What prompted you as a student to pursue a research career in finance?
“After graduating with my bachelor's degree, I was not sure in which direction I wanted to go. At the time, I was required to undertake an internship by the university before starting my master's degree. This was a prerequisite for students who did not specialize in business administration in grammar school – I had specialized in humanities with Latin.”
“While interning for an insurance company in Spain in 1991, I found myself with lots of time on my hands. I also happened to have in my possession the first edition of John Hull’s book on option pricing1 and another one on capital market theory from a different author. As I read through the pages, I was immediately fascinated by derivatives and financial markets. So when I returned for my master’s studies, I knew I wanted to specialize in finance and economics, with a preference for quantitative aspects of the theory.”
“Since I had read the relevant books during my internship in Spain, the courses I enrolled in at the university were a repetition of the material I had covered. So I sneaked into specialized math courses at ETH Zurich, one about stochastic processes and the other about the history and philosophy of mathematics.”
“To help finance my studies, I also worked for an innovative private bank that was developing structured products in 1993. I was part of a small team consisting of three students. And we were given lots of flexibility to implement our ideas in a market that was just emerging. These turned out to be great experiences as they gave me a deep theoretical understanding and insight into the practical relevance – or irrelevance – of the theory.”
Since then you’ve specialized further, combining financial research and sustainability. How did you embark on this journey?
“After completing my doctoral studies in finance, I worked full-time in the financial industry for almost four years. That said, I still enjoyed doing research in my spare time. So after some time, I finally I gave in to my passion and decided to pursue an academic career. I became a professor in 2002 and garnered years of experience in researching and teaching quantitative finance at institutions such as the Federal Reserve Bank of New York, Imperial College and University of Zurich.”
“But after becoming a family man in 2016, I felt compelled to direct my energy and focus toward something that could help my children's future, and perhaps society more generally. At least, when they grow up, I can tell them that I tried to use my knowledge and skills for something good. That was the tipping point for me to focus on climate finance. At the same time, my interest in machine learning and natural language processing (NLP) began to develop due to my curiosity for new (and quantitative) technologies. Plus, I found great value in combining climate finance with NLP.”
This is a rapidly expanding field of study. What’s on your research agenda right now?
“I am enthusiastic about climate finance-related topics, ranging from asset pricing to climate risk management, disclosure and financial economics. Combining these with artificial intelligence (AI) also intrigues me, particularly NLP. Significant strides have been made in terms of NLP through the use of neural networks and machine learning techniques. I personally got a glimpse of what is feasible with NLP during my first visit to Google in 2019. After a few years, I am now back at Google as a visiting researcher to further deepen my understanding.”
What’s your view on how alternative data and techniques can contribute to sustainability?
“There is a large amount of untapped alternative data that can be used for sustainable investments and sustainable financial products. This data is available in modular form, i.e., figures, graphs, images, tables and text. However, the abundance of data also creates problems. Therefore, we need the right tools to retrieve relevant information, perform the correct analysis and communicate the results. This is the only way to achieve the transparency we need.”
“In addition, the raw data and methods used should be disclosed and open source. Hence, for good scientific practice, we need replicability of the measures we use to address the climate crisis, and not rely on third-party metrics where we cannot determine what they measure and how they do so.”
Talking about transparency in sustainability, how can greenwashing be avoided?
“Greenwashing is a ubiquitous term these days. I understand greenwashing as being a special case of targeted misinformation to consumers, investors and the public. Industry associations go to great lengths to set their own standards and disclosure rules. They promote these initiatives as part of high-profile campaigns to create a more positive brand or industry image among the public.”
“These efforts are certainly laudable. But while I believe in the principle of the free market, certainly in those areas where it works effectively, self-regulation is not enough to prevent greenwashing. Therefore, government regulation is necessary to get greenwashing under control. Unfortunately, as the European Union’s green-labeling of gas proves, this opens a Pandora's box of political processes and lobbying.”
“Therefore, another important prerequisite is to promote the maturity of consumers and investors. In a recent study,2 we found that knowledge about sustainable finance is limited even in Switzerland. So if we educate the public, then the transition to a more sustainable economy will be less subject to compromise caused by the slowness of political change.”
What’s the future of quantitative and sustainable investing?
“If you had asked me this question five years ago, and compared my answer with today's reality, you would have probably felt that academics simply have no idea in which direction the world is developing. Who would have thought back then that entirely new questions would arise in sustainable finance thanks to quantitative methods? With the help of AI, this quantification of sustainability can be pushed even further.”
“In particular, I believe we will make great progress in combining numerical, satellite and textual data. At the same time, however, there is a growing need for the development of methods that can present this information in a way that is transparent and clearly understandable, even for the layperson. Also, for good scientific practice, data and methods should be open source and easily accessible for everyone.”
The contents of this document have not been reviewed by the Securities and Futures Commission ("SFC") in Hong Kong. If you are in any doubt about any of the contents of this document, you should obtain independent professional advice. This document has been distributed by Robeco Hong Kong Limited (‘Robeco’). Robeco is regulated by the SFC in Hong Kong. This document has been prepared on a confidential basis solely for the recipient and is for information purposes only. Any reproduction or distribution of this documentation, in whole or in part, or the disclosure of its contents, without the prior written consent of Robeco, is prohibited. By accepting this documentation, the recipient agrees to the foregoing This document is intended to provide the reader with information on Robeco’s specific capabilities, but does not constitute a recommendation to buy or sell certain securities or investment products. Investment decisions should only be based on the relevant prospectus and on thorough financial, fiscal and legal advice. Please refer to the relevant offering documents for details including the risk factors before making any investment decisions. The contents of this document are based upon sources of information believed to be reliable. This document is not intended for distribution to or use by any person or entity in any jurisdiction or country where such distribution or use would be contrary to local law or regulation. Investment Involves risks. Historical returns are provided for illustrative purposes only and do not necessarily reflect Robeco’s expectations for the future. The value of your investments may fluctuate. Past performance is no indication of current or future performance.