Technical analysis = Behavioral finance
Andy Lo and the Adaptive Market Hypothesis
Why does technical analysis work? It is critical that practitioners understand why their models work:
We propose a framework for the intelligent application of these models:
. Understand the investment environment that you are in.
. Build a model to exploit conditions in the current environment in a systematic and disciplined way.
. Know the bet that you are making and know the limitations of the model. Most of all, know when they are going to fail.
Knowing that an investment model or technique works is not sufficient. You need to know why it works. Otherwise you will never understand the weaknesses of your investment approach and when it might fail.
We believe we know the answer. Technical analysis amounts to behavioral finance.
It fell on the shoulders of leading behavioral finance academic, Andy Lo of M.I.T., to approach technical analysis from a behavior finance viewpoint. He co-authored a seminal paper titled: Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation where he and his co-authors found significant incremental returns from technical patterns:
"Technical analysis, also known as "charting," has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis - the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution-conditioned on specific technical indicators such as head-and-shoulders or double- bottoms - we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value."
Lo followed up his work with a book, co-authored with Jasmina Hasanhodzic, called The Evolution Of Technical Analysis: Financial Prediction From Babylonian Tablets To Bloomberg Terminals in which he expanded on his views. Technical analysis is really a form of human pattern recognition, which is another application of behavioral finance.
Lo and Hasanhodzic began the background research for their book with interviews with 14 leading practitioners of technical analysis and an extensive study of its history. Technical analysis goes back to the Babylonians. Their research shows uses of technical analysis in the middle ages, Renaissance, and 19th century, before it was adopted on a more widespread basis for the study of financial markets.
In their book, Lo and Hasanhodzic took steps toward automating commonly used technical analysis techniques. They studied the prices of 25 randomly chosen securities over a four-year period. Using pattern recognition technology, they identified when certain signals occurred in the data. These signals, such as head-and-shoulders patterns, were those that were identified in the interviews they conducted and, in their model, indicated when a stock should be bought or sold.
They compared the returns of their statistical buy-and-sell cues produced to those of a "buy-and-hold" portfolio - owning all 25 securities over the four year period. They found that there was a statistically significant difference in the distribution of the returns generated through technical analysis compared to the returns of the buy-and-hold portfolio. The data shows, according to Lo and Hasanhodzic, that technical analysis produces what the quantitative community calls "information value," which is a prerequisite for creating a trading strategy from which profits can be derived.
Andy Lo, in contrast to the early finance theorists, is the father of the "Adaptive Market Hypothesis" - the theory that the market is inefficient, but market inefficiencies change over time. One group of traders find an anomaly that they can exploit, e.g. low P/E stocks outperform, but eventually that advantage fades over time. However, other anomalies appear in their place. It falls to the task of pattern recognition, as practiced by people to find new and emerging market anomalies and opportunities.
Thus, technical analysis falls into the realm of modeling human behavior - which is the point of behavioral finance. Insights into human psychology have remained much the same from ancient Athens to Charles Dow, founder of Dow Jones, to today's "quants," who search for bargains in the form of various cognitive biases (overconfidence, overreaction, loss aversion, and herding, to name the most widely known). High-speed traders can accentuate the wizards' edge.
As long as humans, not robots, make markets, bubbles and crashes will be a reality. Behavioral finance, which is the logical extension of technical analysis, offers some hope of recognizing and profiting from the swings.
Courtesy of Cam Hui, Qwestfunds.com