From Black Holes to Black-Scholes

EP 003 QuantPy Insights Podcast | Davide Bufalini | The Journey from Academia to Quant Finance

📈 About This Episode:
Today, we have a very special guest, Davide Bufalini, who has transitioned from academia having studied a PhD in theoretical physics to the world of quantitative finance. In this episode, we discuss some of the largest challenges, transferrable skills and recommendations for making the transition from university to the quant industry.

🔑 Key Takeaways (Guest Perspective):
From solving one of the biggest challenges in theoretical physics to understanding market flows and behaviour, what I learned in my PhD applies to my job on a daily basis. To successfully transition to quant finance, it is crucial to have the right motivations, strong math fundamentals, have studied the right books, and have asked experienced practitioners about their opinions at the right time.

🎯 Who Should Watch?
If you're a university student/researcher intrigued by a career in quantitative finance, or a seasoned quant looking to diversify your skill set and advance your career, this episode is for you!

🏛 Guest Background, Motivations, Insights and Resources (Guest Perspective)
My PhD focused on one of the biggest challenges in theoretical physics: solving the black hole information paradox, first formulated by Stephen Hawking. I contributed to this issue within the framework of string theory, today's leading theory of quantum gravity.

Despite the stimulating and interesting topic, the academic lifestyle was not something that I wanted to pursue because of many issues, unfortunately common to numerous researchers. While deciding to change career, I learned more about the fascinating world of quant finance, how I could continue to have fun with math, and apply my skills to new exciting challenges.

🎓 Useful skillset from PhD to Quant?
From solving supergravity equations to the Black-Scholes’ PDE, from expectations values of operator products to expectations under martingale measures, the overlap between the fields is broader than what it seems at first glance. Problem-solving skills, research abilities, statistical physics, differential equations, Fourier transforms, and Lebesgue integrals: all of these concepts apply to my job, and help in understand research papers and books with relative ease.

🔋 What skillset do you use every day?
Daily, I program in Python and use traditional and stochastic calculus to actively produce work. To read and understand research papers, knowledge of hypergeometric functions and complex analysis has been proven useful. Most importantly, my approach to solving problems is still very similar to that of the PhD, and the rigorous imprint and technical background is likewise crucial in a field such as quantitative finance.

📚 Recommended Books & Resources

BASICS & OPTION PRICING
1. Wilmott - Paul Wilmott introduces Quantitative Finance
2. Baxter, Rennie - Financial Calculus
3. Bjork - Arbitrage Theory in Continuous Time
4. The two books by Steven Shreve (a classic!)
I strongly recommend following the above order, and I recommend studying the Black-Scholes model and the Greeks, as a minimum requirement. Note that the list is non-exhaustive.
5. [Advanced, and only for physicists with a strong math background] Labordere - Analysis, Geometry, and Modeling in Finance: Advanced Methods in Option Pricing

INTERVIEWS
Joshi, Denson, Downes - Quant Job Interview Questions And Answers
Crack - Heard on the Street: Quantitative Questions from Wall Street Job Interviews
Wilmott - Frequently Asked Questions in Quantitative Finance

CODING SKILLS
Python for research and AI, machine learning, and deep learning.
C++ or other low latency language for front office roles
Big banks and institutions may have their proprietary programming language, so understanding the logic behind programming and algorithms is crucial.

★ A data driven path to getting a job in Quant Finance
https://www.quantpykit.com/

★ QuantPy GitHub
Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy

Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise.