Financial markets are not macro information efficient. This means that investment decisions miss out on ample relevant macroeconomic data and facts. Information goes to waste due to research costs, trading restrictions, and external effects. Evidence of macro information inefficiency includes sluggishness of position changes, the popularity of simple investment rules, and the prevalence of herding.  A simple and practical enhancement of macro information efficiency is the construction of quantamental indicators. A quantamental indicator is a time series that represents the state of an investment-relevant fundamental feature in real-time. The term ‘fundamental’ means that these data inform directly on economic activity, unlike market prices, which inform only indirectly. The key benefits of quantamental indicators are that [1] they fit machine learning pipelines and algorithmic trading tools, thus making a broad set of macro information tradable, [2] they support the consistent use of macro information, [3] they can be applied across traders (or programs), strategy types and asset classes and are, thus, cost-efficient.

The below post is part of this site’s updated summary on macro information inefficiency.

What is macro information efficiency?

Macro information here refers to public information on the economy and its key sectors that is relevant for the pricing of assets and derivatives. This type of information includes economic data (on growth, inflation, confidence, and so forth), government and corporate balance sheets, financial market data (including turnover and open interest), social and political developments, and even environmental and weather trends.

The information efficiency of an asset market is defined as the extent to which the price of the asset reflects available information. Importantly, the efficiency of a market does not mean efficient use of information across all market participants. Different individuals or institutions have different capacities to buy, collect and use data. This is one key reason why there is trading and rational herding behavior (view post here). Herding is rational for uninformed but flexible traders when important releases are forthcoming and some market participants are likely to have private advance information. An information efficient market produces, researches, and applies macro information to the extent that investment returns and social benefits exceed information costs.

Why are markets not (macro) information efficient?

The principal obstacles to information efficiency are costs, trading restrictions, and external effects.

In their seminal article “On the Impossibility of Informationally Efficient Markets” Grossman and Stiglitz explained that since price-relevant information comes at a cost it will only be procured to the extent that inefficient markets allow translating it into sufficient returns: ” The only way informed traders can earn a return on their activity of information gathering, is if they can use their information to take positions in the market which are ‘better’ than the positions of uninformed traders…Hence the assumptions that all markets, including that for information, are always in equilibrium and always perfectly arbitraged are inconsistent when arbitrage is costly” (view journal article here). The theory here shows what practitioners already know: investment in information involves a trade-off between cost and return, with no guarantee that markets set asset prices close to their fundamental value.

Theory and practice show that investment managers only collect information and engage in research if costs are contained, the overall market is uninformed, and the information advantage does not become general knowledge:

  • First, information cost must not exceed related expected returns. Genuine value-generating macroeconomic and financial research requires experience, quantitative skills and systems, and a lot of legwork. This means that information costs easily add up to large numbers in practice. Many essential areas of this research, such as real-time economic data or advanced modeling, are beyond the scope of most portfolio management teams, even at large institutions.
    Thus, standard economic data are notoriously hard to interpret and require considerable adjustments. Economists often disagree on their interpretation of data and do not normally update their predictions continuously. Even the most popular and highest-quality economic data, such as U.S. labor market reports, need in-depth research to extract information (view post here). Also, forecasts are also not easily comparable across countries due to different conventions and biases.
    All this gives rise to rational information inattentiveness of markets (view post here). This means that market participants update their information set sporadically, rather than continuously. Rational inattentiveness reflects costs of acquiring information or costs of re-optimizing investment decisions. There is empirical evidence that inattentiveness causes sticky expectations and goes some way in explaining price momentum after important relevant news, such as corporate earnings releases (view post here).
    Moreover, understanding the relationship between economic information and asset prices requires experience and econometric skills. Data science has come a long way in providing powerful tools for analysis and model construction. However, in the data-constrained macro space, the success of statistical models hinges on good judgment and real in-depth understanding of methods, models, and data, all of which remain in short supply. Therefore, most, institutional investors prefer simple relations, often condensed in the three main categories of risk premia strategies, i.e. carry, momentum, and relative value (view post here).
  • Second, investor research only pays off when the overall market is not already well informed. Put simply, research must result in a significant information advantage. This can be a serious obstacle because the information content of prices with respect to known fundamentals tends to grow faster than the information content of private research. This discourages fundamental research and can lead to over-reliance on price information (view post here). Experimental research has confirmed that traders do not invest in information if they believe that others have already done so and that market prices already reflect this research (view post here). For a profitable investment management business, it is crucial to invest in relevant information where or when others do not.
  • Third, the information advantage must remain confidential. In particular, market makers must not suspect that their counterpart is in possession of superior information (view post here). A value trader with a reputation of being well informed is easily ‘front run’ when giving orders to market makers. As pointed out by Bouchaud, Farmer and Lillo (2009): “If I know that you are rational, and I know that you have different information than I have, when I see you trade and the price rises I can infer the importance of your information and thus I should change my own valuation.”

Moreover, research alone does not produce efficient markets. Financial markets research translates into price information only if it is acted upon. Alas, the link between research and actual investment flows is often tenuous, for various reasons.

  • Taking positions in accordance with research is frequently obstructed by institutional rules and regulations. For example, many funds face limitations to leverage and short selling or are prohibited from investing in specific asset classes, currencies and sectors.
  • For some institutions market access is limited and trading costs can be prohibitively high. For example, in OTC (over-the-counter) markets bid-offer spreads vary across counterparties(view post here), favoring clients with high volumes and sophistication. Since institutional investment strategies in forwards, swaps, and options that are sensitive to transaction cost implementation depend on the institution’s standing with market makers.
  • Often enough investment managers simply do not fully trust their researchers, possibly due to conflicts of interest. Portfolio managers sometimes denigrate research to elevate their own role in profit generation. Researchers sometimes gear their research towards company politics and reputation rather than investment value.
  • Finally, there is evidence that financial decision-making under uncertainty is far from rational and subject to a range of behavioral biases, such as the illusion of control, anchoring bias, sunk-cost bias, and gambler’s fallacy (view post here). This implies irrational neglect of optimal strategies.

What is the evidence for macro inefficiency?

Macro information inefficiency is consistent with evidence of numerous behavioral biases of both retail and professional investors.

  • Thus, survey evidence suggests that retail investors adjust positions sluggishly to changing beliefs and that their beliefs themselves defy classic rationality (view post here). Sluggishness manifests in two ways. First, the sensitivity of portfolio choices to beliefs is small. Second, the timing of trades does not depend much on belief changes. Contrary to standard rationality, investors cling stubbornly to diverse beliefs with little convergence overtime.
  • Macro information inefficiency also explains the dominance of simple investment rules with little fundamental research. In practice, asset allocation often just follows past performance (view post here), simplistic highly stylized factors (view post here), risk parity and valuation ratios (view post here), or simply market capitalization and benchmark index conventions (view post here). Even active portfolio managers often find it more practical to produce “fake alpha” through receiving risk premia on exposure to non-directional conventional factors and strategies rather than to generate true investor value (view post here).
  • Furthermore, information inefficiency explains why momentum trading has been a profitable trading strategy, even in the best researched and most liquid markets (view post here) and is widely used as a trading style to protect against adverse macro trends (view post here). There is ample evidence of herding and sequential dissemination of information in markets with great macroeconomic importance, including currencies (view post hereand here). And there is evidence simple fundamentals trend following has yielded significant returns in equity markets in past decades (view post here). All these phenomena testify to the sluggishness of market responses to broad shifts in fundamental conditions.
  • Finally, experimental research has added evidence for mispricing of assets relative to their fundamental values. Academic studies support a wide range of causes for such mispricing, including asset supply, peer performance pressure, overconfidence in private information (view post here), speculative overpricing, risk aversion, confusion about macroeconomic signals and – more generally – inexperience and cognitive limitations of market participants (view post here). In particular, as pointed out by Bouattour and Martinez (2019), “laboratory experiments…find that market efficiency is reduced when the fundamental value of stocks is volatile…The more volatile the fundamental value, the more the informational efficiency is reduced…Also, participants under-react to information announcements. This under-reaction, which is more pronounced in markets with information asymmetry between subjects…is not corrected during trading periods.”

Enhancing macro efficiency with quantamental indicators

Investment managers can contribute to and benefit from information efficiency. A simple and practical approach is [i] to create indicators with meaningful macroeconomic and market information and [ii] to condense them into meaningful conceptual quantamental indicators and related trading factors that can guide investment strategies.

A quantamental indicator is a time series that represents the state of an investment-relevant fundamental feature in real-time. The term “fundamental” here means that data inform directly on economic activity. This separates them from market data, which dominate conventional algorithmic trading, but provide such information only indirectly.

Fundamental features can be from the macroeconomic, social (macro behavioral), corporate and environmental space. Real-time dating means that indicator values correspond to the state of public information at the associated date. Values change in accordance with new data releases or re-estimations of relevant models. The impact of new data releases should be calculated based on data vintages, i.e. the state of time series at a specific release date. Re-estimation can be simulated through statistical learning.

The usage of quantamental indicators and factors has great benefits, particularly in the macro space:

  • Quantamental indicators fit machine learning pipelines and algorithmic trading tools, thus making a broad set of macro information tradable. In international macro trading, there are far too many economic data series to keep track of, even for the most diligent investment manager. Selected pre-filtered macro factors effectively outsource part of the production of information to research and data science. Importantly, one factor can be used by multiple traders or programs, as each will customize its own version and implementation around it. Thereby, fixed costs in the production of factors and – more importantly – their building blocks can be shared across traders, making investment research more cost-efficient.
  • Quantamental factors support consistency in the application of fundamental information. Often enough investors are myopic: they overrate latest “fashionable” factors that happened to coincide with recent market moves, regardless of causality and long-term relations. This constitutes an informal version of “overfitting” of information (view post here), also called “scapegoat theory” (view post here). Overfitting leads to misinterpretation of fundamental information, crowded positions and setback risk.
  • Quantamental macro factors are applicable across markets and asset classes. This means that they have use in multiple strategies and are more cost efficient than sporadic bespoke research. Detecting changes in economic growth, for example, matters for both equity and FX strategies. If returns across asset class strategies have little or no correlation (view post hereon equity and FX), quantamental factors can become important building blocks for diversified multi-strategy portfolios.

For practical purposes and in order to avoid double-counting, misinterpreting and forgetting information it is helpful to structure quantamental factors into three groups.

  • Valuation gaps are defined as differences between the market price of an asset or derivative and its estimated value. There are two basic methods of tracking valuation gaps. The first is to estimate an asset’s fundamental value directly, maybe based on discounted cash flows, and then compare it with the quoted price. Conscientious estimation of fundamental value gaps is one of the most challenging strategies in asset management. It requires advanced financial modeling and often long waiting times for payoff (view summary). The second method works indirectly, by efficiently tracking or “nowcasting” the trend in key valuation-relevant factors and then estimating the gap between their actual values and market perceptions. Macroeconomic trends are powerful asset return factors because they affect risk aversion and risk-neutral valuations of securities at the same time (view summary).
  • Implicit subsidies in financial markets are premia paid through transactions that have motives other than conventional risk-return optimization. They manifest as expected returns over and above the risk-free rate and conventional risk premia. Implicit subsidies are a bit like fees for services. Typically, subsidies are paid by central banks, governments, highly-regulated institutions or non-financial institutions (view summary).
  • Endogenous market risk or setback risk refers to uncertainty regarding the interaction of financial market participants, as opposed to uncertainty about traded assets’ fundamental value. Endogenous market risk often manifests as feedback loops after some exogenous shock hits the market. An important type is setback risk, which refers to the asymmetry of the upside and downside potential of a trade that arises from market positioning. Setback risk is a proclivity to incur outsized mark-to-market losses even if the fundamental value proposition of the trade remains perfectly valid. Related macro factors typically measure the positioning or “crowdedness” in a trade as well as the probability that investors will exit the trade in the near term (view summary).

Importantly, these three types of indicators are complementary, not competing. Indeed, powerful trading factors can be built on combinations of the above principal indicators. Thus, a price-value gap often arises as a consequence of implicit subsidies, meaning that the subsidized asset becomes overpriced for a reason. Also, typically setback risks arise alongside subsidies due to positioning, causing sudden large losses to subsidy receivers.

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Ralph Sueppel is founder and director of SRSV, a project dedicated to socially responsible macro trading strategies. He has worked in economics and finance for over 25 years for investment banks, the European Central Bank and leading hedge funds. At present, he is head of research and quantitative strategies at Macrosynergy Partners.