Fundamentals Or Technicals: Which Provides Better Predictive Value?

Over the last six months, I have been writing articles on Seeking Alpha, in which I have provided you with many predictions in both the equity markets and the precious metals markets. In fact, in one article, I called for a top in the gold market through the use of Elliott Wave analysis, a form of technical analysis, which turned out to be within several dollars of the actual top that gold later hit.

Yet, what I noticed was that I was receiving one comment after another (some even quite personal and scathing) about how technical analysis cannot work, even after I had just utilized it to call a top in a market where most were expecting a move over $2,000 and did not see a top coming.

The following is just a sampling of some of the comments, which I am sure, are quite representative of many investors’ and commenters’ sentiments (I am leaving out all the positive responses that I received, as that does not make good fodder for an article):

“There is no way you can understand what is going on in gold by doing technical analysis.”

You are “following an outdated script with very little relevance as to what is happening now.”

“Because of fundamentals, gold does not work very well for technical analysis, for charting.”

“Technical analysis is nothing but a wind sock. It is not now nor will it ever be a crystal ball.”

“Your TA is useless. You don't understand the fundamentals because you only look to the past. Gold bulls are forward thinking. The times they are a changing ...”


Clearly, there are very strong feelings, which were exhibited by the readership, that technical analysis cannot provide any predictive value, while fundamental analysis can. This is actually a very widely held belief within the investment world. But just because it has been a mainstay in the past, does that necessarily make it an accurate assumption? In fact, did not the populist believe that the world was flat at a point in time?

What is Fundamental Analysis?

Fundamental analysis is generally defined as a method of evaluation that attempts to measure “value” by examining related “current” economic, financial and other qualitative and quantitative factors. Fundamental analysts will utilize “current” macroeconomic factors (like the overall economy and industry conditions) and company-specific factors (like financial condition and management).

What are the flaws with this methodology?

Market fundamentals are the existing conditions of a market based upon historical data. In order to utilize this information for predictive purposes, economists will employ a form of trend extrapolation. This effectively presumes that the current market conditions will continue indefinitely into the future, until they do not. This is possibly the crudest form of linear extrapolation. If we wait for the underlying “fundamentals” to change, are we not already within a different trend within the market that is now changing underlying fundamentals?

Therefore, is this the appropriate methodology to utilize to be able to identify a change in the “current market conditions” before they occur?

Fundamental Analysis based In “Random Walk” Theory

The Random Walk, or Efficient Market, theory is partly based upon the fact that ALL investors (1) make informed and (2) rational decisions, however, (3) exogenous events affect markets and cause changes to investment values. Based upon this theory, since no one can predict exogenous events, then the market must be unpredictable and will move randomly.

However, there are numerous issues with each of the three foundations which support this theory.

First, do you truly believe that ALL investors make “informed” decisions? Do all investors have access to the same information so that all investors within the market all make “informed” decisions? I think not.

Second, do you truly believe that ALL investors make rational decisions? This presupposes that there is no emotion involved in decision making, as all decisions by investors are made “rationally.” In fact, this notion has been dispelled by former Fed Chairman Alan Greenspan. In his testimony before the Joint Economic Committee, Mr. Greenspan noted that the stock market is driven by “human psychology” and “waves of optimism and pessimism.”

Lastly, we really have to begin to question whether exogenous events actually move markets. Don’t you ever question why markets go up after seemingly bad news or go down after seemingly good news?

Social experiments have actually been conducted which resulted in price patterns that mirror those found in the stock market. In 1997, the Europhysics Letters published a study conducted by Caldarelli, Marsili and Zhang, in which subjects simulated trading currencies, however, there were no exogenous factors that were involved in potentially affecting the trading pattern. Their specific goal was to observe financial market psychology “in the absence of external factors.”

One of the noted findings was that the trading behavior of the participants were “very similar to that observed in the real economy,“ wherein the price distributions were based on Phi (.618).

Their ultimate conclusion would surprise the most avid trader today:

In spite of the simplicity of our model and of the strategies of the single participants, and the outright exclusion of economic external factors, we find a market which behaves surprisingly realistically. These results suggest that a stock market can be considered as a self-organized critical system: The system reaches dynamically an equilibrium state characterized by fluctuations of any size, without the need of any parameter fine tuning or external driving.


Marsili was quoted as saying that “the understanding that we got is that the statistics of price histories in financial markets can be understood as the result of internal interaction and not the fundamental interaction with the external world.”

In August 1998, the Atlanta Journal-Constitution published an article by Tom Walker, who conducted his own study of 42 years’ worth of “surprise” news events and the stock market’s corresponding reactions. His conclusion, which will be surprising to most, was that it was exceptionally difficult to identify a connection between market trading and dramatic surprise news.

Based upon Walker's study and conclusions, even if you had the news beforehand, you would still not be able to determine the direction of the market only based upon such news.

Robert Prechter Jr., in his book The Wave Principle of Human Social Behavior, in which he cites many of these instances, concludes

once you realize that even if you got [the news] in advance, you could not forecast the stock market. Though these facts are counter-intuitive, it does not take a dedicated market student long to observe the acausality of news to the stock market.


R.N. Elliott, in his 1946 publication of Nature’s Law, probably puts it best when he said:

At best, news is the tardy recognition of forces that have already been at work for some time and is startling only to those unaware of the trend.


Experts denouncing the foundational support for “Random Walk” and Fundamental Analysis

The Random Walk Theory has been around for quite some time now. In fact, the October 10, 2008 publication of Fortune Magazine noted that “for two decades, finance professors have taught EMH as if it were as indisputable as the laws of gravity.” However, since that time, many experts have come out criticizing the basis behind the EMH.

In the Fall 1993 publication of the Review of Financial Economics, Stephen R. Cunningham - Professor in the Department of Economics at UCON, published his research, and I quote:

Neither the Samuelson-Fama tests for efficient markets nor the popularly used augmented Dickey-Fuller 1979 test for unit roots can successfully discriminate between a fully deterministic time series, generated from a non-linear process, and a random walk. A researcher applying these methods to a simply nonlinear price process would be misled into believing that such a series is a random walk.


In the 1998 July/August issue of Bloomberg’s Personal Finance, James Rogers - Professor of Security Analysis at Columbia Business School, was quoted as saying: "The random walk theory is absurd."

Another example is registered in the October 23, 1987 issue of The Wall Street Journal, wherein Robert Schiller - Professor of Economics at Yale, was quoted as saying: "The efficient market hypothesis is the most remarkable error in the history of economic theory."

Finally, in a paper written by Profession Hernan Cortes Douglas, former Luksic Scholar at Harvard University, former Deputy Research Administrator at the World Bank, and former Senior Economist at the IMF, he noted the following regarding those engaged in “fundamental” analysis for predictive purposes:

The historical data say that they cannot succeed; financial markets never collapse when things look bad. In fact, quite the contrary is true. Before contractions begin, macroeconomic flows always look fine. That is why the vast majority of economists always proclaim the economy to be in excellent health just before it swoons. Despite these failures, indeed despite repeating almost precisely those failures, economists have continued to pore over the same macroeconomic fundamentals for clues to the future. If the conventional macroeconomic approach is useless even in retrospect, if it cannot explain or understand an outcome when we know what it is, has it a prayer of doing so when the goal is assessing the future?


What drives market movement? Social Mood and Herding

As we mentioned before, Mr. Greenspan noted that markets are driven by “human psychology” and “waves of optimism and pessimism.” Ultimately, as Mr. Greenspan correctly recognized, it is social mood that will move markets. This is why news does not cause a change in the trend of the market, unless that trend is already set to change. In fact, have you ever wondered why a market will continue to go up after the announcement of bad news, or down after the announcement of good news?

Bernard Baruch, an exceptionally successful American financier and stock market speculator, who lived between 1870-1965, identified the following long ago:

All economic movements, by their very nature, are motivated by crowd psychology. Without due recognition of crowd-thinking ... our theories of economics leave much to be desired. ... It has always seemed to me that the periodic madness which afflict mankind must reflect some deeply rooted trait in human nature – a trait akin to the force that motivates the migration of birds or the rush of lemmings to the sea ... It is a force wholly impalpable ... yet, knowledge of it is necessary to right judgments on passing events.


This is why any investor who is able to rise above news and emotion, and identify the prevailing social moods and trends, will have a significant advantage over other investors, as Mr. Baruch clearly understood.

This is exactly why I use Elliott Wave in my analysis. In theory, it understands that public sentiment and mass psychology moves in 5 waves within a primary trend, and 3 waves in a counter-trend. Once a 5 wave move in public sentiment is completed, then it is time for the subconscious sentiment of the public to shift in the opposite direction, which is simply a natural cause of events in the human psyche, and not the operative effect from some form of “news.”

This mass form of progression and regression seems to be hard wired deep within the psyche all living creatures, and that is what we have come to know today as the “herding principle.” Humans are hard wired for herding within their basal ganglia and limbic system within their brain, which is a biological response they share with all animals. In fact, in a study performed by Dr. Joseph Ledoux, a psychologist at the Center for Neural Science at NYU, he noted that emotion and the reaction caused by such emotion occur independent and prior to, the ability of the brain to reason.

Furthermore, in 1996, Robert Olson published a study in the Financial Analysts Journal in which he studied the effects of herding upon “expert” fundamental analysts’ predictions of corporate earnings. After studying 4000 corporate earnings estimates, he arrived at the following conclusion:

Experts’ earnings predictions exhibit positive bias and disappointing accuracy. These shortcomings are usually attributed to some combination of incomplete knowledge, incompetence, and/or misrepresentation. This article suggests that the human desire for consensus leads to herding behavior among earnings forecasters.


In a paper entitled “Large Financial Crashes,” published in 1997 in Physica A., a publication of the European Physical Society, the authors, within their conclusions, present a nice summation for the overall herding phenomena within financial markets:

Stock markets are fascinating structures with analogies to what is arguably the most complex dynamical system found in natural sciences, i.e., the human mind. Instead of the usual interpretation of the Efficient Market Hypothesis in which traders extract and incorporate consciously (by their action) all information contained in market prices, we propose that the market as a whole can exhibit an “emergent” behavior not shared by any of its constituents. In other words, we have in mind the process of the emergence of intelligent behavior at a macroscopic scale that individuals at the microscopic scales have no idea of. This process has been discussed in biology for instance in the animal populations such as ant colonies or in connection with the emergence of consciousness.


In fact, one commenter to one of my articles on Seeking Alpha made the following astute point regarding how news affects these subconscious herding trends:

Compare the market to a stream of ants marching by in, generally, a single direction. Run a stick across their path and there will be some momentary confusion and reaction to the direct stimuli but very soon afterwards the original parade of ants continues and the stimulus is forgotten.


So, based upon much research, it does seem that the market may be considered to be on a path that is determined by a mass form of herding that is given direction by social mood. It sure does explain the oft asked question of why markets go up when bad news is announced or vice versa. It also takes out all the guess work in attempting to determine the next “news event” that may move markets. So, it then takes us to the next obvious question.

How do we know when the prevailing trend of the herd can change?

As we also noted before in the studies performed by Caldarelli, Marsili and Zhang, trading behavior within the herd display price distributions based on Phi (.618). This basically means that markets will move forward and move backward based upon mathematical relationships within their movements.

Phi is a number which exhibits many unusual mathematical properties, and is also the solution to a quadratic equation. These concepts have been understood by Plato, Pythagoras, Bernoulli, Da Vinci and Newton. Historic structures have been built by architects of famous Greek structures, such as the Parthenon, based upon the concept of Phi, and even as far back as the architects of the Great Pyramid of Giza in Egypt, who recorded their knowledge of Phi as the building block for all man nearly 5,000 years ago.

What makes Phi even more unusual is that it can be derived in many ways and is exhibited in relationships throughout the universe, such as proportions within the human body, plants, DNA, the solar system, music, population growth, and the stock market.

The Elliott Wave theory is also based upon Phi, as Elliott Wave postulates that markets move in 5 steps forward, and three steps back (a Phi relationship). Phi, when used appropriately in conjunction with Elliott Wave, can be an incredibly predictive market tool. The internal wave structure within an Elliott Wave analysis must display relationships based upon Phi in order to be able to appropriately predict the next move within a market with any form of accuracy. In fact, after you identify the appropriate Elliott Wave pattern within a market, you are often able to then identify the next move of the market with shocking accuracy based upon a Phi-based target.

As an example, on August 21 2011, I wrote an article on Seeking Alpha calling for a potential top in gold at the $1,915 level. It was published the following day. Specifically, I stated that:

The next potential levels that can provide a top for gold are the Fibonacci 2.00, 2.236 and 2.382 extensions. This would relate to top projections of $1,915, $2,111, and $2,232 levels. Again, since we are most probably in the final stages of this parabolic fifth wave “blow-off-top,” I would seriously consider anything approaching the $1,915 level to be a potential target for a top at this time.


On Tuesday, August 23, gold hit $1,913 an ounce and immediately reversed. On Wednesday August 24, 2011, gold fell more than $100, one of the steepest declines in its history.

As another example, on August 30, 2011, I wrote another article on Seeking Alpha calling for a market top in silver with a high end target of 42.90, but that it must remain below 44.30 for the correction to ensue. In fact, I even provided a downside target of 26.80 in the futures within that same article even before silver began a correction. Remember, this was also at a time when almost all analysts were bullish and calling for a move over $50.

On September 2, 2011, the silver futures reached a high of 43.72, and then began a downside correction which spiked as low as 26.36 twenty four days later.

Lastly, on November 7, 2011, I published an article on Seeking Alpha calling for a market top between 1265-1275 by November 10, followed by a 100+ point decline which would end by Thanksgiving. On November 8, the market closed at 1275.95, and on November 9 began a decline which took 100+ points off the market, and which ended on Thanksgiving. Although my initial expectation before the decline began was that the market would find support at the .500 Fibonacci retracement level (a Phi-based calculation) of the market rally from the October 4 low, the market did not find support until it reached the .618 retracement level (another Phi-based calculation) during the Thanksgiving holiday, which I then noted after the decline began and the market showed indications of projecting to this lower level.

For those that still question how well this method can really predict market direction, I will leave you with the following prediction made by Ralph Nelson Elliott in August of 1941:

[1941] should mark the final correction of the 13 year pattern of defeatism. This termination will also mark the beginning of a new Supercylce wave (V), comparable in many respects with the long [advance] from 1857 to 1929. Supercycle (V) is not expected to culminate until about 2012.


Current market outlook

While the technical top of the market was, arguably, in 2007, I think we can all recognize the power of the prediction made by Elliott over 70 years ago. But 2012 is definitely setting up as a year for a potential market crash, the likes of which have never yet been experienced. Based upon my current Elliott Wave pattern, we can very well see a multi-year top made in the near future. Even though we are currently setting up to see one final rally into the end of the year, and possibly lasting into the early part of 2012, if this pattern follows its current projected path, the early part of 2012 will kick off a massive market decline that may take years to complete.


While there is no Holy Grail to predict market movement, as there is nothing that is 100% accurate and Elliott Wave may be used only in a highly probabilistic sense, I have attempted to juxtapose two prominent market theories in my research above.

Whereas I do recognize that many will still disagree with my proposition that lagging information cannot provide a basis from which to predict the next move within markets that are non-linear based, I am hoping that this article may at least open some of those eyes to the fact that market direction may actually be driven my mass psychology, as noted by many including Alan Greenspan and Bernard Baruch, and not by exogenous events.

I am also hoping that those that have read this article will take note that there is a mathematically-based method with which we can attempt to track movements in social mood, which has been proven to generate relatively accurate and highly probable targets for turning points within our financial markets.

Authors Note: I would like to note that many of the studies that I have presented in this article have already been compiled within a wonderfully written two set series by Robert Prechter entitled “Socionomics; The Science of History and Social Prediction,” which I have used for much of my source material.