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50788-01 - Lecture: Computational Finance & AI (3 CP)
| Semester | spring semester 2026 |
| Course frequency | Every spring sem. |
| Lecturers | Dietmar Maringer (dietmar.maringer@unibas.ch, Assessor) |
| Content | Computing is an indispensable part of finance: in pricing financial instruments such as derivatives, managing portfolios and running trading strategies, in risk management and data analysis. This course introduces students to computational tools and methods used in finance. Topics include: *) modelling and simulation: efficient implementations of deterministic and non-deterministic models (pricing trees, Monte Carlo methods, artificial markets, agent-based models) *) trading and portfolios (single and multi period, rebalancing strategies, backtesting) *) machine learning and AI (algorithmic trading, ML-based asset pricing, learning and self-adaptation) The course will be hands on, with the better part of lectures dealing with the implementation of techniques and models. |
| Learning objectives | Being able to implement financial models using Python, and being able to solve quantitative problems in finance. |
| Bibliography | There is no designated textbook; useful resources include: *) M Gilli, D Maringer, E Schumann, Numerical Methods and Optimization in Finance, 2nd edition, Academic Press 2019. *) A Arratia, Computational Finance: An Introductory Course with R, Atlantis Press 2012. *) P Brandimarte, Numerical Methods in Economics and Finance, Wiley, 2nd ed, 2006. *) P Brandimarte, Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics, Wiley 2014. *) K Cuthbertson and D Nitzsche, Quantitative Financial Economics, Wiley, 2nd ed., 2004. *) JC Duan, WK Härdle, JE Gentle (eds), Handbook of Computational Finance, Springer 2014. *) G Fusai, A Roncoroni, Implementing Models in Quantitative Finance: Methods and Cases, Springer 2008. *) P Glasserman, Monte Carlo Methods in Finance and Engineering, Springer 2003. Specific recommendations and additional literature to be announced during the course. |
| Weblink | Weblink |
| Admission requirements | *) Solid knowledge of financial theory. *) Solid background in quantitative methods (in particular statistics/econometrics and empirical finance). *) Throughout the course, we will use Python to implement methods and concepts, and perform experiments. Participants are expected to have at least a basic knowledge of programming as taught in "58989-01 Computing for Business and Economics". |
| Course application | Registration: Please enroll in the Online Services (services.unibas.ch); Eucor-Students and mobility students of other Swiss Universities or the FHNW first have to register at the University of Basel BEFORE the start of the course and receive their login data by post (e-mail address of the University of Basel). Processing time up to a week! Detailed information can be found here: https://www.unibas.ch/de/Studium/Mobilitaet.html After successful registration you can enroll for the course in the Online Services (services.unibas.ch). Applies to everyone: Enrolment = Registration for the course and the exam! |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Course auditors welcome |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Thursday | 14.15-20.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
Dates
| Date | Time | Room |
|---|---|---|
| Thursday 19.02.2026 | 14.15-20.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 26.02.2026 | 14.15-20.00 | Fasnachtsferien |
| Thursday 05.03.2026 | 14.15-20.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 12.03.2026 | 14.15-20.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 19.03.2026 | 14.15-20.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Thursday 26.03.2026 | 14.15-20.00 | Wirtschaftswissenschaftliche Fakultät, Grosses PC-Labor S18 HG.37 |
| Modules |
Module: Core Courses in Finance and Money (Master's Studies: Finance and Money) Module: Field Electives in Economics and Public Policy (Master's Studies: Economics and Public Policy) Module: Field Electives in Finance and Money (Master's Studies: Finance and Money) Module: Risk Analysis (Master's Studies: Actuarial Science) Module: Specific Electives in Business and Economics (Master's Studies: Business and Economics) Module: Specific Electives in Data Science and Computational Economics (Master's Studies: Business and Economics) |
| Assessment format | record of achievement |
| Assessment details | Combination of active participation, assignment(s), and a final written examination. written examination: The date will be published during the first week of lectures. |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,1 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |