During down periods in the stock market (and
September is historically the worst month of the year), it pays to watch the
market, as well as develop a watch list of quality stocks, in anticipation
of the next market turnaround. I still expect the market to take off but not
until late October. During this period, I like to analyze our methods and
approaches. Today I'll share some of my work designed to improve our
entries into and exits from pullback trades.

Two major approaches to trading stocks utilize
pullbacks and breakouts. The first offers well defined risk management
while the second offers the benefit of no immediate overhead resistance.
Here, I share some of the work that I've been conducting with TradeStation
to better define the risk/profit characteristics of two common pullback
schemes: 5-day minimums and three consecutive lower lows.

Strong stocks (ranked
by earnings and earnings revision fundamentals) that are undervalued, according to their PEG ratios, trend higher
with increasing daily highs until selling pressure builds and then
overwhelms. Here, owners are taking profit, and short sellers are
anticipating an overbought reversal. A multi-day pullback follows,
deepening until increasing buying pressure causes the bullish uptrend to
resume. A stock undergoing this sequence has a chart that resembles a
rising series of hills and valleys (example).
The pullback trader buys the pullback at the point it resumes its upward
trend.

In this study, twenty fundamentally sound
stocks (2 each from 10 major sectors) were back tested over 4.2 years
between 6/14/00 and 8/26/05 (1,534 days or a total of 30,720 days for the 20
stocks). The period encompasses extremes in both bullish and bearish
pressure, a period over which the S&P lost 19 percent of its value. My aim
is to determine the best pullback scheme via both profit and risk (drawdown)
criteria for these type stocks.

Five factors were
explored (two extremes for each) through a fractional factorial
characterization: (1) first 1/2 position profit target taken after either a
2.5 percent or a 5.0 percent gain ($0.50 or $1.00 for a $20 stock, $1.00 or
$2.00 for a $40 stock, $2.00 or $4.00 for a $80 stock); (2) utilizing either
a 5 percent or 10 percent positional stop loss of last resort (like O'Neil's
7.5 percent stop loss where the position is sold out after this stop is
hit); (3) either a 2- or 5-day trailing stop for the 2nd 1/2 profit once
this stop exceeds the initial stop set 11 cents below the pullback trough;
(4) pullback depth either greater than 5 percent or greater than 10 percent
defined by the high 4 days ago and today's low; and (5) pullback type,
either 3 lower lows or a 5-day minimum. Four factors were held constant for
each run: (1) 20-day moving average must be greater than 50-day moving
average (pullback from bullish trend); (2) today's price must be greater
than its 200-day moving average; (3) today's 50-day moving average must be
greater than it was 10 days ago (uptrend); and (4) a 20-day time stop ends
positions that haven't been stopped out otherwise. Each trade invested
$10,000.

The chart below
shows the factor space investigated for the 5 factors and the total profit
generated from trading the 20 stocks. Only half the combinations in the
space are needed to define factor effects. Least-squares modeling (R^{2}
0.96, edf 10, RMSE $3592) defines the following profit map:

` `Total trading profit =
$27,343 +

+ $ 2,181 (when
using a 5.0 percent 1st target)

+ $ 850 (when using a 10.0 percent
positional stop)

+ $ 2,414 (when using a 5-day trailing stop
for 2nd half profit target)

+ $13,946 (when using a >5 percent pullback
depth)

+ $ 324 (when using 5-day minimum pullback
definition)

{for alternate factor settings, reverse the
sign of its coefficient, i.e., increasing the selectivity of the pullback
depth to >10 percent reduces profit by $13,946}

Profit was
maximized by (1) a 5 percent 1st profit target, (2) a 10 percent positional
stop, (3) a 5-day trailing stop for the 2nd half profit target, (4) a >5
percent pullback depth, and (5) the 5-day minimum method. This combination
would have produced a profit of $44,877 (90% on a $50,000 investment) over a
period when the S&P lost 19%.

__Sectors and Stocks Used in
the Optimization__

Sectors (Stocks): Health
Services (AMED, CVH), Diversified Services (ASF, NTRI), Manufacturing (ASVI,
CAT),

Energy (CDIS, HYDL), Specialty Retail (CHS, URBN), Metals & Mining (HW,
POT), Drugs (LIFC, USNA),

Computer Software (MCRS, QSII), Materials & Construction (MTH, TOL),
Consumer Non-Durable (PARL, PVH)

The following table
documents trading characteristics for each of the 16 sets of conditions
identified in the chart above. For example, factor combination 13 generated
$46,797 in profit from 335 winning trades and 201 losing trades (a 1.67
win-to-loss ratio), and the average, maximum trade drawdown was $718
producing a 3.26 profit-to-drawdown ratio. Ideally, one would like to
maximize profit and maximize both win-to-loss and profit-to-drawdown
ratios.

Factor combination
13 offers the best combination of profit and profit-to-drawdown ratio,
though the ratio of winning-to-losing trades is one of the third worst.
With the exception of the final positional stop at 5 percent, the settings
are those described in the above least-squares model. Its average max
drawdown at $718 is 1.4 percent of the $50,000 account.

Optimization for Pullback Factors |

Trade Condition |
Total Profit ($) |
Winning Trades (#) |
Losing Trades (#) |
Ratio |
Average Max Draw Down ($) |
Average Profit to Avg Max Draw Down |

1 |
6,913 |
85 |
47 |
1.81 |
581 |
0.74 |

2 |
37,180 |
236 |
110 |
2.15 |
572 |
3.25 |

3 |
38,558 |
245 |
108 |
2.27 |
648 |
2.98 |

4 |
17,383 |
93 |
43 |
2.16 |
704 |
1.54 |

5 |
34,424 |
224 |
90 |
2.49 |
600 |
2.87 |

6 |
18,683 |
77 |
46 |
1.67 |
627 |
1.86 |

7 |
42,701 |
216 |
106 |
2.04 |
718 |
3 |

8 |
15,941 |
93 |
36 |
2.58 |
780 |
1.28 |

9 |
35,990 |
426 |
192 |
2.22 |
708 |
2.54 |

10 |
17,191 |
137 |
83 |
1.65 |
652 |
1.46 |

11 |
43,312 |
399 |
191 |
2.09 |
798 |
2.71 |

12 |
5,859 |
156 |
73 |
2.14 |
728 |
0.45 |

13 |
46,797 |
335 |
201 |
1.67 |
718 |
3.26 |

14 |
10,400 |
129 |
72 |
1.79 |
684 |
0.84 |

15 |
46,983 |
387 |
163 |
2.37 |
851 |
2.76 |

16 |
14,803 |
133 |
72 |
1.85 |
856 |
0.96 |

The following two
charts highlight the relationships in the above table.