Financial Engineering

Bachelor 2023/2024

Type: Elective course (Data Science and Business Analytics)

Area of studies: Applied Mathematics and Information Science

Delivered by: Big Data and Information Retrieval School

Where: Faculty of Computer Science

When: 3 year, 1, 2 module

Mode of studies: offline

Open to: students of one campus

Instructors: Асриев Артем Владимирович, Сивакова София Федоровна

Language: English

ECTS credits: 4

Contact hours: 56

Full Syllabus

Abstract

The course introduces new areas of interest and applications of financial modeling. It provides an introduction to methods of quantitative research and product development in financial markets.

Learning Objectives

The course introduces new areas of interest and applications of financial modeling. It provides an introduction to methods of quantitative research and product development in financial markets.

Expected Learning Outcomes

Have general understanding on what the stock market is, how it works, the role and basic methods of quantitative research and development in investment banking industry;
Apply modeling at varying levels of financial decision-making;
Demonstrate the wide applicability of mathematical models while, at the same time, identifying their limitations, risks involved and possible misuse

Course Contents

The field of Applied Mathematics
Quantitative Researcher - a lucrative career path in financial industry
Stock market and its evolution
Types of financial assets
Sources of information about financial markets
Financial markets and Government: who is the boss?
Financial market participants: investors and brokers, trading venues, etc
Trading process
Investment decision making
The role of financial engineering in the financial industry
Models and products
Financial data and basic analytics: trading volume, price volatility, quote spread and depth
Proprietary trading
Market making and high frequency trading
Trading strategies and backtesting (in- and out-of-sample)
Pairs trading strategy
Basic trading indicators: Bollinger’s Bands and RSI
Smart Order Routers
Risk Management: market and operational risk
Drawdown (realized and unrealized) – a measure of trading strategy risk
Risk models
Volume forecasting model
Fair Value model
Transaction cost models, permanent and temporary market impact and opportunity costs
Types of post-trade transaction cost analysis
Algo trading: robot versus human trading, types, how algo trading works
Smart order routers
Client order execution: approaches to find a tradeoff between market impact and opportunity cost
Percentage of Volume algo trading strategy
Profile (TWAP/VWAP) algo trading strategy
Implementation Shortfall algo trading strategy
Approaches to quantitatively address operational risk issues in algo trading
Algo trading development process
Introduction to portfolio optimization

Assessment Elements

Paper Portfolio Project (PPP)
Quantitative research Project (QRP)
Exam

Interim Assessment

2023/2024 2nd module
0.5 * Exam + 0.25 * Paper Portfolio Project (PPP) + 0.25 * Quantitative research Project (QRP)

Bibliography

Recommended Core Bibliography

S. Christian Albright, & Wayne L. Winston. (2019). Business Analytics: Data Analysis & Decision Making, Edition 7. Cengage Learning.

Recommended Additional Bibliography

Harris, L. (2002). Trading and Exchanges : Market Microstructure for Practitioners. Oxford: Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2096842

Course Syllabus