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  0.57 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

1, 23  - View Full Page