Significance of Consensus Positioning Scores on Stock Returns, Schemes and Mind Maps of Communication

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CrowdThnk White Paper

How Can Investors Profit from

Knowing Market Positioning?

Authors: Hans Kullberg & Qiwei Shi

March 2019

Abstract

If you’re in the Investment Management Industry, your primary goal is two-fold: 1) Maximize

the absolute return to your clients and 2) Minimize the risk taken to maximize profits. As

stewards of your client’s capital, achieving these objectives is synonymous with success.

Whether you’re a fundamental investor using discretionary inputs such as valuations, charts,

growth prospects and catalysts or if you’re a systematic investor using quantitative factors to

achieve investment profits, knowing the Market Positioning of the equities in your portfolio can

impact your investment strategy. Consensus Market Positioning – defined here as the relative

crowdedness of a stock attributable to many investors purchasing or not purchasing an equity

within a portfolio – can significantly dictate the magnitude and direction of an existing

stock. As all stocks are not created equal, differences in market positioning can lead to disparate

returns for investors. In this paper, we reveal the significant edge one can achieve by knowing

consensus market positioning and incorporating it into their overall investment framework.

The Importance of Understanding the Consensus Market Positioning

Market positioning has played an outsized role in dictating market direction for decades. The

tech bubble crash in 200 1 saw some of the most popular, crowded stocks like Amazon fall

precipitously as investors headed for the exits. In the Great Financial Crisis of 2008, even the

solid, blue-chip names like General Electric plummeted as investors liquidated their portfolio.

CrowdThnk has observed a strong relationship between crowded, extreme overweight stocks

and negative forward-looking returns during times of high volatility. Even more, during times

of panic, crowded stocks tend to experience the most severe degree of pain relative to the overall

market. The analogy is that of a crowded room within a burning building in which everybody is

trying to head for the exits – it’s a painful escape when the panic sets in.

If one wants to understand what the future holds, the best place to start is by intimately

understanding the present. Under normal market conditions with low volatility, the impact of

market positioning can be opaque and difficult to assess with the naked eye. However, by taking

a discerning approach to uncovering the data, one can gain insight into why market positioning

data is so valuable and how it can help investors achieve their two-fold investment objectives.

CrowdThnk objectively quantifies this market positioning on a scale from 0 to 10 with 0

representing Extreme Underweight and 10 representing Extreme Overweight scores.

The Problem with Measuring Market Positioning

Historically, investors have estimated market positioning by surveying their peer group and

asking what’s in their portfolio in almost a ‘finger-in-the-wind’ type of approach. More

analytically-oriented investors aggregate and parse through data such as SEC 13-F Reports,

Short Interest data and Bank Reports, all of which uncover a small fraction of the entire

landscape. However, it is very difficult to quantify and aggregate view of the entire market

positioning picture. Some of the primary problems entail:

Key Insights from CrowdThnk’s 10 - Year Market Positioning Study

1. Over the past 10-year bull run, all stocks have tended to rally no matter where the

positioning is, based on returns. However, there are important deviations in absolute

return with respect to positioning score bucket. (Exhibit A)

2. It is very interesting that Underweight stocks tend to rally more than the

Overweight stocks, everything else held equal. The overall relationship seems to

be linear, that is, the more overweight a stock is, the less it rallies going

forward. The data concludes that the magnitude of the stock move is directly related

to is positioning score.

3. When accounting for different underlying volatilities of returns and standardizing it

using a Z-Score, this effect is even more prominent as Overweight Stock’s forward

return Z-score becomes negative and Underweight stocks forward return

Z-score becomes positive for all sectors. That implies that a stock’s return is

handicapped by overweight positioning, meaning that the more crowded a stock is, the

less it can be expected to return holding all other things constant.

4. Looking at individual sectors, we find that the sectors that tend to rally more when

positioning is Overweight include: Communication Services, Consumer

Discretionary, Information Technology. The ones that tend to rally more when

positioning is Underweight include: Consumer Staples, Health Care,

Industrials, Information Technology. This may represent a bias towards

momentum-driven versus mean-reversion driven stocks. These reflect particular

tendencies and characteristics of these sectors over our 10-year study period, subject

to change in the future.

5. For quantitative investors, a potential model using these insights would be to go Long

the extremely underweight stocks and Short the extremely overweight

stocks on a systematic basis. Variables in the model include rotation frequency,

positioning score filters, and individual stock selection among other things. An

alternative strategy, benefitting from alpha and beta sources, would be to buy

underweight stocks outright upon crossing a lower boundary threshold

filter given the positive skew, positive expected return with favorable probabilities.

6. Looking at the probability of attaining a positive weekly performance, all things being

equal, extreme underweight stocks exhibit a stronger tendency to deliver a positive

return. When breaking down the Positioning Scores by bucket, underweight stocks

demonstrates probabilities between 56-62% of positive, forward-looking weekly

performance while extreme overweight stocks underwhelm at 52-55% probabilities,

differing by sector.

Exhibit A. Stock Returns have been positive over the past 10 years across sectors. However,

stocks have exhibited a significant degree of variation with regard to Positioning Score. (Note:

Percentages reflect the average weekly return with regard to respective Positioning bucket)

Overweight Underweight

P: [0,1] P: [1,2] P: [2,3] P: [3,4] P: [4,5] P: [5,6] P: [6,7] P: [7,8] P: [8,9] P: [9,10]

Ov erall 0.69% 0.60% 0.50% 0.43% 0.40% 0.40% 0.29% 0.22% 0.18% 0.16%

Communication Serv ices 0.40% 0.54% 0.47% 0.30% 0.47% 0.51% 0.26% 0.12% 0.23% 0.31%

Consumer Discretionary 0.33% 0.51% 0.54% 0.45% 0.45% 0.39% 0.26% 0.28% 0.25% 0.25%

Consumer Staples 1.01% 0.30% 0.34% 0.46% 0.36% 0.44% 0.29% 0.10% 0.16% 0.21%

Energy 0.47% 0.44% 0.25% -0.03% 0.08% 0.13% 0.09% 0.29% 0.11% -0.08%

Financials 0.48% 0.67% 0.39% 0.60% 0.37% 0.31% 0.22% 0.17% 0.12% 0.16%

Health Care 0.97% 0.73% 0.74% 0.52% 0.28% 0.42% 0.41% 0.23% 0.27% 0.17%

I ndustrials 1.14% 0.65% 0.56% 0.41% 0.41% 0.55% 0.35% 0.24% 0.13% 0.12%

I nformation Technology 0.92% 0.69% 0.56% 0.56% 0.68% 0.43% 0.37% 0.28% 0.15% 0.23%

Materials 0.64% 0.55% 0.54% 0.23% 0.18% 0.36% 0.08% 0.32% 0.22% 0.08%

Real Estate 0.80% 0.63% 0.59% 0.52% 0.45% 0.40% 0.31% 0.19% 0.13% 0.07%

Utilities 0.37% 0.81% 0.42% 0.41% 0.46% 0.34% 0.30% 0.10% 0.17% 0.10%

Exhibit C. While a rising market over the past 10 years has led to positive forward-looking

returns across sectors, the out-of-favor stocks with low Position Scores tend to exhibit a higher

tendency to achieve positive returns. (Note: Probabilities denote the percentage of time a stock

incurs a positive weekly return)

Skewness of Distribution of Returns

Given the positive expected return profile and significant probability of a positive return at an

extreme underweight positioning score, it’s natural to assume that the return profile for negative

versus positive instances may present a negative skew. Skewness measure the degree of

asymmetry of a distribution around its mean. Positive skewness indicates a distribution with an

asymmetric tail extending toward more positive values whereas Negative skewness indicates a

distribution with an asymmetric tail extending toward more negative values. Interestingly, the

data depicts a mixed bag of Skewness profiles, differing across Sectors with respect to

Positioning Score. A few examples stand out such as Consumer Staples at 0-1 score and

Communication Services at 7-8 scores, both over 2.5 in Skewness level. However, there is no

pattern or uniformity in Skewness within our sample data with respect to positioning. It is

significant to mention that extreme underweight positioning score buckets don’t exhibit a strong

tendency for negative skewness or fat tails on the negative end of the distribution.

Exhibit D. The Dataset does not reveal any strong relationships of Skewness with respect to

Positioning score bucket when looking at the distribution of returns.

Overweight Underweight

P: [0,1] P: [1,2] P: [2,3] P: [3,4] P: [4,5] P: [5,6] P: [6,7] P: [7,8] P: [8,9] P: [9,10]

Ov erall -0.04 0.54 0.05 -0.06 0.24 0.15 -0.06 0.33 0.15 0.

Communication Serv ices 0.24 0.80 -0.15 0.35 -0.02 0.56 0.18 2.51 0.02 0.

Consumer Discretionary 0.11 -0.17 0.05 -0.01 0.01 0.19 -0.21 0.22 0.04 0.

Consumer Staples 2.62 -0.41 -1.38 0.07 -0.44 1.23 0.24 -0.23 0.71 -0.

Energy -0.11 1.83 -0.30 0.05 1.59 -0.25 -0.16 -0.17 -0.01 -0.

Financials 0.19 -0.15 -0.28 -0.04 -0.02 0.04 0.11 0.02 -0.32 -0.

Health Care 0.90 -0.39 1.38 0.26 0.18 0.50 -0.05 0.39 1.04 0.

I ndustrials 0.07 -0.14 -0.06 -0.11 -0.43 -0.01 -0.28 0.09 -0.34 -0.

I nformation Technology 0.14 0.31 0.07 -0.08 0.73 -0.16 -0.05 -0.03 0.40 0.

Materials 0.07 0.00 0.22 -0.52 -0.08 0.57 -0.36 0.02 -0.39 -0.

Real Estate -0.09 -0.28 -0.36 -0.05 -0.20 -0.34 -0.34 -0.23 -0.24 -0.

Utilities -5.89 5.90 -0.46 -2.50 -0.04 -0.25 2.21 -0.40 -0.47 -0.

Exhibit F. 1 - Week Forward Return Distribution for Extreme Underweight Stocks

Exhibit G. 1 - Week Forward Return Distribution for Extreme Overweight Stocks

Summary

Market Positioning plays an integral role in dictating the future evolution of a stock’s price path.

By understanding an intimately knowing a stock’s market positioning or crowdedness, one can

have an outsized advantage in predicting the stock’s future direction.

Another way of analyzing the data is by using an analogy to horserace betting. Similar to the

odds payout of betting on a highly favored horse, while the probability you win may be high

when going along with a crowded stock, in many cases the payoff may be small. That is, on a

weekly basis, crowded stocks still tend to move higher, but when compared to out-of-favor,

underweight stocks, the weekly return is minimal. Furthermore, extreme underweight stocks

have a higher probability of moving in a positive direction on a week-to-week basis when

compared to extreme overweight stocks. When accounting for disparate underlying volatilities

of the stock universe by factoring in its Z-Score, extreme underweight stocks exhibit a positive

risk/reward scenario. Finally, the Skewness distribution of returns amongst sectors with respect

to positioning score doesn’t raise any alarming observations nor reveal outstanding patterns.

By knowing and quantifying the crowdedness of stocks into a unique metric such as

CrowdThnk’s Consensus Positioning Score, investors can achieve their dual mandate of

maximizing absolute returns while minimizing risk associated with crowded markets.