this is from a book called evidence based technical analysis...
is it really true that statistical models applied to financial market is far more accurate than charting ?
Is that book by David Aronson ?, if so its a great into to basic statistics.
Charts are not the market, they're just an abstract view of the market, and as such they introduce a bias into the way you perceive reality. Statistics just give a different view of the the same information, or in some cases a more detailed view of information that's not immediately apparent.
In what way isn't a chart accurate ? it only shows 4 prices occurring over some arbitrary time period
Sociologists happen to use statistics to measure people's wants and needs and thinking all the time. Using statistics in trading is just a tool to measure trader/investor psychology.
"Evidence-Based Technical Analysis" : the book
Most books and articles about technical analysis focus on applying a specific technique in pursuit of success in the markets. This one is different in that it outlines an entirely new process of thinking, and through the application of this new thought process, success can be attained. Part I of Evidence-Based Technical Analysis is called, "Methodological, Psychological, Philosophical, and Statistical Foundations" and Aronson uses this title as an outline to define the processes which should underlie system development.
The scientific method changed the world, and made the advances of modern society possible. Until now, technical analysis has been considered more of an art than a science to many practitioners and escaped the scrutiny of the scientific method. With recent advances in computing power and analysis software, it is now possible for virtually anyone to search through years of data and identify seemingly profitable trading rules. Aronson presents the scientific method, combined with the philosophy of science as explained by Karl Popper, as an antidote to this very real danger.
Well designed experiments in any scientific inquiry are based upon a verifiable hypothesis grounded in detailed observations. Popper contributed the concept of falsification to this framework, which readily lends itself to mechanical trading system design. As Aronson writes, "Popper's central contention was that a scientific inquiry was unable to prove a hypothesis to be true. Rather, science was limited to identifying which hypotheses were false."
In technical analysis, we can never prove that if the NYSE Advance-Decline Line reaches a new high, the Dow Jones Industrial Average will always be higher thirty days later. But, we can test this hypothesis to see if it is not true. This simple example illustrates the beginning of Aronson's scientific approach to the markets.
Many of the dangers of data mining and curve fitting are grounded in psychology, and Aronson thoroughly explains many of the common problems that can contribute to inaccurate observations. Carefully studying his sections on logic and psychology should lead to better market observations, which should lead to profitable systems.
The chapters on statistical analysis are worth more than the price of the book in itself. Aronson presents a clear primer on statistics, and leaves the reader with all they need to understand how to design a statistically valid experiment. In what may very well be a publishing first, he presents clear, detailed and understandable descriptions of bootstrap and Monte Carlo randomization methods.
This book is well-researched and presents actionable ideas to advance the study of technical analysis. Although none of the rules Aronson tested proved to be statistically significant, he helpfully devotes a section to explaining the limitations of his test results. Armed with this information, and the knowledge provided in the rest of the book, the thoughtful analyst can develop better insights into the market and perform better backtests to identify profitable strategies.