Body Wave

Body Wave

The fundamental error of the Elliott Wave Theory

The premise of the Elliott Wave theory is that human behavior, as manifested in the movement of exchange can be modeled mathematically as a set of guidelines expressed by a deterministic function. This conclusion from a mistaken analogy between Stock price movements and other phenomena in nature. For centuries, people have made there were patterns in snail shells, planetary orbits, ocean tides, and many other phenomena. Often these models were so consistent that can be assigned to mathematics with a high degree of accuracy. It is essential was not necessary to understand why a particular mathematical function, has been able to predict a natural tendency. In other words, the basic inputs (causal variables) model can remain unknown for centuries, despite the mathematical model to predict accurately tracking model. For example, people can predict the movements of celestial bodies, long before had been identified and understood the main entrance behind it (gravity) head model.

If you want to understand why the Elliott Wave theory is wrong, it is important to distinguish between two very different types of knowledge (and / or ignorance) about an entry in the underlying. The first, which I call the "Gateway ID "or" input "existence, is simply a decision that the entry exists at all. The second, which I call the entry level "is the quantitative (or at least relatively qualitative ordinal) for the underlying value of entry once their existence and identity was determined.

Existence of entry and entry level are two very different things. To illustrate this idea with the example of moving sky, astronomers had no principles (1) found that a force has not yet been called "gravity" that is, exists at all (they were aware of the existence of entry), or (2) how to assign a value (High Input) to this position, regardless of whether it was further identified or not they exist. Therefore, it was an unknown "unknown".

Once gravity and computational tools that have been discovered, models of celestial bodies suddenly make sense. The history of science is full of examples of these discoveries underlying input (S), explaining in particular the mathematical models of natural patterns. What is important to note here is that explanations of these models were more often (1) are relatively few (2) is not exhaustive and / or capable of perfect mathematical model. In For blue body movements, we know that the basic calculation of the gravitational mass and the distance is not sufficient to predict the motion of a planet with a 100% accuracy, because the relativistic effects and others in the game (including even changes in mass due to dissipation of the atmosphere, etc.) But the fact is that the errors are small, in other words, our knowledge of the existence of entry and entry level for the celestial motion is robust enough to facilitate launching probes to other planets.

A brief recap: (1) a clear pattern is observed in nature, (2) a mathematical model that expresses the tendency grows vigorously and has predictive power, (3) entries mysterious causal underlying natural phenomena are ultimately discovered / identified, (4) a way to correctly calculate the values of inflows is finally discovered / Identified, and (5) the model is robust now (though perhaps not 100%) understands.

This brings us to another fundamental error the Elliott Wave theory. Like a table of actions, we see a series of vaguely visible role models, some of which seem to repeat sporadically. We then ask whether it is possible that these models can be modeled mathematically in the same manner as other were natural phenomena. For this is true, we should first (1) identify key inputs that the model, and (2) allocate these tickets in their respective degrees of value. Although we lost a few inputs, our model should remain sufficiently robust if we take the key.

Therefore, it is possible? The answer is no.

We can state the reason as follows: Stock prices do not move because unknown unknowns that have not yet been identified and assessed then. We are not looking for something mysterious and new – Actually, you know the handle of the main entrance identities firmly explaining stock price. We also know that these entries (often interrelated) in turn depend on a host of others, added entries, we can not accurately value, although sometimes some of them may be estimated correctly. And here is our cornerstone theoretical: we already know, no a priori theory or mathematical model can predict precise values (input degrees) of some of these entries.

Or, more detailed and precise, and I know that not theory or a mathematical model can accurately predict the values of members at any arbitrary subset of the input side, if subset arbitrary contains only members that they are substantially independent. For purposes of our analysis, we focus on subsets of variables composed of two members only (ie pairs of input side).

To clarify this, more specifically, we will start by looking at a very basic important "main entrance" to enter all the math model to predict the price movement of the S & P 500 next year. Despite a large influx, the exact value of "Next year earnings, which not only predict the direction of stock prices, while knowing the exact value of this entry would give a great advantage and it is likely that any model more accurate. If you support this position with precise knowledge of the U.S. dollar against other currencies the end of next year, would be the most robust model. There are probably a dozen or less as the most "large" inputs which, when put together, they produce a mathematical model that has been surprisingly intuitive if the real (quantitative qualitative or ordinal values) of these inputs are known with certainty. The fact that many of these inputs (economic, behavioral, social, etc.) are correlated to varying degrees does not change anything. Nor does it matter that these entries dozen could be "exchanged" with a dozen entries similar produce a similar result (ie tell my side year earnings Nasdaq stocks Instead, my S & P pricing model continues to run strongly because of the correlation). As mentioned above, what is important is that dozens of (more or less) interchangeable main entrance " or "classes of inputs, are themselves a number of functions (in fact, thousands of millions) of other inputs, no secondary mathematical model (including EWT) is unpredictable.

But how can we say that we know that prior? Firstly, we can show (in practice, in fact, not only in theory) that in all the billions in subentries, there are innumerable pairs of two entries which can be expressed in terms of predicting the other, no matter how hard we try. In general, common sense to define only founded as pairs of inputs (eg, "the average temperature in summer in North America" and "likelihood that the Chinese Minister Finance unexpectedly dies quickly "are two entries that have tiny mathematical interdependence, while both affect the S & P 500 and at least a very small part). By definition, if both inputs are high that any pair has shown a fairly strong correlation (or relationship Mathematics strong enough), then either one of the inputs would be superfluous. This is because the particular entry would not add much explanatory power of a mathematical model that already contains another entry in the pair. However, we found in place (in practice) when we do correctly estimate subentries own our significant improvement in higher overall forecast. In addition, we also know that values of our countless "added entries not interdependent" are not totally random. If they were completely random, then we would be treated as errors (or potentially ignored) in our mathematical model principal. Note that using the word "totally" because all these entries reflect a certain degree of chance in force (even before it has been deconstructed in quantum physics, even if possible). Now we know how these secondary entrances are not completely random? Since it is sometimes possible to predict with great precision, the values of some of these inputs using methods of direct observation, which is imperfect, but reliable, fundamental analysis. For example, an analyst of care rolls up his sleeves, spread tons of data and accurate (or at least robust) provides for the maize crop year next. At the same time, correctly predicted some changes in some tax rates next year, after reading 21 articles on the subject. He looks at historical data, and estimated corn yields and tax rates show no significant correlation or other mathematical relationship between them.

It connects these two values in different areas of a mathematical model greater than he used to predict the benefits of agriculture for the next year. Then, connect the model number in a bigger and use it to predict the value the S & P 500 a little more precise than it would otherwise.

Note our analyst has reached a value of each entry "deterministic" components from the data base, and in total isolation of the contribution of mathematics in other couples. Now, for the wave theory Elliott no sense, should take the absurd position that these two entries, in fact, are in fact related mathematically to a higher level functional the analyst understands (perhaps you might refer to him as holistic or Biblical?). With all due respect, I think this would be the theory Elliott waves should be the responsibility of proving that interdependence analyst mathematics, and not vice versa. Would be tested for each pair (or at least a heck of a lot of them) for the main mathematical model is valid.

What made our analyst is to use two secondary entrances, each having somewhat the appearance of "miraculous" criteria (1) indivi fundamental analysis for most data, (2) is not mathematically interdependent with his counterpart, and (3) part of the grounds of the future value of the market. There is nothing strange in that if you look at the problem with deterministic bias. Now, the fact that these three criteria are met shows that both inputs can not both variables in deterministic mathematical function of any value of the final product (the market) they know already. If so, you'd be able to use the mathematical function predict robust reverse one of the entries themselves in terms of another. Try to use Elliott Wave to predict tax rates next year in terms of maize crop, and you'll see what I mean. At the same time, nobody could argue that the fees have absolutely no influence on the S & P 500. Repeatable this argument with thousands of other examples. Each time, he will not be "global" function (as Elliott Wave theory) would be able to strongly predict the independent variables in the opposite direction. For its absolute (and not just marginal) the nature, thousands of forecast errors does not "empty" between himself as a whole to produce a result converge. the Elliott wave theory can not work by definition.

In this stage of the argument, an Elliott Wave can cry stick fault. How could you use Elliott wave "upside" to predict the value of a variable on a mass basis, when you do not know all the other variables (and their influences on) within the function as well? My answer to this question is that you can not have both. If you accept the idea that fundamental analysis to all the work (which I do), so that also means accepting the idea that strong enough (although almost perfect) mathematical models of stock movements can be constructed with values determined by rigorous fundamental analysis (otherwise, would not make sense to analyze fundamental). Now choose from one of these basic models to choose who cares. One by one, strip the rigorous, independent, determined the value of each variable and replace by the value (of this particular variable thereof) which is to connect the Elliott Wave prediction for the final output (ie the future value of market shares), all other variables as well. Soon, the model starts to spit nonsense forecasts retro-cultures, the tax rate, Alt-A mortgage default, or any other variable that attention rightly, the name may contribute to movements in the stock market. I mention this because many followers of Elliott Wave claims to be used in conjunction with fundamental analysis, as if the two methods are complementary. Ironically, they are in conflict. In accordance with the logic of the rebuttal on the theory of Elliott wave, each input variable correctly identified by fundamental analysis, a reason again why the rest of unspecified input variables are not part of a deterministic function mathematics.

As you can see, the relationship of inputs to values that underlie the actions are very different relationships underlying causes natural patterns RN Elliott intrigued. When chemists of the 19th century could not understand why the periodic table of elements are predictable and recurring patterns, was the concept of electrons / orbital finally explained all this (as the severity made by astronomers). For operators company, it is an analogy, no overall strength, particles or waves that will one day be identified, rated and then connect to an equation to predict future. Instead, there are dozens of key factors have been identified, but whose values are known to be unpredictable in its entirety, although sometimes they can predict some of them individually through rigorous analysis of its components.

Proponents of the method Elliott Wave is likely to argue that my theoretical arguments are not relevant if the theory yields results EW business. I will not insist the many arguments against the idea that the success of an individual merchant is necessarily perpetual test the validity of the underlying methodology he / she uses. But I will say it is impossible to prove or disprove the validity of the theory of Elliott Wave in terms of concrete results. Praxeological Therefore, a purely theoretical analysis is necessary.

Finally, I note that in practice, most professionals admit that their Elliott Wave system is designed only as a general guide "with the knowledge of many possible (, random and additional variability is introduced). Otherwise words, an "ideal model wave", according to the theory, but in practice, there may be exceptions to this trend. Nothing in this entry refutes the arguments exposed, or strengthen the predictive power of the waves underlying Elliott.

Five Orvin

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