Technical Analysis Is Nonsense

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A typical price chart for a stock will show the price of the stock over time. A chartist looks for patterns in charts, patterns he believes are predictive. Is there any scientific evidence that there are some truly predictive patterns in stock price charts? No, but there is scientific evidence that chartists see patterns in random processes (Malkiel 2007). According to Thomas Kida, there is a wealth of scientific research going back nearly half a century demonstrating that "charting is useless." "Technical analysis can't beat the market" (Kida 2003: p. 122). The long and the short of it is: past prices of stocks are extremely poor predictors of future prices. Why are there so many technical analysts working for brokerage firms, then? Because they recommend a lot of trades and trades generate commissions (Malkiel 2007). Does the fact that the scientific evidence shows that technical analysis is no better than chance in predicting the price of stocks mean that nobody ever makes any money buying and selling stocks following the advice of a technical analyst? Of course not. By chance alone many brokers are going to make recommendations that make money for their clients. The illusion is in thinking that technical analysis provides a magic formula that will continue to bring success forever and ever. No, in the long run, there will be some big winners and some big losers, but the average among most of the investors will be no better than what you would get if you let a monkey throw darts at a list of all the stocks listed on the New York Stock Exchange.

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Technical anlysis is doomed to fail by the statistical fact that stock prices are nearly random; the market's patterns from the past provide no clue about its future. Not suprisingly, studies conducted by academicians at universities like MIT, Chicago, and Stanford dating as far back as the 1960s have found that the technical theories do not beat the market, especially after deducting transaction fees. It is amazing that technical analysis still exists on Wall Street. One cynical view is that technicians generate higher commissions for brokers because they recommend frequent movement in and out of the market.

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1. Economists’ predictions are no better than guesses Forecasting skill of economists is no better than guessimates by Main Street investors. 2. Government economists often worse than guesses Sherden discovered that predictions made by the elite economists on the President’s Council of Economic Advisors, the Federal Reserve Board, and even the non-partisan Congressional Budget Office were actually worse than guessing. 3. Long-term accuracy is impossible The accuracy of forecasting declines the longer the lead times. 4. Turning points cannot be predicted Economists cannot predict the crucial turning points in the economy, confirming Siegel’s research. Worse, the vast majority of all long-term predictions fail. 5. No specific forecasters are better than the rest of pack Sherden also learned that no particular forecasters were consistently more accurate. 6. No forecaster was more expert with specific statistics No forecaster has consistently higher skills in predicting any one economic statistic. 7. No one ideological orientation was better No ideology perspective consistently produced superior forecasts. 8. Consensus forecasts do not improve accuracy But still, the press and their readers love those lists, averages and consensus forecasts. 9. Psychological bias distorts forecasters and their forecasts Some economists are naturally optimistic and bullish. Others are naturally pessimistic bears. Some are conservative, some progressive. Why? Look inside their brains at their DNA, Every economist has mental biases and political ideologies that distort their choice of research topics and data selection, and therefore, skew their predictions. 10. Increased sophistication does not improve accuracy Sorry folks, but all the new scientific methods, technologies, algorithms and computer models of the economy can make forecasts worse. At least give skilled Wall Street insiders an even better edge over naive retail investors. 11. No improvement over the years Finally, Sherden says there’s no evidence that economic forecasting has improved in recent decades, despite vast new technologies. In recent years the science of forecasting has been deteriorating more as partisan politics intensifies, along with global macro trends, high-frequency trading, Wall Street’s market manipulations, and unpredictable black swan events. All increase volatility, uncertainty and create countless new ways for forecasters to invent new illusions of economic accuracy, while hardening the mental biases of forecasters.

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This paper provides evidence that “illusory correlations”—a well-documented source of cognitive bias—lead some agents to be imperfectly rational noise traders. We focus on the head-and-shoulders chart pattern, considered by technical analysts to provide one of the most reliable trading signals. Our findings indicate that the pattern is associated with a substantial rise in trading volume even though it does not profitably predict directional movements. We further substantiate the connection between head-and-shoulders trading and imperfectly rational noise trading by showing that the pattern is associated with lower bid-ask spreads.

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We conduct an extensive examination of the profitability of technical analysis in ten emerging foreign exchange markets. Studying 25,988 trading strategies for emerging foreign exchange markets, we find that the best rules can sometimes generate an annual mean excess return of more than 30%. Based on standard tests, we find hundreds to thousands of seemingly significant profitable strategies. However, almost all of these profits vanish once the data snooping bias is taken into account. Overall, we show that the profitability of technical analysis is illusory.

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The primary aim of this study is to investigate the validity and predictability of technical analysis in eight Asian equity markets. We employ the bootstrap tests of White (2000) and Hansen (2005) to determine whether any superior trading rule is found to exist amongst the ‘universe’ of technical trading rules identified by Sullivan et al. (1999). We use these powerful bootstrap tests to ascertain the profitability of technical analysis, along with two institutional adjustments for non-synchronous trading and transaction costs. The empirical results indicate that these three elements, data snooping, non-synchronous trading and transaction costs, have significant impact on the overall performance of technical analysis; indeed, the results for these eight Asian stock markets support the efficient market hypothesis, demonstrating that the generation of economic profits through the use of technical analysis is extremely unlikely with these particular markets.

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The main purpose of this paper is to investigate the validity and predictability of technical analysis in the Taiwan stock market. Bootstrapped tests of White (2000) and of Hansen (2005) are employed to ascertain whether there exists a superior trading rule among two broadly used sets of technical analysis. One coming from Brock et al. (1992) and the other from Sullivan et al. (1999). Moreover, this study brings together powerful bootstrapped tests along with two institutional adjustments to ascertain the efficacy of technical analysis: (1) non-synchronous trading and (2) transaction costs. The empirical results indicate that this triad-data snooping, non-synchronous trading and transaction costs, has a great impact on the performance of technical analysis. In fact, the Taiwan stock market stands for market efficiency, and economical profits cannot be rendered from technical analysis in this market.

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► We conduct an intraday technical analysis of individual stocks on the Nikkei 225. ► We investigate technical rules constructed by the information in the limit order book. ► We also investigate technical rules constructed by past price information. ► Past prices do not contribute to profiting in intraday trading. ► Non-execution and picking-off risks are too large to make profits in intraday trading.

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As opposed to fundamental analysis, financial chartism (or technical analysis) is based on the assumption that future movements of a traded asset can be inferred from past regularities. In spite, or maybe by virtue of its lack of scientific grounds, it enjoys an ever-growing popularity among amateur traders, as countless websites provide free, simple and powerful tools for making professional-looking charts and even automate the detection of trends. Though technical analysts account for the validity of chart patterns in terms of market psychology, constructivist explanations also abound among trading communities: it is all as if economic agents reflexively used technical analysis as a means of coordination on the market. Markers like moving averages and trend lines serve as self-referent indicators whose efficacy only gets reinforced as more agents use it as pivotal points. Performativity is the technical analyst’s most natural language: explicit mentions to mechanisms such as the self-fulfilling prophecy or the Keynesian beauty contest are the only justifications s/he needs. Some chartists even claim their discipline does not contradict the efficient-market hypothesis: if prices already reflect all past publicly available information, one may skip the hassle of dissecting balance sheets or mastering pricing models, focusing instead on candlestick or diagram patterns which, by conjecture, contain all the necessary information to explain price movements. Drawing on the comparative analysis of nonprofessional trading blogs, our paper examines how these major epistemic modes of justification of technical analysis have come to support each other, in a context of proliferation of charting tools.

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