






Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community for help and clear up your study doubts
Discover the best universities in your country according to Docsity users
Free resources
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
This paper explores the importance of market positioning in determining stock returns and reveals the edge investors can gain by incorporating consensus positioning scores. the historical challenges in measuring market positioning, the impact of positioning on stock returns, and the sectors that tend to rally more based on positioning. It also suggests an alternative strategy for buying underweight stocks.
Typology: Schemes and Mind Maps
1 / 11
This page cannot be seen from the preview
Don't miss anything!
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.
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.
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:
positioning is, based on returns. However, there are important deviations in absolute
return with respect to positioning score bucket. (Exhibit A)
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.
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.
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.
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.
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
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.