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. Building on this foundation, the comprehensive program also provides students with skills needed to succeed in presenting ideas and concepts in a professional business setting.
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 and professional business skills 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.
Along with their diploma, students who successfully complete the MSc in Financial Engineering program receive a shareable, verified version of their degree from Credly, the largest and most-connected digital credential network.
Upon completion of the program, you will be able to:
The MSc in Financial Engineering Program consists of nine graduate-level courses and a culminating Capstone course. Students take one course at a time in a prescribed sequence. There is a two-week break between courses (one week for the grading process and one week for subsequent course registration).
In this pilot course for the MScFE program, students are introduced to the world of professional finance: markets, products, participants, and regulation. The activities within financial markets will be discussed, including trading, financing, brokering, pricing, hedging, optimizing, and managing risk. Throughout the course, students identify a list of significant factors that affect the financial industry. Students will be able to interact with web apps that illustrate these concepts. Understanding the asset classes, activities, and influential aspects of the financial landscape will provide a solid foundation on which students will build mathematical and computational tools to develop models for financial engineering. No background in finance is required.
This course introduces students to financial data: the source of energy for financial models. Students will learn how to apply Python to properly select, import, filter, structure, visualize, summarize, and analyze financial data for interest rates, equities, cryptocurrencies, ETFs, securitized products, and other asset classes. Students will also learn how to prepare data to be used in models for financial markets, from which decisions can be made, and how to accomplish fundamental analysis with accounting data, technical analysis with trading data, statistical analysis with transformed data, and sentiment analysis with textual data. Software engineering, visualization techniques, probability and statistics, linear algebra, and presentation skills will be developed throughout the course. The ultimate goal of this course is to build foundational skills that enable students to understand the type of data needed depending on their goals, how to source it, structure it, shape it, build with it, and discover what it tells. At their best, financial engineers turn data into empirically based, well-calibrated financial models whose output provides investors and risk managers with sound decisions in the uncertain world of finance.
This course provides a comprehensive introduction to financial econometrics. Students will learn how to model probability distributions of returns, including graphical, Bayesian, and non-parametrical methods. They will also learn how to model univariate time series, focusing on their moving average, autocorrelations, and volatilities, including GARCH models. Students will build additional tools to see how two financial series can relate to each other, using correlation, vector autoregressions, and cointegration. Further, they will build the statistical foundation and Python coding skills to run econometric models to apply in financial decision making. Finally, they will see how the ideas of bias, variance, and overfitting apply to machine learning.
Derivative Pricing is a hands-on course focused on pricing options. Students will build a conceptual background that deepens their understanding of why classical calculus is not sufficient for detecting rates of change in stochastic processes. Course content focuses on the concept of no-arbitrage and perfect replication using the world of stochastic calculus, including the Black-Scholes Model. Students will be able to construct pricing models such as binomial trees and finite difference methods to price an array of vanilla and exotic options. They will also measure sensitivities of the price to variables, such as the underlying price, volatility, time, interest rates, and carry costs. Finally, some extensions to classical models, such as the Heston Model and jump models will be addressed. Much of the course will include Python illustrations to build practical skills.
In this course, students increase their knowledge of modeling stochastic processes. Students will investigate advanced volatility models that upgrade Black Scholes parameters to variables, increasing their stochastic modeling skills to address heteroskedasticity and variable costs as well as jump diffusions. Students will dive into Markov processes, including hidden Markov process and Markov decision process to financial applications, and will build a mathematical foundation for deep learnings, a tool they will use for machine learnings. Overall, students will be able to evaluate the assumptions, benefits, and difficulties associated with stochastic models.
Computational Finance is an advanced computing course that builds skills in optimization, calibration, and simulation. Student will use data to calibrate models using a variety of numerical methods, including parametric and non-parametric methods of statistical inference, linear and non-linear methods, and deterministic and stochastic programming methods. Where problems of skewness and heteroskedasticity occur, students will use techniques to handle non-normality. Students will learn how to run simulations, from classical Monte Carlo methods to Markov Chain Monte Carlo simulation, to agent-based simulations. Student will be able to calibrate the models they learned in the Derivative Pricing course through numerical, computational, and machine learning techniques. Python will be used to illustrate these models, from which students will adapt and apply to fit their own data sets. Once students have calibrated models or optimized portfolios, they will interpret the coefficients and apply the results to financial decision making.
This course addresses the fundamentals of machine learning. It continues the topics from the Financial Econometrics course whereby students will be able to apply algorithms to learn from data. Students will cover the mathematical and computational foundations of both the supervised and unsupervised machine learning problems, and they will use Python modules and a Tensorflow framework to predict, explain, or compare outcomes across different financial series. Students will apply machine learning techniques to determine if financial models are overfit, and use methods of regularization, cross-validation, and resampling techniques to mitigate it. In addition, students will develop a theoretical and practical background in deep learning models to improve the power of their financial model predictions.
This course provides students with methodologies and skills to perform portfolio optimization. From the previous coursework, students will have a solid foundation on which to engage in the portfolio management process. In the first two modules, students will review classical methods of portfolio theory, including Markowitz portfolio optimization. Subsequent modules address more modern versions of the portfolio optimization process, including Black-Litterman, probabilistic scenario optimization, prospect theory, Kelly criterion, and risk parity. In addition, advanced econometrics and machine learning methods will be applied to the classical techniques, including the use of neural networks, genetic algorithms, information theory, and reinforcement learning. The course requires students to engage with the mathematical foundations, code implementation, and practical applications of portfolio management across many asset classes.
This course provides students with both classical and modern methods of modeling and managing risk. The course begins by reviewing metrics and models for market, credit, and systemic risk, and applying these ideas to multiple asset classes, including derivatives. Machine learning methods will be integrated with both classical methods like VaR and GARCH and with robust methods like Extreme Value Theory. Then a comprehensive review of Bayesian methods will be given that builds towards a Bayesian network of modeling systemic risk. By taking the course, students will be able to synthesize a complex network and scenario analysis for both portfolio risk and systemic risk.
The Capstone Course is designed to put the students’ knowledge of financial engineering to the test. 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. The goal of the Capstone Course is to ensure that students have met the program outcomes and are able to apply their knowledge and skills to real-world scenarios.
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 11||December 31|
|April 12||April 4|
|July 5||June 28|
|October 4||September 27|