Two years have gone by where so much has happened. From quitting jobs to travelling around 4 continents, relocating, volunteering and studying as I go. It has been an incredible journey so far and even more so considering that I was doing an online degree all along. Complete flexibility, financial savings and exploration of new concepts and ideas that I have never really thought worked like that or existed thanks to World Quant. In addition, getting the chance to create contacts and accomplish tasks that I didn’t know where to begin from can all be contributed to putting in work and not backing down from studying through the last two years. More than 12 courses one after the other with only a week or two breaks in between required consistency and persistence of delivery and studies as exams, projects and coursework in the University had a constant pressure being applied until the end. I could clearly see people falling behind, failing a course, and slowly a group of students that started 2 years ago was a group of completely different ones with only a handful people staying till the end. It has been an amazing journey and it has opened my eyes to a completely new world that I had no previous preconception that existed in the realms of Finance, Data Science & Programming. What looked like a way for me to improve my maths, programming skills and understanding of the markets and trading, has taken me down a rabbit hole of the discovery of many other subjects around it. This article looks at some of the courses, changes and impacts of my studies at World Quant University and what am I going to undertake following this degree.
First of all, I will quickly go through the final tally of all the courses I passed as the University program has drastically been overhauled and the courses have been changed completely. Most of the knowledge and material covered in my studies is very similar although it is reviewed in a different manner with a focus on a different type of learning outcomes and study path. For further information on my first experiences of the University can be found on one of my previous posts here. Overall, I am happy with the course studied which have delivered what I wanted to get out the most of the degree – learning programming and its application on the different financial markets. Please see the courses covered below:
- Financial Markets I – instruments traded in the markets as well as institutions, policies, regulatory frameworks and trading mechanisms.
- Statistics – exploring statistical concepts important for portfolio management.
- Programming in Python I – basics of object-oriented programming and concepts related to Financial Computing.
- Algorithms I – basics concepts for algorithms such as data structures, recursions, sorting, and searching.
- Financial Markets II – examining the way different instruments introduced are assembled to build portfolios in the asset management industry.
- Programming in Python II – Learning different libraries for applied Financial Computing like NumPy, SciPy, Matplotlib. Applying different financial models and simulations on financial data.
- Econometrics – statistical methods as applied to finance in modelling and forecasting the financial markets.
- An alpha design I – introduces the basic concepts and frameworks in creating a positive alpha algorithmic trading strategy. Strategies such as statistical arbitrage, pairs trading, convergence and divergence strategies.
- Algorithms II – covering core knowledge required to understand numerical algorithms for computational finance.
- Risk Management – introductory risk management seeking to introduce a comprehensive overview of the subject. Risk/Return fundamentals, use of derivatives, VaR and other models used to measure risk in different types of securities.
- Alpha Design II – Builds on the previous course introducing new Alpha generation models with applied risk management frameworks and extended concepts of applied trading.
- Machine Learning – with the help of python and knowledge of Econometrics and Statistics this course introduces basic concepts of machine learning with practical examples using logistic regression, neural networks, support vector machines, boosting and decision trees.
- Alpha Design III, with Machine Learning – additional alpha strategies introduced where machine learning and optimization methods are introduced to improve the risk/return profiles of the particular strategies.
- Capstone Course (Dissertation Project) – the final dissertation project gave every student an option to choose between 8 topics were somewhere based on building strategies from scratch and reporting the results, backtests and rational while others were based on building up a paper case based on academic or industry research. This has been interesting as a peer-review by other students have been considered in grading as well as a final video-conferencing presentation and defence of the project in front of the professor and other students where Q&A session also constituted in the successful pass of the final part of the course.
I believe around the midway of 2018 the whole course was revamped and the new learning platform launched where students from the old one, including me, were migrated to the new learning platform. This initiative is one of many that seeks to further improve and develop the learning process in the University. Moreover, an additional introductory data science module was also launched to provide the basics of data science and python to those that may not be coming from a background with an undergraduate degree or knowledge in the particular subject area. This also is a great introductory course, but it would go as far as offering you a taste rather than the full picture of data science for Finance such as the full two-year degree. In terms of the overhaul of the study program, I believe that a very good job has been done over moving to a more mathematical focused course base with solid scientific based studies rationale that resembles quite a lot similar programs offered by top Universities around the world such as Stanford, Cornell, Columbia, Carnegie Mellon and the University of Chicago.
The new program’s courses:
- Financial Markets – the previous two course on Financial Markets have been merged into one covering or the foundational topics and history on the markets. There are additional case studies added on recent developments in HFT and the Dodd-Frank Act.
- Econometrics – the statistics and econometrics course have been merged into one that covers all the subject areas as well as the use of the R language for the application of models on financial data. Econometric, volatility, time series, regression and stochastic models would be examined.
- Discrete-time Stochastic Processes – this is a course that I would like have seen in the previous program as there was evident lack of practising Stochastic Calculus. A course exploring and breaking down different financial models in use to price Options and other derivatives as well as measure different types of risk inherent to the markets.
- Continuous-time Stochastic Processes – this course covers a lot of content that I have limited knowledge in and wasn’t really focused on in the previous program such as Feynman-Kac Theorem, Stochastic Calculus, Risk Neutral pricing. Brownian motion was briefly touched upon in one of the previous program’s courses though I feel that much more attention has been given on this subject matter at present.
- Computational Finance – computational Finance using python covering Monte Carlo methods, options pricing, risk management simulations with CVaR and VaR.
- Portfolio Theory & Asset Pricing – covering CAPM, MVP, SML, CML, APT, Bayesian Portfolio theory among others. Additional areas listed on this courses that I don’t recall studying over are the Stochastic Dynamic Controls, HJB equations, etc.
- Machine Learning in Finance – this course I believe to be the equivalent of the Machine Learning course from the previous program, though it is very likely there will be improvements in it.
- Case Studies in Risk Management – this is a very interesting course with practical applications and case studies that cover the most important topic in asset management. I believe to be a practical copy with a twist to the Risk Management course from the previous program.
- Data Feeds and Technology – major course providing some real-life implementation of knowledge and gathering of financial data for your models from different sources. A focused course on getting the right type of data through the use of python is of massive help getting proper exposure to real-life implementation of the studies and knowledge learned so far. API data feeds with Python as well as Excel/VBA for finance provides you with great work tools for your professional as well as personal work in Finance. This knowledge will also give you an edge on interviews. Technical analysis and charting are also covered to close the whole picture of data gathering and analysis – one of the main drivers in discretionary trading.
- Capstone Project – I believe this would be quite similar to the final project dissertation I have done in my studies where you have to develop full-fledged project and deliverables throughout the whole course.
- Capstone Examination – This one is new as it looks like a comprehensive exam covering all the program. In the previous program we had a constant barrage of different exams of different sizes with different weights towards your final grade for your course, but not a separate exam course.
So far we have covered my initial experience as well as the digital transformation of the platform and the whole course. I can’t leave without noting the international nature of the whole faculty and the students taking part in the program. People from many different countries, continents and walks of life. There were seasoned bankers from New York and London doing the course just out of curiosity and its ability to provide complete flexibility and any missing points that they may have missed or to supplement their daily work. There were Finance professionals from Africa that were using the knowledge in this course to pivot from a Financial role to a Quantitatively Analytical one or Banking. An area which definitely can be improved upon is the interactions between students and further encouragement of online networking and exchange of contacts. While I have managed to expand my network through LinkedIn I think many students fell short on this matter. Many people found challenging the constant pressure of courses and exams coming one after the other where people have taken breaks, time-off or dropped out.
Some key points about the content:
- Valuable action-oriented content – providing with practical examples that are directly applicable in the real world with live examples of trading different instruments that can be implemented straight away with your exchange
- Time commitment – a maximum of 16 hours spent each week on any one course even during the final Capstone project where a dissertation of 98989 thousand words was written. I did the research paper project while many others opt for the Technical one of building a particular strategy from a list of topics including the data, rationale, backtesting and results.
- Self-learning friendly – constant progression from one course to the next one as there wasn’t a week in the whole period where there wasn’t a submission or a deadline of a group comment on the forum, online exam, mini-project submission.
There were students questioning the accreditation and the nature of the university, though it has to be noted the early stages of this educational start-up and the non-profit venture of the University. Yes, you don’t get to have direct exposure to Fortune 500 companies, Graduate Recruiters and in-person relationships with potential business acquaintances and a proper network as you would in a full-time degree course. Although you do save the massive amount of time, debt and wages you are going to sacrifice to do a full-time program. For example, the proposed total budget for the Berkeley MFE is $111,465. The outcome of the results from the degree and the skills and knowledge entirely depends on your person. The textbooks and materials thought in WQU are no different in any way shape or form to the ones that you have in the MFE courses provided by other Universities. Are you going to get the skills and knowledge that can be applied in the Quantitative space – Yes? Do you have access to the tools and mentorship required to complete the course – Yes? Should you go for it – definitely!! There is only upside to be gained on this matter.
As far as I know, the University currently has a pending accreditation under review and the next few years it will definitely go forward with providing accredited degrees and gaining more credibility as it builds partnerships and relationships with educational bodies both in the US and internationally.
Finally, when it comes down to my person. One of my main focuses after the completion of the degree would be my continuous improvement in data science and programming skills with different datasets and question on the Kaggle Platform. I believe this to be one of the best learning and practical tools for polishing your skills as well as having access to world-class data sets and ranking of your skills. Another area would be trading and algorithmic development where my learning would be expanded on the Quantopian platform with its available financial data feeds and support community. Once I have confidence in my simulations and results I would like to demo trade through the API of exchanges such as FXCM and Crypto facilities . Yes, I am a fan of crypto. The final step would be attending hackathons both python based, data science-based and algorithmic trading ones. I have already done a couple of those and have found them a great source to network, learn new things as well as improve your skills.