7 Ratios To Find The Best Companies To Sell Short by Tim du Toit, Quant Investing

Do you sell individual stocks short?

If you do in this article I have put together a list of ratios and indicators that can help you find companies to short.

I also show you exactly where to find them (screenshots) and how to use them.

How many shares have been sold short?

Short interest percentage

Short Interest percentage (Short Int % in the screener) tells you how many of the company’s shares have already been sold short.

It is calculated as the number of shares sold short divided by the total number of shares outstanding.

This information is only available for companies listed in the USA.

Short interest percentage change

Short Interest Percentage Change (Short Int % Change in the screener) tells you what the change in the company’s shares sold short has been over the past three months.

It is calculated as Current Short Int % – Short Int % 3 months ago. For example, if the current Short Int % is 10% and the Short Int % three months ago was 30% the Short Int % Change is -20%.

This information is also only available for companies listed in the USA.

Where can you find it?

Both ratios can be used as a main screening factor in the screener. You can find them under the Quality heading as shown below.

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Select your output

You can also select Short Interest percentage and Short Interest Percentage Change as output columns of the screener.

Both ratios can be found under the General Info heading as shown below.

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Select companies with the highest short interest

To select companies with the highest short interest use the low side of the slider. This means 0% to 10% will give you a list of companies with the highest percentage of their shares sold short.

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Or use the funnel

You can of course also use the column filter (the small funnel icon below the column heading) to select companies with high short interest.

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Check your long ideas

Remember short interest can not only help you if you are a short seller.

Short sellers are some of the best analysts there are, this means if a large percentage of the shares of a company you are thinking of buying has been sold short I strongly suggest you take another close look at your analysis.

Find companies with manipulated financial statements

The Beneish M-Score

Messod Beneish, an accounting professor at Indiana University’s Kelley School of Business, outlined a quantitative approach to detecting financial statement manipulation in his 1999 paper “The Detection of Earnings Manipulation.”

He based his model on forensic accounting principles, calling it the “probability of manipulation”, “PROBM” model or the Beneish M-Score.

Prof Beneish collected a sample of known earnings manipulating companies. Then he identified their main characteristics and used those characteristics to create a model for detecting manipulation.

Does it detect manipulators?

In back tests the M-Score identified approximately half of the companies involved in earnings manipulation before they were discovered.

The M-Score also correctly identified, ahead of time, 12 of the 17 highest-profile fraud cases in the period 1998 to 2002.

The M-Score can help your returns

In a back test the Beneish M-Score consistently improved stock returns from 1993 to 2007.

During this 15-year period stocks that were identified as potential earnings manipulators returned 9.7% less than stocks that were not identified.

Larger than -1.78 is bad

A M-Score score greater than -1.78 indicates a strong likelihood of a company being a manipulator.

Where to find it in the screener?

You can easily use the Beneish M-Score when looking for ideas in the screener.

Simply select M-Score (Beneish) as one of the output columns of your screen. You can then use the filter function (click the small funnel icon) to screen out companies with a bad Beneish M-Score.

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Find companies that may go bankrupt

Altman Z-score

The Altman Z-Score formula for predicting bankruptcy was published in 1968 by Edward I. Altman, who was, at the time, an Assistant Professor of Finance at New York University.

The formula can be used to predict the probability that a firm will go into bankruptcy within the next two years.

The Z-score uses a few income statement and balance sheet ratios to measure the financial health of a company. You can find more information on how the Altman Z-Score is calculated in the Glossary under the heading Z-Score.

How to interpret the Z-Score

The Z-Score values should be interpreted as follows:

  • Z-score of greater than 2.99 = Safe
  • Z-score between 1.8 and 2.99 = Middle or grey
  • Z-score smaller than 1.80 = Distress

The Altman Z-Score in the screener

You can easily use the Altman Z-Score in any of your favourite screens.

Simply select Altman Z-Score as one of the output columns of your screen. You can then use the filter function (click the funnel icon) to screen out companies with a bad Z-Score of less than 1.80.

In the screenshot below you can see that the Z-Score values are colour coded so you can easily see what is good (green), middle is (black) and distress (Red).

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Can a company repay its debt?

Free cash flow to debt ratio

The free cash flow (FCF) to long-term debt ratio indicates how long it will take a company to pay back its outstanding debt, given its current level of FCF generation.

The ratio thus gives you an indication of the financial health of the company.

Good ratio to generate out-performance

A research study in the book Quantitative Strategies for Achieving Alpha showed that the 20% of companies with a high value of FCF to debt (companies that could easily pay back their debt) would have given you substantially better returns than the market and, as you can imagine, substantially better returns than companies with a low FCF to debt ratio.

To sell short you are thus looking for companies with a weak FCF to debt ratio.

FCF to debt in the screener

You can use FCF to debt ration as one of the four filters in the screener.  Under the heading Quality select Fcf to debt as shown below.

You can also use the column filter function to select only companies with a bad FCF to Debt ratio as shown below.

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Are they cooking the books? – the C-Score

In June 2008 James Montier published a very interesting paper called Cooking the books, or, more sailing under the black flag.

In the article he wrote about the so-called C-Score he developed to identify companies that were cooking the books and how you can use the C-Score to find companies to short.

He developed the C-Score to determine how many of six common earnings manipulating ratios a company is engaging in.

How is the C-Score calculated?

This is how the C-Score is calculated.

It has six inputs, each designed to identify a part of common earnings manipulation:

  1. A growing difference between net income and cash flow from operations.
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