Value Investing

Analyst Neglect And Finding Neglected Companies

Analyst Neglect And Finding Neglected Companies by David Merkel, CFA of Aleph Blog

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 0.570.04 15.12
 Logarithm of Market Capitalization (2.22)0.15(14.69)
 Logarithm of Market Capitalization, squared 0.360.01 31.42
 Basic Materials (0.53)0.53 (1.01)
 Capital Goods 0.390.54 0.74
 Conglomerates (0.70)1.95 (0.36)
 Consumer Cyclical 0.080.55 0.14
 Consumer Non-Cyclical (1.40)0.55 (2.52)
 Energy 2.560.53 4.87
 Financial 0.370.48 0.78
 Health Care 0.050.50 0.11
 Services (0.30)0.49 (0.61)
 Technology 0.820.49 1.67
 Transportation 2.920.66 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


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


TickerCompanySectorExcess analysts
BRK.ABerkshire Hathaway Inc.07 – Financial(25.75)
GEGeneral Electric Company02 – Capital Goods(20.47)
XOMExxon Mobil Corporation06 – Energy(19.32)
CVXChevron Corporation06 – Energy(14.64)
PFEPfizer Inc.08 – Health Care(14.57)
MRKMerck & Co., Inc.08 – Health Care(12.76)
GOOGGoogle Inc10 – Technology(11.44)
JNJJohnson & Johnson08 – Health Care(11.39)
MSFTMicrosoft Corporation10 – Technology(10.39)
PMPhilip Morris International In05 – Consumer Non-Cyclical(10.21)


Too many


TickerCompanySectorExcess analysts
VVisa Inc09 – Services 2.58
DISWalt Disney Company, The09 – Services 2.95
SLBSchlumberger Limited.06 – Energy 4.15
CSCOCisco Systems, Inc.10 – Technology 5.22
QCOMQUALCOMM, Inc.10 – Technology 5.34
ORCLOracle Corporation10 – Technology 5.98
FBFacebook Inc10 – Technology 8.28, Inc.09 – Services 9.34
AAPLApple Inc.10 – Technology 10.57
INTCIntel Corporation10 – Technology 11.85


Large Cap Stocks


TickerCompanySectorExcess analysts
SPGSimon Property Group Inc09 – Services(16.15)
BF.BBrown-Forman Corporation05 – Consumer Non-Cyclical(16.03)
LUKLeucadia National Corp.07 – Financial(15.93)
LLoews Corporation07 – Financial(15.90)
EQREquity Residential09 – Services(15.87)
ARCPAmerican Realty Capital Proper09 – Services(15.75)
IEPIcahn Enterprises LP09 – Services(15.50)
LVNTALiberty Interactive (Ventures09 – Services(15.36)
ABBVAbbVie Inc08 – Health Care(15.01)
GOM CLAlly Financial Inc07 – Financial(14.87)


Too Many


TickerCompanySectorExcess analysts
UAUnder Armour Inc04 – Consumer Cyclical 16.68
BRCMBroadcom Corporation10 – Technology 17.29
RRCRange Resources Corp.06 – Energy 17.33
SWNSouthwestern Energy Company06 – Energy 17.70
RHTRed Hat Inc10 – Technology 18.08
NTAPNetApp Inc.10 – Technology 19.82
CTXSCitrix Systems, Inc.10 – Technology 19.84
COHCoach, Inc.09 – Services 20.87
VMWVMware, Inc.10 – Technology 21.60, inc.10 – Technology 22.64


Mid cap stocks


TickerCompanySectorExcess analysts
FNMAFederal National Mortgage Assc07 – Financial(13.84)
UHALAMERCO11 – Transportation(12.23)
ORealty Income Corp09 – Services(12.06)
CIMChimera Investment Corporation07 – Financial(11.49)
SLGSL Green Realty Corp09 – Services(11.46)
NRFNorthstar Realty Finance Corp.09 – Services(11.34)
FMCCFederal Home Loan Mortgage Cor07 – Financial(11.14)
EXRExtra Space Storage, Inc.11 – Transportation(10.97)
KMRKinder Morgan Management, LLC06 – Energy(10.94)
CWHCommonWealth REIT09 – Services(10.51)


Too Many


TickerCompanySectorExcess analysts
AEOAmerican Eagle Outfitters09 – Services 17.00
DRIDarden Restaurants, Inc.09 – Services 17.40
RVBDRiverbed Technology, Inc.10 – Technology 17.50
CMAComerica Incorporated07 – Financial 17.74
GPNGlobal Payments Inc07 – Financial 18.30
WLLWhiting Petroleum Corp06 – Energy 19.67
DODiamond Offshore Drilling Inc06 – Energy 21.57
URBNUrban Outfitters, Inc.09 – Services 24.06
RDCRowan Companies PLC06 – Energy 24.48
ANFAbercrombie & Fitch Co.09 – Services 26.02



Small cap stocks


TickerCompanySectorExcess analysts
BALTBaltic Trading Ltd11 – Transportation (7.96)
ERAEra Group Inc11 – Transportation (7.45)
PBTPermian Basin Royalty Trust06 – Energy (7.42)
SDRSandRidge Mississippian Trust06 – Energy (7.18)
PHOTGrowlife Inc02 – Capital Goods (6.79)
SBRSabine Royalty Trust06 – Energy (6.74)
CAKCAMAC Energy Inc06 – Energy (6.64)
FITXCreative Edge Nutrition Inc09 – Services (6.57)
BLTABaltia Air Lines Inc11 – Transportation (6.53)
VHCVirnetX Holding Corporation10 – Technology (6.49)


Too many


TickerCompanySectorExcess analysts
WLTWalter Energy, Inc.06 – Energy 12.19
ANGIAngie’s List Inc10 – Technology 12.31
FRANFrancesca’s Holdings Corp09 – Services 12.58
ZUMZZumiez Inc.09 – Services 13.49
GDPGoodrich Petroleum Corp06 – Energy 15.02
DNDNDendreon Corporation08 – Health Care 15.89
ACIArch Coal Inc06 – Energy 16.04
HEROHercules Offshore, Inc.06 – Energy 16.19
AREXApproach Resources Inc.06 – Energy 17.64
AROAeropostale Inc09 – Services 20.80


Microcap Stocks


TickerCompanySectorExcess analysts
SGLBSigma Labs Inc06 – Energy (6.18)
AEGYAlternative Energy Partners In10 – Technology (5.97)
WPWRWell Power Inc06 – Energy (5.83)
TTDZTriton Distribution Systems In10 – Technology (5.53)
SFRXSeafarer Exploration Corp11 – Transportation (5.15)
PTRCPetro River Oil Corp06 – Energy (4.99)
UTRMUnited Treatment CentersInc08 – Health Care (4.82)
BIELBioelectronics Corp08 – Health Care (4.80)
DEWMDewmar International BMC Inc01 – Basic Materials (4.74)
FEECFar East Energy Corp06 – Energy (4.61)


Too many


TickerCompanySectorExcess analysts
PRSSCafePress Inc09 – Services 3.99
SANWS&W Seed Company05 – Consumer Non-Cyclical 4.03
KIORKiOR Inc01 – Basic Materials 4.06
PRXGPernix Group Inc02 – Capital Goods 4.08
EYNONEntergy New Orleans, Inc.12 – Utilities 4.17
PARFParadise, Inc.05 – Consumer Non-Cyclical 4.40
SUMRSummer Infant, Inc.05 – Consumer Non-Cyclical 4.52
LANDGladstone Land Corp05 – Consumer Non-Cyclical 4.57
JRCCJames River Coal Company06 – Energy 6.38
GNKGenco Shipping & Trading Limit11 – 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.


Full disclosure: long BRK/B

Analysts ratings 1 Analyst Neglect
Analyst Neglect