PV Crossovers - Parallax Financial Research

7/26/2019 PV Crossovers - Parallax Financial Research

1/8

SPRING 1991

ISSUE 37

A PUBLICATION OF THE MARKET TECHNICIANS ASSOCIATION

71 BROADWAY, 2ND FLOOR, C/O NYSSA NEW YORK, NEW YORK 10006 (212) 344-1266


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The Use of Price-Volume Crossover Patterns

in Technical Analysis

S. Kris Kaufman and Marc Chaikin

Abstract: Background and Methods. The routine

use of price-volume crossover signals as a means of

forecasting future stock or commodity price move-

ment has been gaining popularity lately due to the

availability of sof tware to simplify the analysis. In

this study we tested 24 unique crossover patterns

and identified their forecasting performance. Cross-

overs are classed by both pattern and the elapsed

time for the pattern to develop. For each pattern, we

analyzed how much better one could forecast price

direction 5,10,15, and 20 days in the future, given

that the elapsed time for the cross to develop spanned

TITLE:

Price-Volume Crossover Derivations

the same number of days. We used approximately

one hundred and twenty five thousand days of daily

stock and commodity data bundled together in our

evaluation. At least 100 occurrences for each cross-

over pattern were used in the analysis.

Results: The results suggest that several pat-

terns are significant and could be used to improve

a stock or commodity price forecast. The most nega-

tive cross within the test window was II-B, which

occurs when price drops on decreasing volume, rises

on light volume and then drops again on increasing

volume. It wa s interesting that the converse pattern

PRICE-TIME VIEW

A

2

4

P

Fi

I

0 P

C

E 1

o,,j ,

3 PRICE-VOLUME VIEW

4

2

I I I I I I I I>

TIME

\

P

R

VOLUME-TIME VIEW

f

X>

C

1

E 3

VOLUME

TIME

Figure 1: DERIVATION OF PRICE-VOLUME CROSSOVER

MTA JOURNAL I SPRING 1991 35


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I-B is not as bullish as II-B is bearish. The I-B results

show that you will generally move up immediately

after the cross, only to fall later. The utility of this

technique is its ease of use by computer and the in-

tegration of price and volume which is achieved.

Introduction

Market technicians usually study time-based

charts in order to identify price and volume patterns.

There is a large body of technical knowledge about

the combinations of price and volume behavior that

lead certain types of market price action [Bring et

al.]. The price-volume chart provides a different way

of viewing the data. In fact, the two major reasons

for using this view are that it leads to a single indi-

cator integrating price and volume and the data can

be easily tracked by computer. The identification of

these patterns on the price-volume chart has been

linked to the crossing o f two price-volume lines. Ben

Cracker, for one, has long been a proponent of price-

volume charting and has studied some of the basic

patterns as well as pattern groupings. In this study

we will test each of the 24 basic single cross patterns

on four time periods using very diverse stock and

commodity data.

Crossover Patterns

The price-volume view is formed by plotting

the closing price on a vertical axis versus volume on

a horizontal axis (Figure 1). Each day a new point

is added and then connected to the previous days

point by drawing a line. For this discussion we will

refer to the final segment as the most recent

price-volume line. The initial segment is the one

which occurred further back in the past and is

crossed by the final segment. Crossover patterns are

TITLE:

PriceVolume Crossover Patterns I

P

FINAL SEGMENT:

PRICE INCREASES

R

VOLUME INCREASES

I

C

I

i

E

VC .Ij:J(E

5 I -0.2 5 I -1.4

5 I -1.6

A

10 I 0.8

15 I 0.4

~ y-2.1 lB lg~ -~~~ : ,viiii

VOLUME

VOLUME

VOLUME

VOLUME

VOLUME

VOLUME

Figure 2

36 MTA JOURNAL /SPRING 1991


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classified by the direction of the initial and final

segments, and by the elapsed time between them.

The pattern shown in figure 1 shows price rising on

increased volume (1 to 21, then declining with the

same level of volume (2 to 31, before finally rising

on decreasing volume (3 to 4) to complete the pat-

tern. The dashed line indicates that an unknown

number of days may have elapsed between the initial

and final segments. There are 24 patterns in all (Fig-

ures 2 through 5). The patterns have been divided

into four groups based on the direction of the final

segment. Each of the figures shows one of the four

final segment possibilities with all possible initial

segment choices.

Analysis

The best way to judge the benefit of using cross-

over signals is to analyze the actual price action

following a certain crossover. Within a particular

time frame, more than one crossover pattern may

complete They have different initial segments with

the same final segment, but they still occur together

in terms of the evaluation. We have chosen a simple

least-squares approach to solve for the relative im-

portance of the crossovers. This approach neatly sep-

arates each pattern by assigning it a weight as part

of a linear sum. Figure 6 shows the least-squares ma-

trix equation Aij * Wj = Bi, where the As are zeros

or ones depending on whether one of the 24 cross-

overs occurred within the analysis window, the Ws

are the unknown pattern weights, and the Bs are

the answers to what happened next in the market.

If the market went up after 5 days (or any fmed num-

her), a plus one is entered. Otherwise, a minus one

is used. Since w e have so much data (roughly 125,000

days), the problem is very well constrained. The re-

TITLE:

Price-Volume Crossover Patterns II

FINAL SEGMENT:

PRICE DECREASES

VOLUME INCREASES

P

R

I

C

E

I

\

VOLUME

5 I -3.9

5 I -8.3

5 I -0.2

A

10

I

-0.7

8

10 I -5.5

C

10 I -1.7

15 I -1.1

15 I -3.8

15 I -2.8

20 I -0.5

20 I -2.3

20 I -2.0

; L+ ; ix 1 \

VOLUME

VOLUME

VOLUME

5 I -4.6

5 I -4.1

5 I -8.1

10 I -1.3

10 I -0.8

10 I -0.9

D 15 I -2.9

20 I 1.6

~ \ iE g:i:l iF gi

VOLUME

VOLUME

VOLUME

Figure 3

MTA JOURNAL I SPRING 1991 37


5/8

sulting weights will tell us whether a pattern is

bearish (W < 0) or bullish. Also note that we added

a constant term to the weighted sum model to pick

up any price trend bias over the whole data set. This

turned out to be positive 1 to 2% which is due to the

80s bull market.

The equation was solved for four cases. All

crossovers occurring within 5 days coupled with the

resulting price behavior 5 days out, and the same

for 10,15, and 20 days. The results are shown in the

upper right hand corner of each pattern on figures

2 through 5. The weights may be interpreted as an

average percentage deviation from the random case.

In other words a 5.2 weight indicates that the pat-

tern predicted higher prices about 5% better than

random. When the weights flip-flop between positive

and negative without any clear pattern, the cross-

over has little or no significance in forecasting.

In evaluating the results it is apparent that all

patterns with the same final segment do not have

the same forecasting utility (see Figure 7). One

might expect that days following a move higher on

increasing volume (figure 2 patterns) would always

be more positive, independent of the crossover. Our

results show that patterns I-C and I-F predict very

negative price action 5days out, before turning and

becoming very positive later. Other members of that

group, including I-A, I-B, and I-C show no clear pat-

tern, while I-D is positive early and negative late,

the opposite of I-C and I-F. Group II however, does

show universally negative behavior, but there are

two patterns which are much more negative than the

rest. Table 1 is a summary of the signiticant results.

Discussion

The results suggest that several of the price-

TITLE:

Price-Volume Crossover Patterns Ill

P

FINAL SEGMENT:

PRICE DECREASES

R

VOLUME DECREASES

II-

/

E

VOLUME

5 I -7.0

5 I -6.4

5 1 -6.2

10 I -0.9

10 I -2.5

10 I -5.6

iA +-= iB kii:- / Jjl:::

VOLUME

VOLUME VOLUME

5 1 -0.8

5

I -0.9

5 I -5.4

10 I

1.2

10 I -0.2

10 I -4.8

iD j-t:: iE g=- ~ F Ii::

VOLUME

VOLUME

VOLUME

Figure 4

38 MTA JOURNAL / SPRING 1991


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volume crossover patterns are significant and could

be used to improve a stock or commodity price fore-

cast by generally +/- 5%. It is interesting to note

that down moves are much more easily forecast

using price-volume patterns than up moves. Also, the

group II pattern results suggest that any time one

sees a price drop on heavy volume, a hasty exit is

in order.

The most negative cross within the test win-

dow was II-B, which occurs when price drops on

decreasing volume, rises on light volume and then

drops again on increasing volume. It was very

interesting to note that the converse pattern I-B is

not as bullish as II-B is bearish. The I-B results show

that you will generally move up immediately after

the cross, only to fall later.

The use of the price-volume crossover techni-

que is a practical way of integrating two charts into

one for analysis. Since a computer can easily be pro-

grammed to pick out these patterns, this indicator

should continue to gain in popularity over time. Fur-

ther study should be devoted to combining crossover

patterns with other technical indicators, analyzing

weekly and monthly data, and also to special se-

quences of these patterns.

REFERENCES

Cracker, Benjamin B., The Computerized Investor, T he Cracker

Report, 320 West California Blvd., Pasadena, CA 91105, Volume

M.

Pring, Martin, Tech nical Analysis Explained, McGraw-Hill,

Second Edition, 1985.

TITLE:

Price-Volume Crossover Patterns IV

PRICE INCREASES

INAL SEGMENT:

VOLUME DECREASES

P

R

I/

C

T

E

VOLUME

5 I -3.1

5 I -0.8

A 10 I 2.5

B

10 I 4.4

C

10 I 0.8

15 I 2.6

15 I 2.8 15 I 0.0

20 I 2.5

20 1 3.1

20 1 1.4

; \ ; x ; .p,102

VOLUME

VOLUME

VOLUME

5 1 -9.2

5 1 -3.9

5 I 5.2

D

10 I -2.8

15 I -2.5

; x-1.3 iE gy= (F


7/8

r

TITLE:

Crossover Evaluation using Lea3bSqwre~~

MATRIX EQUATION:

125,000

Days

of Data

24 Patterns

>

0 0 1 0 0 0 1 o....

A

IS THERE A NO. 3

CROSSOVER TODAY

1 = YES

O-NO

PAlTERN

WEIGHTS

Wl

w2

w3

--

=

WAS THE MARKET HIGHER

OR LOWER 5 DAYS IN THE

/

FUTURE?

IL

1

1

-1

-1

1

-1

--

-1 = LOWER

1 = HIGHER

Figure

6: LEAST-SGUARES EOUAllON TO SOLVE FOR CROSSOVER WEIGHTING

TITLE:

Crossover Pattern Evaluation

P

R

I

c 2

SET OF ALL MARKET DAYS WHEN

PRICE WAS UP ON DECREASED

VOLUME FROM THE PRIOR DAY

VOLUME

ON INCREASED VOLUME

Flgure 7: PATTERN EVALUATION: HOW DIFFERENT ARE CROSSOVER CASES FROM THE NORM 3

40

MTA JOUFWAL I SFFLING 1991


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TABLE 1

Price Action

Pattern Description

Early Late

I-C Price is up strongly on increased volume followed later by price up less

with more volume.

DOWN UP

I-D Price is down strongly on slightly decreased volume followed later by price

moving up strongly with increasing volume. UP DOWN

I-F Price is up somewhat on greatly increased volume followed later by price

up more on less volume

DOWN UP

II

(all) Price decreases on increasing volume. Note that II-B and II-F are very

negative patterns within the group.

DOWN DOWN

III-A Price is up slightly on large volume increase followed later by a strong

price decrease on decreased volume.

DOWN UP

III-B Price moves down on increased volume followed by price drop on decreased

volume.

DOWN DOWN

III-C Price is down strongly on slightly lower volume followed later by smaller

decline on much lower volume. DOWN DOWN

III-F Price is down slightly on much lower volume followed by greater decline

on slightly decreased volume.

DOWN DOWN

IV-A Price is down slightly on increased volume followed by a higher price on

decreased volume.

DOWN UP

IV-B Price increases on increased volume followed by a price increase on

decreasing volume

-

UP

IV-D Price decreases strongly on slightly higher volume followed by a rising

price on much lower volume.

DOWN DOWN

Kris Kaufman is a senior g eophysicist with a leading

oil exploration software company and president of

Parallax Financial Research. Parallax publishe s the

PRECISION TURN trend change indicator quarterly

and provides computer research and consulting services

to several Wall Street firms.

Marc Chaikin g raduated from Brown University with

a degree in Finance He was the head o f the Options

Department at Tucke r Anthony fir five years Later Marc

joined Drezel Burnham Lambert and for five years he

worked with technically oriented traders and investors.

Two years ago he, along with his partner Bob Brogan,

firmed Bomar S ecurities, L.P, a technica l research bou-

tique with a computer-based product which gives buy-

side portfolio managers and block trading desks quick

and easy access to technica l data. Marc is a frequent

guest analyst on FNN, is often quoted in Investors Daily

and recently wrote an article fir Wall Street Computer

Reviews July issue titled Tech nical Analysis Systems:

A Users Perspective:

MTA JOURNAL / SPRING 1991 41

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