Algorithms in Finance (for non-U.S.E. students) (7,5 ECTS) (2023/2024: periode 3)

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Cursusdoel

Learning goals
At the end of the course, the student is able to:
  • understand the financial theory behind financial algorithms and analytics;
  • develop financial algorithms for trading;
  • understand the role of financial algorithms in modern financial markets and fintech developments.

Vakinhoudelijk

The course is organized as a BYOD course, which means that you have to bring own laptop. (BYOD stands for Bring Your Own Device)

Contents
With the rise of high frequency trading on stock markets during the past decade, algorithmic trading plays an increasingly important role on stock markets, for example to exploit mispricing. In the course students will learn to build financial algorithms based on clear and feasible valuation logic. During the sessions students will learn what valuation techniques can be applied to a range of financial products. Moreover, the role of financial algorithms in modern finance and fintech will be discussed. This course is given with the support of an internationally leading firm in the field of market making. This firm trades a wide range of products: listed derivatives, cash equities, exchange–traded funds, bonds and foreign currencies. This cooperation enables students to test their algorithms by applying them to actual price data of a wide range of financial products. The programming of algorithms will be done in Python, a programming language that is used more and more in firms that work with big data and comprehending it is therefore helpful for your future career. The students are expected to have some basic knowledge of Python; a small document discussing the expected level will be distributed via Blackboard prior to the start of the course. The first two weeks consist of two sessions per week where we discuss the valuation of stocks, bonds, and derivatives. In addition, there are two Python workshops which are optional to join. From course week three onwards, each course week will consist of a lecture and a workshop. The lectures discuss arbitrage setups used in financial markets, such as simple pairs trading using stocks, but also delta hedging using options. The workshops will be geared towards the development of algorithms to be built to exploit mispricing in stocks or derivatives.

Prerequisites
The students are expected to have some basic knowledge of Python; a small document discussing the expected level will be distributed via Blackboard prior to the start of the course. Interested students can contact the course coordinator before enrolling to discuss whether their programming skills are sufficient.

Assessment method
  1. This course comes with two assignments in small teams in which students design a trading strategy. The deliverable of both assignments is a report (3-6 pages) which contains at least the following four items: (i) the description of methods to analyse the data and develop the trading strategy, (ii) a detailed description of the logic behind each strategy, (iii) a visualization of the performance of the strategy, and (iv) discussion and possible aspects which can still be improved. In addition, also the notebook containing programming code has to handed in. The assignments count for 65% of the final grade.
  2. Written exams (midterm and final). The main component of the exams will be the lecture materials for all course weeks.

Course materials
To be announced on Blackboard.


Evaluation matrix.
Midterm Exam 10% Final Exam 25% Assignments 65%
is able to understand the relations between financial markets and business economics x x x
is able to distinguish the main features of financial products and markets x x x
is able to value stocks based asset pricing models x x x
understands the mechanics of (electronic) trading floors x x x
is able to value the futures contracts and to profit from mispricing x x x
is able to understand the sensitivities of the Black Scholes model ('the greeks’) x x
understands statistical arbitrage strategies and other elementary trading strategies x x
has knowledge of the risks of trading financial instruments x x
is able to contribute in teams and meet deadlines x
is able to build increasingly functional computer trading algorithms x

Werkvormen

College

Workshops

Toetsing

Midterm exam

Verplicht | Weging 10% | Minimum cijfer 1,0 | ECTS 0,75

Retake

Niet verplicht | Weging 1% | Minimum cijfer 1,0 | ECTS 7,5

Eindtoets

Verplicht | Weging 25% | Minimum cijfer 1,0 | ECTS 1,88

Opdracht

Verplicht | Weging 65% | Minimum cijfer 1,0 | ECTS 4,88

Ingangseisen en voorkennis

Ingangseisen

Er is geen informatie over verplichte ingangseisen bekend.

Voorkennis

Er is geen informatie over benodigde voorkennis bekend.

Voertalen

  • Engels

Cursusmomenten

Gerelateerde studies

Tentamens

Type Datum Tijd Locatie
Midterm exam dinsdag 20 februari 2024 09:00 - 10:00
Eindtoets dinsdag 9 april 2024 09:10 - 10:30
Retake dinsdag 2 juli 2024 11:00 - 12:20

Verplicht materiaal

Er is geen informatie over de verplichte literatuur bekend


Aanbevolen materiaal

  • BOEK
    Ganapathy Vidyamurthy. Pairs Trading: Quantative Methods and Analysis (1e druk). Wiley.
    ISBN: 978-0471460671

Opmerkingen

Elective for non-economics students.

Coördinator

dr. T. Walther t.walther@uu.nl

Docent

Inschrijving

Deze cursus is open voor bijvakkers. Controleer wel of er aanvullende ingangseisen gelden.

Inschrijving
Van maandag 30 oktober 2023 tot en met vrijdag 24 november 2023

Inschrijving niet geopend
Permanente link naar de cursuspagina
Laat in de Cursus-Catalogus zien