The largest financial engineering program in the world is entirely free and online for everyone.
This field is on the rise as financial innovation across the globe drives demand for analytics and data science training.
From evaluating statistics to econometric modeling, our educators teach advanced skills that can be used in the majority of industries. Graduates are prepared for sought-after positions in securities, banking, and financial management, and can also apply their skills at general manufacturing and service firms as quantitative analysts.
Learn more about the field in this article from Towards Data Science.
“Hiring skilled students who come from institutions like WorldQuant University is a business imperative.”
Former Managing Director and Head of Technology & Businesses Services Group, BMO Capital Markets
Designed by industry experts, WorldQuant University’s accredited program integrates mathematical, statistical, and computer science tools with finance theory in a completely online and collaborative setting. Graduates are positioned to excel in today’s highly collaborative, fast-paced, professional environments.
The two-year program consists of nine graduate-level courses and a Capstone Course during which students complete a culminating project. The courses are sequentially taught and build on one another. Taking one course at a time allows you to earn your degree without disrupting your life.
All courses are delivered in an online group setting and focus on applied projects.
Upon completion of the program, you will be able to:
The MSc in Financial Engineering Program consists of nine graduate-level courses as well as a Capstone course. There is a one-week break between each course.
The Financial Markets course serves as an introduction to the field of Financial Engineering. It covers foundational topics including: The History of Financial Markets and Insurance; Market Regulation; Money Markets; and Bond Markets and Trading.
In this course, students apply statistical techniques to the analysis of econometric data, starting with an introduction to the R statistical programming languages that students will use to build econometric models including multiple linear regression models, time series models, and stochastic volatility models.
In this course, students will gain an enhanced comprehension of Discrete-time Stochastic Processes including: understanding the language of measure-theoretic probability, defining trading strategies in discrete time, and creating replicating portfolios.
This course covers key stochastic processes such as Brownian Motion, Stochastic Calculus including the Ito integral, the Black-Scholes Model, and Levy processes.
This course provides a comprehensive introduction to computational finance with a key focus on Monte Carlo Methods in Python, Option Pricing, and Risk Management.
In this course, students are introduced to principles and applications of statistical learning and machine learning, and will. During the course, students examine feasibility of learning, measures of fit and lift, and a number of learning paradigms such as logistic regression, neural networks, support vector machines, boosting, decision trees, and both supervised and unsupervised learning. At the end of the course students are also introduced to the latest trends in machine learning in finance.
The course introduces students to single-period asset pricing including the MVP theory, CAPM, SML and CML. The course also covers multi-period asset pricing (Multi-period portfolio theory, CAPM and APT), Active Frontiers, Bayesian Portfolio Theory and Indexation.
This course uses case studies of historical financial crises to expound on the need for risk management in the modern business environment. Each module highlights the major risks faced by business and society including credit, market, operational, strategic, reputation and enterprise-wide management risk.
In this course, case studies are used as a method of understanding and analyzing various data sets. The course begins with an introduction to R for Data Science, then explores C# for finance programs and concludes with a focus on distributed ledger technologies.
In the Capstone Course students practically apply their understanding of the program content by accomplishing project milestones from developing a problem statement, identifying the required technology to find a solution to the problem, submitting multiple drafts for peer review and instructor feedback, and finalizing and presenting their fully-developed project.
Keywords: Momentum, Trading Strategies, Trend-Following, Options, Regime-Shift, Black-Scholes, VIX Index, Hid- den Markov Model, Average True Range, Moving Averages, Straddle, Dynamic Hedging
This research project seeks to examine the relationship between momentum, stocks and options trading strategies. First, we examine a simple momentum trading strategy for stocks. These discussions are then extended and applied to options trading. Further, we explore how changes in economic conditions can cause trading performances to change from long-term averages and techniques that can be used to mitigate the impact of volatility and regime-shifts on trading performance. Read more.
Financial engineers pursue professional roles such as quantitative researchers, quantitative developers, quantitative traders, algorithmic traders, and portfolio managers for financial institutions.
Some focus on public policy, working for governments developing state and federal financial policies, or conducting research at think tanks.
There is a tremendous amount of fluidity between different financial-engineering careers, as well as transferable skills that allow professionals to easily move between these opportunities.
Our students are career-driven, computer-savvy quantitative thinkers. They have fully completed a bachelor’s degree and are interested in a future in financial engineering.
Students come from a wide range of countries and have diverse backgrounds. They want to advance their career and seek life-changing education. They are persistent, resilient, and committed to meeting the demands of our rigorous program and to mastering advanced concepts. They understand the value of collaborative work and value sharing knowledge as much as acquiring it.
Students are expected to commit 25 hours per week between lecture videos, assignments, group projects, and individual study.
Online instruction is best supported by access to the following essentials:
Detailed information about WorldQuant University, the program, requirements for admission, academic policies, and other considerations are available in the WorldQuant University Catalog.
There are four start dates every year.
|Start Date||Application Deadline|
|January 5||December 22|
|April 6||March 23|
|July 6||June 22|
|October 5||September 21|