5010593: C20 Statistics

Description

• An Introduction to tidy statistics and programming in RSourcing and downloading Financial Data (e.g. from Refinitiv Datastream and Eikon)Supervised vs. unsupervised learningLinear and multiple regressionsClassification problemsPrincipal components and clustering

Learning Outcomes

• Students understand and can apply simple and multiple linear regressions as well as corresponding diagnostic tests.Students understand the pitfalls related to financial time series and know the corresponding methods and tools to overcome them.Students understand the concepts of supervised and unsupervised learning, can give examples and apply such methods to financial datasets.

Lectures Method

Interactive lectures, exercises

Literature

• DeFusco, R. A., McLeavey, D. W., Pinto, J. E., Runkle, D. E., & Anson, M. J. P. (2015). Quantitative Investment Analysis (3 ed.). Hoboken, New Jersey: Wiley.Groebner, D. F., Shannon, P. W., & Fry, P. C. (2017). Business Statistics: A Decision-Making Approach (10 ed.). Boston: Pearson.James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R (1st ed.). Springer.