A New Paradigm for Stock Market Investing
Maximizing Return and Minimizing Risk
By Short Term Trading Strategies

Table of Contents
Introduction

The Evolution of Market Theory
Traditional Market Concepts- The Random Walk
Traditional Market Concepts - Modern Portfolio Theory
Traditional Market Concepts - The Ground Rules For Stock Investment
More Recent Concepts - The Application of Chaos Theory
Market Characteristics of a Chaos Model - Dependence
Market Characteristics of a Chaos Model - Sensitivity to Initial Conditions
Market Characteristics of A Chaos Model - Fractal Self Affinity

Elements of The System
Requirements For Success
Element1: Forecasting Accuracy
Element 2: Strength of The Move
Element 3: Disciplined Trade Management

The New Investment Strategy
Challenging The Buy and Hold Ground Rule
Challenging The Portfolio Diversification Ground Rule
System Performance
Performance - The Ultimate Qualifier
Actual Trading Examples
Simulated Performance of an Investment Pool
Summary

Over the past 30 years the development of the science of chaos and its application to modeling the movements of capital markets has succeeded in challenging the fundamental assumptions underlying the mainstream capital market theories and investment ground rules. By viewing markets as fractal processes, guided by nonlinear functions, the latest models reveal short term dependencies in price action rendering them to be forecastable over the short run and thus setting aside conclusions drawn from the Random Walk hypothesis and Efficient Market Theory. In the new model short term dependencies can occur wherein an impulse affecting market direction is forecastable over the near term but quickly loses it’s influence as it decays in strength or is overcome by another impulse. The resulting structure resembles cyclical action with irregular periods and amplitudes.

This paper traces the evolution of thought about market dynamics from the widely accepted Random Walk and Efficient Market concepts to the emerging application of Chaos Theory and non-linear mathematics.

By developing a quantifiable definition of risk relative to price movement and by trading the fractal nature of price movement using a reliable forecasting technique, we explain how risk of capital loss is minimized and return on capital maximized by the short term strategies that we employ.
Next, the paper identifies the essential elements of a mechanical trading system necessary to realize the extraordinary return potential of the methodology and validates the proprietary components of such a system.
Finally, the paper presents examples of the methodology in action and documents the performance realized through both simulated and actual trading experience.

Traditional Market Concepts
The Random Walk

The traditional methods of stock market investing have their foundations in the widely accepted conclusions of Efficient Market Theory. Under this theory price at any one time reflects all there is to know about the underlying security, and price movement is entirely due to a rational investor reaction to new information. If today’s change in price is caused only by today’s unexpected news and yesterday’s news is already reflected in the price, then today’s price movement and yesterday’s price are independent. If this independence holds true then it is concluded that the expectation of tomorrow’s price change will be a normal probability distribution, a characteristic of random behavior. Price is said to be non-serially correlated in time. This version of Efficient Market Theory is commonly called the random walk theory.

This random walk version of Efficient Market Theory continued to develop as the mainstream model of market movement despite substantial empirical evidence arguing against a normal distribution of future returns. By the mid 1980’s both academic and investment communities had largely accepted this model. The empirical evidence which disagreed with the normal distribution assumption and suggested the existence of periods of price dependence rather than independence, were ignored or dismissed as unimportant anomalies. It was argued that even if they did exist, the penalties of high transaction costs and taxes would render them impractical for the investor to exploit. The random walk theorists did acknowledge, however, that despite the random behavior in the short run, there was a high expectation for positive returns for stocks over the long term. Next came the development of Modern Portfolio Theory to try to capture those expectations.

Traditional Market Concepts - Modern Portfolio Theory

Despite the potential for positive returns through stock investment, there was still the issue of risk associated with the normal distributions of returns in the short term. To help the investment community deal with minimizing this risk, Modern Portfolio Theory was born. Modern Portfolio Theory divided risk into two components:

(1) Systematic risk which was associated with the movements of the market in general and
(2) (2) Unsystematic risk which was associated with the particulars of the underlying security.

The theory demonstrated that by proper diversification in an investment portfolio the unsystematic risk could be reduced or eliminated, leaving the investor to deal only with market risk.

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