On the 15 June 2015, I graduate with a Master of Arts in Finance and Hispanic Studies. I got 2:1 and I were so sure that from now on everything is going to be easy, the hard part is done, a job is on the way and I am sorted. I was setting myself up for disappointment. Once you finish school and the bubble bursts, you are out in the real world where things always take longer than planned and most of the times don’t work out at all. It took me 6 months to get my first Graduate job in Finance in an Accounts Department. The job was a good learning experience with stable, but slow progression structure. After half a year at the job, I realized that I needed something else. I needed to be challenged at work and to be at the tip of new Financial and Technological developments. I needed a job that would be intellectually stimulating and offer out-of-the-routine daily problems to be solved.
Contemplating for a while and deriving from past experiences, interests, and skills I decided that trading and programming would provide me with this working environment. Although I lacked knowledge, experience, and skills in both spheres. So I set on improving both and that’s how I found out about World Quant University (WQU) and their Master of Financial Engineering program. I knew that the skills learned from my first degree were not enough and outdated with the current requirements of the labour market. I knew that I needed to learn to program and improve my maths if I wanted to progress and embark on an exciting career. Although I didn’t have money, I didn’t have time for my full-time job and I was aimlessly searching for solutions without a structure. That’s where WQU came in my professional development.
WQU, teaching and student dynamics
The University offers the part-time distance-learning program free of charge which offers to teach you all the skills in Mathematical Finance and Programming to fill up a gap in the labour market. The whole concept sounded like a too-good-to-be-true sales pitch to lure you in and then make you pay thousands of dollars in subsequent upsells and payments. An MFE in the states cost circa $100,000. How come this is free? As I have subsequently learned The World Quant Foundation created by Igor Tulchinsky the founder of the World Quant LLC, is an organization which seeks to benefit society with free education provision, scholarship and targeted charitable donations. The focal point of its initiative is the World Quant University. The university HQ is based out of New Orleans, Louisiana. I believe that is the case for educational legal and cost-effective reasons. The University is currently only licensed by the Board of Regents of the State of Louisiana. As far as I know, they have applied for professional accreditation which is pending in 2019-2020.
So what is Financial Engineering really?
Financial engineering applies mathematical and quantitative methods to financial problems. This multidisciplinary field combines financial theory, mathematics applications, engineering methods, and programming practices. The combination of these areas gives you the tools and skills to tackle modern problems in many other industries not only Finance. In terms of teaching, all the courses have a dedicated professor who looks after a cohort of students and can be contacted through email, the student forum and some professors even offer their social links such as Twitter and LinkedIn. You are interacting with real people and professors that have designated degrees and can be researched online, rather than being served a bunch of videos, tests, and textbooks online without human interaction. In addition, there is a constant contact between other students from your cohort, which creates an almost exact same learning environment as a full-time University education without the physical contact with other students. Meeting others can be done through Facebook Groups, WhatsApp groups, Twitter and LinkedIn. Piazza is the official class forum platform used for collaboration, questions, and comments around assignments and coursework. It also provides a tool for additional grading by professors as the quality of interactions and responses between students is being judged. You can collaborate with each other on a daily basis and ask questions. In addition, you can build connections and increase your network through LinkedIn adds and referrals between students and professors.
The distance learning aspect of the degree may be of concern to some people. Although the materials, structure, lecture, and delivery has not been undermined in any way because of the lack of physical classes and participation. The courses are 100% online and the only thing you need is a laptop and internet connection. I have not felt even once at loss for lacking certain aspects of full-time education in a person-to-person environment.
Over a two year period, you are offered an opportunity to pass the 14 courses 1 by 1 doing it part-time from the comfort of your home or wherever you are in the world. For example, I travelled for 6 months in Latin America while doing my courses whenever I got a chance to connect to the internet. From an island in the Carribean, through the Jungles of Colombia and the deserts of Peru, tried, tested and passed course after course. In terms of study materials, the university states that it will cost you around 1000$ for books and materials, around 50-70 per course. Although I have not spent more than $100 so far as I didn’t feel the need to buy some of the books or I simply already knew about the particular subject. The lectures and exams are delivered through the edX platform powered by Cyanna. During the course of the studies, you are tested with exams, tests assignments, discussions posts, projects, and short essays.
All the courses covered in the MFE:
- Financial Markets I – The Financial Markets I course is intended as an introduction to Financial Markets. The course discusses the instruments traded in the markets, the institutions that support and frame the markets, the trading mechanisms, and the regulatory structure.
- Statistics – The Statistics course expounds on basic statistical concepts that are important in portfolio management. The goal is to understand the strengths and weaknesses of statistics in interpreting and analyzing data. Basics of programming with R is covered as well.
- Programming in Python I – The Programming in Python I course covers the basics of Python Programming as it relates to Financial Computing. You will learn about Python, object-oriented programming concepts, build simple numerical programs, create functions, explore scoping, recursion, variables, modules, files, tuples, lists and higher-order functions.
- Algorithms I – the Algorithms I course covers the basic concepts of Algorithms. You will learn about algorithms and their role in computing as well as examine data structures, recursion, sorting, and searching.
- Financial Markets II – the Financial Markets II course builds upon the foundation course Financial Markets I to demonstrate how the various instruments that were previously introduced are assembled to build portfolios.
- Programming in Python II – the Programming in Python II course covers advanced Python concepts related to Financial Computing. You will learn how to create financial calculators, calculate interest rates, examine closing price and trading volume, use Python to calculate comparisons among stocks and analyze high-frequency data, compare return versus volatility, write and debug Python code, and use modules. Libraries such as numpy, scipy, pandas, and matplotlib will be used throughout the course.
- Econometrics – This course provides an introduction to the modelling and forecasting of financial markets, with a thorough grounding in basic regression and inference, and moving on to more advanced time-series models like GARCH and cointegration.
- Alpha Design I – The Alpha Design I course will introduce the basic concepts related to statistical arbitrage within and across asset classes. Launching off this starting point, the course further delves into various aspects of the framework dealing with the intricacies involved in developing an alpha model. The course covers revolve around statistical arbitrage, performance and risk measures and other statistical considerations. Python is used extensively to develop strategies and you will get a chance to you World Quants backtesting platform and test your first alphas.
- Algorithms II – The Algorithms course covers the core knowledge required to understand numerical algorithms for computational finance. You will learn about advanced design and analysis techniques, examine Monte Carlo simulations, explore parallel algorithms and be introduced to machine learning. Students will design and test their own algorithms on the markets.
- Risk Management – The Risk Management course is an introductory risk management course that seeks to present a comprehensive overview of risk management to the uninitiated students.
- Alpha Design II – a deeper understanding of Alpha development and demonstrates the application of these concepts in the real world of trading.
- Machine Learning – The Machine Learning course covers the basic concepts of machine learning where you can learn about principles and applications of statistical learning, machine learning, and tools therein.
- Alpha Design III with Machine Learning – Learning course will elaborate in even more detail and increasing complexity a variety of alpha strategies.
- Capstone Course – putting it all together. I haven’t got that far, though I presume that you will have to build a fully functional algorithmic trading strategy ready to be implemented.
In this program, you are getting taught by a mix of practitioners and academic on the basis of a course structure and materials 100% focused in practical terms rather than academic research. During my Master at the University of Aberdeen, Financial subjects felt too academical and static far away from real-world applications while every single course and assignment done in WQU has direct applications and used cases. Once you have graduated from WQU you can start looking at a number of employment paths. Financial engineers pursue professional roles as quantitative researchers, quantitative developers, quantitative traders, algorithmic traders, and portfolio managers for financial institutions. Although once you have the statistical, mathematical, and programming skills you can take this into areas where Data Science is needed or there is a lot of work with large quantities of data. In addition, you can expand further on your programming and machine learning knowledge and seek opportunities working on AI projects. Once you have the tools, then data is data, no matter if the numbers represent stock prices, medical records, social media data, etc. You can embark on a rewarding career in many other fields apart from Finance. Finally, I would like to thank WQU staff, the World Quant Charity Foundation and Igor Tulchinsky for giving me and 1000s of students around the world the opportunity to reinvent ourselves, improve and get an edge in a fast-paced exponentially changing labour market.