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Basics of Financial Investing – Part 12

Timing Strategies That Don’t Work

As Mark Twain once said, ‘‘It ain’t what you don’t know but what you know that ain’t so that gets you into so much trouble.’’ Knowing what does not work may be as helpful as knowing what does.

Avoiding mistakes can be every bit as important to successful portfolio management as taking advantage of profitable opportunities. This point is especially valid for efforts to time the market. A lot of self-appointed experts think they know how to time the market. You need to be able to see the shortcomings to their approaches. That way, you are much less likely to make the mistake of following their misguided advice.

Technical analysis as practiced by chart-readers is a waste of time. Financial economists have differing views on many matters. Those same financial economists do, however, almost universally agree upon one concept: the validity of what is called the weak form of the efficient market hypothesis, or, as it was initially called, the random walk hypothesis. According to this hypothesis, security prices move randomly with respect to past price movements.

Therefore, information on past prices and returns is said to be of no value in predicting future price changes. If this weak form hypothesis is correct, efforts to forecast where stock prices are going next by looking at charts of where they have been are bound to fail. That is not to say that the charts will never provide a correct signal. Rather, the signals derived from the charts are no better than those obtained from flipping a coin. They are, in a given instance, about as likely to be incorrect as correct. Even a broken clock shows the correct time twice a day.

Numerous studies utilizing a variety of statistical techniques have explored this matter. Virtually all of them have come to the same conclusion: Stock prices move in a random fashion relative to a history of their past levels. You cannot tell anything about where a security price is going from where it has been.

This conclusion not only means that you should not waste your time and money on books or articles dedicated to technical analysis, but also that you should not look for patterns in your everyday stock-trading. You might, for example, see a stock bounce back and forth between two apparent barriers. Suppose you see it reach a high of 22 and fall back to 20 and then climb back to 22 before falling back to 20. After several weeks of trading in the 20–22 range, you may conclude that you could make some money by buying at 20 and then selling at 22. You buy some of the stock the next time the price falls to 20 and then put in your sell order at 22. Well, you may be lucky and sell at 22. But according to the weak form of the efficient market hypothesis, that stock is as likely to fall to 18 as it is to go to 22. Seeing patterns where none exists is a well-known phenomena in psychology.

The ‘‘January Indicator’’ Is A Sham!

According to the so called January indicator, if the stock market rises in January, the odds are about two-to-one that it will be up for the year. Historical research supports this proposition. The only problem with this so-called January indicator is that the stock market generally rises about two years out of three. January performance is no better at predicting the year’s performance than is random chance.

Similarly, the idea that if the market falls two years in a row, it is very unlikely to fall in the third year has no incremental predictive value. Since the market rises in about two-thirds of the years, the odds are still about one-in-three that it will fall in a given year, regardless of its prior history. While the stock market rarely falls three years in succession, it did decline in 2002 after declines in 2000 and 2001.

Hot Tips Are Radioactive

Suppose your broker, bartender, brother, or bootblack tells you that he knows, on good authority, that important new information on a particular stock is about to be released. He goes on to say that once the market learns what he already knows, the stock is a sure bet for a big rise. He urges you to buy before the news comes out. Consider the following possibilities:

First, the person spreading the hot tip/rumor could be trying to stimulate interest in the stock in order to cause its price to rise. That person can then unload his or her own position into a rising market. This strategy is called pump and dump. Second, the person supplying the hot tip sincerely believes the rumor and that the stock’s price will rise, but his or her information could be incorrect (the rumor is false). Third, the person actually knows something, but the recommendation is based on nonpublic information.

In all three of the above-mentioned circumstances, a trade based on the tip would be inadvisable. Clearly, if the tip is designed (illegally) to inflate the price, a buy is likely to turn out poorly for you or anyone else who trades on it. Once the market learns that the rumor is false, the stock price will almost certainly fall, perhaps dramatically. By then the source of the hot tip has probably dumped his or her own stock. He pumped; you bought; he dumped; you were left holding the bag.

Similarly, if the recommender is mistaken, investment results will be random at best. The third situation, however, is the most dangerous. Trading on material nonpublic information is illegal. The person providing you with access to such information for trading purposes is violating the law. You, too, become a lawbreaker if you knowingly trade on material nonpublic information.

The penalties for trading on inside information can be severe. If you think that the odds of getting caught are small, think again. The regulators are likely to be very interested in any unusual trading activity that precedes a significant announcement. Once they become suspicious, the authorities will closely examine the relevant transactions in order to identify those investors who entered into profitable trades just prior to the public announcement.

They will then explore who among the traders had a relationship with someone who had access to nonpublic information. Once they have the evidence, the regulators will vigorously pursue the case in the courts (just ask Martha Stewart).

Don’t Expect A ‘‘Greater Fool’’ To Bail You Out

Investors sometimes try to hop on to a stock whose price is rising, believing such theories as the trend is your friend or don’t fight the tape. The stock is said to have a lot of momentum, which will continue to carry it higher, or so some may believe.

If, however, the price rise’s underpinning is fluff, the market will soon enough realize its error and the price will stop rising. Then the price may start falling and eventually collapse. Make sure that you aren’t buying into the idea that the price of this wildly overpriced stock will go even higher, allowing you to get out at a nice profit. You may find out that you were the greatest fool. You bailed the last guy out. But now no greater fool is available to pull your chestnuts out of the fire.

Beware Of Back-test Rules And Strategies. Data-Snooping May Explain The Results

Much of what we have learned about stock market behavior has been derived from studies of past tendencies and performance. The January effect, performance of low PE stocks, stocks of bankrupt firms, companies that engage in reverse stock splits, and many other well-documented market tendencies have been explored by examining how the market reacted under particular circumstances in the past (back-testing). And yet, by no means do all trading rules formulated from an analysis of the past continue to perform as well as past history would suggest.

Data-snooping is one potential pitfall of rules derived from back-testing. The history of a concept called relative strength illustrates the danger of relying inappropriately on evidence from back-testing. In his dissertation (PhD in finance, 1960s) Robert Levy introduced a concept that he called ‘‘relative strength.’’ Levy claimed that stocks that outperform the market over a pre-specified time period exhibited a type of performance that tended to persist.

Such evidence of relative strength could, according to Levy, be taken as a positive (buy) signal. These stocks would, again according to Levy, tend to outperform the market at least for a time thereafter. Levy proceeded to formulate and test a rule that purported to identify stocks possessing relative strength. Levy’s own back-testing seemed to show that stocks with relative strength were likely to produce above-average returns. He even formed a company to sell information on which stocks currently possessed relative strength.

Michael Jensen, a highly respected financial economist, realized that the patterns that Levy had claimed to find were inconsistent with weak form market efficiency. Jensen, like most financial economists, was well aware the vast array of empirical research in support of the weak form of market efficiency. He was therefore quite skeptical of Levy’s findings and conclusions.

Accordingly, Jensen carefully retested Levy’s concept. When Jensen applied Levy’s approach to a new set of data, relative strength’s apparent success in identifying profitable stock trade opportunities disappeared.

Jensen’s reexamination of Levy’s approach revealed the problem: Levy had both designed and tested his trading rules on the same data set. Levy worked the data very hard to find his rules. He tested many different forms of his rules until he found a set of rules that ‘‘worked’’ on that particular set of data (time period, set of stocks). If you try enough different formulations of a trading rule, you will eventually find one that appears to work on the data set that you tested. Indeed, according to one self-appointed expert: Enough monkeys and enough typewriters and one will write King Lear. Levy had snooped through the data until he found a rule that appeared to work on his test set. His trading rule’s apparent success, however, was not due to an underlying tendency of the market. Rather, just by chance, it was successful on the particular data set that he tested. That is why it did not hold up on a different data set. Levy was guilty of data-snooping.

Careful researchers seek to avoid being misled by this data-snooping problem. They begin their analysis by developing a specific theory without examining the relevant data too closely. Only when they have constructed a well-formulated concept that they believe may be worth exploring do they start testing it on an actual data set. Thus they try to avoid being led by the data. Second, they test only a few permutations and combinations of their concept. By avoiding tests of many different formulations, they reduce the possibility that what seems to work was just a statistical artifact of the particular data set studied. Third, and most important, they retest their relationships on a second, holdout sample of data. If, for example, a relationship is found for 1996–2005 data, it is retested on 1985–1995 data to see if the same relationship is replicated. If the relationship performs about as well on the holdout sample as on the original sample, the likelihood that it will continue to perform in the future is enhanced. Even this approach provides no guarantees. Financial markets may work one way at one time and a different way in a different time. Still, carefully retesting your finding on a second data set greatly reduces the likelihood that your results are a statistical artifact.

Whenever you learn about an investment strategy that has been tested on historical data and seemed to work well in the past, be wary of the possibility that the prior results may have been produced by data-snooping. Explore this matter before you act on such a back-tested rule.

Be Wary Of The ‘‘100-Year Storm”. Such Storms Tend To Occur Much More Frequently Than Once A Century

Many investment strategies are based on the belief that the future will be very much like the past. Indeed, those who advocate such strategies may believe that you can depend upon the apparent regularities that they have observed to continue to behave as if there is a law on the subject. Deviations from the expected relationships and tendencies are thought, by some self-appointed experts, to be as rare as one-hundred-year storms. All too often neglected is the possibility that the world will change. Hundred-year storms seem to occur much more frequently than advertised.

Some trading rules work very well right up to the time when they stop working. Consider an example. Many investment strategies have been based on the general tendency for long-term interest rates to be higher than short-term interest rates. And indeed, short rates are lower than long-raters, most of the time. But they are not always lower.

When short-term rates are below long-term rates, one who can borrow at the lower, short-term rates and lend at the higher, long-term rates will earn the spread in the rates. If the short rate is 4 percent and the long rate is 7 percent, a rate spread of 3 percent can be earned on a borrow-short-and-lend-long transaction. This borrow-short-and-lend-long strategy is, in fact, used extensively by commercial banks and other similar financial institutions (e.g., savings and loan associations). Banks generally try to hedge their positions in various ways (e.g., by making variable-rate loans). The strategy does, however, involve significant risks, even for banks.

Many investment strategies rely implicitly upon a particular type of relationship continuing into the future. For example, one who uses margin borrowing (interest charges based on a short-term rate) to invest in long-term junk bonds (a long-term, high-yield, high-risk bond) is relying upon the continuations of two relationships: (1) that the yield curve will continue to be upward-sloping; (2) that the default rate on junk bonds will remain relatively low. The strategy is effective just as long as these two relationships both continue to hold. When either of the relationships falls apart, so does the strategy. In the 1980s, a number of insurance companies and savings and loan associations employed a junk bond strategy, with disastrous results. They expected that their junk bond portfolios would continue to yield more than the cost of their short-term borrowings. The hundred-year storm that they encountered was a period of rising short rates, rising default rates, and collapsing junk bond prices. The bankruptcy of many S&Ls and insurance companies was the result. Various strategies involving derivatives may have at their core a dependence upon a similar kind of relationship. Orange County’s (California) bankruptcy was one of a number of disasters that resulted from employing this type of strategy.

Short rates do not always stay below long rates. Sometimes the level of interest rates rises so high that almost everyone in the market is expecting them to fall from their current high levels. Investors rush into the long end of the market in order to lock in the high rates before they start to fall. When that happens, short-term rates often rise above long-term rates. Nor do interest rates always stay within a particular range. A rise in inflation fears generally causes the Fed to shift toward a tighter monetary policy. The result of this Fed tightening will be not only a rise in the general level of interest rates, but quite possibly a disproportionate rise in short-term rates. This phenomenon can result in short-term rates that are higher than long-term rates (a so-called inverted yield curve). This inversion of the yield curve has happened on more than a few occasions.

Return to our example of the strategy of financing a long-term investment yielding 7 percent with short-term borrowing at 4 percent. A crisis in the Middle East could lead to a shortage in crude oil and a dramatic rise in inflationary expectations. The Fed could then tighten up on the money supply. Short-term rates could rise from 4 to 9 percent while long-term rates increase only from 7 to 8 percent. The investor could soon need to start financing the long-term asset not at a rather low 4 percent, but at a much higher 9 percent.

Since the long-term asset was purchased to yield 7 percent, the positive interest spread has turned into a negative 2 percent (7% - 9% = -2%). That negative yield spread, however, is not the worst of it. The bonds that were purchased when their yields were 7 percent are now trading in a market where they must be priced to yield 8 percent. Thus their market values would have fallen substantially. Accordingly, the investor not only faces a significantly negative yield spread, but his or her assets have also suffered a loss in market value. Even worse, the decline in portfolio value could lead to a margin call. The investor could be forced to sell his or her long-term assets at a substantial loss.

So we see how a strategy that seemed to work effectively most of the time might well encounter market conditions that would lead to large losses. The above scenario is not hypothetical. It has happened to a number of very sophisticated investors (including one with a Nobel Prize in economics). They were hit by what they thought was a hundred-year storm (a financial crisis in Russia) only a few years after they started their firm (Long-Term Capital Management). At first their firm’s strategy was very successful. This initial success added to their confidence (hubris) and willingness to take additional risks by using ever-increasing amounts of leverage. When (after a default in Russian bonds) the market stopped performing the way they had expected it to, their exposure was great, as was that of their lenders. Only the Fed’s intervention avoided a major financial panic.

A second strategy that can lead to trouble is to borrow at relatively low rates and invest the borrowed funds in higher-rate, higher-risk assets. So called sub-prime and asset-based lenders who extend credit at high interest rates to borrowers who have weak credit ratings represent one example of such an approach. Another example involved insurance companies and S&Ls that invested heavily in junk bonds.

True, higher risk is usually associated with higher returns. Moreover, a diversified portfolio of high-risk investments has generally offered a higher return than a diversified portfolio of investment-grade bonds (even after taking account of default losses). But, and it is a big but, what usually happens does not always happen. Indeed, at various times the market has turned very negative on low-quality bonds. Such bonds may on occasion (such as during a severe recession) experience unusually high default rates. Similarly, at times, the loss rate on loans to weak credit borrowers may turn out to be much higher than had been expected. In these cases the promised premium yield on high-risk instruments may be well below the level needed to offset the losses on those issues that do default. At such times a strategy that relied upon a tendency which generally worked in the past will fail because of that hundred-year storm. Some kind of hundred-year storm seems to occur every several years.


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