In a video roundtable series produced by ourselves and Institutional Investor, asset managers share their insights on turning today’s most pressing challenges into opportunities. In the second video in this series, the panel discussed the balance of art and science involved in creating resilient portfolios.
Why are global risks more challenging for asset managers?
U-Wen Kok, Chief Investment Officer, Victory RS Developed Markets Group opens the video with the following view: “Risks, as a global manager, have definitely been a challenge because borders have become blurred because of technology, and all the data and the dissemination of data. And because of that, when there’s geopolitical tension risk, for example, you’re going to see markets go crazy, and that has reverberations across the different regions.”
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The art of scientific data
According to Pradeep Menon, Managing Director: “Risk management itself is a combination of art and science. There’s no substitute for expertise. Who is running these models? What models are more applicable where? I think the underlying theme is transparency, in that you need to communicate this — whether it’s your regulator, whether it’s your client, whether it’s your investor, whoever it is — and say, ‘Look, what are you comfortable with?'”
The science lays the groundwork for the art that investment professionals bring to the equation. The science for investors isn’t just about big data—it’s about the right data, captured in the right way, and a methodology that is efficient and consistent.
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Once the data science has done its part, the art of managing portfolio risks takes the lead, and that art is defined by expertise.
Who is creating risk scenarios and running the models? Which models are applicable where? What assumptions are being made, and will all of this work for a particular client?
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The importance of trust
Risk management is nothing without transparency and effective communication. For an investment professional, there’s no substitute for trust in the information that underpins your math, models and assumptions.
Missing the first video in this series? See what the panel said about finding alpha.
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