On Finding Neglected Companies

On Finding Neglected Companies

David J. Merkel

While at RealMoney, I wrote a short series on data-mining.  Copies of the articles are here: (onetwo). I enjoyed writing them, and the most pleasant surprise was the favorable email from readers and fellow columnists. As a follow up, on April 13th, 2005, I wrote an article on analyst coverage — and neglect. Today, I am writing the same article but as of today, with even more detail, and comparisons to prior analyses.

As it was, in my Finacorp years, I wrote a similar piece to this but it has been lost; I can’t find a copy of it, and Finacorp is in the ash-heap of financial firms. (Big heap, that.)

For a variety of reasons, sell-side analysts do not cover companies and sectors evenly. For one, they have biases that are related to how the sell-side analyst’s employer makes money. It is my contention that companies with less analyst coverage than would be expected offer an opportunity to profit for investors who are willing to sit down and analyze these lesser-analyzed companies and sectors.

I am a quantitative analyst, but I try to be intellectually honest about my models and not demand more from them than they can deliver. That’s why I have relatively few useful models, maybe a dozen or so, when there are hundreds of models used by quantitative analysts in the aggregate.

Why do I use so few? Many quantitative analysts re-analyze (torture) their data too many times, until they find a relationship that fits well. These same analysts then get surprised when the model doesn’t work when applied to the real markets, because of the calculated relationship being a statistical accident, or because of other forms of implementation shortfall — bid-ask spreads, market impact, commissions, etc.

This is one of the main reasons I tend not to trust most of the “advanced” quantitative research coming out of the sell side. Aside from torturing the data until it will confess to anything (re-analyzing), many sell-side quantitative analysts don’t appreciate the statistical limitations of the models they use. For instance, ordinary least squares regression is used properly less than 20% of the time in sell-side research, in my opinion.

Sell-side firms make money two ways.They can make via executing trades, so volume is a proxy for profitability.They can make money by helping companies raise capital, and they won’t hire firms that don’t cover them.Thus another proxy for profitability is market capitalization.

Thus trading volume and market capitalization are major factors influencing analyst coverage. Aside from that, I found that the sector a company belongs to has an effect on the number of analysts covering it.

I limited my inquiry to include companies that had a market capitalization of over $10 million, US companies only, and no ETFs.

I used ordinary least squares regression covering a data set of 4,604 companies. The regression explained 82% of the variation in analyst coverage. Each of the Volume and market cap variables used were significantly different from zero at probabilities of less than one in one million. As for the sector variables, they were statistically significant as a group, but not individually.Here’s a list of the variables:

Variable  Coefficients  Standard Error  t-Statistic
 Logarithm of 3-month average volume  0.57 0.04  15.12
 Logarithm of Market Capitalization  (2.22) 0.15 (14.69)
 Logarithm of Market Capitalization, squared  0.36 0.01  31.42
 Basic Materials  (0.53) 0.53  (1.01)
 Capital Goods  0.39 0.54  0.74
 Conglomerates  (0.70) 1.95  (0.36)
 Consumer Cyclical  0.08 0.55  0.14
 Consumer Non-Cyclical  (1.40) 0.55  (2.52)
 Energy  2.56 0.53  4.87
 Financial  0.37 0.48  0.78
 Health Care  0.05 0.50  0.11
 Services  (0.30) 0.49  (0.61)
 Technology  0.82 0.49  1.67
 Transportation  2.92 0.66  4.40
 Utilities  (1.10) 0.60  (1.82)


In short, the variables that I used contained data on market capitalization, volume and market sector.

An increasing market capitalization tends to attract more analysts. At a market cap of $522 million, market capitalization as a factor adds no net analysts. At the highest market cap in my study, Apple [AAPL] at $469 billion, the model indicates that 11 fewer analysts should cover the company. The smallest companies in my study would have 3.3 fewer analysts as compared with a company with a market cap of $522 million.


Market Cap  Analyst additions
 10.00  2.30
 30.00  3.40
100.00  4.61
300.00  5.70
522.20  6.26
 1,000.00  6.91
 3,000.00  8.01
10,000.00  9.21
30,000.00  10.31
100,000.00  11.51
300,000.00  12.61
469,400.30  13.06


The intuitive reasoning behind this is that larger companies do more capital markets transactions. Capital markets transactions are highly profitable for investment banks, so they have analysts cover large companies in the hope that when a company floats more stock or debt, or engages in a merger or acquisition, the company will use that investment bank for the transaction.

Investment banks also make some money from trading. Access to sell-side research is sometimes limited to those who do enough commission volume with the investment bank. It’s not surprising that companies with high amounts of turnover in their shares have more analysts covering them. The following table gives a feel for how many additional analysts cover a company relative to its daily trading volume. A simple rule of thumb is that (on average) as trading volume quintuples, a firm gains an additional analyst, and when trading volume falls by 80%, it loses an analyst.


Daily Trading Volume (3 mo avg) Analyst Additions
3 0.6
10 1.3
30 1.9
100 2.6
300 3.2
1,000 3.9
3,000 4.5
10,000 5.2
30,000 5.8
100,000 6.5
300,000 7.1
1,000,000 7.8
3,000,000 8.4
4,660,440 8.7


An additional bit of the intuition for why increased trading volume attracts more analysts is that volume is in one sense a measure of disagreement. Investors disagree about the value of a stock, so one buys what another sells. Sell-side analysts note this as well; stocks with high trading volumes relative to their market capitalizations are controversial stocks, and analysts often want to make their reputation by getting the analysis of a controversial stock right. Or they just might feel forced to cover the stock because it would look funny to omit a controversial company.

Analyst Neglect

The first two variables that I considered, market capitalization and volume, have intuitive stories behind them as to why the level of analysts ordinarily varies. But analyst coverage also varies by industry sector, and the reasons are less intuitive to me there.


Please note that my regression had no constant term, so the constant got embedded in the industry factors. Using the Transportation sector as a benchmark makes the analysis easier to explain. Here’s an example: On average, a Utilities company that has the same market cap and trading volume as a Transportation company would attract four fewer analysts.


Sector  Addl Analysts  Fewer than Transports
 Transportation  2.92
 Energy  2.56  (0.37)
 Technology  0.82  (2.10)
 Capital Goods  0.39  (2.53)
 Financial  0.37  (2.55)
 Consumer Cyclical  0.08  (2.84)
 Health Care  0.05  (2.87)
 Services  (0.30)  (3.22)
 Basic Materials  (0.53)  (3.46)
 Conglomerates  (0.70)  (3.63)
 Utilities  (1.10)  (4.02)
 Consumer Non-Cyclical  (1.40)  (4.32)


Why is that? I can think of two reasons. First, the companies in the sectors at the top of my table are perceived to have better growth prospects than those at the bottom. Second, the sectors at the top of the table are more volatile than those toward the bottom (though basic materials would argue against that). As an aside, companies in the conglomerates sector get less coverage because they are hard for a specialist analyst to understand.


My summary reason is that “cooler” sectors attract more analysts than duller sectors. To the extent that this is the common factor behind the variation of analyst coverage across sectors, I would argue that sectors toward the bottom of the list are unfairly neglected by analysts and may offer better opportunities for individual investors to profit through analysis of undercovered companies in those sectors.

Malign Neglect

Now, my model did not explain 100% of the variation in analyst coverage. It explained 82%, which leaves 18% unexplained. Some of the unexplained variation is due to the fact that no model can be perfect. But the unexplained variation can be used to reveal the companies that my model predicted most poorly. Why is that useful? If my model approximates “the way the world should be,” then the degree of under- and over-coverage by analysts will reveal where too many or few analysts are looking. The following tables lists the largest company variations between reality and my model, split by market cap group.


Behemoth Stocks


Ticker Company Sector Excess analysts
BRK.A Berkshire Hathaway Inc. 07 – Financial (25.75)
GE General Electric Company 02 – Capital Goods (20.47)
XOM Exxon Mobil Corporation 06 – Energy (19.32)
CVX Chevron Corporation 06 – Energy (14.64)
PFE Pfizer Inc. 08 – Health Care (14.57)
MRK Merck & Co., Inc. 08 – Health Care (12.76)
GOOG Google Inc 10 – Technology (11.44)
JNJ Johnson & Johnson 08 – Health Care (11.39)
MSFT Microsoft Corporation 10 – Technology (10.39)
PM Philip Morris International In 05 – Consumer Non-Cyclical (10.21)


Too many


Ticker Company Sector Excess analysts
V Visa Inc 09 – Services  2.58
DIS Walt Disney Company, The 09 – Services  2.95
SLB Schlumberger Limited. 06 – Energy  4.15
CSCO Cisco Systems, Inc. 10 – Technology  5.22
QCOM QUALCOMM, Inc. 10 – Technology  5.34
ORCL Oracle Corporation 10 – Technology  5.98
FB Facebook Inc 10 – Technology  8.28
AMZN Amazon.com, Inc. 09 – Services  9.34
AAPL Apple Inc. 10 – Technology  10.57
INTC Intel Corporation 10 – Technology  11.85


Large Cap Stocks


Ticker Company Sector Excess analysts
SPG Simon Property Group Inc 09 – Services (16.15)
BF.B Brown-Forman Corporation 05 – Consumer Non-Cyclical (16.03)
LUK Leucadia National Corp. 07 – Financial (15.93)
L Loews Corporation 07 – Financial (15.90)
EQR Equity Residential 09 – Services (15.87)
ARCP American Realty Capital Proper 09 – Services (15.75)
IEP Icahn Enterprises LP 09 – Services (15.50)
LVNTA Liberty Interactive (Ventures 09 – Services (15.36)
ABBV AbbVie Inc 08 – Health Care (15.01)
GOM CL Ally Financial Inc 07 – Financial (14.87)


Too Many


Ticker Company Sector Excess analysts
UA Under Armour Inc 04 – Consumer Cyclical  16.68
BRCM Broadcom Corporation 10 – Technology  17.29
RRC Range Resources Corp. 06 – Energy  17.33
SWN Southwestern Energy Company 06 – Energy  17.70
RHT Red Hat Inc 10 – Technology  18.08
NTAP NetApp Inc. 10 – Technology  19.82
CTXS Citrix Systems, Inc. 10 – Technology  19.84
COH Coach, Inc. 09 – Services  20.87
VMW VMware, Inc. 10 – Technology  21.60
CRM salesforce.com, inc. 10 – Technology  22.64


Mid cap stocks


Ticker Company Sector Excess analysts
FNMA Federal National Mortgage Assc 07 – Financial (13.84)
UHAL AMERCO 11 – Transportation (12.23)
O Realty Income Corp 09 – Services (12.06)
CIM Chimera Investment Corporation 07 – Financial (11.49)
SLG SL Green Realty Corp 09 – Services (11.46)
NRF Northstar Realty Finance Corp. 09 – Services (11.34)
FMCC Federal Home Loan Mortgage Cor 07 – Financial (11.14)
EXR Extra Space Storage, Inc. 11 – Transportation (10.97)
KMR Kinder Morgan Management, LLC 06 – Energy (10.94)
CWH CommonWealth REIT 09 – Services (10.51)


Too Many


Ticker Company Sector Excess analysts
AEO American Eagle Outfitters 09 – Services  17.00
DRI Darden Restaurants, Inc. 09 – Services  17.40
RVBD Riverbed Technology, Inc. 10 – Technology  17.50
CMA Comerica Incorporated 07 – Financial  17.74
GPN Global Payments Inc 07 – Financial  18.30
WLL Whiting Petroleum Corp 06 – Energy  19.67
DO Diamond Offshore Drilling Inc 06 – Energy  21.57
URBN Urban Outfitters, Inc. 09 – Services  24.06
RDC Rowan Companies PLC 06 – Energy  24.48
ANF Abercrombie & Fitch Co. 09 – Services  26.02



Small cap stocks


Ticker Company Sector Excess analysts
BALT Baltic Trading Ltd 11 – Transportation  (7.96)
ERA Era Group Inc 11 – Transportation  (7.45)
PBT Permian Basin Royalty Trust 06 – Energy  (7.42)
SDR SandRidge Mississippian Trust 06 – Energy  (7.18)
PHOT Growlife Inc 02 – Capital Goods  (6.79)
SBR Sabine Royalty Trust 06 – Energy  (6.74)
CAK CAMAC Energy Inc 06 – Energy  (6.64)
FITX Creative Edge Nutrition Inc 09 – Services  (6.57)
BLTA Baltia Air Lines Inc 11 – Transportation  (6.53)
VHC VirnetX Holding Corporation 10 – Technology  (6.49)


Too many


Ticker Company Sector Excess analysts
WLT Walter Energy, Inc. 06 – Energy  12.19
ANGI Angie’s List Inc 10 – Technology  12.31
FRAN Francesca’s Holdings Corp 09 – Services  12.58
ZUMZ Zumiez Inc. 09 – Services  13.49
GDP Goodrich Petroleum Corp 06 – Energy  15.02
DNDN Dendreon Corporation 08 – Health Care  15.89
ACI Arch Coal Inc 06 – Energy  16.04
HERO Hercules Offshore, Inc. 06 – Energy  16.19
AREX Approach Resources Inc. 06 – Energy  17.64
ARO Aeropostale Inc 09 – Services  20.80


Microcap Stocks


Ticker Company Sector Excess analysts
SGLB Sigma Labs Inc 06 – Energy  (6.18)
AEGY Alternative Energy Partners In 10 – Technology  (5.97)
WPWR Well Power Inc 06 – Energy  (5.83)
TTDZ Triton Distribution Systems In 10 – Technology  (5.53)
SFRX Seafarer Exploration Corp 11 – Transportation  (5.15)
PTRC Petro River Oil Corp 06 – Energy  (4.99)
UTRM United Treatment CentersInc 08 – Health Care  (4.82)
BIEL Bioelectronics Corp 08 – Health Care  (4.80)
DEWM Dewmar International BMC Inc 01 – Basic Materials  (4.74)
FEEC Far East Energy Corp 06 – Energy  (4.61)


Too many


Ticker Company Sector Excess analysts
PRSS CafePress Inc 09 – Services  3.99
SANW S&W Seed Company 05 – Consumer Non-Cyclical  4.03
KIOR KiOR Inc 01 – Basic Materials  4.06
PRXG Pernix Group Inc 02 – Capital Goods  4.08
EYNON Entergy New Orleans, Inc. 12 – Utilities  4.17
PARF Paradise, Inc. 05 – Consumer Non-Cyclical  4.40
SUMR Summer Infant, Inc. 05 – Consumer Non-Cyclical  4.52
LAND Gladstone Land Corp 05 – Consumer Non-Cyclical  4.57
JRCC James River Coal Company 06 – Energy  6.38
GNK Genco Shipping & Trading Limit 11 – Transportation  7.11

My advice to readers is to consider buying companies that have fewer analysts studying them than the model would indicate.  This method is certainly not perfect but it does point out spots where Wall Street is not focusing its efforts, and might provide some opportunities.

About bambooinnovator
Kee Koon Boon (“KB”) is the co-founder and director of HERO Investment Management which provides specialized fund management and investment advisory services to the ARCHEA Asia HERO Innovators Fund (www.heroinnovator.com), the only Asian SMID-cap tech-focused fund in the industry. KB is an internationally featured investor rooted in the principles of value investing for over a decade as a fund manager and analyst in the Asian capital markets who started his career at a boutique hedge fund in Singapore where he was with the firm since 2002 and was also part of the core investment committee in significantly outperforming the index in the 10-year-plus-old flagship Asian fund. He was also the portfolio manager for Asia-Pacific equities at Korea’s largest mutual fund company. Prior to setting up the H.E.R.O. Innovators Fund, KB was the Chief Investment Officer & CEO of a Singapore Registered Fund Management Company (RFMC) where he is responsible for listed Asian equity investments. KB had taught accounting at the Singapore Management University (SMU) as a faculty member and also pioneered the 15-week course on Accounting Fraud in Asia as an official module at SMU. KB remains grateful and honored to be invited by Singapore’s financial regulator Monetary Authority of Singapore (MAS) to present to their top management team about implementing a world’s first fact-based forward-looking fraud detection framework to bring about benefits for the capital markets in Singapore and for the public and investment community. KB also served the community in sharing his insights in writing articles about value investing and corporate governance in the media that include Business Times, Straits Times, Jakarta Post, Manual of Ideas, Investopedia, TedXWallStreet. He had also presented in top investment, banking and finance conferences in America, Italy, Sydney, Cape Town, HK, China. He has trained CEOs, entrepreneurs, CFOs, management executives in business strategy & business model innovation in Singapore, HK and China.

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