Traditionally, teams from around the world compete in a series of in-person regional and global rounds. In 2020, all meetings, presentations, and evaluations were conducted online through platforms like Zoom and Google Meet. In some regions, teams submitted pre-recorded video presentations, while in others, live virtual judging sessions were held.

Despite the challenges, the transition to a virtual format preserved the essence of the competition—critical thinking, team collaboration, and technical execution. In fact, this shift made it even more tech-intensive, encouraging students to hone their skills in tools like MATLAB, Python, R, and Excel to communicate their solutions effectively in an online environment.

Real-World Learning through Case Studies

The competition centered around the famous Long-Term Capital Management (LTCM) case study—a financial crisis that shook the world of hedge funds in the late 1990s. Students were expected to understand the structure of LTCM’s investment strategies and identify the flaws that led to its collapse. More importantly, they had to suggest modern strategies that could avoid similar risks using analytical tools and programming techniques.

By working with real market data, performing simulations, and analyzing risk exposure, students didn’t just revisit history—they reimagined it. The Challenge encouraged them to blend financial theory with programming, data modeling, and teamwork to solve high-stakes problems in a professional setting.

Integrating the MATLAB Challenge

A unique feature of the 2020 Challenge was the addition of a MATLAB Challenge. It was designed to complement the LTCM case study and gave students a direct opportunity to demonstrate their programming and data analysis skills using MATLAB.

The MATLAB Challenge centered around pairs trading, a market-neutral strategy that uses statistical measures to identify opportunities in highly correlated assets. As a starting point, students were given a reference example involving Brent Crude Oil and West Texas Intermediate. This example used cointegration to determine the price relationship between the two commodities and build a trading strategy based on those insights.

While the reference model helped students get comfortable with the application of pairs trading in MATLAB, the real challenge lay ahead: create an original pairs trading strategy using stocks from the S&P 500 index.

What Students Were Expected to Do

Participants had full freedom to choose their preferred analytical techniques—some used cointegration, others opted for correlation or advanced econometric models. The only requirement was to develop a well-structured strategy using a tool of their choice. Many chose MATLAB for its robustness in matrix computation, data visualization, and statistical modeling.

Key deliverables included:

• A written or slide-based presentation explaining their trading strategy
• In-sample and out-of-sample backtested results
• Performance analysis using risk measures such as Value-at-Risk (VaR), Sharpe ratio, and drawdown
• A detailed risk assessment of market exposure and liquidity challenges
• Comparison of their strategy with the LTCM case

The final submissions were judged not just on profitability but also on logic, model transparency, presentation clarity, and how effectively risk factors were addressed.

Participation and Scoring

The competition drew 20 teams who submitted high-quality entries for the MATLAB Challenge. The challenge played a decisive role in the competition, serving as a tie-breaker for regional rounds and helping identify the most technically capable teams for the finals.

The top 12 teams made it to the final round, where their MATLAB Challenge entries were evaluated on:

• Clarity and persuasiveness of presentation
• Analytical depth and technical rigor
• Appropriate selection and use of risk measures
• Trading strategy performance (both absolute and risk-adjusted)

Each team could score a maximum of 100 points. The results were:

• 1st Place – Team 9893, Baruch College (100 points)
• 2nd Place – Risky Toronto, University of Toronto (85 points)
• Tie for 3rd Place – Team Morpheus, Rutgers University, and UNB MQIM-2020, University of New Brunswick (80 points)

Team 9893 stood out with a perfect score and an impressive annualized return of 12.3% from their back-tested trading strategy developed in MATLAB. Their performance demonstrated not only technical competence but also a clear understanding of risk management and financial modeling.

How MATLAB Made a Difference

MATLAB was instrumental in simplifying complex financial analysis tasks. Students used it for:

• Data acquisition and pre-processing: Working with stock price histories and cleaning datasets
• Statistical testing: Checking for stationarity and cointegration between stock pairs
• Backtesting: Simulating historical performance under real market conditions
• Visualization: Presenting insights through graphs, performance charts, and heatmaps
• Risk metrics computation: Automating the calculation of VaR, standard deviation, beta, and more

For teams with strong coding fundamentals, MATLAB acted as a powerful bridge between financial theory and real-world strategy execution. It also allowed students to demonstrate skills highly relevant in finance roles such as risk analysts, quantitative traders, and financial engineers.

Strategies and Reflections from Top Teams

Team 9893 – Baruch College

This team stood out for their collaborative approach and technical depth. Using MATLAB, they built a cointegration-based pairs trading model and tested it across various timeframes. The team divided tasks based on individual strengths—data handling, strategy logic, backtesting, and reporting—then brought everything together in a series of group reviews.

They described the experience as one of the best practical applications of what they had studied in university. The competition gave them exposure to real market dynamics, team collaboration, and performance under pressure. They also noted that the skills developed using MATLAB would benefit them across many roles in finance and analytics.

Team Morpheus – Rutgers University

Team Morpheus combined numerical computing with a deep understanding of market dynamics. They used MATLAB for matrix operations, risk modeling, and visualization of performance. Each member took on a role based on technical and soft skills—ranging from coding to writing to presentation design.

Their biggest takeaway was learning how real-world financial risk evolves and affects firms systematically. MATLAB helped them understand these patterns through simulations, giving them a quantitative edge. They also appreciated how the tool handled complex data transformations smoothly.

Learning Beyond the Classroom

What makes the PRMIA Risk Management Challenge special isn’t just its competitive nature, but how it mirrors real job roles in the finance and risk industry. Students aren’t just solving theoretical problems—they’re simulating the kind of decisions and strategies professionals have to make, often under uncertainty.

For those using MATLAB in their studies or preparing for finance-related careers, participating in challenges like this can be a game-changer. Not only does it help you build a strong portfolio of work, but it also gives you exposure to teamwork, deadlines, presentations, and analytical depth.

Conclusion

The 2020 PRMIA Risk Management Challenge is a shining example of how education and technology can merge to create meaningful learning experiences. Despite the disruption caused by the pandemic, students across the globe rose to the occasion and delivered outstanding solutions, proving that learning doesn't stop when classrooms close.

MATLAB played a pivotal role in helping students turn theory into action. Whether it was data modeling, algorithm development, or risk analysis, teams that used MATLAB gained an advantage in expressing their ideas with precision and clarity.

If you're a student or academic working in risk management, quantitative finance, or data-driven modeling, participating in competitions like this—and learning tools like MATLAB—can help you stand out. It’s not just about winning; it’s about learning how to solve real problems with the tools that professionals use every day.