Financial Engineering

2023/2024

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

Big Data and Information Retrieval School

Course type:

Elective course

When:

3 year, 1, 2 module

Instructors

Асриев Артем Владимирович

Сивакова София Федоровна

Course 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
  0.25 * Quantitative research Project (QRP) + 0.5 * Exam + 0.25 * Paper Portfolio Project (PPP)

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

Authors

• Галевская Софья Андреевна
• Стоякина Елена Игоревна
• Samonenko Ilia Iurevich