Welcome to The Index Investor

Global Macro Analysis and Asset Allocation Insights


In August 2019, we published "The Next Downturn: How Different? How Deep? How Long?"
In light of the high uncertainty surrounding the economic consequences of COVID-19, we are making it available for free.

Former Bank of England Governor Mervyn King has observed that we now live in a world of radical uncertainty that has become a much more dangerous place for investors.
In their latest global survey, PwC found that CEOs agree, noting that, "No matter where CEOs look or from where they are looking, the path forward is fraught with uncertainty."

Researchers have found that when uncertainty rises, evolution has primed human beings to become much more prone to conformity and to rely more on imitating what others are doing (so-called "social learning").

Paradoxically then, as uncertainty increases, people — including many sophisticated investors — are more likely to become attracted to a smaller (not larger) number of narratives about the future. In other words, as uncertainty increases the conventional wisdom grows stronger, even as it is becoming more fragile and downside risks are rising.

Today's highly interconnected socio-technical systems — including financial markets — are extremely vulnerable to small changes in information which trigger feelings (especially fear) and behavior that spread quickly, and are then further amplified by algorithms of various types. The result is sudden, non-linear change.

Since 1997, our mission has been to help investors, corporate, and government leaders to better anticipate, more accurately assess, and adapt in time to emerging macro threats.

To do this, we focus on providing insights about the evolving dynamics of the global macro system and the emergent threats they produce
that lie beyond the detection horizon and analytical capabilities of quantitative algorithmic methods. We translate these into probability forecasts for different macro regimes and portfolio asset allocation recommendations.

That's our edge.

Rather than statistical and machine learning, our approach more closely resembles estimative intelligence analysis. Specifically, we collect and synthesize high value information in the areas of technology, energy and the environment, the economy, national security, society, politics, whose complex interactions over time produce the effects we later observe in the form of investor beliefs and behaviors and financial market valuations, volume, and returns.

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Compared to new quantitative data, new qualitative data diffuses more slowly across market participants, and is only gradually incorporated into asset prices. This time delay, plus the accuracy of one's insights, enables investors to avoid large losses by taking action before the market's dominant narrative changes.

Using this methodology, we provided subscribers with advance warning of both the
2008 and 2000 financial crises (more about the history of Index Investor since its founding in 1997 can be found here, and about our core investment beliefs can be found here, including 15 year results for our model index portfolios).

Each month, we provide subscribers with 12 and 36 month probability forecasts for four possible macro regimes (which can be though of as factors) under which different asset classes deliver relatively better or worse returns. These regimes include Normal Times, High Uncertainty, High Inflation, and Persistent Deflation.

We also provide a narrative forecast that makes explicit the new evidence and logic we have used to reach our conclusions, and the implications of our conclusions for portfolio allocation decisions.

Here's what one subscriber recently wrote to us: "I am delighted to be able to get your analysis again. We get everything from Wall Street, and they all seem to be saying the same thing. Your take is greatly appreciated." Another said "your research is unique. There's nothing else like this out there."

You can
download a free sample copy of a recent issue to get a better understanding of what we provide subscribers each month.

back issues and Research Library are both free. Investors can browse our curated content on a wide range of issues affecting medium and long-term asset class valuations, including technological, environmental, economic, national security, social, demographic, political, and financial market trends and uncertainties, as well as potential "grey swan" wildcards like infectious disease, cyber, and large-scale electromagnetic events.
Here's what you'll get in each monthly issue of The Index Investor:

(1) Narrative forecasts and quantitative probability estimates for macro system and financial market regime changes over the next 12 and 36 months, and their implications for portfolio asset allocation.

(2) Estimated asset class over/under valuations and updated market stress indicators (e.g., levels of uncertainty, herding, liquidity, and credit risk.

3) A cumulative, chronological "Evidence File", that contains two kinds of high value information that we have used to update our monthly forecasts. The first are "indicators" that cause us to either increase or decrease our uncertainty about the values of different parameters in our mental model of the complex macro system. The second are "surprises" that increase our uncertainty about the structure of that model. Evidence is categorized by month, with surprises highlighted, and divided into separate sections covering developments in technology, energy and the environment, the economy, national security, society, politics, financial markets and investor behavior, and two potential "wildcards": health and infectious disease, and cyber and electromagnetic events.

Our Evidence File helps subscribers better understand the trajectory and dynamics of important new developments in each of these areas.

(4) In between monthly publications, we publish flash updates — on our
blog, via email, and via our Twitter @indexllc — if and when we obtain high value information that results in a substantial change to a forecast probability.

(5) A
feature article providing an in-depth analysis of either a key macro-uncertainty (e.g., how close the system is to one or more critical thresholds) or an aspect of making good investment decisions in the face of complexity and uncertainty. These articles typically synthesize a broad range of academic research and practitioner experience to provide thought provoking insights about critical issues facing investors and their advisors.

We know that our subscribers' attention is their scarcest resource, so our goal is to maximize the return on the time you invest each month reading The Index Investor.

Our analytical process is based on methods and tools developed over the past seven years at our affiliate, Britten Coyne Partners, which provides consulting services and education courses to executive teams and boards on strategic risk governance and management.

At The Index Investor, we engage in
anticipatory thinking to identify what could happen (e.g., different macro regimes and related events); forecasting, to estimate the probability that regimes and events will happen, and the impact they will have if they do (e.g., on macro variables and broad asset class returns); and warning, when emerging risks develop to the point that they pose imminent threats to asset class valuations.

With respect to what could happen, we are acutely conscious of the conclusion reached by a
1983 CIA study of failed forecasts: "each involved historical discontinuity, and, in the early stages…unlikely outcomes. The basic problem was…situations in which trend continuity and precedent were of marginal, if not counterproductive value."

We also keep in mind the economist Rudi Dornbusch's famous warning: "Crises take a much longer time coming than you think, then happen much faster than you would have thought."

When it comes to forecasting, we know that in complex socio-technical systems that are constantly evolving, the accuracy of statistical or machine learning based forecasting methods declines exponentially as the time horizon lengthens, since the historical data set on which they were trained will (depending on the speed and effectiveness of any retraining cycle) bear less and less resemblance to the distribution of outcomes the system is likely to produce in the future.

Under these circumstances, forecast accuracy over longer time horizons depends on causal and counterfactual reasoning about the possible future effects of multiple interacting trends and uncertainties that are hard to quantify. In this regard, we differ with those who believe that you don't need theory or experience if you have enough data to analyze; we believe they are still critical.

Our forecasting process also draws on lessons
Tom Coyne learned from spending four years as a member of the Good Judgment Project team, which won the Intelligence Advanced Research Projects Activity’s forecasting tournament with forecast accuracy that was more than 50% better than the tournament's control groups (the team's experience is described in Professor Philip Tetlock's book, “Superforecasting").

To implement our forecasting process, we use Peter Pirolli and Stuart Card's information foraging and sensemaking model, which is described in their classic article, "
The Sensemaking Process and Leverage Points for Analyst Technology”:
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Successful investing is ultimately based on accurate forecasting. Even passive investors need forecasts to avoid large downside losses that can substantially reduce the probability of achieving their long-term goals. Besides improving your own methods, as uncertainty increases the best way to improve the accuracy of your forecasts is to combine them with forecasts made by others, especially those that are based on different information sources and/or models.

In order for subscribers to increase the accuracy of the forecasts they use in their investment processes, our forecasts can (and should) be combined with forecasts they obtain from other sources. Ideally, these forecasts will be based on substantially different underlying information and methodologies.

We provide subscribers with tools that enable them to combine forecasts to improve predictive accuracy, including the “extremizing” methodology used by the Good Judgment Project.

In sum, our goal is to provide you with an ongoing understanding of the complex macro dynamics driving asset class valuations and returns, and quantitative forecasts and detailed narratives that can help you (and/or your clients) to avoid large losses.

We also provide custom research as well as speaker services on how to increase forecast accuracy, understanding the differences between active, passive, and index investing, and how to overcome the individual, group, and organizational obstacles to making good decisions in the face of uncertainty. Our speaker offerings include seminars for advisors' clients and speeches for larger groups. Click here to learn more.

You can also
send us feedback about how to improve The Index Investor to better meet your needs. Or you can subscribe.

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