Technical Analysis and Behavioral Finance- 123docz.net

Technical analysis attempts to exploit recurring and predictable patterns in stock prices to generate superior investment performance. Technicians do not deny the value of fundamental information, but believe that prices only gradually close in on intrinsic value. As fundamentals shift, astute traders can exploit the adjustment to a new equilibrium. 20

For example, one of the best-documented behavioral tendencies is the disposition effect, which refers to the tendency of investors to hold on to losing investments. Behavioral investors seem reluctant to realize losses. This disposition effect can lead to momentum in stock prices even if fundamental values follow a random walk. 21 The fact that the demand of “disposition investors” for a company’s shares depends on the price history of those shares means that prices close in on fundamental values only over time, consistent with the central motivation of technical analysis.

20 E. F. Fama, “Market Efficiency, Long-Term Returns, and Behavioral Finance,” Journal of Financial Economics 49 (September 1998), pp. 283–306.

21 Mark Grinblatt and Bing Han, “Prospect Theory, Mental Accounting, and Momentum,” Journal of Financial Economics 78 (November 2005), pp. 311–39.

Practical traders, who believe themselves to be quite exempt from any intellectual influences, are usually slaves of some defunct mathematician. That is what Keynes might have said had he considered the faith placed by some investors in the work of Leonardo of Pisa, a 12th and 13th century number-cruncher.

Better known as Fibonacci, Leonardo produced the sequence formed by adding consecutive components of a series—1, 1, 2, 3, 5, 8 and so on. Numbers in this series crop up frequently in nature and the relationship between components tends towards 1.618, a figure known as the golden ratio in architecture and design.

If it works for plants (and appears in “The Da Vinci Code”), why shouldn’t it work for financial markets? Some traders believe that markets will change trend when they reach, say, 61.8% of the previous high, or are 61.8% above their low.

Believers in Fibonacci numbers are part of a school known as technical analysis, or chartism, which believes the future movement of asset prices can be divined from past data. But there is bad news for the numerologists. A new study * by Professor Roy Batchelor and Richard Ramyar of the Cass Business School, finds no evidence that Fibonacci numbers work in American stockmarkets.

This research may well fall on stony ground. Experience suggests that chartists defend their territory with an almost religious zeal. But their arguments are often anecdotal: “If technical analysis doesn’t work, how come so-and-so is a multi-millionaire?” This “survivorship bias” ignores the

many traders whose losses from using charts drive them out of the market. Furthermore, the recommendations of technical analysts can be so hedged about with qualifica- tions that they can validate almost any market outcome.

If the efficient market theory is correct, technical analysis should not work at all; the prevailing market price should reflect all information, including past price movements. However, academic fashion has moved in favor of behavioral finance, which suggests that investors may not be completely rational and that their psychological biases could cause prices to deviate from their “correct” level.

Technical analysts also make the perfectly fair argument that those who analyze markets on the basis of fundamentals (such as economic statistics or corporate profits) are no more successful.

All that talk of long waves is distinctly mystical and seems to take the deterministic view of history that human activity is subject to some pre-ordained pattern. Chartists fall prey to their own behavioral flaw, finding “confir- mation” of patterns everywhere, as if they were reading clouds in their coffee futures.

Besides, technical analysis tends to increase trading activity, creating extra costs. Hedge funds may be able to rise above these costs; small investors will not. As illusion- ists often proclaim, don’t try this at home.

*“No Magic in the Dow—Debunking Fibonacci’s Code,” working paper, Cass Business School, September 2006.

Source: The Economist, September 21, 2006.

Behavioral biases may also be consistent with technical analysts’ use of volume data.

An important behavioral trait noted above is overconfidence, a systematic tendency to overestimate one’s abilities. As traders become overconfident, they may trade more, induc- ing an association between trading volume and market returns. 22 Technical analysis thus uses volume data as well as price history to direct trading strategy.

Finally, technicians believe that market fundamentals can be perturbed by irrational or behavioral factors, sometimes labeled sentiment variables. More or less random price fluctuations will accompany any underlying price trend, creating opportunities to exploit corrections as these fluctuations dissipate. The nearby box explores the link between technical analysis and behavioral finance.

Trends and Corrections

Much of technical analysis seeks to uncover trends in market prices. This is in effect a search for momentum. Momentum can be absolute, in which case one searches for upward price trends, or relative, in which case the analyst looks to invest in one sector over another (or even take on a long-short position in the two sectors). Relative strength statistics (see the previous chapter) are designed to uncover these potential opportunities.

22 S. Gervais and T. Odean, “Learning to Be Overconfident,” Review of Financial Studies 14 (2001), pp. 1–27.

Dow Theory The grandfather of trend analysis is the Dow theory, named after its creator Charles Dow (who established The Wall Street Journal ). Many of today’s more technically sophisticated methods are essentially variants of Dow’s approach.

The Dow theory posits three forces simultaneously affecting stock prices:

1. The primary trend is the long-term movement of prices, lasting from several months to several years.

2. Secondary or intermediate trends are caused by short-term deviations of prices from the underlying trend line. These deviations are eliminated via corrections when prices revert back to trend values.

3. Tertiary or minor trends are daily fluctuations of little importance.

Figure 12.3 represents these three components of stock price movements. In this figure, the primary trend is upward, but intermediate trends result in short-lived market declines lasting a few weeks. The intraday minor trends have no long-run impact on price.

Figure 12.4 depicts the course of the DJIA during 1988. The primary trend is upward, as evidenced by the fact that each market peak is higher than the previous peak (point F versus D versus B ). Similarly, each low is higher than the previous low ( E versus C versus A ).

This pattern of upward-moving “tops” and “bottoms” is one of the key ways to identify the underlying primary trend. Notice in Figure 12.4 that, despite the upward primary trend, intermediate trends still can lead to short periods of declining prices (points B through C, or D through E ).

In evaluating the Dow theory, don’t forget the lessons of the efficient market hypothesis.

The Dow theory is based on a notion of predictably recurring price patterns. Yet the EMH holds that if any pattern is exploitable, many investors would attempt to profit from such predictability, which would ultimately move stock prices and cause the trading strategy to self-destruct. While Figure 12.3 certainly appears to describe a classic upward primary trend, we have to wonder whether we can see that trend only after the fact. Recognizing patterns as they emerge is far more difficult.

Recent variations on the Dow theory are the Elliott wave theory and the theory of Kondratieff waves. Like the Dow theory, the idea behind Elliott waves is that stock prices can be described by a set of wave patterns. Long-term and short-term wave cycles are Figure 12.3 Dow theory trends

Trends

Intermediate

Trend Minor

Trend

Primary Trend

superimposed and result in a complicated pattern of price movements, but by interpret- ing the cycles, one can, according to the theory, predict broad movements. Similarly, Kondratieff waves are named after a Russian economist who asserted that the macro- economy (and therefore the stock market) moves in broad waves lasting between 48 and 60 years. The Kondratieff waves are therefore analogous to Dow’s primary trend, although they are of far longer duration. Kondratieff’s assertion is hard to evaluate empirically, however, because cycles that last about 50 years provide only two independent data points per century, which is hardly enough data to test the predictive power of the theory.

Moving Averages The moving average of a stock index is the average level of the index over a given interval of time. For example, a 52-week moving average tracks the average index value over the most recent 52 weeks. Each week, the moving average is recomputed by dropping the oldest observation and adding the latest. Figure 12.5 is a

Figure 12.4 Dow Jones Industrial Average in 1988

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov

C D

E 2180 F

2160

1980 19601940 1920 19001880 2140 21202100 2080 20602040 2020 2000

Figure 12.5 Moving average for Hewlett-Packard (HPQ)

Source: Yahoo! Finance, November 1, 2009 ( finance.yahoo.com ). Reproduced with permission of Yahoo!

50 45 40 35 30

HPQ as of 30-Oct-2009

HPQ 50–day MA

Jan09 Mar09 May09 Jul09 Sep09

A B

moving average chart for Hewlett-Packard. Notice that the moving average plot (the col- ored curve) is a “smoothed” version of the original data series (dark curve).

After a period in which prices have generally been falling, the moving average will be above the current price (because the moving average “averages in” the older and higher prices). When prices have been rising, the moving average will be below the current price.

When the market price breaks through the moving average line from below, as at point A in Figure 12.5 , it is taken as a bullish signal because it signifies a shift from a falling trend (with prices below the moving average) to a rising trend (with prices above the moving average). Conversely, when prices fall below the moving average, as at point B, it’s considered time to sell.

There is some variation in the length of the moving average considered most predictive of market movements. Two popular measures are 200-day and 53-week moving averages.

Example 12.4 Moving Averages

Consider the following price data. Each observation represents the closing level of the Dow Jones Industrial Average (DJIA) on the last trading day of the week. The 5-week moving average for each week is the average of the DJIA over the previous 5 weeks. For example, the first entry, for week 5, is the average of the index value between weeks 1 and 5: 10,290, 10,380, 10,399, 10,379, and 10,450. The next entry is the average of the index values between weeks 2 and 6, and so on.

Week DJIA

5-Week Moving

Average Week DJIA

5-Week Moving Average

1 10,290 11 10,590 10,555

2 10,380 12 10,652 10,586

3 10,399 13 10,625 10,598

4 10,379 14 10,657 10,624

5 10,450 10,380 15 10,699 10,645

6 10,513 10,424 16 10,647 10,656

7 10,500 10,448 17 10,610 10,648

8 10,565 10,481 18 10,595 10,642

9 10,524 10,510 19 10,499 10,610

10 10,597 10,540 20 10,466 10,563

Figure 12.6 plots the level of the index and the 5-week moving average. Notice that while the index itself moves up and down rather abruptly, the moving average is a rela- tively smooth series, because the impact of each week’s price movement is averaged with that of the previous weeks. Week 16 is a bearish point according to the moving average rule. The price series crosses from above the moving average to below it, signifying the beginning of a downward trend in stock prices.

Breadth The breadth of the market is a measure of the extent to which movement in a market index is reflected widely in the price movements of all the stocks in the market. The most common measure of breadth is the spread between the number of stocks that advance and decline in price. If advances outnumber declines by a wide margin, then the market is

viewed as being stronger because the rally is widespread. These numbers are reported in The Wall Street Journal (see Figure 12.7 ).

Some analysts cumulate breadth data each day as in Table 12.1 . The cumulative breadth for each day is obtained by adding that day’s net advances (or declines) to the previous day’s total. The direction of the cumu- lated series is then used to discern broad market trends. Analysts might use a moving average of cumulative breadth to gauge broad trends.

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 favorable omen of con- tinued price increases when they are

associated with increased trading volume. Similarly, market reversals are considered more bearish when associated with higher volume. The trin statistic is defined as

Trin5 Volume declining/Number declining Volume advancing/Number advancing Figure 12.6 Moving averages

10,700 10,600 10,500 10,400 10,300 10,200 10,100 10,000

Week

Dow Jones Industrial Average

1 3 5 7 9 11 13 15 17 19

DJIA

Moving Average

Figure 12.7 Market Diary

Source: The Wall Street Journal Online, November 2, 2009. Reprinted by permission of Dow Jones & Company, Inc. via Copyright Clearance Center, Inc. © 2009 Dow Jones & Company, Inc. All Rights Reserved Worldwide.

Issues Advancing Declining unchanged Total

Issues at

New 52 Week High New 52 Week Low

Share Volume Total

Advancing Declining Unchanged

NYSE 1,604 1,434 97 3,135

28 14

1,504,894,769 795,587,220 681,280,499 28,027,050

Amex 234 223 67 524

4 10

18,612,688 9,216,888 7,688,900 1,706,900 Nasdaq

1,277 1,414 108 2,799

25 65

2,397,479,912 1,226,163,683 1,121,231,398 50,084,831

4.02 p.m. EST 11/02/09

Trading Diary: Volume, Advancers, Decliners

Markets Diary

Therefore, trin is the ratio of average volume in declining issues to average volume in advancing issues. Ratios above 1.0 are considered bearish because the falling stocks would then have higher average volume than the advancing stocks, indicating net selling pressure.

Using the data in Figure 12.7 , trin for the NYSE on this day was 681,280,499/1,434

795,587,220/1,6045.96

Note, however, that for every buyer, there must be a seller of stock. Rising volume in a rising market should not necessarily indicate a larger imbalance of buyers versus sellers.

For example, a trin statistic above 1.0, which is considered bearish, could equally well be interpreted as indicating that there is more buying activity in declining issues.

Confidence Index Barron’s computes a confidence index using data from the bond market. The presumption is that actions of bond traders reveal trends that will emerge soon in the stock market.

The confidence index is the ratio of the average yield on 10 top-rated corporate bonds divided by the average yield on 10 intermediate-grade corporate bonds. The ratio will always be below 100% because higher rated bonds will offer lower promised yields to maturity. When bond traders are optimistic about the economy, however, they might require smaller default premiums on lower rated debt. Hence, the yield spread will narrow, and the confidence index will approach 100%. Therefore, higher values of the confidence index are bullish signals.

Put/Call Ratio Call options give investors the right to buy a stock at a fixed “exercise”

price and therefore are a way of betting on stock price increases. Put options give the right to sell a stock at a fixed price and therefore are a way of betting on stock price decreases. 23 The ratio of outstanding put options to outstanding call options is called the put/call ratio.

Typically, the put/call ratio hovers around 65%. Because put options do well in falling markets while call options do well in rising markets, deviations of the ratio from historical norms are considered to be a signal of market sentiment and therefore predictive of market movements.

23 Puts and calls were defined in Chapter 2, Section 2.5. They are discussed more fully in Chapter 20.

Table 12.1

Breadth

Day Advances Declines Net Advances Cumulative Breadth

1 1,302 1,248 54 54

2 1,417 1,140 277 331

3 1,203 1,272 269 262

4 1,012 1,622 2610 2348

5 1,133 1,504 2371 2719

Note: The sum of advances plus declines varies across days because some stock prices are unchanged.

CONCEPT CHECK

Yields on lower rated debt will rise after fears of recession have spread through the economy. This will reduce the confidence index. Should the stock market now be expected to fall or will it already have fallen?

Interestingly, however, a change in the ratio can be given a bullish or a bearish inter- pretation. Many technicians see an increase in the ratio as bearish, as it indicates growing interest in put options as a hedge against market declines. Thus, a rising ratio is taken as a sign of broad investor pessimism and a coming market decline. Contrarian investors, however, believe that a good time to buy is when the rest of the market is bearish because stock prices are then unduly depressed. Therefore, they would take an increase in the put/

call ratio as a signal of a buy opportunity.

A Warning

The search for patterns in stock market prices is nearly irresistible, and the ability of the human eye to discern apparent patterns is remarkable. Unfortunately, it is possible to perceive patterns that really don’t exist. Consider Figure 12.8 , which presents simulated and actual values of the Dow Jones Industrial Average during 1956 taken from a famous study by Harry Roberts. 24 In Figure 12.8B , the market appears to present 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 heading lower, and it is time to sell your stocks. Figure 12.8A also looks like a “typical”

stock market 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 12.8A is based on the real data. The graph in panel B was generated using “returns” created by a random-number generator. These returns

24 H. Roberts, “Stock Market ‘Patterns’ and Financial Analysis: Methodological Suggestions,” Journal of Finance 14 (March 1959), pp. 11–25.

Figure 12.8 Actual and simulated levels for stock market prices of 52 weeks

Source: Harry Roberts, “Stock Market ‘Patterns’ and Financial Analysis: Methodological Suggestions,” Journal of Finance 14 (March 1959), pp. 11–25. Reprinted by permission of the publisher, Blackwell Publishing, Inc.

Friday closing levels, December 30, 1955–December 28, 1956, Dow Jones Industrial Average A

5 10 15 20 25 30 35 40 45 50 525

520 515 510 505 500 495 490 485 480 475 470 465 460

Level

Week

5 10 15 20 25 30 35 40 45 50 485

480 475 470 465 460 455 450 445 440 435 430 425 420 B

Level

Week

V isit us at www .mhhe.com/bkm

by construction were patternless, but the simulated price path that is plotted appears to follow a pattern much like that of panel A.

Figure 12.9 shows the weekly price changes behind the two panels in Figure 12.8 . 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 mining. After the fact, you can always find patterns and trading rules that would have generated 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 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 question is whether there is reason to believe that what worked in the past should continue to work in the future.

Figure 12.9 Actual and simulated changes in weekly stock prices for 52 weeks

Changes from Friday to Friday (closing) January 6, 1956–December 28, 1956, Dow Jones Industrial Average 25

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−301 5 10 15 20 25 30 35 40 45 50 Week

Change

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Change

1. Behavioral finance focuses on systematic irrationalities that characterize investor decision mak- ing. These “behavioral shortcomings” may be consistent with several efficient market anomalies.

2. Among the information processing errors uncovered in the psychology literature are memory bias, overconfidence, conservatism, and representativeness. Behavioral tendencies include fram- ing, mental accounting, regret avoidance, and loss aversion.

3. Limits to arbitrage activity impede the ability of rational investors to exploit pricing errors induced by behavioral investors. For example, fundamental risk means that even if a security is mispriced, it still can be risky to attempt to exploit the mispricing. This limits the actions of arbi- trageurs who take positions in mispriced securities. Other limits to arbitrage are implementation costs, model risk, and costs to short-selling. Occasional failures of the Law of One Price suggest that limits to arbitrage are sometimes severe.

SUMMARY

Technical Analysis and Behavioral Finance

Financial Markets and the Economy

Stock and Bond Market Indexes