Introduction
My name is Florentin Zander. I grew up in Germany and moved to the US for university. I felt that the American approach to undergraduate study – exploration and the flexibility of discovery – was best suited to me. I found a passion in economics, diving deeper into empirically testing mathematical models in behavioral economics. I sought a job in finance to work on real-world applications of my modeling & data skills. Following an internship at an investment bank, I decided to pursue full-time opportunities that would prioritize improving my data science skills.
I discovered an interesting and exciting niche within the quantitative trading world: fundamental analysis at a market making firm. The two seem entirely opposed. One studies the long-term value of assets, the other uses heaps of high frequency data to provide prices on trading exchanges. I believe, however, that the latter can benefit from understanding the former. When events happen that cannot be neatly identified by dense datasets, an algorithm ends up losing to those who understand what’s changed. To the market maker, the stock symbol for the company Apple, “AAPL”, is nothing more than 4 letters and a large data frame. But when Tim Cook announces an expansion into the eyewear market, someone needs to do the math on what that is worth.
In many ways, this role unites two things I found exciting in my studies of economics. I try to make sense of data every day and employ all the skills I acquired in my statistics courses. At the same time, my analysis is deeply rooted in understanding why things have fundamental economic value, and how unforeseen events may change that. Economics is a social science because it tries to apply models to a world that has no finite solution. Fundamental analysis is not that far off.
Finance Fellow
career options
Finance Fellow
skills
What are the main hard skills you use on a daily basis in your current job?
At the core of each of my deliverables is the question: what is an asset worth? Financial valuation techniques often use future expected cash flows to answer that question. The analyst’s job is to accurately forecast key performance indicators (KPIs), understand their variance, and integrate them into a financial model to project future cash flows. Other valuation techniques may use peer companies or historical trading data to value an asset.
Working with data requires an acute understanding of statistics. Common techniques we employ are linear regression, covariance matrices, and hypothesis testing. In the real world, data is rarely as clean as in theoretical settings. For example, as the magnitude of returns of one security increases, so does the variance of returns of a peer security. If variance is not constant along x, a lot of the assumptions used for conventional modeling break down. Navigating these situations is challenging.
To accelerate research, I frequently use coding languages like python. A lot of the studies I do require clean data frames. Additionally, one dataset often is not enough to answer the question at hand. Merging datasets cleanly and putting together summary statistics, visualizations, and analyses is crucial for success in quantitative trading.
While about half of my time is spent on research, the other half requires active decision-making around placing bets in the equity market. Understanding how to make consistent decisions in a risk-taking context is challenging and requires practice.
What are the main soft skills you use on a daily basis in your current job?
My work sits alongside that of traders and quantitative researchers. This requires effective communication and teamwork across disciplines. Traders can trade better if I communicate my thoughts more clearly. Similarly, when quantitative researchers are putting together new models, they may ask for alternative data that our team furnishes.
Unlike working in a classroom environment, real-world problems often are not as straightforward as theoretical ones. Data is not as dense as you might have hoped, the features you are looking to incorporate into a model are not all observable, and independent variables rarely occur naturally. This requires creative thinking about how to come to a reasoned conclusion, even if all assumptions for effective modeling do not hold.
In trading, you are never afforded unlimited time to solve a problem. When an event that may impact asset prices takes place, quick thinking and decision-making wins the race. Constraining time means that good intuition is crucial in a career like this. Figuring out quickly which approach might be the best for the unique requirements of any given situation will help with achieving success.
Florentin
’s personal path
Tell us about your personal journey in
Finance Fellow
:
My university studies embodied the spirit of an American liberal arts education. I placed discovery first, meaning I took courses as disparate as comparative constitutional law, surrealism, and behavioral economics. Ultimately, I realized that I most enjoyed putting together hypotheses and testing their rigor. While many fields allow for putting forth an argument, fewer accommodate testing a hypothesis objectively. I realized I enjoyed both constructing theoretical behavioral models in economics and then empirically evaluating their merit.
For example, in my senior thesis, I examined risk aversion and risk-seeking behavior in a multi-period model. My research confirmed the hypothesis: using people’s stock trading behavior, I concluded that perception of gain or loss (and the subsequent trading behavior) is path dependent. The thesis required employing many of the skills I had acquired over 4 years of studying economics, including coding in R, employing techniques to evaluate statistical significance, and understanding the mathematical principles behind prospect theory.
Quantitative trading embodies a very similar spirit. The wealth of data that financial markets provide creates an exciting playground to test hypotheses and receive near immediate feedback on their quality. Accordingly, in my last year at Yale, I set out to find full-time opportunities in this field. This required a fair bit of preparation on top of what I had learned in the classroom. Many of these interviews involve answering brainteasers, playing trading simulations, and demonstrating consistent thinking even when risk is involved. I practiced using online materials and some of the literature on the topic. Speaking to traders is the best way to get a sense of what these interviews might look like. Sending out an email to schedule a phone call might be daunting, but this was the best way I managed to learn more about the industry.
What would you tell your younger you regarding building your current career?
Sometimes things that seem daunting come with far less risk than we make them out to be. I frequently felt like I was not ready to get on the phone with trading professionals and ask them about their work, so I would not send them a cold email. Similarly, I was apprehensive about applying to jobs that seemed out of my depth. The downside to giving these things a shot is far less than it seems: in the worst case, they end up a learning experience.
Final thoughts & tips
Navigating the process of starting a career in finance is not easy. Learning about the experiences of people who have gone through it is a great first step. What lies ahead is a long process of trial and error that may require the perseverance to explore many avenues. Finding a seat that you are truly happy in makes that journey well worth the work.
Resources to dig in more
A Practical Guide to Quantitative Finance Interviews
This is the primary resource for the interview process in quantitative finance. Fundamental analysts do not need to master all the concepts in this resource, but for trading and quantitative research, this book covers most of the skills needed to succeed.
Investment Banking: Valuation, LBOs, M&A, and IPOs, University Edition (Wiley Finance)
This book provides a comprehensive overview for the accounting and financial modeling skills required to succeed in a fundamental analyst seat. The book is appropriate for candidates interviewing at investment banks, hedge funds, and private equity firms.
Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market
This book is an exciting read that provides insight into the emergence of quantitative trading. It is a light read and a great introduction to the principles of modern algorithmic trading.
Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage
This book is a mathematical introduction to quantitative trading. The focus is on portfolio construction and provides useful models for thinking about how quantitative trading works.
