• A
  • A
  • A
  • АБВ
  • АБВ
  • АБВ
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта

Бакалаврская программа «Прикладной анализ данных»

Business Analytics and Financial Engineering

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
7
Кредиты
Статус:
Курс обязательный
Когда читается:
3-й курс, 1-4 модуль

Преподаватели


Анпилогов Вадим Васильевич


Асриев Артем Владимирович


Сивакова София Федоровна

Course Syllabus

Abstract

The course extends and reinforces existing knowledge and 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. Prerequisites: If taken as part of a BSc degree, this course may be attempted upon completion of courses on Probability Theory and/or Statistics, Mathematics and/or Calculus and Programming.
Learning Objectives

Learning Objectives

  • Students will study analysis in the business theoretical background
  • The goal of the course is to give students a better grasp of quantitative subjects.
  • This course provides students with an ability to handle a range of mathematical and statistical models which helps them be more inquisitive, more precise, more accurate in their statements, more selective in their use of data.
  • The course extends and reinforces existing knowledge and introduces new areas of interest and applications of modelling in the ever-widening field of management.
Expected Learning Outcomes

Expected Learning Outcomes

  • At the end of the course and having completed the essential reading and activities students should be able to apply modeling at varying levels of decision-making
  • At the end of the course and having completed the essential reading and activities students should be able to demonstrate the wide applicability of mathematical models while, at the same time, identifying their limitations and possible misuse
  • At the end of the course and having completed the essential reading and activities students should be able to have general understanding on what the stock market is, how it works and the role and basic methods of quantitative research and development in investment banking industry
  • At the end of the course and having completed the essential reading and activities students should be able to understand basic principles of how to approach complex multivariate datasets with the aim of extracting the important message contained within the large amount of data which is often available
Course Contents

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
  • Financial market participants: investors and brokers, trading venues, etc
  • Trading process
  • Investment decision making
  • Financial engineering
  • Data analysis and decision-making
  • Software solutions for data analysis: Excel, Tableau, Python
  • Time series data
  • Outliers and missing values
  • Pivot tables
  • Probability distributions
  • Decision making under uncertainty
  • Methods for selecting random samples
  • Nonparametric tests
  • Stepwise regression
  • Time series forecasting
  • Regression-based trend models
  • The random walk model
  • Autoregressive and moving average models
  • Exponential smoothing
  • Seasonal models
  • Introduction to linear programming
  • Product mix models
  • Sensitivity analysis
  • Monte Carlo simulation
  • Applied simulation examples
  • Financial data and analytics
  • Proprietary trading
  • Market Making
  • Basic trading indicators: Bollinger’s Bands and RSI
  • Smart Order Routers
  • Risk Management: market VS operational risk
  • Fair Value model
  • Risk models
  • Trading strategies and backtesting
  • Algo trading
  • Transaction cost analysis: pre- and post-trade
  • Introduction to portfolio optimization
Assessment Elements

Assessment Elements

  • non-blocking Seminar participation
  • non-blocking Paper Portfolio coursework (current state)
  • non-blocking 2nd module exam
  • non-blocking Exam
  • non-blocking Paper Portfolio coursework (final grade)
  • non-blocking Quantitative Research coursework
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.3 * Seminar participation + 0.2 * Paper Portfolio coursework (current state) + 0.5 * 2nd module exam
  • 2022/2023 4th module
    0.25*1st semester grade+0.25*3rd module exam+0.25*grade for PP+0.25*grade for QR
Bibliography

Bibliography

Recommended Core Bibliography

  • S. Christian Albright, Wayne L. Winston, Mark Broadie, Peter Kolesar, Lawrence L. Lapin, William D. Whisler, & Jack W. Calhoun. (n.d.). Data Analysis and Decision Making, Fourth Edition. Http://Www.Cengagebrain.Com/Content/Albright76125_0538476125_01.01_toc.Pdf.

Recommended Additional Bibliography

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