Welcome to The Index Investor

Global Macro Analysis and Asset Allocation Insights

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Download a Free Sample Copy of our January 2020 Issue, which includes a feature article on "Global Macro Risk Dynamics in the 2020s and Beyond".


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 led 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 competing 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 hyperconnected socio-technical systems — including financial markets — are therefore more vulnerable than ever before 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 an increasing probability of sudden, non-linear changes in asset class valuations and returns.


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


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 then translate these into probability forecasts for different macro regimes, and use them to adjust our model portfolio's asset allocation. That's our edge — and potentially yours too, if you
subscribe.

Rather than statistical or machine learning, our approach more closely resembles estimative intelligence analysis, which employs a combination of bottom-up and top-down sensemaking processes. These were described in Pirolli and Card's classic article, "The Sensemaking Process and Leverage Points for Analyst Technology”, which includes this very useful graphic:


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For 23 years, we've made sense of global macro uncertainties, and provided subscribers with advance warning of the
2008, 2000 and 2020 financial crises, to help them avoid the portfolio losses they triggered.

More about the history of Index Investor since its founding in 1997 can be found
here. Our core beliefs about the nature of financial markets, asset allocation, and active versus passive investing can be found here, including 15 year results for our model index portfolios.

Here's what one subscriber recently wrote to us: "I am delighted to get your analysis. 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."


We are affiliated with
Britten Coyne Partners, which provides strategic risk related consulting services to management teams and boards, and the Strategic Risk Institute LLC, which provides online and in-person education offerings leading to a Certificate in Strategic Risk Governance and Management.


Here's what you'll get in each monthly issue of The Index Investor — all for just $250 a year.


(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 12 and 36 month probability estimates for four possible macro regimes: Normal Times, High Uncertainty, High Inflation, and Persistent Deflation.

(3) Asset Allocation implications of our analyses.

(4) 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 and divided into separate sections covering the areas that drive global macro dynamics, including technology, energy and the environment, the economy, national security, society, politics, financial markets and investor behavior, as well as two potential "wildcards": health and infectious disease, and cyber and electromagnetic events.

From an AI perspective, each month we provide a tagged data set that can be combined with similar inputs from other sources, and subsequently analyzed using Natural Language Processing methods.

(5) In between monthly publications, we publish flash updates — on our
blog, on our LinkedIn page, 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.

For example, based on our previous research and writing on
pandemic threats, we started warning about the danger posed by COVID-19 on January 27th — well ahead of the S&P 500's peak on February 19th.

(6) 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. Here's a list of the feature articles we're recently published.

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

Our
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.

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.

Successful investing is ultimately based on accurate forecasting. Here's a brief description of our methodology:


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 their mental models, enables astute investors to avoid large losses by taking action before the market's dominant narrative changes.

To achieve this goal, we use a method called Multipath Analysis, in which we collect and synthesize high value information (threat indicators and surprises) in the areas of technology, health and disease, energy and the environment, the economy, national security, society, and politics.

Their complex interaction over time produces the effects we later observe in the form of investor beliefs and behavior, from which emerge financial market valuations, volumes, and returns.

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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 results that were more than 50% more accurate than the tournament's control group of professional intelligence analysts (the team's experience is described in Professor Philip Tetlock's book, Superforecasting").

Perhaps most important, over our years of forecasting we have always kept in mind 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." That's still true — and equally dangerous — today.

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