This post briefly summarizes why markets are struggling to price macroeconomic trends efficiently. The summary is identical to this blog’s page “Systematic Value III: Information Efficient Tracking of Macro Trends”, which is subject to continuous updates.

The main social value of macro trading is the adaption of broad macro prices (exchange rates, interest rates, credit spreads, equity valuations, and commodity prices) to changing macroeconomic conditions. Concurrent investor value is created through a timely shift into profitable positions following the early detection of fundamental drifts.

Markets are not information efficient

The global networking of electronic media has allowed greater quantities and detail of information to be provided to financial markets and the public in general. Similarly, the available quantity of economic and financial data has expanded rapidly. Analytic tools, infrastructure, and support programs for dealing with the increased amount of information have multiplied as well.

However, none of these developments means that markets have come even close to exhausting their potential for information efficiency. Indeed, a famous paper “On the Impossibility of Informationally Efficient Markets” by Grossman and Stiglitz (1980) underscores that information is costly and will only be procured if inefficient markets allow to translate it into sufficient returns. More recent theoretical work suggests that value traders only invest in research and information if (i) information cost is not too high, (ii) overall markets are poorly informed, and (iii) market makers do not suspect that value traders are not well informed (view post here). Put differently, information efficiency is a complex trade-off, with no guarantee that markets as a whole price assets close to their fundamental value.

This may explain why in the past, simple trend following has been a profitable trading strategy, even in the best researched and most liquid markets in the world (view post here). Also, there is ample evidence of herding and sequential dissemination of information in markets with great macroeconomic importance, including currencies (view post hereand here). These phenomena testify to the sluggishness of market responses to broad shifts in fundamental conditions. Using information efficiently often involves tedious work that many investors, even professionals, like to avoid.

  • The tedium of cleaning data: Economic data reflect many influences and often only a small part of them is what we think they are. For example, changes in industrial production data often are governed more by working day effects, weather, and accounting problems than by the genuine direction of economic trends. Even the most critical and advanced economic reports used by financial markets, typically U.S. data, require some modification and benchmarking (view post here). In short, the interpretation of any economic report and most financial data needs in-depth study of its calculation and, typically, a method of adjustment. This “dirty ground work” is beyond the resources of most portfolio managers and often shunned even by economists.
  • The tedium of building consistent theories: Institutional investors like simple relations. Indeed, the most popular simple principles have given rise to stylized algorithmic trading strategies, most famously the three main categories of risk premia strategies, i.e. carry. momentum, and relative value (view post here). By contrast, even modest complexity is generally disliked, partly because investment managers are short of time and patience, and because their daily duties are geared towards the techniques of trading and accounting, and not macroeconomic theory. And the construction of even a small mathematical model is beyond threach of most institutions, even those that label themselves as “quantitative”. Meanwhile, little help is coming from the academic world, which largely indulges in excesses of complexity and deficits of relevance.
  • The tedium of aligning positions and information: Even if investors have unearthed a market-relevant piece of information, they often lack the mandate or tradable instruments to use it properly. For example, country-specific information that points to rising capital inflows into, say, Brazil, does not mean one should be long the Brazilian currency or stock market. Instead, the correct position is a long position in Brazilian assets hedged for the influence of global markets or even relative to comparable assets. Hence, true information value generation can be gained from correct alignment and position calibration.

The relevance of macroeconomic data

There is consensus that market prices that underpin an economic equilibrium adjust in accordance to information about macroeconomic development. This applies to broad equity indices, benchmark bond yields, broad credit index spreads, exchange rates, and commodity prices. Such information can come in form of data or anecdotal evidence. Beyond obvious directional relations (such as low inflation supporting low interest rates) there are also links in volatility: uncertainty about the economic and financial state of an economy is conducive to higher volatility in market prices (view post here on evidence for commodity markets).

Meaningful macroeconomic trend indicators

While there are many economic time series, few of them provide on their own reliable and relevant information for broad macroeconomic and market trends. That is why market participants must create their own indicators, based on available statistics, quantitative methods, and a good deal of judgment. A macroeconomic trend indicator can be defined as a time series representing a meaningful macroeconomic trend that can be mapped to the performance of tradable assets. For example, concepts of real trade-weighted exchange rates have in themselves a bearing on a country’s competitiveness and hence some influence of its currency’s future trajectory and its equity market’s relative performance (view post here).

Meaningful market-based macroeconomic indicators

Financial market create economic realities, because they shape the conditions for economic activity, international trade, and financial flows. Therefore, market data are a subset of information on macroeconomic conditions. Whilst they are more easy to collect and interpret than non-financial information, it often takes nonetheless considerable theoretical and statistical preparation to extract relevant information.

For example, it is fairly easy to extract the influence of exchange rates on competitiveness, through following a real effective exchange rate concept. However, it is more difficult to measure other relevant concepts, such as insurance costs for currency exposure, which is highly relevant for financial and real economic investors, and which depends on the relation between expected realized and options-implied volatility (volatility risk premium, view post here).