Welcome to The Index Investor

Global Macro Analysis and Asset Allocation Insights

Consistently good investment decisions result from a high quality process combined with high quality information. Yet as former Bank of England Governor Mervyn King has observed, we now live in a world of radical uncertainty that has become a much more dangerous place for investors.

We are inundated with a constant flow of data, and struggle to understand what it all means. For example, if we have 5 data points, there are also up to 10 relationships between them that we have to consider — our "sensemaking challenge" therefore involves 15 pieces of information. A more complex system with 25 data points, has 300 interactions between them, and the sensemaking challenge now involves 325 pieces of information.

This illustrates a critical point: As the amount of data increases, our sensemaking challenge becomes exponentially more difficult, which leads to greater uncertainty.

Here is an example of what this means in practice: Consider the difficulty of integrating and assessing all these distruptive macro trends (and related uncertainties) to derive insights about future asset class valuations and returns.

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 and expectations about the future. In other words, as uncertainty increases the conventional wisdom grows stronger, even as it is becoming more fragile.

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

Our mission is to help investors develop an edge in anticipating downside threats, accurately assessing them, and adapting to them in time to avoid large losses.

To do this, we focus on the evolving dynamics of the macro system and the uncertainties and emergent threats that lie beyond the detection horizon and analytical capabilities of quantitative algorithmic methods. Rather than statistical and machine learning, our approach more closely resembles estimative intelligence analysis. Our approach is grounded in
research findings that macro uncertainty shocks drive many other sources of variability in the economy, financial markets, and beyond.

Specifically, we collect and synthesize high value information in the areas of technology, the economy, national security, society, and politics, whose complex interactions over time produce the effects we later observe in financial markets.

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 integrates the evidence and logic we have used to reach our conclusions.

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."

You can
download a free 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, economic, environmental, national security, social, demographic, and political trends and uncertainties, as well as potential "grey swan" wildcards like environmental, infectious disease, cyber, and large-scale electromagnetic events.

Here's what you'll get in each monthly issue of The Index Investor:

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

(2) Narrative forecasts and quantitative probability estimates for macro system and financial market regime changes over the next 12 and 36 months. In our forecasting methodology, possible regimes include the Normal Regime, where equities perform well; a High Uncertainty Regime, where negative asset class valuation changes of 20% or more can quickly occur; a High Inflation Regime, and a Persistent Deflation Regime.

(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, the economy, national security, society, politics, financial markets and investor behavior, and three potential "wildcards", which cover developments in energy and the environment, health and infectious disease, and cyber and electromagnetic events.

The cumulative Evidence File helps subscribers to better understand the chronological trajectory and dynamics of developments in each of these critical 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 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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n 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
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