Northeastern University - MS in Quantitative Finance

Coursework and Summary

The program is mostly quantitative, with about 20-30% of the content varying based on the electives you choose. From the second semester onward, the curriculum becomes increasingly quant-heavy, with courses such as Empirical Methods, Data Analytics in Finance, Computational Methods in Finance, and Quantitative Portfolio Management, where Python-based modeling, econometrics, and quantitative strategies are heavily emphasized. It is not comparable to other quantitative course in the market as the first semester is quite basic for someone with a stats/quant background.

Career Services:
Career services offered some support with resumes and cover letters, but the guidance wasn’t at the level expected for industry-ready preparation. Since the program is relatively new, they made efforts to connect students with alumni; however, few graduates had entered quantitative roles, which limited the effectiveness of those connections. Additionally, almost no dedicated company fairs or recruiting events were organized for the program.

Alumni Outcomes:
Graduates from the program have pursued a range of finance and investment roles, including positions as financial analysts, middle-office trading operations analysts, and FP&A professionals.

A small number (approximately 5–10 graduates) have joined boutique investment banks, while a few others have secured equity research roles at top-tier firms.

In terms of quantitative career paths, only a limited number of alumni (maybe around 5–10 individuals) appear to have entered roles in quantitative risk management, portfolio analytics, or related areas at mid-sized banks and consulting firms, smaller asset managers.

(Note: this data is just based on personal observation and informal discussions, not official placement data)

Overall Conclusion for people looking to break into quantitative roles through this course:
This program offers a mix of quantitative and traditional finance in the first semester and becomes increasingly quant-focused in the second and third semesters, depending on the few electives you choose. It’s not a great fit for those aiming for quant research or trading roles at firms like Citadel, Jane Street, etc. But with the right electives and consistent networking, you can position yourself well for roles in portfolio analytics, quantitative risk (market or credit), and other hybrid quant-finance positions while developing a technical and financial foundation.

Detailed Course structure:

Core Courses:
Investment Analysis - Great Professor
In Investment Analysis, we began by understanding how securities trade across equity, bond, and money markets, and explored the historical relationship between risk and return — including why equities earn a long-term premium. We then examined investor preferences using utility theory, covering concepts like risk aversion, certainty equivalents, and how investors balance expected return and variance. Building on that foundation, the course moved into portfolio theory, including Markowitz optimization, the Capital Allocation Line, and the Single Index Model for simplifying portfolio covariance. We also analyzed performance metrics such as Sharpe ratio, Treynor ratio, Jensen’s alpha, and Value-at-Risk (VaR).The final segment focused on asset pricing models, starting with the CAPM and extending to multifactor frameworks and the Arbitrage Pricing Theory (APT). We also introduced the mathematical basis of portfolio optimization using Lagrangian multipliers and Kuhn-Tucker conditions. Overall, the course provided a comprehensive understanding of how assets are priced, portfolios are constructed, and risk is quantified.

Empirical Methods (Econometrics) - Great Professor
In Empirical Methods, we focused on applying statistical and econometric techniques to analyze real-world financial data. The course emphasized hypothesis testing, regression analysis, and model diagnostics to derive meaningful insights from empirical relationships in asset returns, macroeconomic variables, and risk factors. We worked extensively in Python, performing time-series and cross-sectional regressions, testing for stationarity and autocorrelation, and interpreting R-squared, t-stats, and p-values in the context of financial modeling.

Fundamental of Financial Math and Financial Markets
In Fundamentals of Financial Math and Financial Markets, we built the math foundation for finance. We used linear algebra, integrations to work with portfolio optimization and regressions, calculus to study how prices and investments change, and optimization methods to find the best portfolio choices under constraints.
Data Analytics in Finance (Best Course in terms of learning and applying quant strategies as it is project based)
In Data Analytics in Finance, we focused on using Python to solve real financial problems. We started with data tools like NumPy and Pandas for cleaning and analyzing datasets, then applied them to time series analysis of returns and risk. We coded multifactor models, built and back tested trading strategies, and implemented risk measures. The course was project-based, so it gave me hands-on practice translating financial theory into working models and analytics, which is exactly what’s needed in more quantitative heavy management roles.
Derivatives and Risk Analytics (Good Course with the project, but not a great professor)
In Derivatives and Risk Analytics, we studied the main derivative instruments, futures, forwards, and options, and how they can be used to hedge or take on risk. We covered pricing using binomial trees, risk-neutral valuation, and the Black-Scholes model, and also learned how to measure risk using the Greeks, including delta-hedging. A key part of the course was the trading project, where my group designed and executed option strategies like spreads and straddles in a simulated setting, then analyzed performance and wrote a professional report. That experience showed me how theory translates into practice and gave me hands-on exposure to risk management through derivatives trading.
Not-Core Quant Heavy Courses:
Quantitative Portfolio Management (Great Course and Great Professor)
In Quantitative Portfolio Management, I learned how to apply quantitative methods to build and evaluate investment strategies. We covered active management, factor models like Fama-French, and portfolio construction techniques such as mean-variance optimization. A big part of the course was hands-on, using Matlab to backtest strategies like momentum and to analyze risk exposures with economic and fundamental factors. What I found most valuable was understanding not just how to generate alpha, but also how to measure whether performance truly came from skill or from exposure to certain risks. This gave me both the theoretical foundation and the coding experience to think like a practitioner in portfolio management and risk analysis.
Computational Methods in Finance (Excellent Professor and Most Quant Heavy)
In Computational Finance, we applied coding and numerical methods to real finance problems. The first half focused on time series econometrics, modeling stationarity, forecasting, and using ARIMA-type models. The second half was simulation-based: we studied Monte Carlo methods, Brownian motion, and applied them to option pricing. The emphasis was on Python implementation, so I built models that forecast returns, simulated random price paths, and priced options through Monte Carlo. The course gave me hands-on practice in the computational techniques that are widely used in risk analytics, derivatives modeling, and stress testing.
Other Practical Non-core Course:
360 Huntington Fund
At Northeastern, you can serve as an first as an analyst in the first Sem and later work in a managerial role for the $2 million 360 Huntington Fund, one of the university’s student-run fund that carries three academic credits over three semesters. The experience offered a valuable perspective on global market trends and real-world portfolio management.

The fund provided hands-on exposure to Bloomberg analytics, equity research, and portfolio construction using real capital. But again, most financial models were DCF-based rather than quantitative or algorithmic.

This degree program lags behind most other MFE/Quantitative Finance masters programs by most metrics. Though the tuition is considerably lower than many other programs, particularly at other private universities, the poor quality of academics and career prospects make it difficult to recommend even considering this advantage.

Coursework/academics:

The standard first-semester courses consist of Corporate Finance, Investment Analysis, and Financial Mathematics. The former two primarily provide a foundation in fundamental finance, while the latter feels more like a crash course on statistics, linear algebra, calculus, and basic optimization. For students without a quantitative background, the Financial Mathematics course provides intro-level knowledge on these topics, but those coming from math-oriented undergrad majors will not see much benefit.

The required second-semester courses begin to scratch the surface of skills necessary for the quantitative finance industry, but go little beyond that. Furthermore, there is a lack of electives available that thoroughly develop these skills. The highest-level math course available, Computational Methods in Finance, rehashes much of Financial Mathematics while providing only a slightly deeper dive into optimization and introduces stochastic processes. The issue is that the course only introduces you to these more advanced topics without requiring true knowledge of them - they show up only trivially, if at all, on exams, meaning many of the students passing the course have not gained these skills.

Most of the other electives available stay within the fundamental finance domain. They would be passable curriculum for a non-quantitative finance degree, but the content is still not particularly rigorous and generous grading curves mean that most students can obtain their degrees without being knowledgeable about the topics studied.

Students:

Unfortunately, Northeastern relies on their reputation gained from the impressive career placements of its undergraduate students. However, its masters programs are much less selective and rely mainly on full-tuition-paying international students as a source of revenue for other departments. MSQF is no exception.

Most students come from non-quantitative backgrounds with the vast majority being undergraduate business majors. Rather than selecting more qualified applicants and creating a more rigorous curriculum, the university instead reduces the intensity of coursework to be suitable for the students it accepts. The instructors and generally highly credentialed practitioners and/or academics, but teach in accordance with what students are able to digest. One professor indicated that they wished they could teach more in-depth concepts but couldn't due to the students' abilities; another that they taught the same material to their graduate and undergraduate students.

In project groups, it became apparent that my partners had very little knowledge of the course material as well as the basic mathematical/statistical tools used. Additionally, they failed to catch glaring errors in the data they produced - the type that should be obvious to a lay person looking at the graph, let alone a qualified quantitative analyst. Nevertheless, high grades were handed out.

Many students lack clarity and knowledge about the industry, mentioning that they hope to break into IB despite pursuing a quantitative finance degree. Many of the international students indicated that they are taking the program only to facilitate working in the US.

Unfortunately, the program operates this way as a cash cow with little regard to the quality of the students.

Careers:

Northeastern markets heavily on their career prospects, which is a reputation well earned at the undergraduate level. However, placements for this program are abysmal. The program is intended to be completed in three semesters - Fall-Spring-Fall - allowing students to pursue summer internships before graduating. However, most students struggle to obtain relevant internships. The ones who achieve the best outcomes typically work in FP&A. A handful of students end up taking jobs in administrative functions with the university. I have not heard of any students breaking into quant research/trading/development.

Career services are almost non-existent and insourced networking opportunities are negligible. Additionally, the program is only a few years old, meaning there is not an expansive alumni network.

Overall:

This program offers little benefit for students looking to advance their careers, and almost no chance of developing the necessary skills for the quant industry. The only conceivable benefit would be for international students trying to find work in the US - but this degree will not help obtain a job in the relevant industries. If your goal is to break into quantitative finance, you will be sorely disappointed and $60k in the hole.

It's disappointing because Northeastern has the resources and potential to turn this into a high-quality program, but chooses instead to use it as a source of easy cash.