SAS for Finance : Forecasting and Data Analysis Techniques with Real-World Examples to Build Powerful Financial Models.

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 decisions -- Adhering 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 REG -- ARIMA -- 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 imputation -- Summary -- 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 analysis -- Survival 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.

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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.