SAS for Finance电子书

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SAS for Finance

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Preface

Who this book is for

What this book covers

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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

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

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

Budget and Demand Forecasting

The need for the Markov model

Business problem

Markovian model approach

ARIMA model approach

Markov method for imputation

Summary

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

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

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

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