Tuesday 15th June: Day 2 WQF
Abstract: The market group serves as “eyes and ears” of the Federal Reserve System and Treasure Department in terms of monetary policy, financial stability policy, foreign exchange policy and debt management policy. One of the key responsibility of the market group is market monitoring, which provides the rigorous analysis of how the financial markets are reacting to, and interpreting monetary policy. One importance piece of this process is the event studies of FOMC communications and other policy communications such as governor and Fed president’s speeches/interviews. This is time sensitive work, and currently is very human resource intensive because it is conducted manually. Built with advanced AI techniques, the MarketScribe is an NLP tool transcribe and summarize the public speeches, and visualize the market impact of these communications on a word-to-word basis. The tool outputs a word document including the summary and full transcription of the speech. The tool also includes an interactive dashboard to visualize the transcription analysis together with the market data movement. It is now been utilized in the market continues group for the quick overview of the market during the FOMC time.
Senior Knowledge Engineer in Technology Innovation, Federal Reserve Bank of New York
Knarig Arabshian: Senior Associate Knowledge Engineer in Technology Innovation, Federal Reserve Bank of New York
I am a Senior Associate Knowledge Engineer in Technology Strategy & Innovation at theFederal Reserve Bank of New York where I conduct research in semantic web technologies and text analytics for structuring financial data.
Previously, I was an Assistant Professor in the Computer Science Department at Hofstra University in Hempstead, NY and a Member of Technical Staff at Bell Labs in Murray Hill, NJ. I have also taught as an Adjunct Professor at Columbia University twice. I received my PhD in Computer Science from Columbia University in 2008, where I worked in theIRT Lab under the advisment of Henning Schulzrinne.
Senior Associate, Enterprise Architecture, Federal Reserve Bank of New York
Xue Rui: Senior Associate, Enterprise Architecture, Federal Reserve Bank of New York
Xue graduates from University of Notre Dame with Ph.D in physics and Master in Electric Engineering. She is a Senior Associate in Federal Reserve Bank of New York. Her work focuses on developing and deploying NLP and AI technology in the bank. Prior to joining the Federal Reserve Bank, Xue has been working as Scientist in General Electric Global Research Center. Xue’s research interests focus on artificial intelligence, including natural language processing, image analysis, and computer vision. She holds 20 + peer reviewed publications and 10 + patents.
Abstract: We apply facial recognition methods to FOMC press conference videos, and quantify one of the most important aspects of nonverbal communication – facial expressions. Using minute-level data, we align our nonverbal communication measure with a set of financial assets to estimate the impact of the Federal Reserve Chairs’ facial expressions on investor expectations. We find that investors adversely react to negative expressions revealed during the press conference, even when controlling for the verbal component of the press conference and additional explanatory variables. The effect is heightened in meetings that draw more attention and when the Chair is discussing forward guidance.
Senior Quantitative Analyst, Federal Reserve Bank of Richmond
Sophia Kazinnik: Senior Quantitative Analyst, Federal Reserve Bank of Richmond
Sophia is currently a Sr. Quantitative Analyst within the Supervision, Regulation and Credit (QSR) department at the Federal Reserve Bank of Richmond. Prior to joining the Richmond Fed, Sophia worked at the University of Houston, teaching courses in economics. She received her bachelor’s degree from the Tel Aviv University (Tel-Aviv, Israel), and earned her doctoral degree in economics from the University of Houston. In her current role, she works on development and production of analytical tools related to financial risk management. Her research interests focus on applying Natural Language Processing (NLP) techniques in the realm of financial economics.
- What are QR Financial Services currently doing and what should they be doing to attract more female talent?
- What can Universities and Recruitment companies do to help?
- What strategies are financial companies using at present if any?
- What are QR Financial Services currently doing and what should they be doing to retain female talent?
- What top positions besides Asset Management can QF- profiled women occupy?
- For each position open, the percentage of female CVs submitted is very small (if not none). Why is this happening and how can universities/headhunters/companies work together to improve the numbers?
- At more senior levels the number of women is even lower than at entry level which means that the female population retention rate is low or/and women are not being promoted. Discuss.
- Mentoring programmes that could specifically help Diversity & Inclusion.
- Are quantitative positions too specialised which prevents women (and men) to move horizontally to different (and possibly more senior) roles?
Quantitative Developer, NatWest Markets
Burcu Karabork: Quantitative Developer, NatWest Markets
Burcu joined NWM in 2012 on the Technology Graduate Scheme and is currently a quant developer at NWM. She holds an MEng (Hons) in Aeronautical Engineering from the University of Bristol. She has spent the last few years working on the bank’s unified risk engine and has more recently returned to her more maths-centric roots in the eFI space.
Principal, Greenwich Street Advisors, LLC
Head of Foundational Credit Risk Modeling team for Wholesale and Retail portfolios, Citi
Marina Balzac: Head of Foundational Credit Risk Modeling team for Wholesale and Retail portfolios at Citi
Head of Cross-Asset Quantitative Research team, Société Générale
Sandrine Ungari: Head of Cross-Asset Quantitative Research team, Société Générale
Sandrine Ungari is currently Head of Cross-Asset Quantitative Research team at Société Générale. Within the Cross-Asset Research group, the Quantitative Research team is active in risk premia strategies, derivatives and structured products, portfolio risk modelling, and provides research to investors worldwide. The group has been recognised as a market leader in quantitative research, and was ranked #1 in the Extel survey in the Quantitative Strategies category. Sandrine’s research topics cover systematic strategies across asset classes, interest rate modeling, machine learning, statistical analysis and portfolio construction. She joined Société Générale in 2006. Prior to that, she worked as a quantitative analyst at HBOS Treasury and at Reech Sungard in London. She is a graduate of ENSTA (Paris) and hold a Master’s in Quantitative Finance from Paris VI University. She is a guest lecturer at University Paris Diderot.