Mathematical research does not always produce immediate tangible results, it often takes years for an “elegant” solution, a novel method, to be put into practice. However, there is an area where a new model or algorithm is quickly put to the test, the banking world. The world’s financial markets may find out the next day whether the innovation has worked. This challenge attracted György Ottucsák and Ágnes Jónás to the 15-year-old Hungarian headquarters of Morgan Stanley this year in the fields of machine learning and evolutionary biology.
Morgan Stanley opened its first office in Budapest 15 years ago. In 2006, work began with a mathematical modeling team of a few people, and in the decade and a half since then, the Hungarian headquarters of the global financial institution has expanded to a technology and analysis center of 2,000 people. From technology to risk management, a number of new businesses have entered the Hungarian capital – but the first modeling division is also growing steadily and still offers opportunities for those who would like to face new challenges in quantitative terms.
-The front-line trade support team has more than a hundred quantitative staff, mainly mathematicians, physicists, technical IT specialists, financial analysts, but also biologists, chemists and meteorologists.
Experts develop, inter alia, mathematical models that map the market regularities, price various financial products, and use algorithms to support money market trading. The efficiency of these models and algorithms plays an important role in the firm’s ability to successfully trade in a particular asset class, such as government bonds or corporate equities, possibly in the foreign exchange market.
From theoretical mathematics to government bonds
György Ottucsák joined Morgan Stanley in 2014 and is currently in one of the most important asset classes in the government bond market. deals with modeling. He graduated from the Budapest University of Technology and Economics as a technical computer scientist and wrote his doctoral dissertation on machine learning. At that time he was not yet interested in the practical use of artificial intelligence, but in pure theory. Around this time, even programming was not emphasized in his life, which is now essential in his daily work. After earning his doctorate, he used his knowledge to solve practical problems at startups, but he found the most inspiring task at Morgan Stanley.
“ I have previously applied my knowledge to areas such as forecasting the turnover of retail products.This is an interesting task, but forecasting has no effect on turnover.It will not take significantly more or less of a given product depending on what our model predicted. situation “- explains György Ottucsák.
– If a team develops an algorithm that can perform profitable trades in large batches, this opportunity will soon disappear from the market as others start copying as well. The whole market is learning. If you don’t improve your algorithm, you’ll fall behind, so new challenges await you every day. In other areas of machine learning, such as an image recognition system, we sooner or later get to an accuracy from which there is no more. There is always room for improvement in finance. “
Algorithm competition
Before joining the Morgan Stanley team, György Ottucsák thought that government securities were a stable, not very exciting investment, but they are. in the government bond market, ie in large volumes and with many counterparties, while trying to minimize risk: if you take a position, you want to hedge the risk as quickly as possible with a transaction in the opposite direction. shuffle a portfolio, which is not a trivial task – there is a lot of math behind it.This is now mostly done automatically: algorithms run in the background, price paper and counterparty risk, close the deal, look for hedging opportunities in the market, and buy The algorithms operate under human supervision, and explicit human approval is also required for trades above a certain threshold to conclude a transaction.
“Competition in the money market is very fierce, and here technology is also crucial. Today, only a company that produces good algorithms can trade efficiently and make a profit. This requires good quantitative professionals who come to our team from a wide variety of fields. Once they have entered the Budapest office, they can learn from the most experienced money market professionals, gaining very valuable knowledge that will allow them to use their IT and mathematical expertise to solve practical financial problems. Many smart people work together in the team, 20-30 percent of them have PhD degrees. We learn a lot from each other every day, “says György Ottucsák.
From vinegar to derivatives
Ágnes Jónás came to Morgan Stanley’s mathematical modeling team with certain quantitative knowledge but from a remote area, and received her PhD in population genetics in 2016. In her research, she tried to understand how individuals in a population adapt to environmental change, including time series models.
The young researcher was mainly experimenting with vinegar butterflies, an easy-to-keep and fast-growing species that could be studied in hundreds of generations over a number of years. How the population of genes in a population changes. There are many points in common between population genetics and my current field, the pricing of derivative products and counterparty risk – explains Ágnes Jónás. – We simulate markets with models similar to those used for population dynamics in biology. There we examined how changes in the environment affected the gene pool, here we examined how changes in certain parameters affected, say, oil prices. And we do all this in similar programming languages. “
Clever and open people around the world: they believe in the power of community knowledge
Programming skills are essential for mathematical modeling: no matter how good a theorist is, one slows down the work if you can’t immediately “scrip down” your ideas.
“In my doctoral school, I got used to working with scientists and professionals from many different countries and backgrounds: mathematicians, physicists, and biologists from all over the world. I work in the same environment at Morgan Stanley in Budapest, which is important to me. I am also in constant contact with employees who now work in New York, London, Tokyo or some other office, and it is good to know that everyone has an international career opportunity within the company, “he pointed out.
University Programs, Mathematics for Girls
An important core value of Morgan Stanley is that employees give something back to the community in which they live and work – this is the so-called “giving back” philosophy.For members of the mathematical modeling team this is often done as part of an educational activity. data science) and he leads the team at this university that coordinates Morgan Stanley’s student initiatives, internship program, lab, av
It is the heart of Ágnes Jónás to have a greater proportion of women in science, informatics, technology and mathematics (so-called job fairs). STEM) tracks. In addition to being an active member of the company’s internal network of Women’s Quantitative Finance, she is also doing her best to teach the new generation to make the career attractive to more young girls. Along with many other Morgan Stanley employees in Budapest, she is also a member of the mentoring and teaching team that runs mathematics and programming courses for high school students as part of the SMARTIZ program, initiated by the Women in Science Association (NATE) and supported by Morgan Stanley.
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