Given the range of models we are discussing in this book, is there a need to discuss Markov models? When we speak about forecasting, one of the main inputs is the historical information. This could be in the form of a time series. However, Markov models don't need historical information to be able to forecast. When we build a Markov model, we are interested in the state (value/behavior/phenomenon) of a subject at the present time. We are also interested in the states that the subject can get transitioned to and the transition probabilities involved. A textbook definition of the Markov model would be a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. To understand the terms better, let's look at the states that a car being driven may...
SAS for Finance
By :
SAS for Finance
By:
Overview of this book
SAS is a groundbreaking tool for advanced predictive and statistical analytics used by top banks and financial corporations to establish insights from their financial data.
SAS for Finance offers you the opportunity to leverage the power of SAS analytics in redefining your data. Packed with real-world examples from leading financial institutions, the author discusses statistical models using time series data to resolve business issues.
This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate financial models. You can easily assess the pros and cons of models to suit your unique business needs.
By the end of this book, you will be able to leverage the true power of SAS to design and develop accurate analytical models to gain deeper insights into your financial data.
Table of Contents (9 chapters)
Preface
Free Chapter
Time Series Modeling in the Financial Industry
Forecasting Stock Prices and Portfolio Decisions using Time Series
Credit Risk Management
Budget and Demand Forecasting
Inflation Forecasting for Financial Planning
Managing Customer Loyalty Using Time Series Data
Transforming Time Series – Market Basket and Clustering
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