The authors of a paper published by NBER on March 2000 and titled “The Foundations of Technical Analysis” – Andrew Lo, Harry Mamaysky, and Jiang Wang – claim that:
“Technical analysis, also known as ‘charting’, has been 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 offer a systematic and automatic approach to technical pattern recognition … and apply the method to a large number of US stocks from 1962 to 1996…”
And the conclusion:
” … Over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.”
These hopeful inferences are supported by the work of other scholars, such as Paul Weller of the Finance Department of the university of Iowa. While he admits the limitations of technical analysis – it is a-theoretic and data intensive, pattern over-fitting can be a problem, its rules are often difficult to interpret, and the statistical testing is cumbersome – he insists that “trading rules are picking up patterns in the data not accounted for by standard statistical models” and that the excess returns thus generated are not simply a risk premium.
Technical analysts have flourished and waned in line with the stock exchange bubble. They and their multi-colored charts regularly graced CNBC, the CNN and other market-driving channels. “The Economist” found that many successful fund managers have regularly resorted to technical analysis – including George Soros’ Quantum Hedge fund and Fidelity’s Magellan. Technical analysis may experience a revival now that corporate accounts – the fundament of fundamental analysis – have been rendered moot by seemingly inexhaustible scandals.
The field is the progeny of Charles Dow of Dow Jones fame and the founder of the “Wall Street Journal”. He devised a method to discern cyclical patterns in share prices. Other sages – such as Elliott – put forth complex “wave theories”. Technical analysts now regularly employ dozens of geometric configurations in their divinations.
Technical analysis is defined thus in “The Econometrics of Financial Markets”, a 1997 textbook authored by John Campbell, Andrew Lo, and Craig MacKinlay:
“An approach to investment management based on the belief that historical price series, trading volume, and other market statistics exhibit regularities – often … in the form of geometric patterns … that can be profitably exploited to extrapolate future price movements.”
A less fanciful definition may be the one offered by Edwards and Magee in “Technical Analysis of Stock Trends”:
“The science of recording, usually in graphic form, the actual history of trading (price changes, volume of transactions, etc.) in a certain stock or in ‘the averages’ and then deducing from that pictured history the probable future trend.”
Fundamental analysis is about the study of key statistics from the financial statements of firms as well as background information about the company’s products, business plan, management, industry, the economy, and the marketplace.
Economists, since the 1960’s, sought to rebuff technical analysis. Markets, they say, are efficient and “walk” randomly. Prices reflect all the information known to market players – including all the information pertaining to the future. Technical analysis has often been compared to voodoo, alchemy, and astrology – for instance by Burton Malkiel in his seminal work, “A Random Walk Down Wall Street”.
The paradox is that technicians are more orthodox than the most devout academic. They adhere to the strong version of market efficiency. The market is so efficient, they say, that nothing can be gleaned from fundamental analysis. All fundamental insights, information, and analyses are already reflected in the price. This is why one can deduce future prices from past and present ones.
Jack Schwager, sums it up in his book “Schwager on Futures: Technical Analysis”, quoted by Stockcharts.com:
“One way of viewing it is that markets may witness extended periods of random fluctuation, interspersed with shorter periods of nonrandom behavior. The goal of the chartist is to identify those periods (i.e. major trends).”
Not so, retort the fundamentalists. The fair value of a security or a market can be derived from available information using mathematical models – but is rarely reflected in prices. This is the weak version of the market efficiency hypothesis.
The mathematically convenient idealization of the efficient market, though, has been debunked in numerous studies. These are efficiently summarized in Craig McKinlay and Andrew Lo’s tome “A Non-random Walk Down Wall Street” published in 1999.
Not all markets are strongly efficient. Most of them sport weak or “semi-strong” efficiency. In some markets, a filter model – one that dictates the timing of sales and purchases – could prove useful. This is especially true when the equilibrium price of a share – or of the market as a whole – changes as a result of externalities.
Substantive news, change in management, an oil shock, a terrorist attack, an accounting scandal, an FDA approval, a major contract, or a natural, or man-made disaster – all cause share prices and market indices to break the boundaries of the price band that they have occupied. Technical analysts identify these boundaries and trace breakthroughs and their outcomes in terms of prices.
Technical analysis may be nothing more than a self-fulfilling prophecy, though. The more devotees it has, the stronger it affects the shares or markets it analyses. Investors move in herds and are inclined to seek patterns in the often bewildering marketplace. As opposed to the assumptions underlying the classic theory of portfolio analysis – investors do remember past prices. They hesitate before they cross certain numerical thresholds.
But this herd mentality is also the Achilles heel of technical analysis. If everyone were to follow its guidance – it would have been rendered useless. If everyone were to buy and sell at the same time – based on the same technical advice – price advantages would have been arbitraged away instantaneously. Technical analysis is about privileged information to the privileged few – though not too few, lest prices are not swayed.
Studies cited in Edwin Elton and Martin Gruber’s “Modern Portfolio Theory and Investment Analysis” and elsewhere show that a filter model – trading with technical analysis – is preferable to a “buy and hold” strategy but inferior to trading at random. Trading against recommendations issued by a technical analysis model and with them – yielded the same results. Fama-Blum discovered that the advantage proffered by such models is identical to transaction costs.
The proponents of technical analysis claim that rather than forming investor psychology – it reflects their risk aversion at different price levels. Moreover, the borders between the two forms of analysis – technical and fundamental – are less sharply demarcated nowadays. “Fundamentalists” insert past prices and volume data in their models – and “technicians” incorporate arcana such as the dividend stream and past earnings in theirs.
It is not clear why should fundamental analysis be considered superior to its technical alternative. If prices incorporate all the information known and reflect it – predicting future prices would be impossible regardless of the method employed. Conversely, if prices do not reflect all the information available, then surely investor psychology is as important a factor as the firm’s – now oft-discredited – financial statements?
Prices, after all, are the outcome of numerous interactions among market participants, their greed, fears, hopes, expectations, and risk aversion. Surely studying this emotional and cognitive landscape is as crucial as figuring the effects of cuts in interest rates or a change of CEO?
Still, even if we accept the rigorous version of market efficiency – i.e., as Aswath Damodaran of the Stern Business School at NYU puts it, that market prices are “unbiased estimates of the true value of investments” – prices do react to new information – and, more importantly, to anticipated information. It takes them time to do so. Their reaction constitutes a trend and identifying this trend at its inception can generate excess yields. On this both fundamental and technical analysis are agreed.
Moreover, markets often over-react: they undershoot or overshoot the “true and fair value”. Fundamental analysis calls this oversold and overbought markets. The correction back to equilibrium prices sometimes takes years. A savvy trader can profit from such market failures and excesses.
As quality information becomes ubiquitous and instantaneous, research issued by investment banks discredited, privileged access to information by analysts prohibited, derivatives proliferate, individual participation in the stock market increases, and transaction costs turn negligible – a major rethink of our antiquated financial models is called for.
The maverick Andrew Lo, a professor of finance at the Sloan School of Management at MIT, summed up the lure of technical analysis in lyric terms in an interview he gave to Traders.com’s “Technical Analysis of Stocks and Commodities”, quoted by Arthur Hill in Stockcharts.com:
“The more creativity you bring to the investment process, the more rewarding it will be. The only way to maintain ongoing success, however, is to constantly innovate. That’s much the same in all endeavors. The only way to continue making money, to continue growing and keeping your profit margins healthy, is to constantly come up with new ideas.”