Technical Analysis

Technical Analysis

by Walter Fox

A technique used to forecast the movement of stock prices based on past market data is called technical analysis. Though some have claimed positive results simply considering the variables of price and volume, academic mathematicians scrutinize this as an incomplete analysis of market trends and statistics.

Historically, Technical Analysis stood in contrast with fundamental analysis, which prefers to make a more comprehensive profile of a company before predicting future trends. Technical Analysts argued that if specific company data was relevant, it would have already effected the price or volume of their stocks, and is therefore inherently included in the technical analysis anyway.

The purpose of Technical Analysis is to take the guesswork out of investing. It seeks to increase profits by predicting the future of the markets and trading against those predictions. Fundamental Analysis does the same thing but prefers to use different data, or use data differently. Perhaps more than Fundamental Analysis, Technical Analysis seeks to automate the decision making process by producing a purely quantitative measure of future trends.

“Head and Shoulders,” a graph pattern that shows two equal peaks with a high center peak, is one of the typical patterns a professional technical analyst will utilize in making trading decisions. Some criticize that these patterns are a result of the humanas predisposition to form patterns in the geographically random environment, and are not mathematically valid.

Though technical analysts aim to objectively measure market trends, subjective bias may cause quantitative data to be overlooked. Attributing more or less weight to some statistical patterns or favoring certain charting methods are some factors that can limit quantitative prediction of the market.

The future of Technical Analysis, and Fundamental Analysis too, is giving way to the power and promise of machine learning and artificial intelligence. Machine learning does what all analysis systems attempted “automate the decision-making process” but without the limitation of how much data could be physical processed.

Machine learning has no predisposition to identifying false patterns, and it is able to include disparate data, which on the surface appears to have no correlation to the trends being analyzed. Furthermore, machine learning will identify patterns at any scale. While Analysts tend to look for large (significant) trends, at the machine level, any scale is significant if a trend can truly be identified.

It seems many current analytical paradigms will become less important as tools improve. Though it is unknown whether machines will replace technical analysis, it is certain that they will reveal the shortcomings of our prior techniques and help us improve them.

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