With the market down some 35% this year, the question of the performance of companies that have been beaten down hard becomes very relevant. On the one hand is the conventional wisdom that those that are down will be sold hard in the remainder of the year to realize losses before the end of the service year. On the other hand is the well known effect for companies that are beaten down hard to rally during the first month of the year, in conjunction with a bounceback that in some years has been as much as 50% in the first month. There are so many variables involved in such a study that it is hard to do anything scientific. As a start, I looked at the NASDAQ 100 companies as of November 2007. They were down an average of 5% that year. I compared the performance in the next month of the 10 best performing companies to the 10 worst performing companies as follows:

Performance of 10 best and 10 worst in December 2007

10 best                                 10 worst

company dec 07 perf        company dec 07 perf

Top 10 / Bottom 10

mean 0.5                               mean  -1

The change in NASDAQ 100 for December 07 was -1%. There was thus, no significant difference between the performance of these companies in 2007 although there is some small evidence that except for LEAP, which was the worst in the first 11 months but the best in the last, that the 10 worst performed worse than the 10 best. There was survivor bias in this study since I worked with the performance of the current NASDAQ 100. Other studies one would want to see would be the performance of companies as a function of their goodwill to market value ratios. One would hypothesize that retrospectively those companies with high goodwill to market value ratios would have performed significantly worse than the average during the past several years as goodwill is prone to be valueless in a declining market. Also, with the performance of IPOs in the current environment one would hypothesize that their subsequent performance would be well above average. More studies of this nature are in order.

Michael Pomada writes:

Mr / Mrs DudeA quick & dirty study on the top and bottom 20 turkeys/non-turkeys in the sp500 from 1992-present. This is based on the sp500 membership list as of the beginning of each year, so it disregards changes intrayear, but is adjusted annually. Also, I only include stocks with px>$5, mktcap>$100MM & avg volume >100k shs/day as of November 30. I rank the stocks that meet these criteria on the last trading day of November and here I report the average return of those 20 stocks YTD, then Nov30 to Dec31, then Dec31 to approx Jan15.

So, bottom line,  the 20 stocks that have performed worst YTD shed on avg another -19bps while the best add 2.81% until the end of the year. This is then reversed during the first couple of weeks of Jan with the worst adding +2.59% and the best only adding +40bps.

WORST20 TURKEYS    AVG     SD   CNT        WINpct   Zscr        Zdrft
RETjan-nov30           -43.54   16.66    16            0.00%  -10.45

RETnov30-dec31        -0.19     5.65    16            56.25%  -0.14     -1.00

RETdec31-jan15          2.59     11.38   16           62.50%    0.91      0.89

BEST20 TURKEYS       AVG    SD      CNT         WINpct  Zscr        Zdrft
RETjan-nov30           99.21   38.06     16          100.00%   10.43

RETnov30-dec 31      2.81       5.50     16           68.75%     2.04       1.15

RETdec31-jan15        0.40       5.77     16           56.25%     0.27       0.24

S&P                              AVG    SD       CNT          WINpct   Zscr
SPRETnov30-dec31    1.22     2.95       16            75.00%    1.66

SPRETdec31-jan15     0.05     2.70       16            56.25%    0.08

The 20 worst turkeys on the menu this year: ticker and retYTD:

20 Worst

It must be noted that the "best turkeys" list will always be skewed by pending acquisitions — particularly in a year such as 2008 when most stocks are down YTD. One would need to exclude these stocks. Also, inevitably the resulting portfolios will be heavily exposed to particularly industries (like financials, oil & construction stocks this year) which changes the risk profile of the portfolio such that the S&P is not an accurate comparison for the drift.


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