Taking a Behavioral Aspect on Technical Analysis for Better Trading Results
Financial markets are not only ruled by economic forces. Neoclassical finance has been the prevalent economic theory in the financial world for many years, and claimed that all financial practitioners are making rational decisions. However, empirical evidence and psychological studies have challenged the neoclassical finance in the last decades.
Behavioral finance explains how human psychology affects market prices, and raised as a strong alternative framework to neoclassical finance. Financial markets consist of humans and their psychological set-ups, and therefore the behavior of market participants systematically influence individual decision makers and market prices. According to Ricciardi (2008) “the different behavioral finance theories and concepts that influence an individual’s perception of risk for different types of financial services and investment products are heuristics, overconfidence, prospect theory, loss aversion, representativeness, framing, anchoring, familiarity bias, perceived control, expert knowledge, affect (feelings), and worry.” These concepts are reflected in market prices and charts, and therefore play an important role in technical analysis. By studying the historic prices of markets using charts, one is actually indirectly studying behavioral finance.
Technical analysis is another theory which challenges the neoclassical finance and efficient market hypothesis. Technical analysts believe that certain price patterns will repeat themselves in the future and thus provide profit opportunities. According to a leading technician, Martin J. Pring (1985), “technical approach to investment is essentially a reflection of the idea that the market moves in trends which are determined by the changing attitudes of investors to a variety of economic, monetary, political, and psychological forces. The art of technical analysis is to identify changes in such trends at an early stage and to maintain an investment posture until a reversal of that trend is indicated.” 25 to 30 percent of foreign exchange traders base most of their trades on technical trading signals (Cheung and Chinn, 1999), and technical analysis is used as either a primary or secondary source of trading information by more than 90 percent of foreign exchange market participants in London (Allen and Taylor, 1992). They report that traders base their long-term expectation on fundamentals, but use technical analysis for short-term investing and entry-exit signals. A survey made by Menkhoff and Schmidt (2005) report that 36% of German fund managers allocate their funds using alternative strategies including technical analysis.
According to Rolf Wetzer (2011), “academic interest in technical analysis started in the late 1950s. Ever since the first paper on this subject was written, researchers from universities and institutions, such as central banks, have tried to prove whether technical analysis is worthwhile or whether it is just pure nonsense. For decades, the prevalent regime was the “efficient market hypothesis”, i.e. the idea that market prices discount available information instantly and therefore, not only technical analysis but virtually every kind of analysis is useless. This quarrel has not yet been solved, but for over 20 years there has been a growing body of evidence that technical analysis can be profitable.”
Behavioral models suggest that technical trading strategies may be profitable because they presume that price adjusts sluggishly to new information due to noise, market power, humans’ irrational behavior, and chaos (D. Vasiliou et al., 2008).
Andrew Lo and J. Hasanhodzic (2010) explain that the efficacy of both technical and fundamental analysis is disputed by efficient-market hypothesis which states that stock market prices are essentially unpredictable. However, as long as anomalies in the market exist, technical analysis can be used to exploit inefficient prices.
According to psychologist Scot Heuttel, it takes only two similar particular events for the brain to expect they could occur also in the future. In behavioral finance this is called the “Heuttel bias”. “Financial professionals are a close group, sharing information in real and virtual space, thus becoming prone to emotional herding. The behavior of analysts for instance is closely observed by others and their opinions influence the investment public” (Raluca Qawi, 2010). This also explains the behavioral aspect of forming trends, bubbles or repeating technical patterns.
Technical analysis and behavioral finance can be efficiently combined to better understand the behavior of market prices, and thus provide investors with more relevant information in decision making. Exploring the behavioral aspect of technical analysis will give a practical framework for taking advantage of the human psychology in markets.
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