The Evolution of Technical Analysis: Financial Prediction from Babylonian Tablets to Bloomberg Terminals

In The Evolution of Technical Analysis: Financial Prediction from Babylonian Tablets to Bloomberg Terminals, MIT’s Andrew W. Lo details how the charting of past stock prices for the purpose of identifying trends, patterns, strength, and cycles within market data has allowed traders to make informed investment decisions based in logic, rather than on luck.

Introduction:

Technical analysis—the forecasting of prices based on patterns in past market data—is something of a black sheep in modern economics. Some skeptics view it as kissing cousins with sleazy speculation or gambling, while others regard it as a relic that is only slightly more sophisticated than the reading of chicken entrails. Proponents of quantitative analysis, who take physics as the ideal model of how economic science ought to look, view technical analysis as antiquated and contrived in its very foundations. They demand mathematical proofs of its validity and dismiss as exception bias the strong betting averages and impressive bottom lines of successful technicians. We make it no secret, then, that we regard technical analysis as a legitimate and useful discipline, tarred by spurious associations and deserving of further academic study.

Some of this skepticism is understandable in light of the historical origins (and occasional abuses) of technical analysis. Many of its methods come down to us from the days before computers and the number-crunching-intensive theories they made possible, and not all of its methods have been thoroughly explored within the quantitative frameworks now available. Many terms and concepts in technical analysis can seem abstruse or outmoded; it is easy to see how a discipline that involves eyeballing charts for patterns with names like “head and shoulders” and “cup with a handle” might seem at first blush more akin to astrology than science. However, many of these are merely heuristics developed in the precomputer age when calculating a simple statistic was a formidable task. For instance, the 10-day moving average became a fixture of technical analysis not because it was optimal, but because it was trivially easy to compute. Indeed, there are many such concepts in “classical” technical analysis that could benefit from quantitative reformulation.

Ultimately, however, both technical and quantitative analysis serve similar purposes: They both attempt to predict the future based on models of the past. One is statistical, the other is intuitive. Whereas a quant minimizes a sum of squared residuals to find the best-fitting line given the data, a technician estimates it by looking at the charts, searching for tell-tale patterns, and inferring the thoughts and feelings of other market players. Both approaches have merit. This is not to say that they are equal; clearly, quantitative methods have won hands down, dominating the investment industry because of their demonstrable value-added. But technical analysis is surprisingly resilient and persistent, and in some corners of the financial industry—such as the trading of commodities and currencies—it is still the dominant mode of analysis. This state of affairs suggests that technical analysis may have something to contribute, even to the most sophisticated quant. Fortunately, a slow but sure reconciliation is underway.

Though big strides have been made throughout history and in recent years toward developing a more systematic approach to technical analysis, technicians remain ostracized to this day. For evidence, look no further than the Financial Industry Regulatory Author ity’s official recognition of the Chartered Market Technician designation, which occurred only in 2005. Part of the reason is that technical analysis is often associated with the speculators, bear raiders, and market cornerers of previous eras. As Tony Tabell, a veteran technical analyst and an heir to the technical brokerage business founded by his father Edmund Tabell in the 1930s, explains:

It’s hard to visualize unless you’ve talked to people who were involved how difficult this was in the atmosphere of [the] 1930s and 1940s. The entire brokerage business was a basket case. Volume on the NYSE was under a million shares. This was the 1930s, the Great Depression, nobody had any money, and if they did, they were very leery about investing. Furthermore, technical analysis had been associated with the excesses of the 1920s. All of the various Securities Acts were designed to get rid of the manipulative market operations that had characterized the ’20s. Since technical work to a great degree (certainly point and figure charts) had been originally conceived as a means of detecting pool operations, confessing that you were involved in technical work at that point was sort of equivalent to confessing that you were some kind of a low-level criminal. I saw some [of ] this, because the remainder of this attitude was still kicking around when I started in the business in the 1950s, but I can imagine how incredible it must have been in the ’30s and ’40s.

Contents:

• Ancient Roots
• The Middle Ages and the Renaissance
• Asia
• The New World
• A New Age for Technical Analysis
• Technical Analysis Today
• A Brief History of Randomness and Efficient Markets
• Academic Approaches to Technical Analysis

The Evolution of Technical Analysis: Financial Prediction from Babylonian Tablets to Bloomberg Terminals By Andrew Lo, Jasmina Hasanhodzic pdf