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Dialectical Theory



Introduction

There is no doubt that as a security is "bought" and its value increases from the increase in demand, it can create a snowball effect, where other buyers enter the market in an attempt to catch the beginning of an upward trend. This common phenomenon is the result of market psychology, and often results in an effect known as "following the heard." Frequently, the value of a security overshoots its intrinsic value by way of a counterintuitive phenomenon - that an increase in price causes an increase in demand that results in the overvaluing of a security or of the market as a whole. Dialectical theory suggests that there is an opposing "force" to this upward motion that increases in magnitude as the security rises in value. As the market value becomes inflated to values above its intrinsic value, the likelihood that a correction will occur increases at an exponential rate.

These instances are most pronounced after information becomes public that has a dramatic effect on a company's ability to generate future profits. The most obvious example is the release of quarterly earnings that differ dramatically from earnings expectations. Similar instances can occur after a lawsuit is filed, after a drug is granted FDA approval, or after a novel chemical or therapy enters the commercialization phase. Buying and selling opportunities are most pronounced directly following these events, before the market has a chance to equilibrate. In other words, high volatility and abrupt changes in share price tend to present the greatest opportunities to capitalize on a differential between intrinsic value and market price. This process is analogous to a disruption in a controlled process, where a system oscillates before it reaches its new point of equilibrium. Typically, the greater the jump in share price, the greater the amplitude of the correction and the more profound the application of dialectical reasoning.

The difficulty in dialectical theory is not so much understanding the reason why it occurs, but rather, in quantifying the degree to which a security has been overbought or oversold, namely, calculating the difference between the intrinsic value (including its time value) and the current market price. Contrarian investing is not a new term or investment technique by any means, and it capitalizes on an ever-present market phenomenon. Although the derivatives market has removed much of the liquidity from the market, it has not eliminated the need for contrarian-type methodologies to quantify their occurrences. Furthermore, the high-volume event-based trading instituted by large hedge funds has dramatically increased the volatility of the market as a whole. Although logic would indicate that this type of trading would reduce liquidity and increase the rate at which the market equilibrates, this has not been observed in the majority of cases over the last decade. As a result of these high-volume event-based trades, the VIX (volatility index) has been at very high relative values in recent years and has consistently depicted events when market valuation as a whole was either heavily inflated or deflated with respect to intrinsic value.

MVS Technology

To our knowledge, MVS is the only financial entity that employs cutting-edge cloud computing technology to determine (and quantify) market sentiment - in order to predict the financial decisions that people will make before they make them. Applying results from The Macro Project into the Matrix Equations increases response time when dealing with rapid market swings. At the same time, Laplace transforms are utilized to solve complex differential equations in order to determine the equilibration of the market after a rapid, high-volume trading event.

Random Verses Predictable Outcomes

At MVS, we consider the market to be semi-predictable: that is, on an instantaneous timeframe, the market behaves like a Brownian motion and its randomness can be modeled by a stochastic function, where each future outcome does not depend on historical outcomes. A variation of the Black-Scholes equation is utilized to model the "random walk" phenomenon.

A well-known shortcoming of many functions that describe Brownian type processes is that they are very insular and do not incorporate dramatic and atypical outcomes. For example, a stochastic equation that models the motion of an ion as it moves around in a vessel will not incorporate how this movement will be altered if the temperature is increased dramatically or if the vessel is subjected to a high voltage differential. This is why a hybrid model that combines a stochastic function with linear, sinusoidal, and exponential functions is necessary to model the market over a longer timeframe. See The Business Cycle

The image below depicts this hybrid model, where the variance (or spread) of the cumulative distribution function increases as the result of the random walk phenomenon. At the same time however, the CDF is subjected to a "force" that changes its mean value as a function of time. This secondary effect of the hybrid equation is the result of new information that would result in the dramatic change of future earnings. To keep the sequential graph simple, the graph is depicting "linear momentum," where the mean changes at a constant rate with respect to time. In a real situation, however, this market "momentum" is seldom linear and the mean value is better modeled by a differential equation.



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