Macro Trends

The importance of tracking macroeconomic change

Why macroeconomic change matters

Perceptions and expectations of economic trends affect the fundamental value of all traded securities. Thus, interest rates often follow expected inflation and operating rates of an economy, equity valuations are linked to economic growth and relative price-wage trends, credit risk is influenced by financial conditions and aggregate balances sheet, and exchange rates depend on external balances, relative economic growth and international investment positions.

Therefore, changes in macro trends, whether abrupt or gradual, are major “market movers”. That is why most investors watch economic data releases carefully and employ economists to analyze them. Empirical studies show that markets are more likely to move significantly on days of key (U.S.) data releases than on other days (view post here). However, markets’ ability to predict and anticipate economic change has remained limited, due to the high costs of exploring and understanding a wide range of economic data in depth. Many traders and investment managers have remained sceptical towards economists and their predictions, since the latter typically work with limited resources and have to reconcile research and marketing purposes of their work.

Importantly, the influence of economic data on market price changes is the stronger the longer the time horizon that we consider. This is because economic changes are typically more persistent than non-fundamental factors. They are therefore a major explanatory variable of medium to long-term price trends.

• For example, it has been estimated that on a quarterly basis more than a third of bond price fluctuations in the U.S. can be explained by deviations in the country’s major published economic data from analyst expectations (view post here). By contrast data surprises explain only 10% on market fluctuations on a daily basis.
• Longer-term research suggests that most of the decline in equilibrium real interest rates from the 1980s to 2010s can be explained by a single fundamental divergence. On the one hand the propensity to save has surged due to demographic changes (view post here), rising inequality of wealth and the reserve accumulation of emerging market central banks. On the other hand investment spending was held back by cheapening capital goods and declining government activity (view post here).
• Also, some economic estimates suggest that all of the real stock market gains in the U.S. since the 1980s are caused by the gradual redistribution of the benefits of productivity gains from workers to shareholders (view post here).
• Similarly, the big cycles in commodity prices have been driven mainly by “demand shocks”, which seem to be related to global macroeconomic changes and have a persistent effects of 10 years or more (view post here).

Economic shocks have more powerful market effects if they change long-term expectations. Thus, a key factor to watch is whether long-term expectations are “anchored”. A prime example for this is inflation: the persistent undershooting of inflation targets in the developed world has made long-term inflation expectations more dubious and susceptible to short-term inflation trends. This “de-anchoring” can be measured (view post here), providing valuable information on the consequences of price shocks for markets.

How macroeconomic change affects markets

Often enough the directional effect of economic change is straightforward, following standard macroeconomic theory and market experience. For example, rising expected inflation and lower unemployment have historically translated into higher low-risk bond yields (view post here).  Also, swings of large commodity-intensive sectors, such as construction in China, have driven global prices for raw materials, such as base metals (view post here). Furthermore, export price changes of “commodity countries” help explaining and even predicting their exchange rate dynamics (view post here).

However, macroeconomic trends can also have multiple effects. Thus, stronger economic growth can be both positive and negative for equity markets, depending on whether the impact on earnings exceeds the effect on long-term yields and discount factors. On these occasions indicators need to be qualified or combined with other complementary indicators.

Sometimes information regarding economic uncertainty can be as valuable as information on economic direction. One can estimate economic uncertainty through various methods, such as keyword frequency in the news, relevant market volatility and forecast dispersions. Such measures help detecting phases of popular fear or panic and complacency (view post here), both of which offer opportunities for professional investors. Indeed, composite measures suggest that uncertainty typically rises abruptly but subsides only gradually.
Unsurprisingly, uncertainty about the economic and financial state in general has been conducive to higher volatility in market prices, including commodities (view post here). Economic uncertainty can also affect directional trends. For example there is evidence  that uncertainty about external balances leads to underperformance of currencies of economies with net capital imports (view post here).

Best practices for tracking macro trends

In a nutshell

Generally, a macro trend indicator can be defined as an updatable time series that represents a meaningful economic or financial trend and that can be mapped to the performance of tradable assets or derivatives positions. There are three major sources of information: economic data, financial market data and expert judgment. Since the range of available data is vast one must condense them into small manageable sets of meaningful indicators. Typically, the correct sequence is to make use of prior knowledge first, including plausible economic or finance models, and then bring in the power of econometric or statistical models. The implementation of statistical methods has become a lot easier in recent years, thanks to the advances in statistical programming, particularly in language ‘R’ (view post here).

Economic data

For all major economies, statistics offices publish wide arrays of economic data series, often with changing definitions, elaborate adjustments, multiple revisions and occasional large distortions. Monitoring economic data consistently is tedious and expensive. Most professional investors find it easier to trade on data surprises than on actual macro trends. It is not uncommon for investment managers to consider an economic report only in respect to its presumed effect on other investors’ expectations and positions and to subsequently forget its contents within hours of its release.

What makes monitoring economies difficult is that there is usually no single series that represents a broad macroeconomic trend on its own in a timely and consistent fashion. For example, even something apparently simple such as an inflation trend requires watching many different data series, such as consumer price growth, “core” inflation measures, price surveys, wage increases, labour market conditions, household spending, exchange rates and inflation derivatives in financial markets. Market conventions have developed on which data releases to follow and which to neglect. However, even following a reduced subset can be challenging, as indicators can carry overlapping information, often send contradictory messages and may be published with varying and frustraingly long time lags.

Therefore, good macroeconomic trend indicators condense information. There are two complementary methods to accomplish this.

• First, logical connections and sound judgment can provide theoretical structure that allows combining different data series in plausible ways so as to generate a combined more intuitive conceptual indicator.
• Second, statistical methods estimate cross-variable and intertemporal relations, thereby helping to shed uninformative data (“dimension reduction”) and to update important trends quickly (“nowcasting”).

Theoretical structure establishes a plausible relation between the observed data and the conceived macroeconomic trend. This is opposite to data mining and requires that we set out a formula based on our understanding of the data and the economy, before we explore the actual data.

• As a simple example, we can combine production data from different sectors by giving each sector the weight it carries in the country’s gross domestic product.
• As a more advanced example, we can check whether rising consumer price inflation is associated with stronger or weaker demand. This helps distinguishing between supply and demand shocks, making it easier to judge whether a price pressure will last or not (view post here).
• In principle, modern macroeconomic theory can also help. True, dynamic stochastic general equilibrium models are often too complex and ambiguous for practical insights. However, simplified static models of the New Keynesian type incorporate important features of dynamic models, while still allowing us to analyze the effect of macro shocks on interest rates, exchange rates and asset prices in simple diagrams (view post here for interest rates and here for exchange rates).

Statistical methods become useful where our prior knowledge on data structure ends They necessarily rely on the available data sample. In respect to economic trends they can accomplish two major goals: dimension reduction and nowcasting.

• Dimension reduction condenses the information content of a multitude of data series into small manageable set of factors or functions. This reduction is important for forecasting with macro variables because many data series have only limited and highly correlated information content. (view post here).
• Nowcasting tracks a meaningful macroeconomic trend in a timely and consistent fashion. An important challenge for macro trend indicators is timeliness. Unlike financial market data economic series have monthly or quarterly frequency, giving only 4-12 observations per year. For example, GDP growth, the broadest measure of economic activity, is typically only published quarterly with one to three months delay. Hence, it is necessary to integrate lower and higher-frequency indicators and to make use of data releases with different time lags.

In recent years, dynamic factor models have become a popular method for both dimension reduction and nowcasting. Dynamic factor models extract the communal underlying factor behind timely economic reports and translate the information of many data series into a single underlying trend (view post here and here). This single underlying trend is then interpreted conceptually, for example as “broad economic growth” or “inflation expectations”. Also, financial conditions of an economy can be estimated by using dynamic factor models that distill a broad array of financial variables (view post here). The estimation process may look daunting, but its basics are intuitive and calculation is executable in statistical programming language R.

It is important to measure local macroeconomic trends with a global perspective. Just looking at domestic indicators is almost never appropriate in an integrated global economy. As a simple example, inflation trends have increasingly become a global phenomenon, as a consequence of globalization and convergent monetary policy regimes. Over the past three decades local inflation has typically been drifting towards global trends in the wake of deviations (view post here). As an example of the global effects of small-country shocks, “capital flow deflection” is a useful conceptual factor for emerging markets that stipulates that one country’s capital inflow restrictions are likely to increase the inflows into other similar countries (view post here). In order to measure this effect one needs to build a time series of capital controls in all major economies in order to distil the specific impact on a single currency.

Financial data

Financial market data are available at much higher frequencies. Also investment professionals often find them easier to understand. However, extracting macro trend information content from financial data can be challenging as one price typically reflects the influence of a ranger of factors. Hence, as with economic data, it takes some theoretical modelling and, often enough, additional statistical methods to translate market prices into macro factors.

• A simple example would be to derive inflation expectations from breakeven inflation, as priced in the inflation swap markets. For this we must at least make an adjustment for the inflation risk premium embedded in the swaps contract, possibly by using the correlation of inflation swaps with broad market benchmarks (view post here).
• A more advanced example would be to check whether rising commodity prices coincide with upward or downward revisions to global industrial activity. This helps distinguishing between commodity supply and global demand shocks; these two can have very different implications for exchange rate, equity and rates markets (view post here).
• Equity and bond market volatility can be decomposed into persistent and transitory components by means of statistical methods. Plausibility and empirical research suggest that the persistent component is associated with macroeconomic fundamentals. This means that persistent volatility is an important signal itself and that its sustainability depends on macroeconomic trends and events (view post here). Meanwhile, the transitory component, if correctly identified, is more closely associated with market sentiment and can indicate mean-reverting price dynamics.
• An example that relies more on statistical estimation would be the measurement non-conventional monetary policy shocks. For this we can estimate changes in the first principal component of bond yields that are independent of policy rates and on monetary policy announcement dates. Non-conventional monetary policy shocks tend to have a profound and lasting impact on most asset markets (view post here).

A global perspective is even more important for financial data than for economic data. The U.S. financial markets in particular have worldwide influence. Empirical research shows that shocks to U.S. monetary policy have a significant impact not only on USD exchange rates, but on foreign-currency risk premia more generally (view post here). Similarly, there is evidence that shocks to the term premium in longer-dated U.S. yields have a persistent subsequent impact on term premia in most other global markets (view post here).

Also, a cross-asset class perspective is important. Markets are still segmented insofar as different institutions and managers specialize on different types of information and assets. This is a form of rational inattention. For example, equity investors naturally focus more on corporate earnings prospects, while fixed income investors pay more attention to macroeconomic trends and monetary policy. As a consequence, investment strategies in one market can often benefit from information provided by another, if one is familiar with “decoding” price signals quickly. Thus, equity markets have historically been more sluggish than bond markets in adjusting discount factors to shifts in relative country inflation (view post here). Similarly, changes in the implied pace of future policy rates, as priced by fed funds futures, have in the past helped predicting equity returns. (view post here).

Expert judgment

As a rule, expert judgment is complement rather than an alternative to statistical methods:

• Experts can explain the meaning of data. Data analysis requires agood understanding of what the data really represent as opposed to what the label says. For example, some business surveys that refer on a particular month actually use data collected in the previous month. We also need expert judgment on the relevance of data. For example, in some countries core inflation (excluding food and energy) is a very important benchmark for policy rates, while in other countries the central bank would only look at headline inflation.
• Experts help detecting data distortions. Data analysis needs timely and regular information on distortions, such as the impact of taxes or regulated prices on inflation statistics or the effect of natural disasters or calendar effects on growth.