPY463r76PyVvh3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Gulati, Harish. |t SAS for Finance : Forecasting and data analysis techniques with real-world examples to build powerful financial models. |d Birmingham : Packt Publishing, ©2018 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5405691 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n BDZ0036924774 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5405691 | ||
| 994 | |a 92 |b IZTAP | ||