An indicator framework can be a great tool in a portfolio manager’s arsenal to understand economic conditions and build better portfolios. This framework collects, collates, and analyses historical data to understand different economic phases. It can help reduce human bias. Let’s use interest rates as an example of an indicator to illustrate this idea, but it can be generalised to any indicator. Changes in rates influence various asset classes differently and it is important to understand this as an investor.

After an aggressive rate hiking cycle that ended in May 2023, interest rates have stayed flat for over a year. The South African repo rate is currently at 8.25%, which the Monetary Policy Committee (MPC) has held for the last six consecutive meetings. And with every ‘hold’ outcome from the MPC meeting, talks of interest rates being cut increase. Historically the cutting cycles have usually been more aggressive than the hiking cycles, and for various reasons, such as growth stimulation.

The big question is, when the rate cutting starts, where should you be looking for returns?

For context, let us take note of the historical points of aggressive hikes and cuts over the past two decades. Notable movements in the below chart; the aggressive hiking cycle in the early 2000s, which was mostly driven by the need for the government to address several economic concerns and to curb inflation; the hiking cycles around 2006 going into the global financial crisis, and the post-Covid hiking spree. And as the saying goes, what goes up must come down.

As a global economic participant, the South African interest rates are not necessarily directly tied to the United States (US), the South African Reserve Bank looks at a variety of factors to determine policy direction. So, our rate moves tend to follow the US, as the latter has a wide impact on the global economy. At the same time, in our current multipolar world, the next cutting cycle may look and be caused by very different things compared to the past ones.And we are now at the point where we expect the beginning of a new interest rate-cutting cycle. But do investors understand the risk and return behaviours of different asset classes during interest rate cycles to best position portfolios? To understand the patterns, we separate the historical periods into hiking, cutting and neutral/flat phases and look at distributions of asset class returns in those phases. We investigate these effects on a coincidental level, how the asset classes perform during phases, and on a leading basis, how the asset classes perform three, six, nine or 12 months after a phase change. The same exercise is performed for multiple indicators to understand how to build better, well-rounded portfolios that are protected against these risks.

The plot above shows the median returns from SA Equities, SA Property, SA Bonds, Gold and the currency. All the returns are in South African rand, except gold which is in US dollar. This separates the effect of the currency from gold returns. Most asset classes tend to do better in cutting cycles than hiking cycles, except for the currency. The interplay between property and bonds is notable in hiking cycles as bonds are more directly affected by interest rates. SA bonds have historically underperformed most other asset classes during hiking cycles, and they perform better in a cutting cycle. While in flat cycles most asset classes perform well, except for gold and currency. Our flexible indicator framework allows us to dive deeper into equities by splitting them into sectors like resources, banks, financials, technology etc. and bonds by splitting them into short-, medium- and long-term bonds, for better insight.

Another consideration is to split the cycles into early and late, hiking and cutting cycles so one can analyse them in more detail. The downside of this approach is that the number of samples per phase decreases as the number of phases increases. Our confidence in the pattern naturally decreases. Also, these types of inferences hold more value when working with broad sectors or asset classes. They won’t work as well when drilling down to the single stock level, because stock-specific effects will dominate.

There will always be challenges to condense the macro-economic information into a time series, and study returns based on it. Reducing the complexity of the real world into a set of numbers has its pitfalls but it is useful. We approach this work from a scientific perspective, aiming to be objective in our analysis. The historical analysis is good for reducing errors from recency bias. For example, because we have been on an aggressive hiking cycle, we may think this time is different.

A combination of this indicator framework and a skilled and experienced portfolio manager can be the best of both worlds. The framework can make up for human biases, but the portfolio manager can help with overcoming the framework’s deficiencies by providing context. The results from this analysis can guide further research and help understanding. The portfolio manager’s goal is to build portfolios that protect against the downside while achieving return targets. Viewing multiple macro indicators and asset classes through this framework and comparing portfolio weights and tilts to these asset classes is crucial in building and managing well-rounded, risk-cognisant portfolios.