Financial Data Science with SAS

Financial Data Science with SAS

by Babatunde O Odusami
Released June 2024
Publisher(s): SAS Institute
ISBN: 9781685800154

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

Explore financial data science using SAS.

Financial Data Science with SAS provides readers with a comprehensive explanation of the theoretical and practical implementation of the various types of analytical techniques and quantitative tools that are used in the financial services industry. This book shows readers how to implement data visualization, simulation, statistical predictive models, machine learning models, and financial optimizations using real-world examples in the SAS Analytics environment. Each chapter ends with practice exercises that include use case scenarios to allow readers to test their knowledge.

Designed for university students and financial professionals interested in boosting their data science skills, Financial Data Science with SAS is an essential reference guide for understanding how data science is used in the financial services industry and for learning how to use SAS to solve complex business problems.

Table of contents

  1. About This Book
  2. About The Author
  3. Acknowledgments
  4. Chapter 1: Financial Data Science: An Overview
    1. Introduction
    2. Data Science and Financial Systems
      1. Data Science
      2. Financial Data Science
    3. Business Applications of Financial Data Science
    4. Financial Information Systems
    5. Components of Information Systems
    6. Financial Intelligence
    7. Financial Econometrics
    8. Data Science Toolkit
      1. Microsoft Excel
      2. IBM SPSS Statistics
      3. Tableau
      4. MATLAB
      5. Python
      6. R
      7. SAS
    9. Working with SAS
      1. Windows in the SAS Windowing Environment
      2. SAS Enterprise Guide
      3. SAS Studio
      4. SAS Enterprise Miner
      5. SAS Model Studio
      6. SAS Statements
      7. SAS Data and Library
    10. Data Science Concepts and Their Finance Applications
      1. Descriptive Analytics
      2. Inferential Statistics
      3. Diagnostic Analytics
      4. Predictive Analytics
      5. Prescriptive Analytics
      6. Machine Intelligence and Machine Learning
    11. Supervised, Unsupervised, and Reinforcement Learning
      1. Supervised Learning
      2. Unsupervised Learning
      3. Reinforcement Learning
      4. Parametric Versus Nonparametric Algorithms
    12. Limitations of Financial Data Science
      1. Performance Degradation
      2. Overfitting Versus Underfitting
    13. Ethics, Biases, Transparency, and Economic Issues
      1. Ethical Issues in Financial Data Science
      2. Bias
      3. Transparency
      4. Economics
      5. Regulatory Landscape
    14. Exercises
  5. Chapter 2: Exploring and Visualizing Financial and Economic Data
    1. Learning and Communicating Using Visualizations
    2. Types of Data Visualization
      1. Descriptive Data Visualization
      2. Diagnostic Data Visualization
    3. Preprocessing Financial Data
      1. Structured Data
      2. Unstructured Data
      3. Semi-Structured Data
    4. SAS Macros
    5. Data Quality
      1. Dimensions of Data Quality
    6. Other Data Wrangling Tools
      1. The SQL Procedure
      2. The DS2 Procedure
    7. Visual Description of the Data
    8. Visualization Options in SAS
      1. ODS Graphics
      2. SAS/GRAPH
    9. Using SAS to Analyze Portfolio Performance
      1. Numerical Performance Measurement
      2. Visual Performance Measurement
      3. Visual Performance Attribution
      4. Numerical Performance Attribution in SAS
    10. Exercises
  6. Chapter 3: Visual Diagnostics of Financial and Economic Data
    1. Visual Diagnostic of the Salient Properties of Financial Data
    2. Dealing with the Anomalous Statistical Properties of Time Series Data
      1. Trends
      2. Cyclicality
      3. Seasonality
      4. Autocorrelation
      5. Heteroscedasticity
      6. Stationarity
      7. Non-Normality
    3. Exercises
  7. Chapter 4: Simulating Financial and Economic Data
    1. Introduction to Simulations
    2. Types of Simulation Models
      1. Deterministic Models
      2. Stochastic Models
    3. Probability Distributions
      1. Discrete Distributions
      2. Continuous Distributions
      3. Multivariate Probability Distribution
    4. Simulating Random Values from Various Probability Distributions
      1. Discrete Distribution
      2. Continuous Distribution
      3. Multivariate Distribution
    5. Stochastic Processes
      1. Gaussian Process
      2. Wiener (Brownian Motion) Process
      3. Random Walk Process
      4. Itô Process
      5. Geometric Brownian Motion
      6. Mean Reverting (Ornstein-Uhlenbeck) Process
    6. Monte Carlo Simulation
      1. Monte Carlo Simulation of Stock Prices
      2. Monte Carlo Simulations for Capital Budgeting
      3. Capital Budgeting Analysis in SAS
    7. Exercises
  8. Chapter 5: Using Simulations for Risk Management
    1. Using Simulations for Risk Management
      1. Extreme Value Theory (EVT)
      2. Value at Risk (VaR) Using GEV Distribution
      3. Value at Risk Using the Generalized Pareto Distribution (GPD)
      4. Copulas
      5. Copula Application in Finance
      6. Portfolio Construction and Value at Risk
    2. Resampling Techniques
      1. Bootstrapping
      2. Bootstrapping in SAS
      3. Bootstrapping Applications in Finance
      4. Bootstrapping Stock Returns
      5. Bootstrapping Option Prices
    3. Exercises
  9. Chapter 6: Predictive Analytics in Finance
    1. Predictive Modeling
      1. Statistical Versus Machine Learning Models
    2. Methods of Estimation
      1. Parametric Method of Estimation
      2. Comparing Parametric Methods of Estimation in SAS Using PROC MODEL
      3. Nonparametric Method of Estimation
    3. Types of Predictive Models
      1. Classification Models
      2. Regression Models
      3. Dimension Reduction Models
      4. Forecasting Models
      5. Time Index Forecasting Models
      6. Outlier Models
    4. Predictive Modeling in SAS
    5. Statistical Predictive Models in SAS
      1. Classification Models
      2. Dimension Reduction Models
      3. Time to Event Models
      4. Forecasting Models
      5. Time-Index Forecasting Models
    6. Other Statistical and Econometric Procedures
    7. Exercises
  10. Chapter 7: Machine Learning Models in Finance
    1. Machine Learning in Finance
      1. Developing an Algorithm-Based Trading Strategy
      2. A Caution on Algorithm-Based Investment Strategies
      3. Conceptualizing our Trading Strategy
      4. Data
      5. Classical Versus High-Performance Computing Procedures
      6. Importing Our Data Into SAS
      7. Data Partitioning
    2. Supervised Learning Models for Predicting Stock Price Movements
      1. Hyperparameter Tuning
      2. Parametric Algorithms
      3. Logistic Regression
      4. Generalized Linear Model (GLM)
      5. Artificial Neural Network
      6. Nonparametric Algorithms
      7. Decision Tree
      8. Support Vector Machine
      9. Random Forest
      10. The Ensemble Model
    3. Unsupervised Learning Algorithms
    4. Model Selection Criteria
      1. Classification Measures
      2. Data Mining Measures
      3. Statistical Measures
      4. Model Comparison
      5. Backtesting our Algorithmic Strategy Against a Passive Investment Strategy in the S&P 500 Index
    5. Closing Thoughts
    6. Exercises
  11. Chapter 8: Introduction to Financial Optimization
    1. Introduction to Optimization
    2. Structure of Optimization Problems
      1. Feasibility
      2. Boundedness
      3. Smoothness
      4. Convexity
    3. Optimization Procedures in SAS
      1. PROC OPTMODEL
    4. Types of Optimization Problems
      1. Linear Optimization or Programming
      2. Simplex Method
      3. Integer Linear Programming (ILP)
      4. Using Arrays, Index Sets, and Data Sets in the OPTMODEL Procedure
      5. Mixed Integer Linear Programming  (MILP) Problems
      6. Non-Linear Optimizations
      7. Interior Point Method
      8. Active Set Method
      9. Quadratic Optimization
    5. Exercises
  12. Chapter 9: Using SAS for Portfolio Optimizations
    1. Modern Portfolio Theory (MPT) and Portfolio Optimization
    2. Mean-Variance Optimization
      1. Return Maximization
      2. Risk Minimization
      3. Using PROC IML for Optimization Problems
      4. Sharpe Ratio Maximization
      5. Using SAS to Create the Efficient Frontier and Capital Allocation Line
    3. Alternatives to the Mean-Variance Optimization
      1. Black-Litterman Portfolio Optimization
      2. Risk Parity Optimization
    4. Stochastic Optimization
      1. Stochastic Portfolio Optimization
      2. Mean-CVaR Portfolio Optimization
      3. Optimizing with Conditional Value-at-Risk
      4. Implementing Mean-CVaR Optimization in SAS
    5. Robust Optimization
      1. Constraint Robustness
      2. Objective Robustness
      3. Uncertainty Set
    6. Robust Portfolio Optimization
      1. Worst-case Mean Robust Optimization
      2. Implementing Robust Portfolio Optimization in SAS
      3. Robust Portfolio Optimization with Scenario Uncertainty Set
      4. Robust Portfolio Optimization with Ellipsoidal Uncertainty Set
    7. Machine Learning and Other Innovations in Portfolio Optimization
    8. Exercises
  13. References

Product information

  • Title: Financial Data Science with SAS
  • Author(s): Babatunde O Odusami
  • Release date: June 2024
  • Publisher(s): SAS Institute
  • ISBN: 9781685800154