DEBUNKING INVESTMENT MYTHS 

Correlation and Causality By George
J. Paulos Editor/Publisher Freebuck.com Associate Editor The Gold Letter One of the most popular
investment themes being promoted for 2005 is the “Year 5 Effect”. The Year 5 Effect is based upon the observation
that every year ending in 5 over the last century experienced a stock market advance. Many market analysts have used this
observation as a justification for a bullish forecast for 2005. For many people this seems like a strong argument since the
statistical evidence is so apparent. There are many such statistical relationships that are commonly used to make predictions
about future trends. Does a statistical relationship such as the Year 5 Effect actually influence future events? Let’s
examine the facts. Humans are extraordinarily
adept at pattern recognition. We can identify patterns in highly incomplete and distorted data. This is an essential survival
skill that animals also possess to a lesser extent. An important type of pattern recognition is called correlation. Correlation is where there is a notable relationship between
two or more patterns. A correlation may be visually apparent such an in stock market graphs or revealed by using a statistical
technique called regression analysis. The statistical approach gives a quantitative measure of the correlation between different
data series. Measuring correlation
via regression analysis is an extremely powerful technique to analyze trends and verify patterns. It is an excellent way to
augment our own natural pattern recognition abilities and give them a rigorous methodology. It is used in science, engineering,
economics, and many other fields to study data and make predictions. These techniques do have limits however and can be easily
misused to make flawed analyses. One of the most common
errors of analysis occurs when correlation
is assumed to imply causation.
In other words, the analyst claims that because two different trends have highly similar patterns, that they have real influence
over each other. Let’s take a simple example. Everybody knows that flipping a coin has a 50/50 chance of hitting heads
or tails. It is possible, although highly unlikely, that a coinflipper could hit 10 heads in a row. After such a run, the
coinflipper could conclude that it is statistically likely that the next flip of the coin would also be heads because of
the previous trend. But of course that would be a false conclusion. The coin always has a 50/50 chance of hitting heads regardless
of the previous results. The past history of the coin has no influence over the future. The run of 10 heads in a row was a
statistical fluke and not likely to be repeated. Statistical flukes
or anomalies are common in large data sets. There is a story about an Economics PhD student who programmed a supercomputer
to perform over a million regression analyses on a large data set of economic statistics. From that project, the student uncovered
many strongly correlated data series including some ludicrous ones such as a high correlation between the US Dow Jones Industrial
Average and milk production in Market technicians
are fond of doing this same kind of statistical research on financial markets looking for trends and correlations. This type
of research is called data mining and
is very easy using modern computers and huge market databases. The good thing is that such data mining will find many good
correlations and repeating patterns in market prices. The bad thing is that most of these correlations will be statistical
anomalies. So how do we actually
identify causality in the markets? There is no mathematical technique to identify whether there is influence between different
markets and trends. This is a purely qualitative effort and is in the realm of fundamental analysis. The analyst must identify
a mechanism where the various items under study can influence each other. If none can be identified, then the correlation
is likely to be an anomaly. On the other hand, correlation can provide excellent confirmation of a suspected causal relationship.
This is the essence of the scientific method: Propose a selfconsistent theory and then find evidence to support or refute
that theory. Statistical analysis can provide good evidence to either confirm or falsify a causal link. In the case of the
Year 5 Effect, is there any possible influence of the fifth year of a decade on stock prices? The Year 5 Effect is an exact
ten year cycle. There are many multiyear cycles that are significant to markets. We know that there is a causal influence
between the 4year election cycle and economic activity because of the effect of fiscal policy. There are other yearly and
seasonal cycles that are related to tax and funding deadlines. But there is no significant policy cycle that correlates to
the fifth year of a decade. No natural or astronomical cycle is tied to a tenyear middecade cycle. It is possible that a
tenyear social cycle exists, but such a cycle would not be so stable as to maintain perfect symmetry for over a century.
In fact, a tenyear middecade cycle seems to be an orphan with little intrinsic market meaning. Investors, however,
can be influenced by such artificial cycles. Once identified, investors often tend to follow such conventions because they
believe that other investors will do the same. This is not an unreasonable belief being that investors are known to be herd
animals. Such reasoning can be selffulfilling as more and more investors participate. But such a sentimentled trend can
also be selfdestructing because it is by definition a market inefficiency that would eventually be arbitraged away. We can conclude that
the Year5 Effect is probably another statistical anomaly, but one that is now in the investor psyche. If large numbers of
investors attempt to play the Year 5 Effect, we can assume that they would invest early in the year then cash out late in
the year for a net neutral effect. Out of any arbitrary
year, the markets are up more often than they are down. This is a result of the influence of general economic growth and inflation.
From that standpoint 2005 is more likely to be up than down, but that can be said of any year. The last digit of the year
has no effect on that. Although the Year
5 Effect was highlighted in this essay, the same kind of reasoning can be applied to any statistical “fact” about
markets. The ability to mine large data sets with fast computers has enabled market technicians to uncover a multitude of
patterns and correlations including the Year 5 Effect. Some are meaningful but others are not. Committing money into the markets
on the basis of pure statistical relationships is just speculation and is not true investing. Pure statistics do not reveal
any truth about the markets and more importantly does not reduce risk. There is no substitute for understanding how markets
work and how your invested money is used. George J. Paulos is Editor/Publisher of Freebuck.com, a website devoted
to wealth preservation and enhancement using alternative investing approaches including precious metals. He is also Associate
Editor of The Gold Letter, a newsletter covering junior mining and natural resource
stocks. TO RETURN TO THE FACTS THE BROKERS, AND THE FINANCIAL REPORTERS, WONT TELL YOU! PAGE 

Enter supporting content here There are always opportunities through which businessmen can profit handsomely if they will only recognize and seize them. J. Paul Getty (1892  1976) PITAGORAS INTERNACIONAL SA.

