# ESF:MKF_AIIF AI in Finance - Course Information

## MKF_AIIF AI in Finance

**Faculty of Economics and Administration**

Autumn 2023

**Extent and Intensity**- 0/0/0. 6 credit(s). Type of Completion: zk (examination).
**Teacher(s)**- Ing. Martina Halousková (lecturer)

prof. Ing. Štefan Lyócsa, PhD. (lecturer) **Guaranteed by**- prof. Ing. Štefan Lyócsa, PhD.

Department of Finance – Faculty of Economics and Administration

Contact Person: Iva Havlíčková

Supplier department: Department of Finance – Faculty of Economics and Administration **Timetable**- Sat 23. 9. 12:00–15:50 VT206, Sat 14. 10. 12:00–15:50 VT206, Sat 2. 12. 12:00–15:50 VT314
**Prerequisites**- SOUHLAS

Students are expected to be familiar with basic concepts of Statistics and Econometrics. **Course Enrolment Limitations**- The course is also offered to the students of the fields other than those the course is directly associated with.
**fields of study / plans the course is directly associated with**- Corporate Finance, Accounting, and Taxes (programme ESF, N-FIN)
- Financial Markets, Institutions and Technologies (programme ESF, N-FIN)

**Course objectives**- The ever increasing amount of data already dictates our understanding of finance. The ability to process, study, interpret and present such data leads to an enormous competitive advantage on the job market; the Machine Learning in Finance gives students the possibility to work towards this advantage. The course is centered on key topics of machine learning with specific emphasis on case studies applied in the context of financial markets, credit and profit scoring, hedonic price models for real estate and used cars. Key topics include: data pre-processing, unsupervised learning methods, predictive modelling via OLS, LASSO, RIDGE, EN, Complete Subset Regressions, Logistic regression, Random Forest. Basic principles of Gradient Boosting, Support Vector Machines or other methods are also discussed. Selected principles discussed in the course are handling of data-snooping bias, hyper-parameter tuning, bagging and boosting, ensemble learning. Course is primarily led in program R.
**Learning outcomes**- After completing the course, the student should be able to: - identify areas where data techniques might be useful, - prepare and design data analysis, - understand key concepts of machine learning, - program and present analysis using R or Python.
**Syllabus**- Introduction 1. Introduction to Artificial Intelligence in Finance – applications in Finance, variance bias trade-off, supervised and unsupervised learning, semi-supervised learning, reinforced learning, critique of machine learning with over-fitting and model interpretability. 2. Data pre-processing – handling missing data part I., outliers, data transformation, feature engineering. Supervised AI in Finance - continuous outcome 3. Standard machine learning framework – multivariate regression, interactions, dummies, model/variable selection, in-sample and out-of-sample approach, loss functions for continuous target variables. Data snooping bias – model confidence set. 4. Regularization techniques – LASSO, Ridge, Elastic net. hyper-parameter tuning – cross-validation, leave-one-out, grid search, Complete subset regressions, Decision trees, Random forest and support vector machines. 5. Tree-based methods - decision trees, pre-pruning, post-pruning, bagging, random forest, boosting trees. Supervised AI in Finance - discrete outcomes 6. Logistic regression – marginal effects. Discrete choice model evaluation – confusion matrix. AUC. 7. LASSO, Ridge, Elastic net, Complete Subset Logistic Regression. 8. Tree-Based methods, decision trees, pre-pruning, post-pruning, bagging, random forest, boosting for discrete outcomes. Unsupervised machine learning 9. Unsupervised machine learning – distance measures, k-means, k-medoids, CLARA, agglomeration clustering, cluster validation and optimization. 10. Principal component analysis, network based feature selection techniques. Further topics in machine learning 11. Forecast combination - ensemble techniques, Time-series models, model based approach to missing data, sample selection. 12. Further modelling options: Linear Discriminant Analysis, Naïve Bayes classification. 12. Further modelling options: Support vector machines, Neural Networks.

**Literature**- COQUERET, G and Guida T AMP.
*Machine Learning for Factor Investing: R Version*. CRC Press, 2020, 341 pp. ISBN 978-0-367-54586-4. info - CLASTER, W B.
*Mathematics and Programming for Machine Learning with R: From the Ground Up*. CRC Press, 2020, 430 pp. ISBN 978-0-367-56194-9. info - NWANGANGA, F and M CHAPPLE.
*Practical Machine Learning in R*. Wiley, 2020. ISBN 1-119-59151-1. info - WILEY, Matt and Joshua F. WILEY.
*Advanced R statistical programming and data models : analysis, machine learning and visualisation*. California: Apress, 2019, xx, 638. ISBN 9781484228715. info - WILEY, Matt and Joshua F. WILEY.
*Advanced R statistical programming and data models : analysis, machine learning and visualisation*. California: Apress, 2019, xx, 638. ISBN 9781484228715. info - LANTZ, Brett.
*Machine learning with R : expert techniques for predictive modeling*. Third edition. Birmingham: Packt, 2019, xiii, 437. ISBN 9781788295864. info - BERNARDI, M and Catania L AMP. The model confidence set package for R.
*International Journal of Computational Economics and Econometrics*. 2018, vol. 8, No 2, p. 144-158. Available from: https://dx.doi.org/10.1504/IJCEE.2018.091037. info - WRIGHT, M N and A ZIEGLER. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.
*JOURNAL OF STATISTICAL SOFTWARE*. LOS ANGELES: JOURNAL STATISTICAL SOFTWARE, 2017, vol. 77, No 1, p. 1-17. ISSN 1548-7660. info - ELLIOTT, G, A GARGANO and A TIMMERMANN. Complete subset regressions.
*Journal of Econometrics*. 2013, vol. 177, No 2, p. 357-373. Available from: https://dx.doi.org/10.1016/j.jeconom.2013.04.017. info

*recommended literature*- JAMES, Gareth R., Daniela WITTEN, Trevor HASTIE and Robert TIBSHIRANI.
*An introduction to statistical learning : with applications in R*. Second edition. New York: Springer, 2021, xv, 607. ISBN 9781071614174. info

*not specified*- COQUERET, G and Guida T AMP.
**Teaching methods**- Lecture notes, problem sets, and case studies are necessary for successful passing of the course. As they are required they will be available in the eLearning module.
**Assessment methods**- Grading is in accordance with the internal guidelines of the Faculty of Economics and Administration of Masaryk’s University and is based on midterm (30%) and final exam (70%). A total of 51% minimum is required to pass. MUNI students who study study abroad have to pass the midterm and the exam.
**Language of instruction**- English
**Further Comments**- Study Materials

The course is taught annually.

- Enrolment Statistics (recent)

- Permalink: https://is.muni.cz/course/econ/autumn2023/MKF_AIIF