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Working in a hands-on learning environment, led by our SAS for Finance expert instructor, students will learn about and explore: Leverage the power of SAS to analyze financial data with ease.Find hidden patterns in your data, predict future trends, and optimize risk management.Learn why leading banks and financial institutions rely on SAS for financial analysis.

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Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

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Certification

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 lesson 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 lesson, 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.

Course Curriculum

    • Time Series Modeling in the Financial Industry 00:00:00
    • Time series illustration 00:00:00
    • The importance of time series 00:00:00
    • Forecasting across industries 00:00:00
    • Characteristics of time series data 00:00:00
    • Challenges in data 00:00:00
    • Good versus bad forecasts 00:00:00
    • Use of time series in the financial industry 00:00:00
    • Forecasting Stock Prices and Portfolio Decisions using Time Series 00:00:00
    • Portfolio forecasting 00:00:00
    • A portfolio demands decisions 00:00:00
    • Forecasting process 00:00:00
    • Visualization of time series data 00:00:00
    • Dealing with multicollinearity 00:00:00
    • Role of autocorrelation 00:00:00
    • Scoring based on PROC REG 00:00:00
    • Recap of key terms 00:00:00
    • Credit Risk Management 00:00:00
    • Risk types 00:00:00
    • Basel norms 00:00:00
    • Credit risk key metrics 00:00:00
    • Aspects of credit risk management 00:00:00
    • PD model build 00:00:00
    • Budget and Demand Forecasting 00:00:00
    • The need for the Markov model 00:00:00
    • Business problem 00:00:00
    • Markovian model approach 00:00:00
    • ARIMA model approach 00:00:00
    • Markov method for imputation 00:00:00
    • Inflation Forecasting for Financial Planning 00:00:00
    • What is inflation? 00:00:00
    • Business case for forecasting inflation 00:00:00
    • Modeling methodology 00:00:00
    • Managing Customer Loyalty Using Time Series Data 00:00:00
    • Advantages of survival modeling 00:00:00
    • Key aspects of survival analysis 00:00:00
    • Business problem 00:00:00
    • Transforming Time Series – Market Basket and Clustering 00:00:00
    • Market basket analysis 00:00:00
    • Segmentation and clustering 00:00:00
    • MBA business problem 00:00:00
    • Data preparation for MBA 00:00:00
    • Assumptions for MBA 00:00:00
    • Analysis of a set size of two 00:00:00
    • A segmentation business problem 00:00:00
    • Segmentation overview 00:00:00
    • Clustering methodologies 00:00:00
    • Segmentation suitability in the current scenario 00:00:00
    • Segmentation modeling 00:00:00

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