Technical analysis
[6] Some aspects of technical analysis began to appear in Amsterdam-based merchant Joseph de la Vega's accounts of the Dutch financial markets in the 17th century.
[7][8] Journalist Charles Dow (1851-1902) compiled and closely analyzed American stock market data, and published some of his conclusions in editorials for The Wall Street Journal.
In 1948, Robert D. Edwards and John Magee published Technical Analysis of Stock Trends which is widely considered to be one of the seminal works of the discipline.
[12] Technical analysts also widely use market indicators of many sorts, some of which are mathematical transformations of price, often including up and down volume, advance/decline data and other inputs.
A technical analyst therefore looks at the history of a security or commodity's trading pattern rather than external drivers such as economic, fundamental and news events.
In this a technician sees strong indications that the down trend is at least pausing and possibly ending, and would likely stop actively selling the stock at that point.
The STA was a founding member of IFTA, has recently celebrated its 50th anniversary and certifies analysts with the Diploma in Technical Analysis.
Since the early 1990s when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity.
In mathematical terms, they are universal function approximators,[27][28] meaning that given the right data and configured correctly, they can capture and model any input-output relationships.
Subsequently, a comprehensive study of the question by Amsterdam economist Gerwin Griffioen concludes that: "for the U.S., Japanese and most Western European stock market indices the recursive out-of-sample forecasting procedure does not show to be profitable, after implementing little transaction costs.
Moreover, for sufficiently high transaction costs it is found, by estimating CAPMs, that technical trading shows no statistically significant risk-corrected out-of-sample forecasting power for almost all of the stock market indices.
[44] In a 2000 paper published in the Journal of Finance, professor Andrew W. Lo of MIT, working with Harry Mamaysky and Jiang Wang found that: 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.
[5]In that same paper Lo wrote that "several academic studies suggest that ... technical analysis may well be an effective means for extracting useful information from market prices.
[48] Author David Aronson says that the theory of behavioral finance blends with the practice of technical analysis: By considering the impact of emotions, cognitive errors, irrational preferences, and the dynamics of group behavior, behavioral finance offers succinct explanations of excess market volatility as well as the excess returns earned by stale information strategies.... cognitive errors may also explain the existence of market inefficiencies that spawn the systematic price movements that allow objective TA [technical analysis] methods to work.
In his book A Random Walk Down Wall Street, Princeton economist Burton Malkiel said that technical forecasting tools such as pattern analysis must ultimately be self-defeating: "The problem is that once such a regularity is known to market participants, people will act in such a way that prevents it from happening in the future.
[51] In the late 1980s, professors Andrew Lo and Craig McKinlay published a paper which cast doubt on the random walk hypothesis.
In a 1999 response to Malkiel, Lo and McKinlay collected empirical papers that questioned the hypothesis' applicability[52] that suggested a non-random and possibly predictive component to stock price movement, though they were careful to point out that rejecting random walk does not necessarily invalidate EMH, which is an entirely separate concept from RWH.
In a 2000 paper, Andrew Lo back-analyzed data from the U.S. from 1962 to 1996 and found that "several technical indicators do provide incremental information and may have some practical value".
[54] They argue that feature transformations used for the description of audio and biosignals can also be used to predict stock market prices successfully which would contradict the random walk hypothesis.
Caginalp and Balenovich in 1994[58] used their asset-flow differential equations model to show that the major patterns of technical analysis could be generated with some basic assumptions.
The results were positive with an overwhelming statistical confidence for each of the patterns using the data set of all S&P 500 stocks daily for the five-year period 1992–1996.
One method for avoiding this noise was discovered in 1995 by Caginalp and Constantine[62] who used a ratio of two essentially identical closed-end funds to eliminate any changes in valuation.
[63] The classification relies on two dimensionless parameters, the Froude number characterizing the relative strength of the acceleration with respect to the velocity and the time horizon forecast dimensionalized to the training period.
In 2011, Caginalp and DeSantis[65] have used large data sets of closed-end funds, where comparison with valuation is possible, in order to determine quantitatively whether key aspects of technical analysis such as trend and resistance have scientific validity.
In 2013, Kim Man Lui and T Chong pointed out that the past findings on technical analysis mostly reported the profitability of specific trading rules for a given set of historical data.
It consisted of reading market information such as price, volume, order size, and so on from a paper strip which ran through a machine called a stock ticker.
This system fell into disuse with the advent of electronic information panels in the late 60's, and later computers, which allow for the easy preparation of charts.
Jesse Livermore, one of the most successful stock market operators of all time, was primarily concerned with ticker tape reading since a young age.
[69] This analysis tool was used both, on the spot, mainly by market professionals, as well as by general public through the printed versions in newspapers showing the data of the negotiations of the previous day, for swing and position trades.
