Interview: Yesim Tokat-Acikel, Managing Director, Head of Multi-Asset Research PGIM Quantitative Solutions

Hi Yesim! We are honoured that you could join us today. Could you give us an introduction into your current role at PGIM and what you do?

I am Managing Director and Head of Multi-Asset Research for PGIM Quantitative Solutions. In this capacity, I am responsible for the research, development, and portfolio management of systematic total and absolute return multi-asset investment solutions. I am also responsible for developing quantitative tools that are used by our discretionary portfolio managers in managing multi-asset portfolios.

What do you think are the biggest challenges facing data scientists/AI experts/quantitative practitioners after the pandemic and why?

The Big Data revolution is here. Technological innovations have given us access to massive amounts of numerical and textual data on a timely basis. Significant advances in data storage, computing, and artificial intelligence allow for processing vast amounts of data effectively and efficiently for more relevant and timely insights into asset fundamentals and prices. We can now analyze and quantify data previously considered solely qualitative and from sources that just several years ago were unavailable. This is exciting.

However, more data does not always result in more useful information; availability of a broader data set can lead to noise or mis-measurement. Additional data sources are sometimes highly correlated with existing data sources. New techniques can also lead to overfitting historical spurious relationships. It’s our responsibility to tease out what is truly innovative information and ascertain its value to the investment process..

Our research this year showed a lot more firms and practitioners talking about NLP than usual – why do you think this is? Where are seeing the optimal utility for NLP and where does it have the potential to go?

Natural Language Processing (NLP) allows quantitative asset managers like us to access and summarize textual data that traditionally has not been used. It opens up a whole new frontier for us. Applications can vary from assessing the tone of analyst conference calls to evaluating central bank statements. These applications open a window into sentiment and yield additional insights. Similarly, NLP can be helpful in analyzing news media, which means asset managers no longer have to rely on individual companies to disclose information. The use of big data is undoubtedly here for the long term but how it is being used is much more in transition, and the precision with which it is used even more so.

Thank you for these in-depth answers Yesim, it's an exciting time for data, yet it definitely brings to question what is truly innovative information and how exactly big data will be used in the long term...

Did you know Yesim is talking at Quant Strats event on May 5th? Don't miss her on the panel 'Exploring the role of NLP in Quantitative Strategies for a competitive advantage' where she will be discussing leveraging NLP and sentiment analysis for better investment decisions and utilizing NLP to expand data sources and provide meaningful market analysis, find out more by downloading the agenda today.