In today’s age of information overload, it is easy to fall into this trap, thinking that more information makes you better prepared to trade.As with all things, there is a difference between quantity and quality. With my approach to trading, you are simply looking for the quality information and focusing on the probability that the next tick will be higher or lower than the previous one.
Figure 1.4 is a good example of a chart that would drive a classic formation technician mad. There are no clear signals being given here. One could draw a box around the whole thing, or outline the right half as a broken channel formation, but all of it would be a subjective, multistable interpretation of an asset price whose current direction is nowhere. At best, one could draw a median line through the whole thing and call that fair value, saying the market is consolidating around that price.
Figure 1.4 Confusing Price Chart (in US$)
Source:Yahoo! Finance
Legendary technical analyst John Murphy has broken down traditional TA using the definitions from statistics:
The field of statistics makes a distinction between descriptive statistics and inductive statistics. Descriptive statistics refers to the graphical presentation of data, such as the price data on a standard bar chart. Inductive statistics refers to generalizations, predictions, or extrapolations that are inferred from that data. Therefore, the price chart itself comes under the heading of the descriptive, while the analysis technicians perform on that price data falls into the realm of the inductive.
Murphy, John, Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications, New York Institute of Finance (1999), p. 18.
While I may agree with John Murphy that the use of past data to make forecasts of future behavior is grounded in solid statistical theory, the problem I have with traditional technical analysis is that descriptive data sets (i.e., price and time charts) and the inductive statistics one builds from them are simply observations without any grounding in a hypothesis. It’s not that there’s anything wrong with statistics per-se; it is the type of statistics one uses that is the issue.
Join Peter in his next article installment to read the conclusion of this series.