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X0X 0 X 0X 0 X 0 116
0 X0
X0X 0 0X 0 X 115
0 X0
0X 0 X
X0 114
0 X0 X
00
X
0 0X 113
X
0X
X 112
Support 0X
X 111
0
XX
110
X0 X
0X 109
0X 108
0
107
106
1993 F MAM J J
Bodie’Kane’Marcus: VI. Active Investment 19. Behavioral Finance and © The McGraw’Hill
Essentials of Investments, Management Technical Analysis Companies, 2003
Fifth Edition




665
19 Behavioral Finance and Technical Analysis




$36
High price
= $34.25
$35
High price = $34.50
Closing price
= $34 Opening price = $34
Stock price




$34


Closing price = $33
Opening price
$33
= $33.50
Low price = $32.50
Low price
$32
= $33.25

$31

Monday Tuesday Wed. Thurs. Friday




F I G U R E 19.6
Candlestick chart



well as the high and low price. The top and bottom of each vertical line represent the high and
low price, respectively. If the price increases during the day (e.g., Monday in Figure 19.6), the
box is shaded, so the analyst knows that the closing price is at the top of the box and the open-
ing price is at the bottom. If the box is left unshaded (e.g., Tuesday), the stock price is under-
stood to have fallen, and the closing price is at the bottom of the box. The vertical lines extend
from the daily high to the daily low price. The chart thus conveys a considerable amount of
information about recent stock price history. Obviously, candlestick charts can be drawn us-
ing either shorter or longer time periods than one-day returns, for example, using intraday or
weekly prices.


A Warning
The search for patterns in stock market prices is nearly irresistible, and the ability of the hu-
man eye to discern apparent patterns is remarkable. Unfortunately, it is possible to perceive
patterns that really don™t exist. Consider Figure 19.7, which presents simulated and actual val-
ues of the Dow Jones Industrial Average during 1956 taken from a famous study by Harry
Roberts (1959). In Figure 19.7B, it appears as though the market presents a classic head-and-
shoulders pattern where the middle hump (the head) is flanked by two shoulders. When the
price index “pierces the right shoulder””a technical trigger point”it is believed to be head-
ing lower, and it is time to sell your stocks. Figure 19.7A also looks like a “typical” stock mar-
ket pattern.
Can you tell which of the two graphs is constructed from the real value of the Dow and
which from the simulated data? Figure 19.7A is based on the real data. The graph in B was
generated using “returns” created by a random number generator. These returns by construc-
tion were patternless, but the simulated price path that is plotted appears to follow a pattern
much like that of A.
Bodie’Kane’Marcus: VI. Active Investment 19. Behavioral Finance and © The McGraw’Hill
Essentials of Investments, Management Technical Analysis Companies, 2003
Fifth Edition




666 Part SIX Active Investment Management




Level Level
525 485
520 480
515 475
510 470
505 465
500 460
495 455
A 490 B 450
485 445
480 440
475 435
470 430
465 425
460 420



5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50
Week Week
Friday closing levels, December 30, 1955”December 28, 1956, Dow Jones Industrial Average




F I G U R E 19.7
Actual and simulated levels for stock market prices of 52 weeks
Note: Friday closing levels, December 30, 1955“December 28, 1956, Dow Jones Industrial Average.
Source: From Harry Roberts, “Stock Market Patterns and Financial Analysis: Methodological Suggestions,” Journal of Finance, March 1959, pp. 5“6.




Change
Change
30
25
25
20
20
15
15
10
10
5
5
A 0
B 0
“5
“5
“10
“10
“15
“15
“20
“20
“25
“25
“30
1 5 10 15 20 25 30 35 40 45 50 1 5 10 15 20 25 30 35 40 45 50
Week Week
Changes from Friday to Friday (closing) January 6, 1956”December 28, 1956, Dow Jones Industrial Average




F I G U R E 19.8
Actual and simulated changes in weekly stock prices for 52 weeks
Note: Changes from Friday to Friday (closing) January 6, 1956“December 28, 1956, Dow Jones Industrial Average.
Source: From Harry Roberts, “Stock Market Patterns and Financial Analysis: Methodological Suggestions,” Journal of Finance, March 1959, pp. 5“6.



Figure 19.8 shows the weekly price changes behind the two panels in Figure 19.7. Here the
randomness in both series”the stock price as well as the simulated sequence”is obvious.
A problem related to the tendency to perceive patterns where they don™t exist is data min-
ing. After the fact, you can always find patterns and trading rules that would have generated
Bodie’Kane’Marcus: VI. Active Investment 19. Behavioral Finance and © The McGraw’Hill
Essentials of Investments, Management Technical Analysis Companies, 2003
Fifth Edition




667
19 Behavioral Finance and Technical Analysis



TA B L E 19.3
The Superbowl as a predictor of stock market returns

Winning S&P 500 Winning S&P 500 Winning S&P 500
Year Conference Return Year Conference Return Year Conference Return

1967 N 1979 A 1991 N
1968 N 1980 A 1992 N
1969 A 1981 A 1993 N
1970 A 1982 N 1994 N
1971 A 1983 N 1995 N
1972 N 1984 A 1996 N
1973 A 1985 N 1997 N
1974 A 1986 N 1998 A
1975 A 1987 N 1999 A
1976 A 1988 N 2000 N
1977 A 1989 N 2001 A
1978 N 1990 N 2002 A ?

Note: N National Football League or Conference
A American Football League or Conference
“ ” means the S&P 500 return (including dividends) in the 12 months (February through February) following the Superbowl was positive.
“ “ means the S&P 500 return was negative.




enormous profits. If you test enough rules, some will have worked in the past. Unfortunately,
picking a theory that would have worked after the fact carries no guarantee of future success.
In this regard, consider an investment rule that worked with uncanny precision between
1967 and 1997. Suppose that in years when an original National Football League team won
the Superbowl (played in mid-to-late January) you had bet on the S&P 500 rising in the fol-
lowing 12 months, and in years when a team from the American Football Conference won,
you bet on a market decline. You would have won this bet in 23 out of 31 of those years.
Given the impressive success rate of this strategy, would you have used it to invest your
money? We hope not. If you had, Table 19.3 shows that you would have lost this bet in three
of the four years ending in 2001.
In evaluating trading rules, you should always ask whether the rule would have seemed
reasonable before you looked at the data. If not, you might be buying into the one arbitrary
rule among many that happened to have worked in the recent past. The hard but crucial ques-
tion is whether there is reason to believe that what worked in the past should continue to work
in the future.


19.7 TECHNICAL INDICATORS
Technical analysts use technical indicators besides charts to assess prospects for market de-
clines or advances. There are three types of technical indicators: sentiment indicators, flow of
funds indicators, and market structure indicators. Sentiment indicators are intended to measure
the expectations of various groups of investors, for example, mutual fund investors, corporate
insiders, or NYSE specialists. Flow of funds indicators are intended to measure the potential
for various investor groups to buy or sell stocks in order to predict the price pressure from
those actions. Finally, market structure indicators monitor price trends and cycles. The chart-
ing techniques described in the last section are examples of market structure indicators. We
will examine a few more market structure indicators in this section.
Bodie’Kane’Marcus: VI. Active Investment 19. Behavioral Finance and © The McGraw’Hill
Essentials of Investments, Management Technical Analysis Companies, 2003
Fifth Edition




668 Part SIX Active Investment Management


Sentiment Indicators
Trin statistic Market volume is sometimes used to measure the strength of a market rise
or fall. Increased investor participation in a market advance or retreat is viewed as a measure
of the significance of the movement. Technicians consider market advances to be a more fa-
vorable omen of continued price increases when they are associated with increased trading

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