As admission decisions start to come in, let's get a tradition going here so we can share the programs we applied, the admits/rejects we received and the program we are ultimately joining.
Let's keep the format uniform and discussions on their separate programs threads. Please share details on each application timeline on the Tracker.
Below is an example.
Admits: MIT MFin, NYU Tandon MFE, CMU MSCF with 20K scholarship, NYU FinMath
Rejects: Baruch MFE, UCB MFE, Princeton MFin
Joining: CMU MSCF
We are pleased to announce the release of our QuantNet 2020 Rankings of Financial Engineering programs, to be available online at 9AM EST on Dec 16th.
Please turn on push notification and like this first post so you can instantly get notified when the rankings is live. You can enable the push notification feature on any mobile device or computer.
The online course An Intuition-Based Options Primer for Financial Engineering: Model-independent relationships vs. Black-Scholes created by Prof. Dan Stefanica and offered by QuantNet will open for enrollment on September 30.
The course covers topics directly relevant to quant job interviews (interview questions videos are included for multiple sections) as well as to graduate studies in financial engineering. It reflects the experience of Prof. Stefanica, a best-selling author and educator in financial engineering, who has been fostering highly successful careers for the graduates of the Baruch MFE program for over 15 years.
The course syllabus and more info can be found at Intuition-Based Options Primer for Financial Engineering
Course Content – Highlights
Interview Questions – Highlights
- Put-Call parity arbitrage with bid-ask spreads
- Convexity of option prices
- Options trading strategies
- Implementing market views using options strategies
- Three variables underlying the Black-Scholes formulas: log moneyness, total standard deviation, present value of the forward price
- Greeks dependence on spot price, volatility, maturity: Black-Scholes framework and intuition
- Estimating dividend rates from market data using OLS
About the author: Dan Stefanica has been the Director of the Baruch MFE Program since its inception in 2002. A best-selling author whose research spans financial engineering, numerical analysis, graph theory, and geophysical fluid dynamics, Dan was a silver medalist at the International Mathematics Olympiad and coached the MIT and NYU teams for the William Lowell Putnam math competition.
- Convexity arbitrage
- Bull spreads, bear spreads, butterfly spreads, straddles, strangles
- Return enhancement strategies
- Implementing market views using options strategies
- Greeks and model-independent relationships
- Time value of options
- Greeks dependence on option spot price, volatility, maturity
- Implied volatility for options trading strategies
Start Date: The course will open for 40 students on September 30. The next registration date will be November 12.
Teaching Assistant: Every student will be assigned a teaching assistant to assist in the learning process. The teaching assistant for the first 40 students will be Prof. Rados Radoicic of the Baruch MFE faculty and co-author of the best-selling quant interviews book "150 Most Frequently Asked Questions on Quant Interviews".
Time Frame: The course must be completed within 16 weeks.
Format: The course consists of five levels, with video lectures, interview questions videos, on-line quizzes, and homeworks. The course concludes with a final exam proctored online by the teaching assistant.
Certification: A Certificate of Completion will be issued to students who pass the final exam and obtain a 70% or higher average. A Certificate of Completion with Distinction will be awarded to students with 90% or higher average.
Registration: Sign up at QuantNet Forms and specify whether you are interested in the September 30 or November 12 enrollment date in the Comments section. Students are assigned to the selected enrollment date in the order in which they register.
Registration for the September 30 enrollment date will close once all 40 seats are assigned.
Dear prospective students,
I recently signed a contract with a top Investment Bank for an Associate C++ Developer position in the Securities Technology Division in New York. Three years ago I had very little programming and math knowledge, and non-relevant work experience. I was just another business admin graduate with ambition to make it in Wall Street one day. Sounds familiar?
Here's what happened:
I came across the C++ Programming certificate in one of my searches for MFE programs in New York, and decided to enroll.
The intro certificate covers a lot of useful stuff, like implementing important data structures such as vectors (dynamic arrays) and stacks (adaptor containers), Object-Oriented hierarchies for polymorphic behavior, and of course several algorithms for file processing, string manipulations, and more. Additionally, it introduces templates and generic programming concepts, libraries such as STL and Boost, and hands-on applications, like Monte Carlo option pricing systems and standard financial derivatives pricing. This is already equivalent with two semesters of C++ at an average U.S. university, in my experience.
It took me two months to complete the certificate with distinction. A few months later, I had the first opportunity for employment by a big Investment Bank that you definitely have heard of. An in-house recruiter reached out to me and asked my availability to talk. He asked me questions about the course, and was mostly interested about the Object-Oriented and Generic programming part. Although I didn't get the job at this point, I was already getting the attention of the big players.
About a year later I took the advanced C++11/14 certificate. It was one of the most challenging courses I ever took, but the results were amazing. Once I listed it on my LinkedIn profile, making sure to have specified parts of the syllabus, I was literally having 2-3 recruiters every week reaching out and suggesting software engineering jobs for me. I wasn't even trying to get a job at that stage.
I gained deep intuition about all the concepts needed to land a job throughout the advanced course: multi-threading, advanced memory management, complexity analysis, how to look up documentation of new libraries, debugging, software design patterns and testing, optimizations and efficiency, edgy modern C++ techniques, systems engineering, and more.
And guess what. When I started going to interviews all my interviewers were asking the exact same things that were exactly taught throughout the certificate: smart pointers, design patterms (Singleton, Strategy, etc.) STL algorithms, multi-threading and thread-safe techniques, and more.
The C++ certificates here are an excellent opportunity to become a master-level and employable C++ programmer from scratch. Looking back, that's exactly the path I needed to take.
I hope you decide to enroll and write your own story one day!
Since writing for this blog in January about the HFT/algo job market, I’ve received many inquiries from students asking about the “requirements” for quant jobs on Wall Street. “Do I need a PhD?” is a frequent question. Each time I receive one of these inquiries, I struggle with the answer. My instinct is no. But when I look at who is working in these jobs, I do see a predominance of PhD’s in the top positions. PhD’s in mathematics, physics, operations research, EE, etc. are common in the quant community. So it’s tempting to tell students that a PhD is helpful, but it feels like the wrong answer. In my gut I know that the people getting these jobs are not getting offers because they have extra letters after their name. The people in these positions are there because they have proved over their academic and professional lives that they are:[prbreak][/prbreak]
But the above is a generic list of attributes for hiring into just about any job. So what is it that makes someone hirable as a quant? The list isn’t long:
- Very smart
- Quantitative thinkers
- Good at figuring things out with minimal guidance
Okay, now combine the two lists, and you have the list of qualifications for a quant.
- Education in advanced math (stochastic calculus, statistics, probability, etc.)
- Good software development skills
- Good data analysis skills
So, back to the question of whether to get a PhD. Should I get a PhD?, asks one student who is angling for a career in quantitative finance. Will it help me? Is it necessary? No, it’s definitely not necessary. Will it help? Empirically, it seems to help. But does it? I’ve finally come to clarity on the subject with the help of a conversation today with the director of a quant group supporting credit trading for a major investment bank. Of the two lists above, the important qualifications are on the first list. This list has nothing to do with your education. Your success in any field depends on the first list. The 2nd list consists of skills, skills that can come from your education or experience. They are enabling skills, but they are not dictators of success. All career success comes from differentiating oneself with respect to the elements on the first list. You can get a PhD, spend the money and the time, but if you don’t differentiate yourself in the fundamental elements of success, the PhD won’t help.
So why are there so many PhD’s in quantitative roles, anyway? I think the answer is pretty obvious. Very smart people with quantitative instincts are drawn to the PhD path. Later they find that they are well suited to a career in finance. They satisfy both lists and hence are successful in quantitative roles in finance. Almost without exception, these are individuals who pursued a PhD based on their interests and passions (EE, Physics, Applied Math, etc.), not people who pursued a PhD as a means to a job in finance. QED: A PhD is not a requirement for a career as a quant in finance.
I feel this article isn’t complete without addressing the MFE degree. The MFE provides students with the fundamental skills utilized in quantitative jobs. If you can afford it, it’s an easy way to satisfy List 2. However, it’s by no means a ticket to success in quantitative finance. I’ll explore the MFE further in my next post, “The MFE, Is it a Contra-indicator?”
As always, you can reach me at email@example.com. LinkedIn profile: www.linkedin.com/in/peterwagner123 (I keep a listing of active quant roles here).