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Statistical Learning of Financial Markets spring 2020

Jan 23, 2020 3 min read

This course is designed for Ph.D. students in Analytics and Data Science at Kennesaw State University. We study statistical learning methods and their applications in financial data analysis.

Textbook

The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Springer, 2nd edition 2009.
Analysis of Financial Time Series by Ruey S. Tsay. Wiley, 3rd edition 2010.

The first book (ESL) discusses the fundamentals of statistical machine learning theory, and it is a popular textbook used in many universities. The second book (FTS) provides a clear explanation of many standard methods in financial econometrics, making it a useful reference for financial data modelling. Combining the two will, hopefully, give students necessary technical skills to conduct research in financial data modelling.

Tentative schedule

Date	Topic	Chapter
week 1	optimization numerical optimization time series basics	FTS Chapter 2 lecture notes
week 2	Overview of Statistical Learning	ESL Chapter 2 slides pdf Tradeoff between linear and fully nonparametric methods; function approximation; bias-variance tradeoff
week 3	Empirical Asset Pricing	FTS Chapter 9 slides pdf Asset pricing test; single and multi-factor models
week 4	Empirical Asset Pricing	FTS Chapter 9 slides pdf Macroeconomic factor pricing; fundamental factor pricing; statistical factor pricing
week 5	Linear Methods for Regression	ESL Chapter 3 slides pdf Linear model; subset selection; ridge regression
week 6	Linear Methods for Regression	ESL Chapter 3 slides pdf lasso; least angle regression; partial least squares method; applications in asset returning modelling
week 7	Linear Methods for Classification	ESL Chapter 4 slides pdf Linear and quadratic discriminant; logistic method; separating hyperplanes; applications in bond credit rating
week 8	Basis Expansion and Regularization	ESL Chapter 5 slides Polynomials and splines; selection of smoothing parameters
week 9	Model Selection	ESL Chapter 7 slides Model complexity; in-sample prediction error; effective number of parameters; cross-validation
week 10	Volatility Models	FTS Chapters 3 and 10 slides ARCH and GARCH modeling; stochastic volatility model; multivariate volatility model
week 11	Model Inference and Averaging	ESL Chapter 8 slides Bootstrap; Bayesian methods; the EM algorithm; bagging
week 12	Additive Models and Trees	ESL Chapter 9 slides Generalized additive model; tree-based methods; applications in asset return modeling
week 13	Boosting and Additive Trees	ESL Chapter 10 slides Boosting linear models; boosting trees; model regularization
week 14	Value at Risk	FTS Chapter 7 slides RiskMetrics approach; econometric approach to multi-period VaR forecast; quantile estimator
week 15	Support Vector Machines	ESL Chapter 12 slides
week 16	Random Forests	ESL Chapter 15 slides Algorithm description; overfitting; variance and de-correlation effect; application in financial time series modeling
week 17	project presentation

Problem Set

Problem sets are assigned on a regular basis and they are graded.