Causality is a feature of life, as it is of capital markets.
It’s time to embrace this basic principle in investment management.
Here’s why and how.
Why causality matters
Causality has been defined in various ways in philosophy, statistics, economics, computer science, and other disciplines. As humans, we want to understand what we encounter, and causality, in its simplest form, gives the reason for a phenomenon. We notice something, then see something else happen, and wonder if and how they might be related. Alternatively, we could also consider whether something would happen in the absence of a certain factor or whether that factor is a necessary prerequisite.
If the presence or absence of one event has an effect on another, we may be able to create something and change reality. If we truly understand an event and how it relates to other events, we may be able to cause an event we prefer to occur, or prevent one we don’t favor from happening, and thus adapt our decision-making accordingly.
Causality is, therefore, a concept of human thought that helps to answer the why of phenomena: it structures the way we interact with our environment.
We analyzed 191 journal articles on causality tests in stock markets published between 2010 and 2020 to identify the most commonly used causality tests. Our methodology was that of a systematic review of the literature, and our analysis focused on the distribution by year; journal reputation; the geographic focus, by country, category or region; commonly discussed topics; and common causation tests and approaches.
Although causality is a broad and complex topic, we organized and mapped the findings of these articles to provide clarity for academics as well as finance and investment professionals so they can better identify current research trends and quickly find additional literature on related topics. We also wanted to encourage them to think about how to include causality assessments in their work. An example of immediate practical relevance: Net Zero Portfolio Management requires thinking in terms of path-dependent impact.
Forecasts vs. Nowcasting with causality
Causal discoveries help us better understand the world around us. By helping us understand the relevant laws of nature, assuming they exist, causality can give us prescriptive evidence for our analysis and guide us to improved decisions. Indeed, causal knowledge and inferences based on it are critical to effective decision making. Nancy Cartwright even suggests that causal laws are needed to distinguish between effective and ineffective strategies.
Throughout the history of science, causality has been among the fundamental research questions and the ultimate goal of many studies. Some of these studies try to make predictions about the future. But anticipating or predicting consequences is only one aspect of causation. Indeed, in describing empirically based causal theories, Michael Joffe confirms that economic theory prioritizes prediction, while the natural sciences aim primarily to show how the world works.
The prospective case of causality
Financial markets are complex, dynamic and forward-looking. They are driven by many heterogeneous market participants with imperfect information and bounded rationality. A causal understanding of their drivers is therefore both attractive and potentially very lucrative. However, given the speed and informational efficiency of markets, discovering causal relationships is not only extremely difficult, but the benefits of doing so are often short-lived as the market assimilates information quickly.
Causal knowledge is attractive because it can affect decisions by changing our expectations about outcomes. It provides information about what information to look for (how each piece of information should be weighted and which variables to target) if we cannot directly manipulate the outcome.
But how do we get this causal knowledge? Can we imagine situations where market participants and companies wonder why or how something happened? But precisely formulating these reverse causal inference questions is an impossible task. It will become a phenomenon afterwards.
Even if all of the above data were accessible and we had understood and interpreted it correctly, we cannot guarantee that we would act appropriately. The statistics and econometrics literature on causality focuses instead on forward causal questions or “effects of causes.” That is, what happens when, or what happens if. . . It does not focus on reverse causal inference or on “causes of effects” – that is, why this happens – with the latter often inspiring the former.
Correlation does not imply causation
In any introductory statistics or Economics 101 course, students learn the mantra “correlation does not imply causation.” Just because two or more things change together does not necessarily mean that one is the reason or cause of the other. However, our heuristic thinking wants to link the two, although correlation is neither necessary nor sufficient to establish causation. Correlation does not explain why or how, but simply points out that changes occur together.
So what’s behind our tendency to mistake correlation for causation? There are at least three biases, according to Michael R. Waldmann, that may provide an explanation. These are representational biases by which we give more weight to certain information; confirmation bias in which we falsify data to confirm our previous thinking; and the illusion of control bias in which we believe we have more influence over our environment than we actually do.
But causation is more than correlation. It indicates that an event, process or state, i.e. the effect or dependent variable, is the result of the occurrence of another event, process or state, or the cause or independent variable. A cause is at least partly responsible for the effect, while the effect is at least partly dependent on the cause. Peter Spirtes, Clark Glymour, and Richard Scheines describe it more formally as a stochastic relationship between events in a probability space where one event causes another event to occur.
Probability is an important aspect as the cause makes the effect more likely. James Woodward explains, however, that causality deals with regularities in a given environment that go beyond associative or probabilistic relationships because it helps us better understand how an effect changes when we manipulate the cause.
Design of research studies
In our study, we systematically reviewed peer-reviewed journal articles on causality in equity or stock markets relevant to investment and finance professionals over the 11-year period. Our sample only included papers that performed causality tests and that focused primarily on equity markets.
Our analysis revealed five essential points about the causality literature:
1. There is a dominant preference for quantitative assessment techniques to measure causality.
Correlation-based techniques were prominent among these, especially the CWJ Granger bivariate causality test. These 27 bivariate Granger tests, along with many multivariate Granger causality tests and Granger causality within nonlinear data, lead us to conclude that causality in stock markets is predominantly understood as prediction.
2. The lack of qualitative assessment techniques underscores a weakness in current causality testing research.
These heuristic-based techniques would further support investment professionals when dealing with uncertainty management or understanding unknown unknowns. This paves the way for new research activities in the coming years.
3. The domain of causality evidence is increasingly shifting from a focus on forecasting to immediate projection.
Rather than predicting consequences, assessing causality can help us understand how an aspect of the world works.
4. The time distribution showed a slight increase in interest in the subject year after year.
The year 2018 was the last of the 11 years of our sample period, with 27 articles published on causality and equity markets. This is 10 more than the annual average.
5. India, the United States and China were the most studied countries in our sample.
Given the size of these countries and their academic communities, this is not surprising. But it does show that there is ample room for causality analysis in the stock markets of other economies.
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All posts are the opinion of the author. Therefore, they should not be construed as investment advice, nor do the views expressed necessarily reflect the views of the CFA Institute or the author’s employer.
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Markus Schuller
Andreas Haberl
