Technical analysis and the modern investment process.
4 min read
What is Technical Analysis?
Technical analysis aims to predict future market direction and trends based on past prices. Support and resistance are two key concepts among a vast array of technical indicators. On a chart, support is where analysts expect buyers to step in and prop up prices after a drop, generally because the security/market has fallen to that price before and then rebounded. Resistance is the opposite; a point where technical analysts expect selling to drive prices lower after a rally. When prices break through either of these, chartists expect it to continue in that direction. Chartists watch moving averages to spot areas of support and resistance. A moving average is simply the average price over a set period of time, which theoretically illustrates a broad trend.
Does Technical Analysis Work?
Technical indicators seem to "work" often enough to maintain their reputation among advocates and fail frequently enough to appease skeptics. However, there are also market environments where fundamental analysis or academically driven quant analysis proves ineffective.
Several academic studies agree with the chartists. Technical analysis may well be an effective means for extracting useful information from market prices. In a rejection of The Random Walk Hypothesis in U.S. Stock Indices, Lo and MacKinlay (1988, 1999) showed past prices may be used to forecast future returns to some degree, a fact any retail chartist takes for granted. Additional indirect support includes studies such as Rouwenhorst (1998), and more direct support examples include Allen and Karjalainen (1999).
It’s reasonable to say technical analysis works at times, is especially suited to short-term trading, and retains relevance as long as humans driven by fear and greed drive markets.
Do Hedge Funds use Technical Analysis?
Many hedge fund non-arbitrage algorithmic trading systems keep a close eye on trends and employ strategies to capitalize on them. There is a growing trend among hedge funds and the overall financial services sector that professes the automation of trading strategies, with the strategies themselves being highly complex and sophisticated technical analysis.
However, technical analysis is markedly more popular among individual traders than hedge funds, with the perception that it’s archaic, less effective, and not scientific. Increases in computational power help bridge this chasm, though a historic linguistic barrier seems part of the equation. Campbell, Lo, and MacKinlay (1997) provide an example of the jargon divide with two statements. First, consider the following statement:
"The presence of clearly identified support and resistance levels, coupled with a one-third retracement parameter when prices lie between them, suggests the presence of strong buying and selling opportunities in the near-term."
Then this one:
"The magnitudes and decay pattern of the first twelve autocorrelations and the statistical significance of the Box-Pierce Q-statistic suggest the presence of a high-frequency predictable component in stock returns."
Both statements indicate past prices contain information for predicting future returns. However, the former is often considered intuitive and subjective while the latter is perceived as statistical and scientific.
Human nature’s inherent eternal need for a good story is also part of the equation. Take the example of Richard Dennis’s famous trading system from the late 80’s. When the strategy rules were revealed, they could be summarized as disciplined technical analysis. Labeling the system “turtles” with the story most anyone could be trained in these methods makes for significantly better print.
The need for higher-level storytelling increases proportionately with fees. Hedge funds require a compelling narrative, especially in the wake of highly publicized blow ups.
In a nut shell, over time, investment managers enhanced the basic trend narrative with academic story telling. In addition to just prices, the story evolved to include additional features such as signal processing, behavioral finance, or crisis alpha. This is not to say these features were not additive; however, none of it changes the basic fact that every investment strategy faces peaks and troughs. It’s a particularly prudent idea to introduce such measures when any hedge fund strategy endures extended periods of .5 Sharpe ratio doldrums.
Hedge funds absolutely use technical analysis. However, when investors pay the high fees hedge funds command, there must also be a correspondingly superior level of storytelling involved. Given evolutions in financial engineering, computing technology, and numerical algorithms, it’s no surprise that the label quantitative finance has overtaken technical analysis in popularity.
AHL co-founder Martin Lueck was even more candid in an interview for Niels Kaastrup-Larsen’s Top Traders Unplugged podcast.
“Having developed these models from… I’ve talked about arcane roots and chartism. Gradually you put more scientific rigor on them, and you find they are signal-processing techniques with certain statistical characteristics, and it all became, in a sense, more respectable.”
Upon closer examination of CTA, quant, or short-term trading strategies, technical analysis is back under the shell and hiding in plain sight.