Issue 57, Winter/Spring 2002

Using the stock market principles outlined by Charles H. Dow, how
could we look at the long wave in stock prices?

Dow published The Wall Street Journal beginning in 1889 and, unfortunately, died in 1902. He wrote during a period of generally rising stock prices from the depression lows in the 1870s to the then all time high in 1901. During that period Dow formulated his theory of the stock market. It consisted of two important components: the cyclical nature of the markets and in the longer cycle, the “third wave,” the need for confirmation between economically different sectors, specifically the industrials and the railroads.

Following an earlier analogy between the stock market and ocean waves during the tidal cycle, Dow hypothesized in his famous Wall Street Journal editorial of January 4, 1902:

“Nothing is more certain than that the market has three well-defined movements which fit into each other. The first is the variation due to local causes and the balance of buying and selling at that particular time. The secondary movement covers a period ranging from 10 days to 60 days, averaging probably between 30 and 40 days. The third movement is the great swing covering from four to six years.”

Some technicians, especially cycle analysts, would quibble with the simplicity of Dow’s breakdown since there is evidence of other waves with periodicity between 40 days and four years. However, cycle analysts would also have to acknowledge that Dow’s breakdown is certainly accurate, though perhaps not inclusive, and that the periods he mentions are, remarkably, still the dominant cyclical movements today.

But Dow stopped short at the four- to six-year cycle, essentially the business cycle. He assumed that stock prices had an underlying uptrend about which these cycles oscillated. This was consistent with his experience at the time. Stock prices (see chart A, Dow Jones Industrial, 1885-1902) had wild gyrations during the late 19th Century, but the underlying trend was generally upward. He undoubtedly would have added a fourth wave, or “long wave,” had he lived to see the 1929-32 crash.

Aside from recognizing that the stock market had a pattern, which is the basis for technical analysis, Dow also recognized, in his theory of confirmation between the Industrial Average and the Railroad Average, that there must be an economic rationale for any signals given by the stock market price action. Most pure technicians conveniently overlook this because it diverges from a strict price analysis. Unfortunately, investment analysts have evolved into three camps since Dow – technicians, fundamentalists and academics – and as seems to be the way of human nature, they generally disregard the other’s work to reinforce their own identity. However, Dow was above all that, (or at least before it), and considered the economic rationale for a cyclical turn in the stock market just as important as the technical.

In the post-1929 era, we now know that the underlying long-term uptrend in stock prices can be severely interrupted. From looking at stock prices going back several hundred years we also note that the 1929-1932 decline was not an anomaly. It occurs with frightening regularity, roughly every 40 to 60 years (see Chart B, Dow Jones Industrial, Reconstructed, 1700-1940). We call this cycle the “long wave” and ponder on how Dow would have analyzed it.

As an aside, there are still many analysts, especially academics, who believe that the long wave is imaginary. Their thesis is based on the assumption that markets don’t have a “memory.” They argue that today’s prices are totally independent of yesterday’s, of last week’s, of last year’s and certainly of 50 years ago prices. Furthermore, since Fourier transforms and other sophisticated mathematical techniques have been unable to identify with certainty such cyclicality, it probably doesn’t exist. On the other hand, new experiments, especially those with non-linear mathematics, are beginning to knock down the “no memory” thesis. Edgar Peters, in his book Chaos and Order in the Capital Markets, suggests that the stock market has at least a four year memory. Professors McKinley and Lo from Wharton and MIT have demonstrated that stock price action is inconsistent with a “no memory” thesis and are now using non-linear mathematics to study prices. Professor Zhuanxin Ding from the University of California has shown that stock prices act as if they had long memories. Even simple moving averages, as studied by two professors at the University of Wisconsin, Dr. William Brock and Blake LeBaron, can generate profitable trading signals from prices alone, an inconsistency with the “no memory” thesis. The Economist wrote in a special section on the Frontiers of Finance on October 9, 1993:

“This was a shock for economists. Might chartists, that disreputable band of mystics, hoodwinking innocent fund managers with their entrail-gazing techniques and their obfuscatory waffle about double-tops and channel break-outs, be right more often than by chance? How could it be?”

Whether we believe in price memory or not, charts of stock prices since the South Sea Bubble in 1720 show that there are obviously times when the stock market experiences enormous, speculative rises and subsequent, disastrous declines. These major events occur at periods considerably longer than Dow’s four- to six-year movements. Furthermore, when we look at other economic data, such as commodity prices, GNP (even U. S. Post Office revenue), etc., we see the same long-term periodicity.

How would Charles Dow have looked at this long wave price action for signals? Probably he would have begun by looking only at the highs and lows of each four- to six-year cycle. Intermediate-term motion would be largely irrelevant to the long wave. Simplistically, he would likely have stated that the long wave was up when the tops and bottoms of the fouryear cycles were making new highs, and conversely, when the tops and bottoms were making new lows, the long wave was declining.

In the last 60 years, this approach would have missed the 1929 crash, but the ultra long-term investor would have sold his stocks in 1930 when the 1929 low, a four-year cycle low, was broken. It would also have told the investor in 1950 that the long wave was turning upward, that it was time to invest in the stock market. Unfortunately, there would have been several false signals. For example, in the 1970s, two four-year cycle lows broke below previous four-year lows, wrongly suggesting that the long wave was headed down again. Also, in the 1930s, after the initial bottom in 1932, several four-year cycle lows were broken between 1937 and 1949, suggesting that the long-term cycle was turning down again after having bottomed in 1932.

False signals also occurred in Dow’s original work on the four-year cycles and are the reason for his turn to confirmation between the Industrial and the Railroad averages. He based his confirming signals on the economic assumption that expansion in industrial profits could be a temporary anomaly but not if the produced goods were being shipped, by railroads, to customers. A confirmation between the two averages in either direction suggested that the new trend was real. 

Unfortunately, over the long wave, the theory of industrials versus railroads breaks down. First, over time, railroads are not always the principal form of transportation for goods (How do you ship the service industry? and how about canals in the 1830s?), and second, the  apparent cause for the long-wave has more to due with capital formation, debt and money than with industrial production.

Money has a price too – the interest rate. Interestingly, interest rates over the past several hundred years have also had a long wave that has corresponded in period, if not in turning points, with the stock market (see Chart C, U.S. Long-Term Interest Rates, Reconstructed, 1700-1940). For this reason, we assume Dow would have looked to the interest rate market for confirmation of a trend change in the long-term stock market.

Looking at interest rate trends, however, is not as simple as looking for a confirmation in trend between industrials and rails. Long wave interest rate cycles do not overlap precisely with long wave stock price cycles (see Chart D, U.S. Long-Term Interest Rates & Dow Jones Industrial Average, Reconstructed, 1700-1940). They will not “confirm” a move to new highs or lows as the rails will the industrials. It is important that one understand more about the history of the long wave direction in interest rates before a signal can be confirmed for the stock market.

The confusing aspect between long wave interest rates and the stock market is that sometimes both can be moving in the same direction and sometimes each can be moving in opposite directions. This is because stock prices have a corporate profit or growth component, as well as an interest rate or alternative investment component. In the former, stock prices rise as a result of economic growth, industrial expansion and profitability along with interest rates; in the latter, stock prices rise as an alternative investment to falling yields on fixed income securities. The latter, as we shall see, is more dangerous.

When we look at the evidence over the past several hundred years we see alternating periods of rising and falling interest rates. These are called “secular” moves and have to do with the expansion and contraction of capital and debt.

Notice in Chart D that the peak in interest rates always precedes the long wave peak in stock prices by many years. When interest rates and the stock market are both rising together, the industrial growth component is dominant. The period after interest rates peak is when stock prices rise as an alternative investment. During that period declining interest rates force yield-conscious investors into alternative investments of lesser quality in order to maintain yield. Since stocks are the most risky and least quality investments, they become the final alternative, especially when their price continues to appreciate as a result of increasing cash flow into the stock market. The recent conversion of government-guaranteed CD deposits into stock mutual funds is typical during this period. Unfortunately, it eventually leads to the declining long wave in stock prices.

Each declining stock market wave has occurred only during a secular decline in interest rates. Over the past several hundred years, you won’t see a long wave decline in stock prices while interest rates are rising. Declining interest rates at first can cause a financial speculation and an enormous rise as yield is chased through lesser quality, but eventually declining interest rates are unhealthy for the long wave in stock prices. With this in mind, Dow would likely have developed the following confirmation rules for the long wave in stock prices:

1. When four-year stock price cycles reach new highs and business-cycle interest rates are rising, the long wave is rising.
2. When four-year stock price cycles break below previous lows and business-cycle interest rates are rising, the long wave is rising.
3. When four-year stock price cycles break above previous highs and business-cycle interest rates are declining, the long wave has been given a warning but is still rising.
4. When four-year stock price cycles break down below previous lows and business-cycle interest rates are declining, the long wave is declining.
5. After a decline, the long wave will not turn up until business cycle interest rates also turn up.

Using this set of rules, let’s walk through the past 75 years using the accompanying Chart E of long-term U.S. interest rates and the Dow Jones Industrial Average since 1900.

From Dow’s death in 1902 both interest rates and the stock market rose.

p4

According to rule #1, the long wave was rising. Interest rates peaked in 1920 and declined through 1946. Declining interest rates are a warning to be confirmed later by a breakdown in the stock market. Thus, under rule #4, when the stock market broke to new lows in August 1930 (DJIA monthly mean = 231), it confirmed the long wave downturn.

During the 1930s and 1940s, while the initial bottom in 1932 turned out to be the actual bottom, the gyrations were large and the stock market trend generally flat. Interest rates declined until the end of World War II. Any upward breakout had to be taken skeptically (rule #5).

Finally, in March 1950, interest rates broke above their earlier business-cycle high (rule #5 and #1). Since rising interest rates are always accompanied by a rising stock market long wave, this was the buy signal. The DJIA was 249 at the time.

In the 1970s, the stock market broke below its prior four-year cycle lows in 1970 and in 1974. However, interest rates were still rising and thus the long wave was still rising (rule #2).

Interest rates finally peaked in September 1981. This was a warning (rule #3), similar to the interest rate peak in 1920, that the long wave was ending. Currently, the stock market has yet to break below a previous four-year cycle low and thereby confirm a new decline in the long wave.

The last four-year low was 2340 in the DJIA in 1990*. Should it be broken before a higher low is established, we will have confirmation of the downturn in the long wave. Would Charles Dow have looked at the long wave in this manner? We don’t know. But his principle of first observing price action simplistically and then confirming it with other markets, using some economic justification, gives us an excellent background for analysis of the long wave and teaches us to remain broad-minded and rational. His legacy is more than just a stock market theory. It is a way of thinking that transcends the narrow confines and pettiness of much investment analysis.

March 30, 1994

*Note: 8064 in the DJIA in 2001, the NASDAQ has already begun its long wave decline.

CDK, 2002

“The nature of risk is highly sensitive to whether we act before or after we have all the information in hand. This is just another way of saying that risk and time are only opposite sides of the same coin, because the availability of information increases with the passage of time. Thus, risk, time and information interact upon one another in complex and subtle ways.”

From keynote address by Peter L. Bernstein upon receiving the Inaugural Distinguished Scholar Award from the Southwestern Economic Association, Dallas, March 4, 1994.

The reader should keep in mind that any discussion of the financial markets is of necessity a discussion of constantly changing statistics and other data. This article was originally written in May 1994 and was submitted to the MTA Journal at that time. Therefore, while the data used herein were current as of May 20, 1994, such data applied to any specific situation described may no longer be applicable. The same caveat applies to the Sequel, which was written and submitted on November 11, 1994 market close, and briefly discusses how each of the theories or methods described herein worked or failed to work during the period subsequent to May 20,1994.

SYNOPSIS

This article outlines the core theories of Charles H. Dow and Edson Gould. Three of Gould’s methods used to forecast stock prices, which are based on quantifying investor psychology, are described and then illustrated using current data. Several forecasts are then made based on how Gould’s three methods and those of the author combine, in the author’s opinion, to operate in current financial markets. Future levels of interest rates, stock prices, an industry group, the technology sector, as well as two individual stocks, are estimated. A sequel, written six months after the original article was submitted, discusses how the forecasts turned out.

DOW’S THEORIES

The granddaddy of all stock market technical studies is the Dow Theory, which was originated by Charles H. Dow around the turn of the century. According to Dow, major bull or bear trends are indicated when the Dow Jones Industrial and Transportation averages, one after the other, set new highs or lows. A divergence between the indices often indicates a potential turning point in the underlying trend of the stock market. Dow set the stage for the later theories, still used and elaborated on by market analysts today, of what may broadly be defined as divergence analysis. That is the study of divergences among and between a broader universe of indices and indicators than were available to Dow. Dow’s theory was used in the context of his basic commandment: “To know values is to know the meaning of the market.”[1] But Dow also said that wise investors, knowing values above all else, buy them when there is no competition from the crowd. Indeed, they buy them from the crowd during periods of mass pessimism, and sell them to the crowd in return for cash during late stages of advancing markets. The stock market as a whole, said Dow, “represents a serious, well-considered effort on the part of farsighted and well-informed men to adjust prices to such values as exist or which are expected to exist in the not too remote future.”[2]

GOULD’S THEORIES

Edson Gould, who first studied the Dow Theory, was a practicing market analyst for more than fifty years between the early 1920s and late 1970s. His main focus was on forecasting the stock market. Though a student of physics and the harmonics of music, as well as business cycles and Greek civilization, each of which he believed helped explain certain aspects of how the stock market behaved, he came to believe, after reading Gustave LeBon’s classic, The Crowd,[3] that “the action of the stock market is nothing more nor less than a manifestation of mass crowd psychology in action.”[4]

The methods and techniques Gould utilized in his service, Findings & Forecasts, attempted to “…integrate the many economic, monetary and psychological factors that set the level and cause the changes in stock prices.”[5] He regarded the economic factors as important but typically late so far as the stock market is concerned. He regarded the monetary factors as crucial for the stock market and typically early. Whereas, he believed that, “Of all three sets of factors, the psychological factors are by far the most important – in fact, the dominant factors affecting the cyclical swings of stock prices.”[6]

THESIS

It follows from the above that one of the most important aspects of all in successfully analyzing the stock market is measuring investor sentiment. The consensus view, the most difficult factor of all to gauge accurately, can be glimpsed at times – and only in part-through not only such transaction-based data as put/call ratios, premiums and open interests, but also poll-based data such as the weekly Investors Intelligence reports of what percentage of investment advisors are bullish or bearish. Whereas the author regularly screens such data for extremes, the theories and methods which are derived from Gould and are discussed below are, in an of themselves, measures of the behavior of the investment crowd and, in his opinion, more practically useful in making and implementing investment decisions. And inasmuch as they are also applied to the monetary factors, a bond market opinion is derived therefrom, as well.

The index and stock charts used to illustrate this article are of:

1. Treasury Bonds Nearest Futures, Monthly
2. Treasury Bonds Nearest Futures, Weekly
3. New York Stock Exchange Financials Index, Weekly Standard & Poor’s 40 Utility Stock Composite, Weekly
4. Standard & Poors 400 Industrial Stocks Composite, Monthly
5. Drug Shares Index, Weekly Close (Sum of BMY, LLY, MKC, MRK, PFE, UPJ x 4.50541)
6. TXB-Hambrecht & Quist Technology Stock Index Less CBOE Biotechnology Stock Index, Weekly Average
7. Merck (MRK), Weekly
8. U.S. Robotics (USRX), Daily

GOULD’S METHODS AND TECHNIQUES

Edson Gould is, perhaps, best known for his monetary rule and valuation barometer: His Three-Step-and-Stumble Rule states that, “Whenever any one of the three rates set by monetary authorities – the rediscount rate, the rate for bank reserve requirements, and margin requirements on stocks – increases three times in succession … invariably … the stock market has subsequently … suffered a sizable setback.”[7] Whereas his Senti-Meter is, “the ratio of the Dow Jones Industrial Average to the average rate of annual cash dividends paid on that average. “[8] When the Senti-Meter reads $30 per $1 of dividends or more it indicates a high and risky market. A reading of $15 or less indicates a relatively low and cheap market.

Lesser known and, perhaps, too arcane for many, the author has found that three of Gould’s methods and techniques are more practically useful in helping decide when and at what levels a given stock or price index is “too” high, or “too” low and what constitutes a sentiment extreme. With this background in mind, let’s examine Gould’s theories and applications of Resistance Line Measurement, Unit Measurement and the Rule of Three, as well as the author’s theory of the Cut-in-Half principle and its opposites.

RESISTANCE LINE MEASUREMENT

According to Gould, “ … the market continually reveals a quantum of mass psychology comprising time and price. It follows that a sharp decline in a short period of time generates as much bearishness as a slow and minor decline over a long period of time.”[9] This theory,  then, is based on three principal determinants of crowd psychology in the market place: price change itself, elapsed time to achieve it and the perceived amount of risk.

The resistance line theory attempts to measure these three elements of mass psychology mathematically, weighing both the vertical price change and the horizontal elapse of time. This measure of potential risk or reward must be keyed off whatever the investor regards as any pair of prices which consist of an important high and low of the particular security’s price history. Four of the charts which are discussed below illustrate how the resistance lines are applied.

The theory is that a trendline rising at one-third (or two-thirds) the rate of an advance movement is likely to produce resistance to subsequent decline, but, if violated, the decline will accelerate from the point of penetration. Similarly, a trendline declining at one-third (or two-thirds) the rate of a decline movement may provide resistance to a subsequent advance, but, if penetrated, the advance will accelerate from that point. Sometimes these resistance lines work, sometimes they don’t; they are not foolproof. But the author uses resistance lines because they seem to be more accurate than ordinary trendlines and – most importantly – because they can be drawn before the subsequent price action takes place.

LONG TREASURY BONDS

Let’s see how resistance line theory may be helpful this year in gauging when Treasury Bonds Nearest Futures, which have been falling mostly since their September 1993 peak, etch a major low. Inasmuch as these Treasuries, Monthly (Chart 1) made a major low in 1981 at 55.156 and more than doubled it at the 1993 high of 122.313, the most important set of resistance lines derive from that low and that high. Referring to the chart, we observe that the May 11, 1994 low of 101.125 slightly broke the rising 2/3 speed line before reversing upward to close May 20 at 105.000. Important here are the facts that this same resistance line approximately defined each of the 1987, 1990 and 1991 Treasury lows. Translated into an opinion on May 20, this means that Treasuries won’t decisively break par this year. Should they do so, it might imply the onset of a renewed inflationary cycle. 

Moreover, the second of Gould’s methods, the unit measurement principle, also helps to determine the importance of the early-May intraday low of 101.125.

UNIT MEASUREMENT

This technique is sometimes helpful in estimating terminal phases of advances and declines, of both individual stocks and market indices. In other words, what constitutes a price which is “too” high or “too” low. Its measurements are expressed in terms of bull and bear “units.” A bull unit consists of the number of points of an initial advance by a stock or price index following the bottom of a prior important decline, succeeded by a subsequent reaction which, however, remains above that bottom and then is followed by a second advance that goes beyond the first one. A bear unit is formed in the same manner but in the opposite direction. These measurements sometimes portend the length of an overall advance (or decline) and indicate levels at which a trend may meet resistance, or, at times, an extreme reversal.

Price action with the primary trend frequently “works off” units three times (sometimes four times), in accordance with the Rule of Three, the basis of which is discussed below. In other words, for a move with the trend, expect three units, but be prepared for the fourth. One other important point about unit measurement is that recognition of the 2-unit level, by a sharp reaction from it, often indicates that following such a reaction the security will go all the way and work off three, or four units. Whereas recognition of that level which is defined by 2-l/3 units, without recognition of the 2-unit level (by resistance from it), is grounds for caution, especially for trend followers, since that is often the hallmark of a contratrend move.

Now we are equipped to develop a second opinion about Treasury Bonds Nearest Futures, Weekly, which is illustrated in Chart 2. Referring to the chart, we observe that these Treasuries etched a bear unit of 4-7/8 points by their initial September 7-22, 1993 decline from 122.313 to 117.438. According to unit measurement theory, then, the contratrend, upward reaction from the 2-bear unit level of 112.563, which was reached at the November 23 low of 112.031, implied that Treasuries would go down all the way – to work off either 3 or 4 bear units to 107.688, or 102.813, respectively. With the actual intraday May 11 low of 101.125, this close – less than 2 percent away-recognition of the theoretically maximum 4-bear unit count, also leads to the conclusion that that was a low in Treasuries of major importance.

INTEREST-SENSITIVE STOCK MARKET INDICES

Because of the importance of the monetary factors, ideally the resistance line and unit measurement theories should also be reflected in interest-sensitive stock market indices. Sure enough, the New York Stock Exchange Financials and Standard & Poors 40 Utilities indices (both on Chart 3) did, so far, in 1994 faithfully reflect both resistance line measurement and unit measurement theory, respectively. Referring to the chart, we observe that the Financials’ week of April 8, 1994 low of 200.01 and all subsequent lows, which were higher (itself a positive divergence), reversed upward above the rising 2/3 speed resistance line from the 1990 low. Gould always said that the ability of a price index to stay above its rising 2/3 speed resistance lines during reactions was the hallmark of a powerful advance.

Whereas the S&P 40 Utilities, which etched a bear unit of 10.29 points by the September 17-October 15, 1993 decline from 189.49 to 179.20, worked off a fairly precise 4-bear unit count to 148.33, compared to the actual May 13 low of 146.85. Close enough. Moreover, these Utilities also respected their rising 2/3 speed resistance line from the 1981 low, which approximated this 4-bear unit count.

It logically follows from each of these two theories that should the aforesaid risk parameters of these three interest-sensitive indices – Treasuries, NYSE Financials, S&P 40 Utilities – be decisively downside penetrated on a closing basis that the bear market in bonds not only had more to go on the downside but also that stocks might have begun a bear market as well. However, the author does not believe that is the case at May 20, 1994, as we examine next.

A STOCK MARKET ROAD-MAP

Gould also said that over longer periods of time unit measurement was useful, too: “A ‘grand unit’ is, as the name implies, a big unit sometimes taking months to complete and years to confirm.”[10] We think this theory has been remarkably accurate since the stock market’s 1982 low and that it is relevant now. Referring to Standard & Poors 400 Industrials (Chart 4) we observe that during the 14-month long 1982-1983 advance from 113.08 to 195.25, a grand bull unit of 82.19 points was etched – the l0-month-long 1983-1984 decline to 167.64 not exceeding the 1982 low.

Thereafter, the S&P 400 steadily rose until hitting the 1986 peak of 282.87, which was less than 2 percent above the 2-bull unit count at 277.46. The subsequent 12 percent reaction to that year’s September low of 252.07 constituted recognition that the unit measurement principle was operative, and that the S&P 400 would go on to work off at least 3 or 4 bull units.

From the 1986 low, the S&P 400 gathered steam and began to accelerate in 1987, reaching the 3-bull unit count of 359.65 in June. That level was potentially an important peak level in accordance with the theory – expect three units. During the next two months the S&P 400 overshot the 3-bull unit count but peaked 9.7 percent higher (intraday) in August, followed by the crash.

From the 1987 crash low, stocks steadily rose until hitting the July 1990 peak of 438.56 which was less than one percent below the 4-bull unit count to 441.84. That was a perfect fourth and final move, according to Gould’s unit measurement theory. During the next three months stocks fell by 21 percent.

Of current relevance, in the author’s opinion and experience, is that sometimes unit counts will work off a double set of units, i.e., 6 or 8 units. This appears to be the current case for the S&P 400 Industrials, which, rising from the October 1990 low of 345.79, recognized the five-bull unit count to 524.03 repeatedly last year by resisting further advance. However, by late-1993 that level was decisively exceeded. This means to us that the theory is saying the stock market should continue to rise until reaching at least the 6-bull unit count to 606.22, before the bull market which began from the 1982 low is over.

In 1994 the S&P 400 Industrials advanced further to reach the 560.88 level in February, before reacting to the April 20th low of 507.36, a drop of 9.5 percent. Referring again to Chart 4, we further observe that during the February-April reaction the rising 2/3 speed resistance line from the 1990 low, which during April was at the 500 level, was effective in defining that month’s low. This means we believe current risk from the May 20th close of 530.88 approximates four percent, say 510, whereas potential reward – to 606.22 – would be a gain of 14 percent. Those seem like good odds.

THE RULE OF THREE

Now, we examine the third of Edson Gould’s theories, the Rule of Three. For reasons about which people have speculated for thousands of years, the number “three” and “four” have a meaning of finality about them. For example, Aristotle said, the “Triad is the number of the whole, inasmuch as it contains a beginning, a middle and an end.” This concept may be deeply rooted in the natural family unit of father, mother and child, which is given religious expression in the concept of the Holy Trinity. The financial markets, which, after all, reflect human emotions, also frequently act in the same way. Sometimes there is a fourth movement, which usually is characterized as a “now and never” action, climactic in nature. (Three strikes you’re out; four balls take a walk). That financial markets and individual stocks typically – but not always – move in a series of three or four steps is apparent in both very short-term moves as well as those encompassing months and even years.

DRUG SHARES INDEX

However, to simply illustrate the Rule of Three we next examine the Drug Shares Index (Chart 5), a composite of Bristol Meyers, Lilly, Marion Merrill Dow, Merck, Pfizer and Upjohn. Between January 8, 1992 and August 13, 1993 the Drugs dropped 42.8 percent in a classic bear market, which consisted of four steps down. Also, helping define the fourth step as the final one was the fact that the August 13, 1993 low of 1011.46 closely approximated the 3-bear unit count from the 1992 peak, at 1012.93.

After rallying 20 percent from the 1993 low, to 1217.04 on January 14, 1994, the Drugs came down again to etch a successful test of last year’s low, at the April 15, 1994 low of 1014.84. In other words, we are confident that a classic double bottom has been put in place for this group. Additionally, as illustrated later, another yardstick of extreme investor behavior targeted both Lilly and Merck as having etched final lows last year and this year.

TECHNOLOGY AND GROWTH STOCKS

No discussion of the stock market would be complete without addressing the role of the technology and growth sectors. They are important not only be- cause they often represent the fastest growing companies, but also, as I stated in my book which was first published in 1970, “…glamour/ growth stocks which, because they are highly volatile – ordinarily two-and-a-half times more so than those in the DJIA – are favorite vehicles of sophisticated investors.”11 This volatility provides greater time opportunity than is available in the behavior of most other stocks.

Edson Gould, “put together the first ‘glamour average’ back in 1960,”[12] though, surprisingly, the pamphlet, A Vital Anatomy, from which we’ve also earlier quoted various Gould statements about his theories and methods, says nothing whatsoever about “glamour” stocks. Having originally gotten this idea from Gould in the late 1960s I created my own “Glamour Price Index,” which consisted of the stocks of eleven highly-regarded, wellknown, technology-oriented companies.[l3] However, in the most recent edition of my book, I noted that in recent years I’ve scrapped my original “Glamour” and several other technology- or growth-based indices in favor of the more representative Hambrecht & Quist Technology Stock Index[14] and its sub-index of even more rapidly growing, smaller companies, the H&Q Growth Stocks Index. But inasmuch as the H&Q indices include stocks in the biotechnology sector, which I believe march to a different tune than other growth and technology types, I also have created two other indices which consist of the numerical values of each of the respective H&Q indices less the CBOE Biotechnology Stock Index. Hence, in the technology and growth sectors, we examine these five different indices:

1. H&Q Technology Stock Index, which is comprised of the publicly traded stocks of 200 technology companies, broadly defined in five basic groups: Computer Hardware, Computer Software, Communications, Semiconductors, Health Care (within which is a Biotechnology sub-index). The index was originally conceived in the 1970s as a priceweighted index. In 1985 it was reconstructed and market capitalization weighted. Changes in the index occur as mergers, acquisitions and failures dictate – not infrequently.
2. H&Q Growth Stock Index is a subset of the Technology Index and is comprised of all companies in the Technology Index which have annual revenues of less than $300 million. Companies are removed every January if they have passed $300 million in revenues.
3. CBOE Biotechnology Stock Index
4. TXB Index, which is the H&Q Technologies Excluding Biotechs
5. GXB Index, which is H&Q Growth Stocks Excluding Biotechs

THE TXB INDEX

Of these five indices, the author thinks the TXB Index is both the most representative of the overall technology sector as well as being the most orthodox in reflecting investor psychology We examine it next. Referring to Chart 6, we observe that between their respective 1990 lows and 1994 highs (through May 20, 1994), whereas the DJIA gained almost 71 percent and the Dow Transportations rose more than 131 percent, the TXB Index more than tripled. So much for volatility!

We also observe that at its March 18, 1994 peak, the TXB Index completed a third step up from its 1991 low. In accordance with the Rule of Three, this allows for either the possibility that that was a final step, or allows for the emergence of a fourth and final higher high, after the current reaction is over. We favor the latter possibility and believe Gould’s two other theories provide well-defined potential risk and reward parameters for the outcome we envisage.

As to risk in the TXB Index, which at May 20, 1994 was down 14-l/2 percent from its March 18 peak, it must not break below the rising 2/3 speed resistance line from the 1991 low, in order to maintain its bullish uptrend, in accordance with Resistance Line Theory. Inasmuch as TXB closed at 303.15 on May 20, and the aforesaid 2/3 speed line was nearing the 287 level, that means we think that risk of this date approximates 5 percent.

Whereas potential reward of a possible fourth and final rise of the TXB Index we think may be estimated through the Unit Measurement method. Referring again to Chart 6, we further observe that the TXB Index etched a bull unit of 99.96 points by its initial advance from the September 20, 1991 low of 112.29 to the April 24, 1992 high of 212.25. The 2-bull unit level of 312.21 was briefly recognized by its one-week reaction from near that level in early 1994, before advancing to the higher March 18 all-time high. Assuming then, that the aforesaid resistance line risk parameter holds on the current reaction, we believe that potential reward from the May 20 level is about 35 percent to the 3-bull unit count at 412.17.

These sound like favorable odds of 7-to-1 between possible risk and reward, in the author’s opinion. I note, too, that an overhead trend, which is projected through the 1992, 1993 and March-1994 peaks and which also parallels the rising 2/3 speed resistance line, approximates the 400 level by year-end 1994, as well. In other words, the author believes that the TXB Index will rise by about one-third before this sector is vulnerable to a bear market.

BEAR MARKET

After the reward area is approximated, that’s from where I think a bear market in technology and growth, as well as one for the stock market, overall, may begin. That there will likely be a bear market between now and 1995 is suggested by the facts that every single “5” year in this century has been an up year, which means that there “should” be an intervening bear market before the 1995 bull market begins. However, an alternative scenario is simply that it will take between now and year-end 1994 for the reward area to be reached.

If that proves to be the case and the stock market rises to record levels and nears the potential reward areas we have outlined herein, by year-end 1994 (possibly narrowly extending into early 1995), that would be the fourth consecutive up year – a possible “final” up year, according to the Rule of Three. In that event, 1995, especially if perceived by “too” many as always an up year since it is a “5” year, would then set the stage for it to become the first down “5” year during the past century, in the author’s opinion, i.e., 1995 happens in second-half 1994.

THE CUT-IN-HALF RULE AND ITS OPPOSITE

The fourth gauge for measuring investor extremes is conceptually the simplest of all – the Cut-In-Half Rule and its opposite. Briefly stated, when an important stock or price index loses 50 percent of its value, a rally or even major reversal often originates from near that level. Keep in mind that the Cut-In-Half Rule and a 50 Percent Retracement are quite different. For example, two stocks each base at the level of 50 and both rise to 100. If one declines to 75, before advancing once again, it has retraced 50 percent of its advance from 50 to 100. However, if the other one drops back to 50 from 100, it has been “cut-in-half.” A textbook example of the Cut-in-Half Rule is shown in Chart 7 of Merck, which we discuss below.

Why the Cut-In-Half Rule and its related spinoffs often work is probably because the investor crowd quantifies 50 percent off the top as “too” cheap. Whereas the opposite is that after an important stock or index doubles it often runs into trouble. At that point, investors tend to take at least some profits. But since some indices, individual stocks, commodities and interest-bearing securities are more volatile than others, this same yardstick is sometimes extended on the way up to a triple, quadruple, quintuple, or even a sextuple, (with low price stocks sometimes squaring their lows). Whereas, on the way down there is sometimes a double cut-in-half (off 75 percent), or – more rarely – a triple cut-in-half (87-l/2 percent off the top). Keep in mind, however, that in applying these Cut-In-Half yardsticks as potential long entry points, one should be satisfied that the company’s balance sheet is not in serious question.

STOCK SELECTIONS

Though it can be repeatedly demonstrated that these four theories of investor behavior are constantly operating in all financial markets, in the author’s opinion, it does not necessarily follow that one can readily use them in every instance. Sometimes the units are not readily discernible and the resistance lines don’t work. Moreover, sometimes there is a fifth step in an overall advance or decline movement, which appears to contradict the Rule of Three – though a case might be made that such a fifth step represents an undercut (or overcut) test of the fourth step.

However, after using these theories over time to make day-to-day investment decisions, I have found that they are valuable when discernible and add confidence to a decision. That is particularly the case when more than one theory appears to be operative in a given situation.

For example, referring to Chart 7 of Merck (MRK @ 30-l/4), we can observe that when it closely approximated its theoretical cut-in-half level of 28-9/ 32 during August 1993, at the actual low of 28-5/8, on a fourth (and presumably final) step down from the January 3, 1992 peak of 56-9/16, it appears to have constituted a classic buying juncture. Thereafter rallying to 38 by January 5, 1994, Merck subsequently tested last year’s low at this year’s April 15, 18 lows both at 28-1/8. This makes me confident that the cut-in-half level was, or  approximated, a final low, especially since there is no great mystery about Merck’s fundamentals and fifty percent off the top seems a reasonable – if not “too” great – a discount for those investors critical of the Clintons’ health care plans.

More volatile technology and growth stocks sometime reflect these theoretical principles of how crowd behavior plays out in the financial markets in an extraordinary way. For example, referring to Chart 8 of U.S. Robotics (USRX @ 30-3/4), a world-wide leader in data communications, we can observe that after doubling the late-1991 low of 12-l/4 by the early-1992 high of 24-1/4, Robotics dropped sharply (off 45 percent). Whereas the early-1993 high of 25-1/2 almost doubled the summer-1992 low of 13-3/8. Then the March 1993 low of 17 was slightly more than doubled at the October high of 35-1/4, whereas the subsequent decline to 23 worked off an almost-perfect 4-bear unit count to 23-1/4.

Moreover, this year’s high of 46 (on March 8) was a perfect double, from which a reaction has begun, with a bear unit of 5-1/4 points already etched and confirmed (by a lower low), and more recently appearing to recognize the 3-bear unit level of 30-1/4, by the May 10-11 lows of 29-1/4, as the new low from which to key off. That the stock of a single company could have gone through so many extreme bull and bear moves, in such a short period of time, shows not only that Alvin Toffler’s “Future Shock”[15] has arrived on Wall Street but also that traditional Wall Street research is incapable of dealing with it effectively. The arrival of “future shock,” what some now call the information age, also presents a challenge to stock market technicians – to do their homework in order to stay ahead of the curve.

May 22, 1994

SEQUEL
At Market Close November 11,1994: What Happened During the Subsequent Six Months

Treasury Bonds Nearest Futures (Charts 1 and 2) perfectly tested their May 11 low at their virtually identical July 11 low of 100.0625 – compared to the May 11, 100.2500 (the numerical value of the Futures are about one point lower than shown on the chart because the Nearest Futures had rolled over from June’s to September’s, and currently December’s). Thereafter Treasuries rallied back to the August 5 high of 105.21875, then slowly eroded until par was broken at the September 22 close of 99.40625.

At that point we conceded the 13-year long uptrend in Treasuries was clearly broken and that the major trend inference of bonds should be assumed as being down. By November 11, Treasuries slumped even more, closing at 96.0625. Moreover, since this break of the grand resistance line of Treasuries also took out the 4-bear unit count level, it implies to us that ultimately at least six bear units will be worked off. That level is 93.0625. However, we never changed our positive stock market opinion because the two interest-sensitive stock market bellwethers we mostly rely on remained intact, notwithstanding the break in bonds.

The NYSE Financials(Chart 3), which had hit an intraday low of 199.95 on April 4, closing May 20 at 214.27, slightly exceeded the 220 level during four days in June, then also slowly eroded until closing November 11 exactly at 199.56. While this does constitute a break of its resistance line, and hence is clearly negative as of November 11, it seems such an obvious “test” of the April 4 low, that it is conceivable to us the Financials may be able to mount at least a weak rally from here.

We draw this tentative conclusion because the third interest-sensitive index, Standard & Poors 40 Utility Stock Index (also on Chart 3), which closed November 11 at 148.51 – still above its May 13 low – we don’t think will take out that level. Not only has the 4-bear unit count level of 148.33 been repeatedly and successfully tested during 15 trading days in October and November but is also defined by these Utilities’ rising 2/3 speed resistance line from the March 1980 low. That is about as precise recognition of a Gouldian-defined risk parameter as it ever gets! Naturally, this also means that a decisive break of it would undoubtedly require some change in our current stock market opinion.

Our Stock Market Road-Map for Standard & Poors 400 Industrials (Chart 4 and Chart 9), successfully tested the April 20 low (507.30) at the June 27 low of 511.90, thereafter rising to an all time high of 564.50 on October 31. Closing November 11 at 550.87, potential reward has now moved down to only 10 percent whereas near-term risk remains about 4 percent (the rising 2/3 speed resistance line moving up to about 527). Not quite as good odds as on May 20.

The Drug Shares Index (Chart 5) was up almost 18 percent at November 11 from May 20 and we think is headed substantially higher. Though gaining almost 30 percent from its April low at the November 11 close of 1316.70, we think the Drugs will work off at least three bull units, a further gain of 25 percent from here. Three bull units were worked off on the way down, so why not three bull units on the way up?

The TXB Index (Technologies Less Biotechs) (Chart 6 and Chart 9), at its June 23 daily close of 293.97, never broke below its 2/3 speed resistance line risk parameter and subsequently rose 28.9 percent from that low to 378.83 on November 9. Obviously the odds of further gain from here have sharply deteriorated, potential remaining reward only a possible additional 8.8 percent, in our opinion. We have chosen to deal with this change of the odds by building cash as specific technology and growth components reach their individual, respective potential reward zones.

Merck (MRK @ 36-3/4) (Chart 7) hit a recovery high of 37-5/8 on November 10 and we believe is headed into the 44-45 zone. That is defined by both a bull unit count and an overhead declining 1/3 speed resistance line.

Whereas U.S. Robotics(USRX @ 38-3/4) (Chart 8) worked off a fourth bear unit at its June 2nd low of 24, then etched a new bull unit of 5-1/2 points by its subsequent initial rise to 29-1/2. USRX went on to slightly exceed the 3-bull unit count of 40-1/2. at the November 9 high of 42-1/4. The maximum upside potential we see from here, is a 4-bull unit count to 46, which would also be a prospective double top with the early-1994 peak.

CONCLUSION

I believe that this real-time experience in using the Gouldian theories amply demonstrates both their usefulness as well as their drawbacks, though only scratching the surface of their potential applications. Their key advantages are that Gould’s quantifications of investor sentiment help one to both reach and act upon specific investment conclusions on a case by case basis, without being held hostage to an endless, self-imposed debate about what to do.

REFERENCES

1. Why Most Investors Are Mostly Wrong Most of the Time, W. X. Scheinman, 1991, Fraser Publishing Company (p. 139)
2. Scheinman, ibid
3. The Crowd, G. LeBon, 1896, Fraser Publishing Company (1982)
4. A Vital Anatomy, E. Gould, (Undated), Anametrics, Inc.
5. Gould, ibid
6. Gould, ibid
7. Gould, ibid
8. Gould, ibid
9. Gould, ibid
10. .Gould, ibid
11. Scheinman, ibid
12. Gould, ibid
13. Scheinman, ibid
14. Hambrecht & Quist Technology and Growth Indices, Michael De Witt and Shiela Ennis, Hambrecht & Quist Incorporated, January 1993
15. Future Shock, A. Toftler, 1971, Bantam Doubleday

BIBLIOGRAPHY
Numbers

• Jung, C.G., Collected Works of C.G. Jung, General Index, (Volume 20, pp. 485-489, “Numbers”), Princeton University Press, 1979
• Menninger, K., Number Words and Number Symbols; A Cultural History of Numbers, The M.I.T Press, 1970
• Von Franz, M-L, Number and Time, Northwestern University Press, 1974

Technology

• Veblen, T., Imperial Germany and the Industrial Revolution, Transaction Publishers (1990 reprint)

“This indicator has always produced huge profits! In fact, you would have doubled your money in just six months!”

Such a claim could be a sales pitch. It could also be an analyst’s enthusiasm about some work just completed. But in either case, such claims appear to be meeting increasing skepticism, perhaps because enough have proven to be based more on fiction than quantifiable fact, perhaps because enough investors have been burned by indicators that have failed to pan out when put to real-time use, perhaps because the combination of everstrengthening computing power and ever-increasing program complexity have made excessive optimization as easy, and dangerous, as ever.

In any case, the need to quantify accurately and thoroughly is greater than ever. Honest and reliable quantification methods, used in the correct way, are needed for increased research credibility. They are needed to impart objectivity. They are needed for effective analysis and for the sound backing of research findings. The alternative is the purely subjective approach that uses trendlines and chart patterns alone, making no attempt to quantify historical activity. But when the quantification process fails to deliver, instead producing misleading messages, the subjective approach is no worse an alternative – a misguided quantification effort can be worse than none at all. The predicament, then, is how to truly add value through quantification.

THE CONCERNS

The major reason for quantifying results is to assess the reliability and value of a current or potential indicator, and the major reason we have indicators is to help us interpret the historical data. The more effective the interpretation of historical market activity, the more accurate the projection about a market’s future course. An indicator can be a useful source of input for developing a market outlook if quantitative methods back its reliability.

But for several reasons, quantification must be handled with care. The initial concern is the data used to develop an indicator. If it’s inaccurate, incomplete, or subject to revision, it can do more harm than good, issuing misleading messages about the market that’s under analysis. The data should be clean and contain as much history as possible. When it comes to data, more is better – the greater the data history, the more numerous the like occurrences, and the greater the number of market cycles under study.

This leads to the second quantification concern, and that’s sample size. The data may be extensive and clean, and the analysis may yield an indicator that foretold the market’s direction with 100% accuracy. But if, for example, the record was based on just three cases, the results would lack statistical significance and predictive value. In contrast, there would be fewer questions regarding the statistical validity of results based on more than 30 observations.

The third consideration is the benchmark, or the standard for comparison. The test of an indicator is not whether it would have produced a profit, but whether the profit would have been any better than a random approach, or no approach at all. Without a benchmark, “random walk” suspicions may haunt the results.[1]

The fourth general concern is the indicator’s robustness, or fitness – the consistency of the results of indicators with similar formulas. If, for example, the analysis would lead to an indicator that used a 30-week moving average to produce signals with an excellent hypothetical track record, how different would the results be using moving averages of 28, 29, 31, or 32 weeks? If the answer was “dramatically worse,” then the indicator’s robustness would be thrown into question, raising the possibility that the historical result was an exception to the rule rather than a good example of the rule. An indicator can be considered “fit” if various alterations of the formula would produce similar results.

Moreover, the non-robust indicator may be a symptom of the fifth concern, and that’s the optimization process. In recent years, much has been written about the dangers of excessive curvefitting and over-optimization, often the result of unharnessed computing power. As analytical programs have become increasingly complex and able to crunch through an ever-expanding multitude of iterations, it has become easy to over-optimize. The risk is that armed with numerous variables to test with minuscule increments, a program may be able to pick out an impressive result that may in fact be attributable to little more than chance. The accuracy rate and gain per annum columns of Figure 1 compare results that include an impressive-looking indicator that stands in isolation (top) with indicators that look less impressive but have similar formulas (bottom). One could have far more confidence using an indicator from the latter group even though none of them could match the results using the impressive-looking indicator from the top group.

What follows from these five concerns is the final general concern of whether the indicator will hold up on a real-time basis. One approach is to build the indicator and then let it operate for a period of time as a real-time test. At the end of the test period, its effectiveness would be assessed. To increase the chances that it will hold up on a real-time basis, the alternatives include out-of-sample testing and blind simulation. An out-of-sample approach might, for example, require optimization over the first half of the date range and then a real-time simulation over the second half. The results from the two halves would then be compared. A blind-simulation approach might include optimization over one period followed by several tests of the indicator over different periods.

Whatever the approach, real-time results are likely to be less impressive than results during an optimization period. The reality of any indicator developed through optimization is that, as history never repeats itself exactly, it is unlikely that any optimized indicator will do as well in the real-time future. The indicator’s creator and user must decide how much deterioration can be lived with, which will help determine whether to keep the indicator or go back to the drawing board.

TRADE-SIGNAL ANALYSIS

With the general concerns in mind, the various quantification methods can be put to use. The first, and perhaps most widely used, is the approach that relies on buy and sell signals, as shown in Figure 2.[2] When the indicator meets the condition that it deems to be bullish for the market in question, it flashes a buy signal, and that signal remains in effect until the indicator meets the condition that it deems to be bearish. A sell signal is then generated and remains in effect until the next buy signal. Since a buy signal is always followed by a sell signal, and since a sell signal is always followed by a buy signal, the approach lends itself to quantification as though the indicator was a trading system, with a long position assumed on a buy signal and closed out on a sell signal, at which point a short position would be held until the next buy signal.

The method’s greatest benefit is that it clearly reveals the indicator’s accuracy rate, a statistic that’s appealing for its simplicity – all else being equal, an indicator that had generated hypothetical profits on 30 of 40 trades would be more appealing than an indicator that had produced hypothetical profits on 15 of 40 trades. Also, the simulated trading system can be used for comparing a number of other statistics, such as the hypothetical per annum return that would have been produced by using the indicator. The per annum return can then be compared to the gain per annum of the benchmark index.

But the method’s greatest benefit may also be its biggest drawback. No single indicator should ever be used as a mechanical trading system – as stated earlier, indicators should instead be used as tools for interpreting market activity. Yet, the hypothetical and actual can be easily confused. Although the signal-based method specifies how a market has done between the periods from one signal to the next, they are not actual records of real-time trading performance. If they were, the results would have to account for the transaction costs per trade, with a negative effect on trading results. Figure 3 summarizes the indicator’s hypothetical trade results before and after the inclusion of a quarter-percent transaction cost, illustrating the impact that transaction costs can have on results. The more numerous the signals, the greater the impact.

Also, as noted in the results, another concern is the maximum draw-down, or the maximum loss between any consecutive signals. But again, as long as it is clear that the indicator is for perspective and not for dictating precise trading actions, indicators with trading signals can provide useful input when determining good periods for entering and exiting the market in question.

ZONE ANALYSIS

In contrast to indicators based on trading signals, indicators based on zone analysis leave little room for doubt about their purpose – they don’t even have buy and sell signals. Rather, zone analysis recognizes black, white and one or more shades of gray. It quantifies the market’s performance with the indicator in various zones, which can be given such labels as “bullish,” “bearish” or “neutral” depending upon the market’s per annum performance during all of the periods in each zone. Each period in a zone spans from the first time the indicator enters the zone to the next observation outside of the zone. Unlike the signal-based approach, the indicator can move from a bullish zone to a neutral zone and back to a bullish zone. An intervening move into a bearish zone is not required.

Zone analysis is therefore appealing for its ability to provide useful perspective without a simulated trading system. The results simply indicate how the market has done with the indicator in each zone. But this type of analysis has land mines of its own. In determining the appropriate levels, the most statistically-preferable approach would be to identify the levels that would keep the indicator in each zone for roughly an equal amount of time. In many cases, however, the greatest gains and losses will occur in extreme zones visited for a small percentage of time, which can be problematic for several reasons:

1. if the time spent in the zone is less than a year, the per annum gain can present an inflated picture of performance;
2. if the small amount of time meant that the indicator made only one sortie into the zone, or even a few, the lack of observations would lend suspicion to the indicator’s future reliability;
3. the indicator’s usefulness must be questioned if it’s neutral for the vast majority of time.

A good compromise between optimal hypothetical returns and statistical relevance would be an indicator that spends about 30% of its time in the high and low zones, like the indicator in Figure 4. For an indicator with more than four years of data, that would ensure at least a year’s worth of time in the high and low zones and would make a deficiency of observations less likely. In effect, the time-in-zone limit prevents excessive optimization by excluding zone-level possibilities would look the most impressive based on per annum gain alone.

Another consideration is that in some cases, a closer examination of the zone performance reveals that the bullish-zone gains and bearish-zone losses occurred with the indicator moving in particular directions. In those cases, the bullish or bearish messages suggested by the per annum results would be misleading for a good portion of the time, as the market might actually have had a consistent tendency, for example, to fall after the indicator’s first move into the bullish zone and to rise after its first move into the bearish zone.

It can therefore be useful to subdivide the zones into rising-in-zone and falling-in-zone, which can have the added benefit of making the information in the neutral zone more useful. This requires definitions for “rising” and “falling.” One way to define those terms is through the indicator’s rate of change. In Figure 5, which applies the approach to the primary stock market model used by Ned Davis Research, the indicator is “rising” in the zone if it’s higher than it was five weeks ago and “falling” if it’s lower. Again, the time spent in the zones and the number of cases are foremost concerns when using this approach.

Alternatively, “rising” and “falling” can be defined using percentage reversals from extremes, in effect using zones and trading signals to confirm one another. In Figure 6, for example, the CRB Index indicator is “rising” and on a sell signal once the indicator has risen from a trough whereas it’s “falling” and on a buy signal after the indicator has declined from a peak. Even though the reversal requirements resulted from optimization, the indicator includes a few poorly-timed signals and would be risky to use on its own. But the signals could be used to provide confirmation with the indicator in its bullish or bearish zone, in this case the same zones as those used in Figure 4. For example, in late 1972 and early 1973 the indicator would have been rising and in the upper zone, a confirmed bearish message. The indicator would then have peaked and started to lose upside momentum, generating a “falling” signal and losing the confirmation. That signal would not be confirmed until the indicator’s subsequent drop into its lower zone.

The chart’s box shows the negative hypothetical returns with the indicator on a sell signal while in the upper zone, and on a buy signal while in the lower zone. In contrast to the rate-of-change approach to subdividing zones, this method fails to address the market action with the indicator in the middle zone. But it does illustrate how zone analysis can be used to in conjunction with trade-signal analysis to gauge the strength of an indicator’s message.

SUBSEQUENT-PERFORMANCE ANALYSIS

In addition to using signals and zones, results can be quantified by gauging market performance over various periods following a specified condition. In contrast to the trade-signal and zone-based quantification methods, a system based on subsequent performance calculates market performance after different specified time periods have elapsed. Once the longest of the time periods passes, the quantification process becomes inactive, remaining dormant until the indicator generates a new signal. In contrast, the other two approaches are always active, calculating market performance with every data update.

The subsequent-performance approach is thus applicable to indicators that are more useful for providing indications about one side of a market, indicating market advances or market declines. And it’s especially useful for indicators with signals that are most effective for a limited amount of time, after which they lose their relevance. The results for a good buy-signal indicator are shown in Figure 7, which lists market performance over several periods following signals produced by a 1.91 ratio of the 10-day advance total to the 10-day decline total.

In its most basic form, the results might list performance over the next five trading days, 10 trading days, etc., summarizing those results with the average gain for each period. However, the results can be misleading if several other questions are not addressed. First of all, how is the average determined? If the mean and the median are close, as they are in Figure 7, then the mean is an acceptable measure. But if the mean is skewed in one direction by one or a few extreme observations, then the median is usually preferable. In both cases, the more observations the better.

Secondly, what’s the benchmark? While the zone approach uses relative performance to quantify results, trade-signal analysis includes a comparison of per annum gains with the buy-hold statistic. Likewise, the subsequent-performance approach can use an all-period gain statistic as a benchmark. In Figure 7, for instance, the average 10-day gain in the Dow Industrials has been 2% following a signal, nearly seven times the 0.3% mean gain for all 10-day periods. This indicates that the market has tended to perform better than normal following signals. That could not be said if the 10-day gain was 0.4% following signals.

A third question is how much risk has there been following a buy-signal system, or reward following a sell-signal system? Using a buy-signal system as an example, one way to address the question would be to list the percentage of cases in which the market was higher over the subsequent period, and to then compare that with the percentage of cases in which the market was higher over any period of the same length. Again using the 10-day span in Figure 7 as an example, the market has been higher after 75% of the signals, yet the market has been up in only 58% of all 10-day periods, supporting the significance of signals. Additional risk information could be provided by determining the average drawdown per signal – i.e., the mean maximum loss from high to low following signals. The mean for the 10-day period, for example, was a maximum loss of 0.7% per signal, suggesting that at some point during the 10-day span, a decline of 0.7% could be considered normal. The opposite approaches could be used with sellsignal indicators, with the results reflecting the chances for the market to follow sell signals by rising, and to what extent.

Along with those questions, the potential for double-counting must be recognized. If, for example, a signal is generated in January and a second signal is generated in February, the four-month performance following the January signal would be the same as the three-month performance following the February signal. This raises the question of whether the three-month return reflects the impact of the first signal or the second one. Moreover, such signal clusters give heavier weight to particular periods of market performance, making the summary statistics more difficult to interpret. Problems related to double-counting can be reduced or eliminated by adding a time requirement. For the signals in Figure 7, for instance, the condition must be met for the first time in 50 days – if the ratio reaches 1.92, drops to 1.90, and then returns to 1.92 two days later, only the first day will have a signal. The time requirement eliminates the potential for double-counting in any of the periods of less than 50 days, though the longer periods still contain some overlap in this example.

Another application of subsequent-performance analysis is shown in Figure 8, which is not prone to any double-counting. The signals require that three conditions are met, all for the first time in year – the Dow Industrials much reach its highest level in a year, another index must reach its highest level in a year, and the joint high must be the first in a year. The significance for the various indices can then be compared in conjunction with their benchmarks – i.e., the various all-period gains. Figure 9 uses 12 of those indices to show how subsequent performance analysis for both buy signals and sell signals can be used together in an indicator. For each time span, the chart’s box lists the market’s performance after buy signals, after sell signals, and for all periods.

REVERSAL-PROBABILITY ANALYSIS

Finally, the subsequent performance approach is useful for assessing the chances of a market reversal. In Figure 10, the “signal” is the market’s year-to-year change at the end of the year, with the signals (years) categorized by the amount of change – years with any amount of change, those with gains of more than 5%, etc. In this case, the subsequent-performance analysis is limited to the year after the various one-year gains. But the analysis takes an additional step in assessing the chances for a bull market peak within the one- and two-year periods after the years with market gains, or a bear market bottom within the one- and two-year periods after the years with market declines.

This analysis requires the use of tops and bottoms identified with objective criteria for bull and bear markets in the Dow Industrials. The reversal dates show that starting with 1900, there have been 30 bull market peaks and 30 bear market bottoms, with no more than a single peak and a single trough in any year. This means that for any given year until 1995, there was a 31% chance for the year to contain a bull market peak and a 31% chance for the year to contain a bear market bottom (30 years with reversals / 95 years).

Using this percentage as a benchmark, it can then be determined whether there’s been a significant increase in the chances for a peak or trough in the year after a one-year gain or loss of at least a certain amount. The chart’s boxes show the peak chances following up years and the trough chances following down years, dividing the number of cases by the number of peaks or troughs. For example, prior to 1995, there had been 31 years with gains in excess of 15% starting with 1899. After those years, there was a 52% chance for a bull market peak in the subsequent year (16 following-years with peaks / 31 years with gains of more than 15%). The chances for a peak within two years increased to 74%, which can be compared to the benchmark chance for at least one peak in 61% of the two-year periods (since several two-year periods contained more than one top, this is not the exact double of the chances for a peak in any given year).

A major difference in this analysis is that in contrast to signals and zones, which depend upon the action of an indicator, this approach depends entirely on time. Each signal occurs after a fixed amount of time (one year), with the signals classified by what they show (a gain of more than 5%, etc.). Depending upon the classification, the risk of a peak or trough can then be assessed.

CONCLUSION

Each one of these methods can help in the effort to assess a market’s upside and downside potential, with the method selected having a lot to do with the nature of the indicator, the time frame, and the frequency of occurrences. The different analytical methods could be used to confirm one another, the confirmation building as the green lights appeared. An alternative would be a common denominator approach in which several of the approaches would be applied to an indicator using a common parameter (i.e., a buy signal at 100). Although the parameter would most likely be less than optimal for any of the individual methods, excessive optimization would be held in check. But whatever approaches are used, it needs to be stressed that each one of them has its own means of deceiving. By better understanding the potential pitfalls of each approach, indicator development can be enhanced, indicator attributes and drawbacks can be better assessed, and the indicator messages can be better interpreted.

The process of developing a market outlook must be based entirely on research, not sales. The goal of research is to determine if something works. The goal of sales is to show that it does work. Yet in market analysis, the lines can blur if the analyst decides how the market is supposed to perform, then selling himself on this view by focusing only on the evidence that supports it. What’s worse is the potential to sell oneself on the value of an indicator by focusing only on those statistics that support one’s view, regardless of their statistical validity. As shown by the various hazards associated with the methods described in this paper, such self-deception is not difficult to do.

Our goals should be objectivity, accuracy, and thoroughness. Using a sound research approach, we can determine the relative value of using any particular indicator in various ways. And we can assess the indicator’s value and role relative to all the other indicators analyzed and quantified in a similar way. The indicator spectrum can then provide more useful input toward a research-based market view.

FOOTNOTES

1. Reference to Burton Malkeil’s A Random Walk Down Wall Street, which argues that stock prices move randomly and thus cannot be forecasted through technical means.
2. The charts that accompany this paper were produced with the Ned Davis Research computer program.

Since winning the third Dow Award in 1996, Tim Hayes has expanded upon “The Quantification Predicament” in writing his first book, “The Research Driven Investor,” published in November 2000 by McGraw-Hill. A Global Equity Strategist for Ned Davis Research, Tim and his team have developed numerous U.S. and global asset allocation indicators and models in recent years, while also developing global market and sector ranking systems and indicators based on 18 market sectors in 16 countries.

The crash of the Hong Kong stock market in October 1997, with its obvious parallels to similar events in the U.S. in 1987 and 1929, once again raises the specter of October as a dark and ominous month for stocks. Is it merely a coincidence that these three crashes all occurred in October? Is there a timing pattern among autumn panics useful to market participants? This article expands upon the observation, originally contained in Chapter 1 of the author’s book, The Spiral Calendar[1], outlining the correlation between the lunar calendar and the stock market panics of 1929 and 1987. This paper examines how the 1997 Hong Kong panic conforms to that earlier model, as well as examines the great autumn panics of the 19th century. Finally, a look at the peculiar international character of panics, and its implications for the possible causes of these panics.

DEFINITION OF TERMS

Panic. The focus of this article is on short-term equity market panics. The crashes of 1929 and 1987 are the obvious examples. I define these panics as one-to-three day, free-fall drops of approximately 20% in the major averages. The term “panic” is preferred over “crash” as the definition of panic stresses the suddenness and irrationality of the event. Panics were originally ascribed to the god Pan simply because there were no obvious fundamental causes for their occurrence.

Collapse. Collapse is used to signify the larger macro market decline lasting weeks or months within which the panic occurs. An example would be the Hong Kong panic of October 1997, occurring within the larger Asian equity and currency collapse that ran from July 1997 to January 1998.

Annual Lunar Calendar. The annual lunar calendar used here is based on the Babylonian calendar, which was the model for the later Jewish calendar. This annual lunar calendar labels the date of the first new moon following the spring equinox as month one, day one; or 1-1. The following date is 1-2. The date of the second new moon after the spring equinox is 2-1, etc. The difficulty with annual lunar calendars, and one of the reasons for their abandonment, is that the solar year does not have an even number of months. Thus, some years in an annual lunar calendar have 12 months, others 13. For our purposes, which focus on the Autumn months, this issue is inconsequential. All calculations use Eastern Standard Time to determine the dates of the lunar phases.

In 1992, this author demonstrated how the panic dates of “Black Tuesday,” October 29, 1929, and “Black Monday,” October 19, 1987 occurred on the same annual lunar calendar date, 7-28. Additionally, the other similar points in the comparisons of those two years, the spring lows, summer highs and autumn failure highs all occurred within one day on the lunar calendar. Figure 1 shows those years in a chart aligned with the lunar calendar, where similar lunar dates are juxtaposed above each other. The panics are marked with arrows. The other similar features are denoted with dashed lines. The chart also includes Hong Kong’s Hang Seng Index for the panic year 1997.

These price moves are extraordinarily large over a very short period of time. Are these panics the largest such declines, or do we selectively remember the October panics and forget those of other months? A scan of daily data of the Dow Jones Industrial Average from 1915, the Hang Seng Index from 1980, The Japanese Nikkei Index from 1950, and the German DAX Index from 1960 for the 10 largest, single-day percentage drops is shown in table 1. Seven of those ten declines were days associated with one of the three panics. Two of the others, the spring 1989 declines in the Hong Kong market, were tied to a fundamental news event, the Tiananmen crisis in China. The final entry is from the German market during the “minicrash” of October 1989, an October event similar to the others, but smaller in magnitude. The point to stress here is that in their breadth and ferocity, these panics lie outside the boundaries of normal price action.

There are no other comparable one-to-three day declines of this magnitude in the data. They represent the very largest percentage drops in the database. This is not normal market behavior. What else ties these events together? The panics occupy virtually identical positions on the annual lunar calendar.

Table 2 shows the percentage declines for each panic in the key fourday time span around the lows. The lunar dates 7-27 and 7-28 are the “dark days,” encompassing the various Black Tuesdays of N.Y. in 1929 and Hong Kong in 1997, and the Black and Blue Mondays in N.Y. in 1987 and 1997 respectively. In each case, lunar date 7-28 marked the end of the panic and the next two days, 7-29 and 7-30 (or 8-1, some lunar months have 29 days, others 30) saw significant retracement rallies. Table 3 groups the data into two-day segments and includes the percentage of these retracement rallies. This table shows the striking similarity of these panics and how that similarity conforms to the annual lunar calendar.

Table 4 pinpoints the precise timing of the panic lows on the lunar calendar. The timing from 1929 is gathered from the news accounts that described stock prices as rallying sharply off their lows in the last fifteen minutes of trading on Black Tuesday, October 29. The 1987 and 1997 times are from available databases for the Dow Industrials and are corrected to Eastern Standard Time. The table also shows the date and time of the nearest lunar phase, the eighth new moon on the annual lunar calendar, as well as the difference in hours between the stock market’s low and the moon’s phase. The timing of these three great panic lows is within twenty-four hours of each other. In other words, all three lows fall within the same onehalf of one percent of the calendar year.

A REVIEW OF THE PRE-1915 AUTUMN PANICS
The Panic of 1907

The so-called panic of 1907 does not fit our short-term panic criteria. There was no market decline of approximately 20% in the span of one-tothree days. The largest, singleday declines were 3% in the Dow Jones Industrial Average during the collapse. There was a collapse  and coincident banking panics, most of which occurred in October of that year. Sobel, in Panic on Wall Street[2] , describes the ending of the collapse. J.P Morgan put together his plan to save the banking system on November 3-5, 1907, 7-28 through 7-30 on the annual lunar calendar. After being closed for Election Day on November 5 (7-30), stocks rallied strongly on lunar 8-1. The crisis was over. The timing of the end of the crisis is consistent with the lunar panic model. The day Morgan realized the banking system was not going to fail, he put into motion a plan to save the banks, which ultimately arrested the decline. That day was lunar 7-28, the same date as the lows of the later 20th century panics.

The Crash of 1873

September 18 and 19, 1873 were labeled “Black Thursday” and “Black Friday” in the collapse of 1873. The Friday selling took prices of major stocks 5 to 25% percent below Thursday’s already collapsed levels. This panic was considered the greatest on Wall Street until 1929. The news accounts describe the same type of free fall and despair as the 20th century counterparts. The annual lunar calendar dates of “Black Thursday” and “Black Friday” were 6-27 and 6-28, one month earlier, but exactly the same lunar days as the 20th century examples. News accounts describe a temporary bottom late on Friday. Saturday, September 20 brought renewed selling and the closure of the exchange after a shortened two-hour trading day. The stock exchange remained closed for a week thereafter. Though on Monday September 22 prices rose sharply in trading in the streets. The timing of the 1873 Autumn panic is consistent with the 20th century results, though exactly one month earlier.

The Crash of 1857

The collapse of 1857 was not a stock market free-fall in the sense of the 20th century panics outlined above. It was a very sharp drop in stocks over a period of nine weeks, accompanied by a number of runs on banks, persistent pressure on the banking system, and sharply rising interest rates. Also, it was international in scope, a facet we’ll address later. Though the selling in the equity markets did not climax in a free-fall panic, the pressure on the banking system did, as the N.Y. banking panic broke out on October 13 and mayhem continued for two days thereafter. Sobel, in the Panic on Wall Street[3], quotes George Strong writing on October 15. “Wall Street blue with collapse. Everything flaccid like a defunct Actina.” On the annual lunar calendar, October 13 and 14, 1857 are 7-27 and 7-28, the same “dark days” as the 20th century examples.

CAUSATION

The correlation between the annual lunar calendar and the timing of the three 20th century panics as well as the supportive data from the 19th century does not prove that an annual lunar calendar position is the cause of those panics. A few examples of anything cannot statistically prove a hypothesis. However, it should be realized that each occurrence is not a 50-50, or true/false proposition. If the Hong Kong panic had occurred on any of the 360 days of 1997 other than lunar 7-27, 7-28, 6-27, or 6-28; then this model would be effectively discredited. Yet the 1997 Hong Kong panic climaxed 5 hours after the timing of the 1987 panic and 20 hours after the 1929 panic on the lunar calendar.

Previous theories explaining panics have not fared well when the next panic came along. In the 19th century, it was widely believed that panics occurred in October specifically because banks’ cash positions were weakened as farmers were paid for the new crop. Yet today, agriculture makes up a much smaller fraction of the world economy than before, yet October panics are still with us. The Federal Reserve System was set up in the belief that if banking panics were prevented, stock market panics would cease to exist as well. That causal theory was disproved by the 1929 crash. The 1929 panic was blamed on low margin levels, yet 1987 happened anyway. In 1987, the finger was pointed at program trading. However, the 1997 panic occurred without any appreciable role by program traders.

The lunar calendar model of panics, alone among theories, not only survived the next panic intact, but its basic tenet was remarkably affirmed by the precise timing of the 1997 low.

The timings of financial collapses do not show a pattern. The 1997 Asian collapse began in July, while the crisis of 1987 and the collapse of 1857 began in August. The 1929 and 1873 examples began in September. Yet in each case, the start of the collapse did not result in immediate widespread panic. Those panics seem to wait for a particular time period on the calendar, the 27th and 28th days of the autumn lunar months, usually October, but in one instance September.

THE INTERNATIONAL QUESTION

The international character of financial crises has been a difficult problem for those who have sought to ascribe causes to collapses and panics. Kindleberger, in Manias, Panics and Crashes writes, “Time and again, observers like Juggler, Mitchell and Morgenstern have observed that financial crises tend to be international, either running parallel from country to country or spreading by one means or another from the country where they originate to other countries.[4] ” And “What is remarkable is that securities prices do the same even when only a few securities can be said to be truly international, that is, are traded on several markets, their prices joined by arbitrage. In 1929 all stock markets crashed simultaneously; the same was largely true in October 1987…It is striking that share prices behaved in parallel almost sixty years apart, even though share prices were thought not to have been integrated in the 1920s as they were in the 1980s.[5]”

The panics of 1987 and 1997 highlighted the international quality of panics. Traders the world over saw these markets dive and then rally in unison. In this wired world, that interconnection is not so extraordinary, though Kindleberger is surprised by the international nature of the 1929 collapse. An examination of the 1857 collapse is more revealing. Kindleberger notes, “What is striking is the concentrated nature of the crises…Clapham observes that it broke out almost at the same moment in the United States, England, and Central Europe, and was felt in South America, South Africa and the Far East.[6] ” Aside from the international nature of the macro collapse, the 1857 collapse affords a unique, controlled database of market behavior in the “dark days” of lunar 7-27 and 7-28 on two continents. In 1857, the Atlantic cable linked America with England by telegraph. In the early days of the collapse, the telegraph cable failed and all communication was done by ship for the remainder of the crisis. The London Times and The New York Times from the period leading up to and through the N.Y. banking panic provide striking evidence of two markets in distress. Wall Street began its rally from the depths of the collapse on October 13, 1857 (lunar 7-27) at the same time the banking panic broke out in N.Y. Table 5 is reprinted from The New York Times of October 14, illustrating the sharp rise in prices underway as contrasted with the lows of October 13. I’ve added the column on the right showing the month’s-end prices. Some issues had made their lows earlier in September, but others were at or near their lows on October 13. What’s clear is that prices began to rally from their depressed levels on lunar 7-27, coincident with the outbreak of the banking panic. This sequence parallels the 1987 experience, when U.S. bond prices began a sharp rally from their lows on lunar 7-27, coincident with the outbreak of the stock panic.

At this same time, Europe was aware of, and sharing in, the collapse in America. In the week leading up to October 13, the bank of England raised their discount rate twice, while Paris, Hamburg and Amsterdam each raised their rates once. Though debt and equity prices traded down sharply, there was no free-fall panic. London stocks and debt bottomed decisively on October 13 at the beginning of the trading day. The London Times of November 2, 1857 summarized the events of October and printed the table of prices labeled here as Table 6. To that table is added the date of the month’s low for each security. Here is the commentary accompanying the table. “The range of Consols (government debt) has been unusually extensive, showing a difference of 4 percent between the highest and lowest prices, although at the conclusion (of the month) the market has returned to the precise position in which it stood at the commencement…In railway shares the fluctuations have also been violent, and the rebound, except in a few cases has not been equal to that of the funds.”

The London Times offered this account of the trading in debt on October 13 in its October 14 edition. “The fluctuations in the funds today (Oct. 13) have again been most rapid and extensive. The market opened with a great weakness at a fall of nearly one and a quarter percent from the heavy prices of last evening. But there was subsequently a considerable reaction and a more healthful tone became apparent in all departments of business.” Now, here’s the account of stock trading on October 13 from The New York Times of October 14. “The stock market this afternoon advanced from 1 to 3 percent, the conviction being general that the basis of business would be changed tomorrow and that a large amount of money held in abeyance since the panic first paralyzed confidence will be set free now that the worst is known…”

The cause of the market low in New York on October 13 is ascribed to the banking panic, yet London bottomed on the same day. The selling, motivated by fear, was pervasive on both side of the Atlantic leading up to October 13. That selling ceased and a vigorous rally commenced on the same day, continents apart, with neither market having access to any timely information from the other. Word of the N. Y. banking panic did not reach London until October 26, and was then reported in The Times the following day.

The sudden, international cessation of distress selling that is a hallmark of 20th century panics also occurred in the crisis of 1857, at a time when no timely communication existed. The international character of panics has been a stumbling block to those who subscribe to local, “fundamental” causes for these panics. Contrarily, a lunar-based model for panics would seem to require an international manifestation of the phenomenon. If the moon is affecting market participants, it should affect them the world over. All the panic examples cited here, from 1857 through 1997, have been international, yet the dearth of communication technology in 1857 provides a datum that cannot be explained as a serial reaction. The international character of panics is distinctively supportive of the lunar model.

USES

Put simply, every market participant should have his calendar marked with the “dark days” of lunar 7-27 and 7-28. Even better, everyone should calculate the time of the eighth new moon and subtract 55 hours from that point. A time window of plus or minus twelve hours from that point is the lunar calendar model for an Autumn panic’s low point. There may not be another October stock panic for sixty years or longer. And the lunar model offers no clues as to which years will see a panic. Yet there can be no doubt, as the trillions of dollars lost during these panics makes plain, market intelligence that can pinpoint when an unfolding panic will climax is invaluable. In 1997, as worldwide markets became unglued in October, the lunar calendar model provided by 1987 and 1929 pointed to late Monday, October 27 as the ideal low point. The dramatic early Tuesday morning low of October 28 demonstrated the model’s effectiveness in real time.

Calendars are complex mechanisms. Calendar research must recognize the importance of both lunar and solar calendars. The annual lunar model for panics points to the 27th and 28th days of the lunar month as the dark days, yet that is only true in the autumn season, the 6th or 7th lunar month. Past studies that purport to find no lunar relationship in markets have treated all lunar phases alike, lumping spring and fall together as well as summer and winter. Likewise, specific seasonal analysis tends to ignore the concurrent lunar calendar. Those who dismiss that October may be a rough month for stocks cite that overall, it is not the worst month for stocks statistically, falling on average .5% since 1915. A proper approach to calendar research suggest that distinctions should be made among Octobers based on the lunar calendar. Here’s the lunar distinction. When there is no full moon between October 3 and 19 inclusive, the Dow has been up 1.5% in October since 1915. In those years with a full moon between those dates, the Dow’s average change is a loss of 1.9%. Seasonal analysis should recognize the lunar distinctions and vice versa. The annual lunar calendar makes those distinctions. When autumn panics are viewed though its prism, the results are remarkable.

FOOTNOTES

1. Carolan. The Spiral Calendar. New Classics Library. 1992
2. Sobel. Panic On Wall Street. Dutton. 1988. pp 318-320
3. ibid. p 106
4. Kindleberger. Manias Panics and Crashes. Basic Books. 1989. p 131
5. ibid. p 131
6. ibid. p. 143

Ask one hundred investors whether this is a bull market or a bear market, and you are likely to find their opinions split evenly down the middle. No one is really certain that the September 2001 low marked the end of the bear market and the start of a new bull market. But, this uncertainty is nothing new. As long as stock exchanges have existed, analysts and investors have always placed heavy emphasis on the difficult task of identifying the primary trend of the stock market. Everyone’s ideal market strategy is, at least in theory, to avoid the ravages of each bear market, and then to move aggressively into stocks after each important market bottom. To further maximize the benefits of a new bull market, time is of the essence. An investor should buy as close to the final low as possible. This is the ‘sweet spot’ for investors – the first few months of a new bull market in which so many stocks rise so dramatically. But, theory and reality, especially in the stock market, are often entirely different matters. To bring this theoretical investment strategy to reality, an investor would need a time-tested method of identifying major market bottoms – as opposed to minor market bottoms – and would have to apply this method quickly, to capture as much of the bull market as possible. Traditional methods of spotting major turning points in the market often leave a great deal to be desired. The financial news typically remains negative for months after a new bull market has begun. The economic indicators offer little help since, historically, the economy does not begin to improve until about six to nine months after the stock market has already turned up from its low. Even some widely accepted technical indicators, such as 200-day moving averages or long-term trendlines, can sometimes take several months to identify a major turning point in the market. To spot an important market bottom, almost as it is  happening, requires a close examination of the forces of supply and demand – the buying and selling that takes place during the decline to the market low, as well as during the subsequent reversal point.

Important market bottoms are preceded by, and result from, important market declines. And, important market declines are, for the most part, a study in the extremes of human emotion. The intensity of their emotions can be statistically measured through their purchases and sales. To clarify, as prices initially begin to weaken, investor psychology slowly shifts from complacency to concern, resulting in increased selling and an acceleration of the decline. As prices drop more quickly, and the news becomes more negative, the psychology shifts from concern to fear. Sooner or later, fear turns to panic, driving prices sharply lower, as investors strive to get out of the market at any price. It is this panic stage that drives prices down to extreme discounts – often well below book values – that is needed to set the stage for the next bull market. Thus, if an investor had a method for identifying and measuring panic selling, at least half the job of spotting major market bottoms would be at hand.

Over the years, a number of market analysts have attempted to define panic selling (often referred to as a selling climax, or capitulation) in terms of extreme activity, such as unusually active volume, a massive number of declining stocks, or a large number of new lows. But, those definitions do not stand up under critical examination, because panic selling must be measured in terms of intensity, rather than just activity. To formulate our definition of panic selling, we reviewed the daily history of both the price changes and the volume of trading for every stock traded on the New York Stock Exchange over a period of 69 years, from 1933 to present. We broke the volume of trading down into two parts – Upside (buyers) Volume and Downside (sellers) Volume. We also compiled the full and fractional dollars of price  change for all NYSE-listed stocks that advanced each day (Points Gained), as well as the full and fractional dollars of price change for all NYSE-listed stocks that declined each day (Points Lost). These four daily totals – Upside Volume and Points Gained, Downside Volume and Points Lost – represent the basic components of Demand and Supply, and have been an integral part of the Lowry Analysis since 1938. (Note: an industrious statistician can compile these totals from the NYSE stock tables in each day’s Wall Street Journal.)

In reviewing these numbers, we found that almost all periods of significant market decline in the past 69 years have contained at least one, and usually more than one, day of panic selling in which Downside Volume equaled 90.0% or more of the total of Upside Volume plus Downside Volume, and Points Lost equaled 90.0% or more of the total of Points Gained plus Points Lost. For example, April 3, 2001 qualified as a valid 90% Downside Day. To clarify, the following table was shown in Lowry’s Daily Market Trend Analysis Report of April 4, 2001:

The historical record shows that 90% Downside Days do not usually occur as a single incident on the bottom day of an important market decline, but typically occur on a number of occasions throughout a major decline, often spread apart by as much as thirty trading days. For example, there were seven such days during the 1962 decline, six during 1970, fourteen during the 1973-74 bear market, two before the bottom in 1987, seven throughout the 1990 decline, and three before the lows of 1998. These 90% Downside Days are a key part of an eventual market bottom, since they show that prices are being deeply discounted, perhaps far beyond rational valuations, and that the desire to sell is being exhausted.

But, there is a second key ingredient to every major market bottom. It is essential to recognize that days of panic selling cannot, by themselves, produce a market reversal, any more than simply lowering the sale price on a house will suddenly produce an enthusiastic buyer. As the Law of Supply and Demand would emphasize, it takes strong Demand, not just a reduction in Supply, to cause prices to rise substantially. It does not matter how much prices are discounted; if investors are not attracted to buy, even at deeply depressed levels, sellers will eventually be forced to discount prices further still, until Demand is eventually rejuvenated. Thus, our 69-year record shows that declines containing two or more 90% Downside Days usually persist, on a trend basis, until investors eventually come rushing back in to snap up what they perceive to be the bargains of the decade and, in the process, produce a 90% Upside Day (in which Points Gained equal 90.0% or more of the sum of Points Gained plus Points Lost, and on which Upside Volume equals 90.0% or more of the sum of Upside plus Downside Volume). These two events – panic selling (one or more 90% Downside Days) and panic buying (a 90% Upside Day, or on rare occasions, two back-to-back 80% Upside Days) – produce very powerful probabilities that a major trend reversal has begun, and that the market’s Sweet Spot is ready to be savored.

Not all of these combination patterns – 90% Down and 90% Up – have occurred at major market bottoms. But, by observing the occurrence of 90% Days, investors have (1) been able to avoid buying too soon in a rapidly declining market, and (2) been able to identify many major turning points in their very early stages – usually far faster than with other forms of fundamental or technical trend analysis. Before reviewing the historical record, a number of general observations regarding 90% Days might help to clarify some of the finer appraisal points associated with this very valuable reversal indicator:

1. A single, isolated 90% Downside Day does not, by itself, have any long term trend implications, since they often occur at the end of short term corrections. But, because they show that investors are in a mood to panic, even an isolated 90% Downside Day should be viewed as an important warning that more could follow. 
2. It usually takes time, and significantly lower prices, for investor psychology to reach the panic stage. Therefore, a 90% Downside Day that occurs quickly after a market high is most commonly associated with a short term market correction, although there are some notable exceptions in the record. This is also true for a single 90% Downside Day (not part of a series) that is triggered by a surprise news announcement.
3. Market declines containing two or more 90% Downside Days often generate a series of additional 90% Downside Days, often spread apart by as much as 30 trading days. Therefore, it should not be assumed that an investor can successfully ride out such a decline without taking defensive measures.
4. Impressive, big-volume “snap-back” rallies lasting from two to seven days commonly follow quickly after 90% Downside Days, and can be very advantageous for nimble traders. But, as a general rule, longerterm investors should not be in a hurry to buy back into a market containing multiple 90% Downside Days, and should probably view snapback rallies as opportunities to move to a more defensive position.
5. On occasion, back-to-back 80% Upside Days (such as August 1 and August 2, 1996) have occurred instead of a single 90% Upside Day to signal the completion of the major reversal pattern. Back-to-back 80% Upside Days are relatively rare except for these reversals from a major market low.
6. In approximately half the cases in the past 69 years, the 90% Upside Day, or the back-to-back 80% Upside Days, which signaled a major market reversal, occurred within five trading days or less of the market low. There are, however, a few notable exceptions, such as January 2, 1975 or August 2, 1996. As a general rule, the longer it takes for buyers to enthusiastically rush in after the market low, the more investors should look for other confirmatory evidence of a market reversal.
7. Investors should be wary of upside days on which only one component (Upside Volume or Points Gained) reaches the 90.0% or more level, while the other component falls short of the 90% level. Such rallies are often short-lived.
8. Back-to-back 90% Upside Days (such as May 31 and June 1, 1988) are a relatively rare development, and have usually been registered near the beginning of important intermediate and longer term trend rallies.

A detailed Appendix is attached, showing each 90% Day (or back-to-back 80% Upside Days) over the past 40 years, since January 1, 1960. But, several examples may make it easier to visualize the concepts presented here. The charts to follow show the Dow Jones Industrial  Average in the months before and after a number of major market bottoms. An oscillator of the intensity of each day’s trading, in terms of both Price and Volume, is also shown on each chart (for simplicity’s sake, the Price and Volume percentages have been combined into a single indicator). The 90% Days, both Downside and Upside, are highlighted with an arrow. The backto-back 80% Upside Days are highlighted with a dot.

During 1962, seven 90% Downside Days were recorded during May and June, the last one occurring two days before the final low. The 90% Upside Day was recorded on June 28, just three days after the low in the Dow Jones Industrial Average.

Five 90% Downside Days were recorded during the final months of the 1969-1970 bear market, the last one occurring one day before the low. The 90% Upside Day occurred on May 27, one day after the market low.

The final months of the 1973-1974 bear market contained four 90% Downside Days (a total of 14 occurred throughout 1973 and 1974), the last occurring on December 2, four days before the final low in the Dow Jones Industrial Average. Back to back 80% Upside Days occurred on December 31, 1994 and January 2, 1995 – an unusually long sixteen days after the 1974 market low. Another 90% Upside Day, a superfluous confirmation of the new bull market, occurred on January 27, 1975, thirty-three days after the bottom day.

Three 90% Downside Days were recorded during the final months of the 1980 decline. The 90% Upside Day occurred on March 28, one day after the market low. Another superfluous 90% Upside Day occurred on April 22, after a successful test of the lows.

In 1987, 90% Downside Days occurred on October 16 and on “Black Monday,” October 19. The 90% Upside Day occurred two days later, on October 21. Then, like aftershocks following a major earthquake, two more 90% Downside Days occurred on the first successful test of the lows in late October, followed by a 90% Upside Day on October 29. The aftershocks continued in December and January, each followed by an equivalent 90% Upside reversal.

Three 90% Downside Days were recorded during July and August 1990. As a demonstration that the record is not perfect, a 90% Upside Day was recorded on August 27. The Dow Jones Industrial Average moved sideways for two weeks before dropping to new lows.

Two more 90% Downside Days were recorded during September and October before back-to-back 80% Upside Days were recorded on Friday, November 9 and Monday, November 12 – twenty days after the market low.

During 1998, three 90% Downside Days were registered during August. The 90% Upside Day occurred just five trading days after the market low, on September 8. Another, superfluous 90% Upside Day was registered on October 15, five days after a successful test of the  September lows.

This review of 90% Days would not be complete without bringing the record up-to-date. And, the recent history may hold a particularly important message for investors: There were no 90% Downside Days recorded during 1999 or 2000. However, the sharp drop in the Dow Jones Industrial Average during the early months of 2001 generated two 90% Downside Days, on March 12 and April 3. But, during the ensuing rally, investor buying enthusiasm was not dynamic enough to generate a 90% Upside Day, leaving the impression that the final lows had not been seen. After just six weeks of rally to the May, 2001 rally peak, the market began to weaken again, eventually plunging to a three-year low in the midst of the September, 2001 tragedy. But, as strange as it may seem, the selling during that decline never reached the panic proportions found near almost all major market bottoms in the past 69 years. Not even a single 90% Downside Day was recorded from May through September. Thus, the probabilities drawn from past experience suggested that stock prices had not been discounted enough to attract a broad sustained buying interest. In short, the final market bottom had not been seen in September 2001. And, the highly selective rally that ensued from the September 2001 low through early January 2002 was, once again, not strong enough to produce a 90% Upside Day, thus adding to the evidence that the final low for the Dow Jones Industrial Average has not yet been reached, and that a period of investor panic, generating a series of 90% Downside Days, may still be ahead.

It is important to recognize that the pattern of 90% Days is not a new, untried, backrecord discovery. The original research was conducted by the Lowry staff twenty-seven years ago, in early 1975. The findings were first reported to the investment community in 1982 at a Market Technicians Association Seminar. Since that time, the history of 90% Days has been recorded day by day, and has proven repeatedly to be a very valuable tool in identifying the extremes of human psychology that occur near major market bottoms. Obviously, no prudent investment program should be based solely on a single indicator. Other measurements of price, volume, breadth, and momentum are needed to monitor the strength of buying versus selling on a continuous daily basis. But, we believe the 90% indicator, as outlined above, will be an enduring, important part of stock market analysis, since it, like the other facets of the Lowry Analysis, is derived directly from the Law of Supply and Demand – the foundation of all macro-economic analysis.

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