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Fund: ETRACS Mthly Pay 2xLev US Sm Cap HiDiv ETN (SMHD)

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SMHD
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Beg. Date
2015-03-12
End. Date
2017-01-31
Avg. Daily Return
0.0730%
Standard Dev.
2.7505%
Day 0 is the last day of the month. Returns are from the prior close to the current close (e.g. the return for day 0 is from close on day -1 to close on day 0).
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Int'l
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Day of MonthAvg RtnStd Devz-ScorePct Pos
-12-0.511.87-1.4930
-11-0.573.12-0.9948
-101.032.132.1557
-91.082.801.7270
-8-0.392.96-0.7639
-70.401.431.0870
-60.492.400.8365
-50.363.510.4057
-4-0.952.05-2.4035
-3-0.372.93-0.7343
-20.433.270.5357
-10.232.100.3752
00.702.191.3657
1-0.071.88-0.3450
2-0.373.31-0.6245
30.732.491.2455
4-0.082.75-0.2641
50.132.510.1141
6-0.683.46-1.0359
7-0.073.49-0.2059
8-0.242.28-0.6536
9-0.603.19-1.0243
10-0.132.62-0.3839
110.613.110.8365
120.552.780.8352


About this page:

This page is designed to help visualize the monthly seasonal pattern that exists in many markets relative to month-end. Many developed markets experience weakness in the mid- to late-month period followed by a strong period going into the end of the month and contiuing through the first few days of the followig month.

Some studies of this effect focus on calendar days. We evaluate this phenomenon from the standpoint of trading days before and after month-end, with time zero defined as the last trading day (last close) of the month. Returns shown are from the prior close through the stated day's close. For example, the return for day -3 is measured from the close of day -4 to the close on day -3, with day -4 being the 4th trading day prior to the last day of the month.

About the z-Score

To put it simply for the non-statisticians, the z-Score is a statistical measure indicating how significantly different one day's average return is from the average of all days. The larger the absolute value of the number, the more likely there is a real difference. The statistical term we are looking at here is the Confidence Level. A z-Score greater than 1.28 indicates an 80% confidence level, 1.64 relates to 90%, and 1.96 relates to 95%. Of course that all assumes a random distribution with independence in the returns which might not apply, but it is likely better than nothing.

Mathematically the z-Score is:
     ([Avg Rtn for Day] - [Avg Rtn for symbol])  
  ([Std Dev for Day] / Sqrt([Number of observations]))









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