Trading the Central Bank - r-squared macro's Emma Dinsmore

Finance Magnates: What do you see happening for central banks and what role does r-squared play?

Emma Dinsmore: We’re a systematic global macro fund, trying to understand where growth and inflation stand in economies and using that to really understand where central banks fit, in terms of fulfilling their mandates and how that is going to impact their policies.

You’ve seen the advent of quantitative easing, then switching into quantitative and qualitative easing - like Japan. That clearly puts central banks in the markets in a way that they have never really historically been.

As central banks that are doing quantitative easing now run up on the limits of the government debt that they’ve been purchasing, the only natural extension if they want to continue with that type of intervention is to move into riskier assets as Japan did. Japan purchases REITs (Real Estate Investment Trust), shares and ETFs. I think you potentially could see that with other central banks.

In some ways, everybody is being held hostage to the Federal Reserve and PBOC (People’s Bank of China), and that affects anybody with a lot of exposure to China – Australia, New Zealand, Japan, Korea, even to a certain extent the ECB (European Central Bank).

FM: How do systematic strategies fit into that story?

ED: With regards to China, we are primarily interested in the pace of the slowdown and how that impacts its trading partners.

For example, a lot of Latin America trades with China – Chile stands out as a huge copper exporter, so does Brazil as a major trading partner. Conversely, Mexico has much less exposure to China, and much more concentrated trade with the US.

We are looking to invest based on expectations of relative outperformance of one economy versus the other given the underlying economic current

This sort of relative play ends up fuelling investments for us, and we are really looking for opportunities like that – where even though a number of countries are impacted by the fall in commodities prices and slowdown in China, one is impacted much worse than the other. We are looking to invest based on expectations of relative outperformance of one economy versus the other given the underlying economic current.

FM: What's your AuM and how long do you hold investments for?

ED: For AuM, I can tell you we are in the low double-digit millions (USD). On holding periods, for most of our investments we tend to be a much longer-term investor than most systematic strategies because it is all fundamental analysis. Three months would be a short-term hold for us, whereas the longest would be two years.

The views we take are about understanding where in a monetary policy cycle an economy is, and those cycles, on the very short end, can last three months, but typically closer to a couple of years. So we’re evaluating where that stands and that term, affects the term of our hold.

FM: How do your systems work?

ED: When it comes to our systematic framework there are two pieces that talk to each other. One is more or less an econometric model that gives us a good sense of how close central banks are to fulfilling their mandates, where their economies stand, and projections for growth and inflation expectations And then a separate system that is focused on current market pricing and understanding based on where fundamentally economies stand, what our assessment of fundamental fair value is. Finally, using that to build a book, we expect assets to return to fundamental fair value over time.

We basically build a pretty diversified book based on these fundamental assessments of value.

FM: How important are qualitative assessments in your methods?

ED: Economic data is mostly quantitative, but at the end of the day central banks are run by humans and humans make qualitative decisions. Hence, a judgement can be reflected in statements that are not reflected in hard data.

Economic data is mostly quantitative, but at the end of the day central banks are run by humans

We are actually really interested in Machine Learning Machine Learning Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained Read this Term, particularly how it applies to sentiment analysis. If you look at the Fed, for example, if they use the word ‘moderate’, that is so much better than the word “modest”. It really implies a much more optimistic outlook on the economy.

We’re looking into trying to understand the significance of that, if it’s predictive and if it augments the hard data, which I think theoretically it should and would.

This interview has been edited and condensed.