Episode 58: QMA Wadhwani’s Sushil Wadhwani

Monetary Policy, Systematic Macro and Keynes

Dr. Sushil Wadhwani is the Chief Investment Officer of PGIM-owned systematic macro manager QMA Wadhwani. Dr. Wadhwani has an impressive resume, including as member of the Monetary Policy Committee of the Bank of England and economist at the London School of Economics.

In this episode, we talked about the sense and nonsense of monetary policy, Keynes and the notion of animal spirits as applied to last year’s pandemic fueled panic, as well as the use of supervised machine learning in systematic macro investing. Please enjoy the show.

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3:00 At the age of 13, you were already interested in the unfolding financial crisis at the time. What peaked your interest?

5:00 On working for Goldman Sachs, while at the London School of Economics

6:00 As a former member of the Monetary Policy Committee of the Bank of England, how do you look back on the last 10 years of unprecedented quantitative easing?

6:30 “In 2008/09, I believe that the central banks saved the world from a much worse outcome. I’m a great fan of QE 1.”

7:30 “But my worry is that, after the last one (March 2021), they overstayed their welcome. We’ve acquired this obsession that inflation has to be just right”

9:00 “I think central banks pay too little attention to their role in asset price misalignments.”

9:50 Are we in a bubble today?

11:50 Bernanke’s paper on central banks focusing on inflation and not asset price misalignments

13:30 In the next crisis, there is a risk that they [central banks] stay behind the curve and let inflation stay high for too long.

15:00 Your particular investment style is systematic macro and it has been described as a combination of quantitative modelling and Keynesian economics. What makes John Maynard Keynes such a big inspiration for you?

17:00 Keynes’ animal spirits. “They played a huge role in the overreaction of markets in March 2020.”

21:00 Integrating the macro picture, using big data. “There is a lot of big data that is completely useless that people are talking about using”

23:00 Using natural language recognition in leading indicators.

25:00 Measuring scientist forecasts on vaccine development and the interplay with GDP

27:30 “We like supervised machine learning, but I’m not a fan of unsupervised machine learning. It is very easy to overfit.”

28:30 What is your take on Modern Monetary Theory?

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