A New Paradigm
for Stock Market Investing
Maximizing
Return and Minimizing Risk
By Short Term
Trading
A Position Paper From Pivot Research & Trading Inc.
3203 Provence Place
Thousand Oaks, Ca. 91362
805-493-4221
James E. White, Principal
Table of Contents
Introduction
The Evolution Of Market Theory
Traditional Market Concepts- The Random Walk
Traditional Market Concepts - Modern Portfolio Theory
Traditional Market Concepts - The Ground Rules For Stock Investment
More Recent Concepts - The Application Of Chaos Theory
Market Characteristics Of A Chaos Model - Dependence
Market Characteristics Of A Chaos Model - Sensitivity To Initial Conditions
Market Characteristics Of A Chaos Model - Fractal Self Affinity
Elements Of The System
Requirements For Success
Element1: Forecasting Accuracy
Element 2: Strength Of The Move
Element 3: Disciplined Trade Management
The New Investment Strategy
Challenging The Buy And Hold Ground Rule
Challenging The Portfolio Diversification Ground Rule
System Performance
Performance - The Ultimate Qualifier
Actual Trading Examples
Simulated Performance Of An Investment Pool
Summary
There is no greater burden than a great opportunity.
Over the past 30 years the development of the science of chaos and its application to modeling the movements of capital markets has succeeded in challenging the fundamental assumptions underlying the mainstream capital market theories and investment groundrules. By viewing markets as fractal processes, guided by nonlinear functions, the latest models reveal short term dependencies in price action rendering them to be forecastable over the short run and thus setting aside conclusions drawn from the Random Walk hypothesis and Efficient Market Theory. In the new model short term dependencies can occur wherein an impulse affecting market direction is forecastable over the near term but quickly loses it’s influence as it decays in strength or is overcome by another impulse. The resulting structure resembles cyclical action with irregular periods and amplitudes.
This paper traces the evolution of thought about market dynamics from the widely accepted Random Walk and Efficient Market concepts to the emerging application of Chaos Theory and non-linear mathematics.
By developing a quantifiable definition of risk relative to price movement and by trading the fractal nature of price movement using a reliable forecasting technique, the author explains how risk of capital loss is minimized and return on capital maximized by the short term strategy.
Next, the paper identifies the essential elements of a stock trading system necessary to realize the extraordinary return potential of the methodology and validates the proprietary components of such a system.
Finally, the paper presents
examples of the methodology in action and documents the performance realized
through both simulated and actual trading experience.
The Random Walk
“ We forge the chains that bind us!”
Charles Dickens
The traditional methods of
stock market investing have their foundations in the widely accepted
conclusions of Efficient Market Theory. Under this theory price at any one time
reflects all there is to know about the underlying security, and price movement
is entirely due to a rational investor reaction to new information. If today’s
change in price is caused only by today’s unexpected news and yesterday’s news
is already reflected in the price, then today’s price movement and yesterday’s
price are independent. If this independence holds true then it is concluded
that the expectation of tomorrow’s price change will be a normal probability
distribution, a characteristic of random behavior. Price is said to be
non-serially correlated in time. This version of Efficient Market Theory is
commonly called the random walk theory.
This random walk version of
Efficient Market Theory continued to develop as the mainstream model of market
movement despite substantial empirical evidence arguing against a normal
distribution of future returns. By the mid 1980’s both academic and investment
communities had largely accepted this model. The empirical evidence which
disagreed with the normal distribution assumption and suggested the existence
of periods of price dependence rather than independence, were ignored or
dismissed as unimportant anomalies. It was argued that even if they did exist,
the penalties of high transaction costs and taxes would render them impractical
for the investor to exploit. The random walk theorists did acknowledge,
however, that despite the random behavior in the short run, there was a high
expectation for positive returns for stocks over the long term. Next came the
development of Modern Portfolio Theory to try to capture those expectations
Despite the potential for
positive returns through stock investment, there was still the issue of risk
associated with the normal distributions of returns in the short term. To help
the investment community deal with minimizing this risk, Modern Portfolio
Theory was born. Modern Portfolio Theory divided risk into two components:
(1) Systematic risk which
was associated with the movements of the stock market in general and (2)
Unsystematic risk which was associated with the particulars of the underlying
security. The theory demonstrated that by proper diversification of stocks in an
investment portfolio the unsystematic risk could be reduced or eliminated,
leaving the investor to deal only with market risk. Data such as shown in
Figure (1) was used to argue that the systematic risk could be minimized by
increasing the holding period of the portfolio but at the penalty of accepting
a lower expected return.
Figure (1) Range of Annual Returns on Common Stocks for
Various Time Periods
(From “A Random Walk Down Wall Street” by Burton G. Malkiel)
Traditional Market
Concepts - The Ground Rules For Stock Investment
“ The wise man does no wrong in changing his habits with the times..”
-Cato
Growing out of these
traditionally accepted theories of market movement have come the widely adopted
ground rules for stock market investment:
- Diversify your portfolio by investing in a variety of sectors or industries.
Much of the public information
disseminated by the investment industry preaches these two ground rules. In
fact, the mutual fund industry is essentially based on these assumed truisms.
Interestingly enough, however, historical evidence has shown that most mutual
funds and investment managers fail to beat the returns produced by the market
as a whole.
The Application Of Chaos
Theory
First of all the science of
chaos is not about chaos at all - at least not as the term is generally
perceived. The word chaos implies disorder, a condition of confusion, unstable
and out of control. The science of chaos, on the other hand, is the study of complex
nonlinear systems and processes. Nonlinear processes are abundant in Nature and
are characterized by seemingly randomness at one level but a structural order
at a higher, global level. They are dynamic in nature, ever evolving and
adapting to a changing set of initial conditions but always maintaining a
recognizable global structure. These complex, dynamic systems are much more
like the way things work in Nature and represent the characteristics of market
movement much closer than the linear models offered by Efficient Market and
Modern Portfolio Theory. Benoit Mandelbrot, a mathematician considered by many
to be the father of fractal mathematics, a key element of chaos theory, in an
article entitled “A Multifractal Walk Down Wall Street” in the February issue
of Scientific American said “ Modern Portfolio theory poses a danger to those
who believe it too strongly and is a powerful challenge for the theoretician.”
“ In the past, money managers embraced the continuity and constrained price movements of modern
portfolio theory because of the absence of strong alternatives. But a money
manager need no longer accept the current financial models at face value.”
Dependence: Feed-Forward of Influence
Edgar Peters, in his book Chaos And Order In The Capital Markets - Second Edition claims that through the application of “re-scaled range” analysis to capital markets that “ In all cases, we find fractal structure and non periodic cycles — conclusive evidence that the capital markets are nonlinear systems and that EMH( efficient market hypothesis) is questionable.”
The application of chaos models to
the U.S., U.K., Japanese and German stock markets has confirmed this
feed-forward characteristic, but has determined that the influence of events
decays over time so that the system loses all memory of initial conditions
after about 42 months. This implies there are limits to forecastability. The
second characteristic of chaotic systems indicates there are even shorter limits to forecastability.
Sensitivity To Initial Conditions
The second characteristic of chaotic
systems is that they are extremely sensitive to the initial influence and
continuously adapt to the newer, ever changing influences. As a consequence of
this adaptive quality and the sensitivity to the input, the forecastability is
inherently limited to the short term. An example of this is given by the
operation of the biggest weather computer in the world, in the European Center
For Medium-Range Weather Forecasting. This computer, using a chaos model,
accepts over 100 million separate weather measurements from around the world,
and makes as many as 400 million calculations per second for three hours of
continuous operation to produce a ten day forecast. Yet beyond two or three
days, the forecasts are speculative, and beyond six or seven days they are
worthless. Chaos theory, then sets definite limits to the forecastability of
complex nonlinear systems such as the stock market.
Fractal Self Affinity
The third
characteristic of importance is the fractal characteristic of self affinity. We
have already referenced Peter’s proof that markets are fractal in nature.
Fractals are geometric shapes that can be separated into parts, each of which
is a reduced-scale version of the whole. The geometric relationship of the
whole to its parts is said to be one of self-affinity.
Financial price - time charts have long been known to have this characteristic. If an observer were to view a number of time series charts composed from different time increments, such as hourly, daily, weekly, monthly, and if the time scales were removed, he would be unable to distinguish between them. In other words, they would all exhibit the same patterns and statistical relationships, regardless of the time increment. Each lower time element appears to be a microcosm of the time element above it. Self- affinity can be observed in financial charts all the way down to the individual tic charts representing each buy and sell transaction of a market.
The basic “fractal” for a price-time chart of market movement is the recurring trace of an impulse move and reaction shown in Figure (2). The impulse move from A to B, over some period of time, is composed of a number of similar “fractals” at the next lower time scale. So if we observe a directed move from A to B on a weekly chart, we can detect smaller similar moves between A and B on a daily chart. The dependency characteristics discussed above indicate that each of these shorter term fractals are forecastable in the near term and until the occurrence of the next impulse.
The implications of these characteristics of market action and how they lead us to a new set of ground rules for the investor are discussed in the next section.
Figure (2) The Fractal Nature Of Price Movement
Challenging The Buy And Hold Ground Rule
“Be not the first by whom the new are tried, nor yet the last to lay the old aside.”
- Alexander Pope
If these new models of market behavior are to be of benefit to the investment community, we must devise a new set of ground rules to guide the investor. The author has developed such a set of ground rules which he believes are a logical extension of the advanced view of market behavior. In this section I will explain how these ground rules are derived from our new understandings of market movement and how they can be used to maximize the potential return over any time period while minimizing the risk of capital loss.
First, let us restate the three characteristics of a nonlinear, dynamic model of the markets.
(1) Markets display dependence of movement so that an impulse today affects the future outcomes. This dependence has a limited life, however, as the influence decays with time.
(2) Markets are extremely sensitive to the initial impulse and quickly adapt to the occurrence of future impulses. This sensitivity and adaptive quality restricts the accuracy of forecasts to the immediate short term only.
(3) Markets are fractal in nature and in responding to the sensitivity described above, create a series of shorter term movements within a longer term movement.
The first ground rule to be rewritten based on these characteristics is the one dictating a buy and hold strategy. The traditional model assumed no dependency in market movements and thus concluded that the investor could not benefit from trading the short term swings which exist in all markets. Our new model allows for dependence in the short term and has shown that forecastability can exist, but only over the short period following the initial impulse. An impulse may be any event that stimulates directional movement.
Due to the fractal nature of
markets, we know that a longer term move is composed of a series of shorter
term moves. If we can apply a forecast to anticipate and trade the short term
moves, we should be able to improve the return over the buy and hold longer
term result. Consider, for example, the hypothetical but realistic price movement
depicted in Figure (3) The price moves up in a series of up impulses (AB, CD,
& EF) and retracements (BC & DE). The buy and hold potential is the
difference between the beginning price at A and the ending price at F, or 9
units. If we were able to perfectly forecast the beginning and end of each of
the short term moves and were able to enter and exit with no commissions or
slippage, our return would have been the sum of the three up moves or AB + CD + EF = 14 units. This is a
56% greater return than buy and hold.
If we were able to trade the down swings as well, the total would have been 19 units or 111% better than buy and hold. Obviously, this is a simplified example to illustrate the potential for improving performance by actively trading the short term swings over a passive buy and hold strategy. Real markets, however, have even greater potential because there are many more tradable swings in a typical period of performance than represented here. However, we do not have perfect knowledge of the beginning and ending points of these swings. Even the latest dynamic models have been unable to produce a perfect simulation and forecast of future market movement. These models can, however, provide a variety of possible future outcomes based on previous history. I contend that perfect knowledge is not necessary, nor do we need to rely on complicated dynamic models. If we can apply a statistically accurate forecasting technique for the immediate future and capture a small portion of the short term profits, the effect of compounding over time will result in gains far exceeding the performance of the market as a whole.
Other traditional arguments against such a short term strategy have been based on two existing penalties for short term traders.
(1) Previously the high commission cost of full service brokerage houses made it difficult to capture profits from short term moves. This barrier of high transaction costs has essentially evaporated with the advent of reliable discount brokerage houses. Today you can trade in and out of a 1000 share block of any stock for as little as $25.00 or less total cost. These deep discounted commissions make even a 1/8 point gain a profitable trade.
(2) The second penalty is the result of the favorable tax treatment given long term profits (capital gain rate) over short term profits (ordinary income rate).This difference in tax treatment defines a minimum by which the short term results must exceed the long term result for the short term strategy to be feasible. Simply stated, the short term return must be greater than the buy and hold return by more than the ratio of tax rates. Tax rates vary year to year but if , for example, the marginal ordinary income rate is twice the capital gain rate, then the short term results must be at least twice the long term results to provide after tax benefit. This is a formidable barrier however I will later show achievable performance that can easily overcome it. So the first new ground rule for the new investing paradigm is
Trade the Short Term Swings Of A
Market And Compound The Results Of Many Small Profits. Challenging The Buy And Hold Ground Rule
Diversification Ground
Rule
The second ground rule of investing to be rewritten is the one dealing with the control of risk through diversification. The traditional model concluded that the investor can control some of the risk associated with stock investing by properly diversifying the securities held in the portfolio. By proper selection of the securities in the portfolio, the positive and negative potentials at any one time are balanced so that the only risk assumed is that of the market as a whole. Diversification was seen as necessary because the traditional model assumed you could not forecast results for an individual security and thus could not control the risk associated with the individual investment decision. This not only subordinates the control of risk for an individual security to that of the whole portfolio, but also places size minimums on the portfolio for which the diversification technique could be used.
Our new view of market movement views risk only as a function of the last impulse impacting the market and argues that risk can be minimized by proper selection of the entry point of a trading decision rather than the selection of the security itself.
To understand this new viewpoint, we will analyze the investment decision process. First, we define risk as the potential for loss of capital. Following traditional market theory, the risk of loss of capital is the penalty incurred for seeking the higher potential returns of the stock market. By abiding to the buy and hold strategy, we must endure the potential for short term losses in order to profit from the long term potential. There are no limits placed on the capital loss to endure. In fact, declines in price are generally seen as opportunities to buy more at a lower price. Within our new model, however, we can place real limits on the risk incurred for a particular investment decision.
When an investor decides to enter a market, he is essentially voting his opinion of the direction that market will follow. If he buys a stock, he believes the stock will increase in price. If he sells short, he believes the stock will decline in price. I propose then that the risk of the investment decision can be measured by the difference between the entry price of the trade and the price at which the investor’s decision is proven to be wrong.
Determining the entry price is easy, but at exactly what price is the investor’s decision proven wrong? To answer that question we will consider the actual short term price movement of a stock as shown in Figure (4). The stock is IBM and data is shown for the period 3/2/99 to 5/7/99.
Each vertical bar on the chart represents the variation in price that IBM traded at during that day. You may conclude that it represents the investor’s view of IBM’s value on that day. Lines have been drawn between significant highs and lows to illustrate the directed movements in price which typically occur. Referring to our dynamic model of market movement, we may consider each turning point, labeled A,B,C & D, as caused by an impulse - an event which reversed the investing public’s opinion of the immediate and anticipated direction of price movement. The extreme price reached during a directed price move we call a pivot point. High pivots represent the maximum valuation given the stock during the period under influence of the upward impulse. Low pivots represent the lowest valuation given the stock during the period under influence of the downward impulse.
I contend that these pivot points
represent the prices where an investor’s decision may be proven wrong. For
example, consider an investor that after pivot B is convinced that IBM is going
to go higher. However, he observes that the immediate impulse and price
direction is down as he sees the daily price making lower lows. On the day
after pivot C, he is encouraged that his opinion is correct as price moves
above the previous day’s high and has a higher low than the previous day. At
this point he has some evidence that the downward impulse has been reversed and
sentiment may now favor an upward movement in price, in line with his opinion.
He may choose to buy the stock here or wait for further confirmation of his
opinion.
Suppose he does buy the stock, but the price reverses and approaches the pivot at C. He would again be uncertain as to the future direction of price. If the price moves below the pivot at C, however, he now knows his decision to go long was wrong because it was based on the pivot price at C being the lowest valuation of IBM for the downward impulse. He would now conclude the downward influence is not yet over or that a new downward impulse is now in effect. In either case, his decision to be long in the stock is incorrect and he should exit the trade until his opinion is again verified. The difference between his entry price and the previous low pivot price represents the risk of capital loss he must endure before his opinion is proven wrong. Each last occurrence of a low pivot therefore becomes the risk point to be considered by the long investor and each last high pivot price becomes the risk point to be considered by the short investor. Since each impulse is deterministic only in the short run, it is only the last impulse - creating a pivot point - that we use to measure risk.
The consequence of this method of controlling risk is that each individual trading decision can be risk controlled. It is unnecessary for the investor to endure substantial declines in value of any one security with the hope that he will be compensated by the positive gains of other securities in the portfolio. The number of securities to be tracked or included in the portfolio can be reduced because the net gain is now due to the movement of the stock over the short term. The investor or money manager can focus on understanding the movements of just a few quality issues rather than continuously reviewing and selecting new issues. This reduces the size of the investment account necessary to achieve superior results over that required by the diversification model.
With this understanding we can now
state the second trading ground rule under the new paradigm.
To Control and Minimize Risk –
Enter Trades As Close To The Last Pivot Point As Possible And Exit If And When
That Pivot Has Been Penetrated.
In this paper I have argued
that a new paradigm is emerging for understanding the movement of markets and
profiting from them. Ralph Acampora, one of the most respected analysts on Wall
Street, is quoted in the summer issue of Omega Research Magazine as saying
“Finally the academics have come around and said “Gee, the market isn’t totally
efficient, you can time it and you have to worry about volatility.” Now
academics are saying the markets are not random.” Acampora hints that
prestigious business schools, such as MIT Sloan and the Wharton School will
soon be teaching market timing techniques to their students.
The ramifications of this enlightened view of market action are tremendous. But how can the individual investor or the professional money manager take advantage of the new insight? What are the essential elements of a system that will allow the active manager to consistently beat the market averages?
During the past seven years I have focused my research on identifying and developing what I believe to be the essential elements of a successful short term trading methodology. In this section I will describe these necessary elements and document the performance attributes which
Element 1: Forecasting Accuracy
“Our doubts are traitors, and make us lose the good we oft might win, by fearing to attempt.”
- William Shakespeare
I have characterized market price movement as a series of reactions to an impulse which changes the investor sentiment for that security. These changes in sentiment have discrete price extremes which we identify as pivot points. I have also said that to minimize risk and maximize return, we need to enter our trades as close to the pivot points as possible. And to accomplish that entry, we must be able to anticipate the occurrence of the pivot. The accuracy and reliability of our methods for forecasting the occurrence of the pivot points will determine the ultimate success of the methodology.
Ideally, we would like our forecasting technique to indicate the exact date of the reversal. To measure the accuracy of a forecasting technique, I count the number of time bars between the actual observed pivot and the forecasted date. If the forecasted date occurs before the actual pivot, it is recorded as a negative number. If it occurs after the actual pivot, it is recorded as a positive number. The plus and minus differences are then summed, regardless of sign, and divided by the number of observations to determine average accuracy. We then report accuracy as plus or minus bars from an actual pivot.
The forecasting technique I use has been under continuous
development and refinement since 1994. It combines proprietary applications of
dynamic cycle analysis with commercially available techniques to reach a
consensus forecast of a pivot date for each security. Table I below provides
the latest accuracy results for these methods. These results were obtained from
hundreds of forecasts made for a variety of NYSE and NASDAQ stocks. This
documents our ability to anticipate a future turning point to within 1.38 days.
Also 69% of the forecast occurred right on or before the actual pivot giving
adequate warning to take action. The third column in the table indicates the
average move from the forecasted pivot was 10 days indicating the presence of a
tradable move. The other important characteristic for profitable trading is the
strength of the move in price, discussed as the next element.
|
ACCURACY + or - Bars From Pivot |
Range Of Accuracy |
Average Duration Of Move |
|
1.38 |
+3 to –5 |
10 |
Table I: Accuracy Of The Forecasting Technique
Element 2: Strength Of The Move
The second required element of the methodology is the ability to forecast price reversals with enough strength of movement to provide an opportunity to capture a profit. The methodology relies on the ability to reliably capture short term profits and compound the results over time. I measure the strength of the move as the difference in price between the forecasted pivot price and the next occurring pivot price. The actual price differential depends on the price of the security and the current volatility of the market. Our analysis indicates that for stocks selling for over $20.00 a share, we can be confident of at least an average of 3 1/2 points per forecasted move.
Another method of measuring
reliability of the forecast is to determine the percentage of opportunities for
a fixed amount of profit versus suffering a fixed amount of loss. The data for
our entry signals indicates we have an 87% probability of recording at least a
5/8 of a point profit versus a 5/8 point loss from any of our forecasted pivots.
Remember that with today’s low discount brokerage cost, we can record profits
on as little as a 1/8 point move for a 1000 share block trade.
The third element of a successful system is the application of disciplined trade management practices. Adherence to strict entry and exit criteria are essential to capture the short term profits and minimize the risk of capital loss. This implies active monitoring of all active trades during the day.
I have been developing and refining the trade practices since 1992 and have in place the disciplined set of rules necessary to assure success. These rules require the confirmation of an anticipated price move by actual market action before a trade is entered. To assist us in this active management, I employ the latest in hardware and software technology for monitoring and alerting as to intraday price movement.
System Performance
The ultimate test for any system or methodology is the result of its operation. The first application of the methodology was tested in 1994. I formed a simulated trading account of $250,000.00 and strictly applied the forecasting methods and trading rules to a variety of NYSE and NASDAQ stocks. During this period I recorded 850 total trades, both long and short. At the end of the year I had 64.3% profitable trades providing a one year return on the $250,000.00 starting capital of 63.7%.
During the later part of the year, I traded a small actual account, totaling 80 trades, 80% profitable and providing a return of 23.4% on equity. In 1994 the S&P index declined 4%.
Refinement of the methodology has continued providing reliable and consistent return. In 1998, in actual trading , the methodology produced an 85% return on trading equity. The actual results are shown in Table II below.
One of the obvious
advantages of this methodology is that it produces positive returns no matter
what direction the market is heading. The investor no longer has to endure a
drawdown on capital or sit on the sidelines during market corrections. Another
advantage is that the investor may choose to be in cash at the end of the day
or the end of the week thus eliminating the impact of unforeseen economic
events.
|
Long Trades |
Short Trades |
Totals |
|
|
Profitable Trades |
30 |
20 |
50 |
|
Losing Trades |
19 |
9 |
28 |
|
Total |
49 |
29 |
78 |
|
Per Cent Profitable |
61.22% |
68.96% |
64.10% |
|
Average Trade Duration (Calendar Days) |
9 |
5 |
7.3 |
|
Return On Invested Capital |
|
|
85.00% |
Table II: Actual Performance Of The Short Term
Trading Methodology
Actual Trading Examples
of the Methodology
The dream of every trader is to be able to forecast turning points with accuracy and reliability. Unfortunately there aren’t many methods available that can do this. The techniques I have developed are based on an algorithm which uses information from the last confirmed pivot location to forecast the next pivot location in time. It then uses the immediate price action in the forecasted time window to confirm the potential of a price reversal. Entry to the trade is by stop order as price moves in the forecasted direction. To illustrate the technique I will show some actual trading sequences from 1998.
Figure (5) below shows the
price movement for Quantum Corporation (QNTM) between 1/2/98 and 3/24/98. The
red and blue bars are indicators of a reversal based on the price action of the
last four bars. They are used as confirming indicators of potential reversals
in the forecasted time window.
From the high at A on 1/5 , I forecasted a reversal window between 1/6 and 1/12. On 1/12 the near term price action signaled a reversal occurring and I entered long at 19.50
As price moved up the next day, the pivot was confirmed and the next reversal was forecasted for 1/14. On the 14th I exited the trade at 20.625 for a profit of 1.125 points. Notice that a confirming indicator of potential reversal did not occur until the 16th so no short entry was signaled. The 15th proved to be the next low with forecasted high reversals for 1/22 and 2/2. Price action around the 22nd did not indicate a reversal however and no trade was taken. A reversal did occur on the second but again without a reversal confirmation. The low on 2/9 was used to forecast a reversal for 2/12. Two trading days later the reversal was signaled and I entered short on 2/18 at 25.8125. Two days later I exited at 24.6875 for a profit of 1.125 points.
A third short was entered on 3/17 without a confirmation of reversal and was exited the next day for a 1.1875 point profit. These three trades netted a total of 3.4375 points before commissions while price closed on the March 18th very close to where it began on January 12th.
One of the advantages of the short term trading methodology
is that it allows you to produce positive returns even in a bear market. For example Figure (6) shows the price action for
Battle Mountain Gold
(BMG) during 1998. Note that from the April high, BMG (and
Gold in general) has been in a bear market. Never the less, if you were
inclined to trade from the long side only, there was ample opportunity to make
significant profits using a short term strategy.
|
|
Entry Price |
Exit Date |
Exit Price |
Gain (Loss) |
Trading Days In Trade |
|
2/25/98 |
5.375 |
3/3/98 |
5.8125 |
0.4375 |
5 |
|
3/19/98 |
5.375 |
3/27/98 |
6.375 |
1.00 |
6 |
|
4/9/98 |
6.625 |
4/23/98 |
7.375 |
0.75 |
9 |
|
6/17/98 |
5.375 |
6/30 |
5.875 |
0.50 |
9 |
|
7/27/98 |
5.0625 |
9/15/98 |
5.50 |
0.4375 |
35 |
|
10/15/98 |
5.25 |
11/5/98 |
6.00 |
0.75 |
15 |
|
12/10/98 |
4.0625 |
12/20/98 |
4.00 |
(0.0625) |
8 |
|
TOTALS AVERAGES |
|
|
|
3.8125 0.545 |
|
The short term strategy achieves a very consistent equity
growth of about 0.77% per week and does not experience the give back of capital
that the buy and hold investor must endure. This further validates the position
that a short term trading strategy, based on reliable forecasting information,
can far out perform the buy and hold strategy and with less risk of capital
loss.
|
|
Net Gain |
Number of Trades |
Accumulated Gain |
Annual Percent |
|
2/17/98 To 2/20/98 |
1785.00 |
24 |
1785.00 |
9.28% |
|
2/23/98 To 2/27/98 |
9400.00 |
29 |
11,185.00 |
29.08% |
|
3/2/98 To 3/6/98 |
2775.00 |
15 |
13,960.00 |
24.20% |
|
3/9/98 To 3/13/98 |
480.00 |
13 |
14,440.00 |
18.77% |
|
3/16/98 To 3/20/98 |
6895.00 |
12 |
21,335.00 |
22.19% |
|
3/23/98 To 3/27/98 |
10,585.00 |
26 |
31,920.00 |
27.66% |
|
3/31/98 To 4/3/98 |
13,530.00 |
23 |
45,450.00 |
33.76% |
|
4/6/98 To 4/10/98 |
27,370.00 |
22 |
72,820.00 |
47.33% |
|
4/13/98 To 4/17/98 |
(480.00) |
12 |
72,340.00 |
41.80% |
|
4/20/98 To 4/24/98 |
(2600.00) |
21 |
69,740.00 |
36.26% |
|
4/27/98 To 5/1/98 |
14,320.00 |
17 |
84,060.00 |
39.74% |
|
5/4/98 To 5/8/98 |
28,575.00 |
19 |
112,635.00 |
48.81% |
|
5/11/98 To 5/15/98 |
(6125.00) |
11 |
106,510.00 |
42.60 |
|
5/18/98 To 5/22/98 |
0.00 |
NO TRADES |
106,510.00 |
39.56% |
|
5/25/98 To 5/29/98 |
7235.00 |
16 |
113,475.00 |
39.43% |
|
6/1/98 To 6/5/98 |
10,285.00 |
29 |
124,030.00 |
40.30% |
|
6/8/98 To 6/12/98 |
8400.00 |
25 |
132,430.00 |
40.51% |
|
6/15/98 To 6/19/98 |
4280.00 |
18 |
136,710.00 |
39.49% |
|
6/22/98 To 6/26/98 |
(4575.00) |
15 |
132,135.00 |
36.16% |
|
6/29/98 To 7/3/98 |
(870.00) |
19 |
131,265.00 |
34.12% |
|
7/6/98 To 7/10/98 |
(7875.00) |
14 |
123,390.00 |
30.55% |
|
7/13/98 To 7/17/98 |
5230.00 |
13 |
128,620.00 |
30.40% |
|
7/20/98 To 7/24/98 |
26,675.00 |
18 |
155,295.00 |
35.11% |
|
7/27/98 To 7/31/98 |
(2015.00) |
16 |
153,280.00 |
33.21% |
|
8/3/98 To 8/7/98 |
14,945.00 |
17 |
168,225.00 |
34.99% |
|
8/10/98 To 8/14/98 |
15,765.00 |
10 |
183,990.00 |
36.80% |
Figure (7) Equity Growth For The Short Term Strategy
Compared To the
Buy And Hold Performance Of The DOW