Seth Godin talks about the need to be remarkable, and even bizarre, to grab the attention of the right people. After all you are unlikely to notice a cow on the road unless it is purple in color. I have talked about short interest before in another post.

Let's change the topic to the color purple.

**Internal Struggle**

I'm still not sure what is more important, the days to cover or the percent short interest. In the last post, I compromised and combined the two metrics. The following is my attempt, on a small data sample and with no back testing, to expand on the idea.

If you multiply the percent short interest * days to cover you are actually double counting the first metric.

**a)(Volume Short / Float)**= percent short interest

**b)(Volume Short /**~~Float~~)* (

**Percent Short Interest**

This short sample is a breakdown of one industry by percent short interest. I generated it in www.finviz.com. If I sort by percent short interest column you can see that there are three types of records, with large day to cover ratios. I derived the average, calculated the standard deviation and mapped the percent short interest to a bell curve using standardization.

Click on the Data Table below to see the results. Lets do a comparison to the days to cover; On a bell curve the first 6 lines are above the first standard deviation, with 4 not applying to the usual definition of short interest ratio. "Investopedia indicates a bullish guideline for the days to cover when it is greater than eight days to cover."

Clickable Data Table

Shares Float | Float Short% | Days To Cover | Avg Vol(K) | standardization |

37.47 | 0.1526 | 5.56 | 1029.41 | 1.713946415 |

27.06 | 0.1468 | 30.18 | 131.66 | 1.6065934 |

19.81 | 0.1413 | 6.86 | 408.23 | 1.504793127 |

72.17 | 0.1345 | 15.01 | 646.65 | 1.378930971 |

37.39 | 0.1340 | 4.74 | 1056.48 | 1.369676401 |

81.8 | 0.1253 | 4.86 | 2107.32 | 1.208646878 |

60.76 | 0.0865 | 8.26 | 636.29 | 0.490492225 |

39.5 | 0.0565 | 2.1 | 1063.13 | -0.06478199 |

4.86 | 0.0516 | 19.26 | 13.02 | -0.15547678 |

9.53 | 0.0495 | 9.69 | 48.62 | -0.19434598 |

9.98 | 0.0442 | 10.47 | 42.1 | -0.29244442 |

18.13 | 0.0418 | 2.44 | 311.36 | -0.33686636 |

39.18 | 0.0414 | 4.33 | 374.9 | -0.34427001 |

48.15 | 0.0375 | 5.84 | 309.1 | -0.41645566 |

7.18 | 0.0347 | 25.43 | 9.8 | -0.46828126 |

289.2 | 0.0193 | 1.68 | 3313.68 | -0.75332202 |

386.71 | 0.0110 | 1.3 | 3280.47 | -0.90694789 |

17.5 | 0.0091 | 6.64 | 24.1 | -0.94211525 |

6.72 | 0.0064 | 1.02 | 42.33 | -0.99208993 |

27.04 | 0.0025 | 1.15 | 57.94 | -1.06427558 |

6.59 | 0.0010 | 0.61 | 10.68 | -1.09203929 |

5.96 | 0.0009 | 0.58 | 8.94 | -1.09389021 |

8.29 | 0.0004 | 0.78 | 4.61 | -1.10314478 |

average | 0.06 | |||

stDev | 0.05402736 | |||

variance | 0.002918956 | |||

skew | 0.694462922 |

**Days to Cover (Short Interest Ratio) Data Table**

Now let's generate an excel data chart of days to cover data. Get a list of industry competitors from ValueLine that your equity candidate belongs with. Or use www.barchart.com to get competitors in that industry. Next, get historic days to cover data from the last 12 months.

The candidate averaged around 4 days to cover over the 12 months, and had a standard deviation of about 1.5 days. In other words, my range of 2 standard deviations gave me {+1 to +7 days}.

The industry standard deviation was different. My sample industry had 11 companies, ranging in all sizes and shapes. The bell curve skewed dramatically to the right and gave a mean of ~6 and standard deviation of 5. This would result in a range of {-4 to +16 } for 2 standard deviations.

There were several months where competitors had the 2 and 3 standard deviations barriers breached on the ratio data table. My initial assessment of the candidate's days to cover was done using the "rule of thumb" proposed above by Investopedia. After including the data of it's competitors, the candidates ratio was near the average days to cover and not really shorted by the bears dramatically!

**Conclusion**

My conclusion is a simple one. Make sure that both metrics exceed the normal standard deviation before acting on them. When you see that the days to cover ratio exceeds the normal limits of the bell curve check to see if the %short interest is in this higher standard deviations also.

**A Purple Cow or in other words, what is remarkable about this?**

The 12 month historical days to cover ratio data showed that this particular industry grew more and more bearish as we approached the present environment. Can I conclude that the last 6 weeks downturn have affected the short sellers? That may be stretching the limits of a simple empirical experiment. What should be noted is that the days to cover ratio is a dynamic indicator that changes with the external environment. That what appears to be a high number for one candidate may be normal for that industry, at this current time.

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