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Issue No. 53 – November 21, 2005 Prescott, Arizona Systems@WiserTrader.com
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1.0 Trading
A relationship has been developed between five stock valuations needed to minimize the probability of loss and ratios to their industry averages.
1.1 Stock Valuations and Industry Averages
The past year has shown that stock prices behave in sometimes surprising ways to valuation metrics. The response to each metric changes with the character of the market and is sensitive to the industry involved. When combining several metrics into a single filter, one can try to determine ranges that result in the greatest gains, gains above a certain percentage value or the minimum probability of loss.
Table 1 compares absolute valuation metrics needed to minimize the probability of loss with ratios to their industry average. While price response to absolute values in the table may vary widely over time, price response to ratios to industry averages should vary less, being sensitive to differences between industries.
Table 1
Valuation Metrics to Minimize the Probability of Loss
|
Valuation Metric |
Stock Value That Reduced Risk of Loss |
Corresponding Ratio of Stock Value to Industry Value |
|
Price to Earnings (PE) |
(7) 10 to 15 |
0.5 to 0.75 (1) |
|
Price to Book (PB) |
< 1 |
0.5 to 0.75 (1) |
|
Price to Sales (PS) |
< 0.5 (1) |
0.5 to 0.75 (1) |
|
Return on Equity (ROE) |
10 to 20 |
2 to 3 |
|
Price to Free Cash Flow (P-FCF) |
< 5 (10) |
(0.5) 0.75 to 1 |
© 2005 Desert Mountain Systems, LLC. Members of wisertrader.com are neither licensed brokers nor licensed advisors. Trades discussed represent recommendations made by the editor for the wisertrader.com portfolio. The newsletter and web site are for information only and should not be considered as personal advice. While it is believed that the posted information is factual, mistakes can be made in transcription. Investors should trade stocks only after verifying all information and consulting with a licensed broker or adviser. Desert Mountain Systems markets third party trading systems but has no other affiliation with trading system companies.
In this study the minimum probability of loss was chosen for two reasons. One is that when losses are minimized, profits tend to take care of themselves. The second is that filters that focus on gains are often so exclusive that no stocks pass the screens. In fact the value ranges in Table 1 may need to be expanded using the numbers in parenthesis in order to find any stocks that pass the screens.
Without expanding the ranges and applying absolute values for the filter to year ago market data, only one stock passed the screen, Smith Motor Xpress (NASDAQ: SMXC). The stock priced at $7.75 gained 50% over the past year after a maximum intermediate decline of 24%.
With the expanded absolute value filter (using values in parenthesis), 14 stocks passed the screen and the average gain after 1 year without a stop loss was 29%. Using, for this case, an optimum 35% stop loss the average gain was 31.9%.
Again without expanding the ranges and applying industry relative values for the filter to year ago market data, only one stock passed that screen, as well, Astro-Med, Inc (NASDAQ: ALOT). The stock priced at $10.71 gained 29% over the past year after a maximum intermediate decline of 1%.
With the expanded industry relative filter (using values in parenthesis), 9 stocks passed the screen and the average gain after 1 year without a stop loss was 29.8%. For this case it is interesting to note that the best stop loss setting of 40% resulted in an average profit of only 29.4%
This exercise is forcing me to reevaluate the use of stop losses when it comes to value stock investing. Before drawing too many conclusions from the above results a commitment should be made to continuously monitor the performance of the filters both on year ago data and on current stock selections. The character of the one year price gain histograms, from which the filters were developed, should also be periodically monitored on a quarterly basis.
2.0 Market Analysis
Slightly bullish sentiment continues for the fourth straight weekly gain for the S&P500 index. Inflation data was neutral, housing was softer than expected, retail sales were good, earnings were good and a raft of stock buy backs and acquisitions left the market with an overall positive bias.
Key industry ETF’s in Table 2A are Biotech and Semiconductors which confirm the NASDAQ. Transportation confirms the Dow Jones Industrial Average according to Dow Theory. Banking and Financials are confirming indicators for the S&P 500. Gold and Real Estate are respective indicators for the inverse health of the currency and the capacity for consumer spending.
Table 2A
Indices, Key Industry ETF’s and Sector SPDR’s
|
|
1 month |
1 wk ago |
2 wks ago |
3 wks ago |
4 wks ago |
|
Dow Jones Industrials |
5.4% |
0.8% |
1.5% |
1.2% |
1.8% |
|
NASDAQ |
7.0% |
1.1% |
1.5% |
3.8% |
0.4% |
|
S & P 500 |
5.8% |
1.1% |
1.2% |
1.8% |
1.6% |
|
Russell 2000 |
6.2% |
0.8% |
1.3% |
3.6% |
0.4% |
|
GLD, Gold |
4.2% |
3.5% |
2.8% |
-3.6% |
1.5% |
|
RKH, Banking |
6.3% |
-0.6% |
3.7% |
0.4% |
2.6% |
|
IYT, Transportation |
14.7% |
1.7% |
3.8% |
5.4% |
3.0% |
|
SMH, Semiconductors |
5.0% |
1.4% |
2.0% |
5.9% |
-4.1% |
|
BBH, Biotechnology |
13.5% |
2.0% |
2.3% |
6.0% |
2.6% |
|
IYR, Real Estate |
7.0% |
1.2% |
4.4% |
-1.2% |
2.5% |
|
OIH, Oil |
9.9% |
4.9% |
-4.7% |
3.2% |
6.5% |
|
XLE, Energy |
6.6% |
3.3% |
-4.1% |
2.5% |
4.9% |
|
XLU, Utilities |
2.7% |
1.7% |
-0.8% |
-0.5% |
2.3% |
|
XLB, Materials |
10.5% |
2.4% |
2.7% |
1.3% |
3.6% |
|
XLI, Industrial |
6.9% |
2.5% |
1.5% |
1.5% |
1.2% |
|
XLK, Technology |
6.8% |
1.9% |
1.2% |
3.4% |
0.2% |
|
XLV, Healthcare |
3.4% |
0.6% |
1.4% |
1.3% |
0.1% |
|
XLF, Financials |
8.0% |
0.3% |
3.2% |
1.1% |
3.2% |
|
XLP, Consumer Staples |
2.3% |
-0.4% |
1.4% |
0.4% |
0.9% |
|
XLY, Consumer Discretionary |
6.0% |
0.5% |
1.4% |
3.9% |
0.1% |
