Far from Random: Using Investor Behavior and Trend Analysis to Forecast Market Movement (Bloomberg F

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Malkiel The million-copy bestseller, revised and updated with new investment strategies for retirement and the most current research into behavioral finance. Read more Our price: Malkiel The best investment guide money can buy, with over 1. Malkiel Simply put, the essential first book for any investor. Malkiel From the million-copy-selling author of A Random Walk Down Wall Street, the perfect guide to investing in the next economic giant. Straight Talk On Investing: Because investor behavior repeats itself so often, technicians believe that recognizable and predictable price patterns will develop on a chart.

Technical analysis is not limited to charting, but it always considers price trends. These surveys gauge the attitude of market participants, specifically whether they are bearish or bullish. Technicians use these surveys to help determine whether a trend will continue or if a reversal could develop; they are most likely to anticipate a change when the surveys report extreme investor sentiment. And because most investors are bullish and invested, one assumes that few buyers remain.

This leaves more potential sellers than buyers, despite the bullish sentiment. This suggests that prices will trend down, and is an example of contrarian trading. Chan have suggested that there is statistical evidence of association relationships between some of the index composite stocks whereas there is no evidence for such a relationship between some index composite others. They show that the price behavior of these Hang Seng index composite stocks is easier to understand than that of the index. The industry is globally represented by the International Federation of Technical Analysts IFTA , which is a federation of regional and national organizations.

Professional technical analysis societies have worked on creating a body of knowledge that describes the field of Technical Analysis.

A body of knowledge is central to the field as a way of defining how and why technical analysis may work. It can then be used by academia, as well as regulatory bodies, in developing proper research and standards for the field. Technical analysis software automates the charting, analysis and reporting functions that support technical analysts in their review and prediction of financial markets e.

Since the early s when the first practically usable types emerged, artificial neural networks ANNs have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal function approximators , [36] [37] meaning that given the right data and configured correctly, they can capture and model any input-output relationships.

As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.

While the advanced mathematical nature of such adaptive systems has kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly neural network software has made the technology more accessible to traders. However, large-scale application is problematic because of the problem of matching the correct neural topology to the market being studied.

Systematic trading is most often employed after testing an investment strategy on historic data. This is known as backtesting. Backtesting is most often performed for technical indicators, but can be applied to most investment strategies e. While traditional backtesting was done by hand, this was usually only performed on human-selected stocks, and was thus prone to prior knowledge in stock selection. With the advent of computers, backtesting can be performed on entire exchanges over decades of historic data in very short amounts of time.

The use of computers does have its drawbacks, being limited to algorithms that a computer can perform. Several trading strategies rely on human interpretation, [41] and are unsuitable for computer processing. John Murphy states that the principal sources of information available to technicians are price, volume and open interest. However, many technical analysts reach outside pure technical analysis, combining other market forecast methods with their technical work.

One advocate for this approach is John Bollinger , who coined the term rational analysis in the middle s for the intersection of technical analysis and fundamental analysis.

Technical analysis is also often combined with quantitative analysis and economics. For example, neural networks may be used to help identify intermarket relationships. Investor and newsletter polls, and magazine cover sentiment indicators, are also used by technical analysts. Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data.

Many investors claim that they experience positive returns, but academic appraisals often find that it has little predictive power. Technical trading strategies were found to be effective in the Chinese marketplace by a recent study that states, "Finally, we find significant positive returns on buy trades generated by the contrarian version of the moving-average crossover rule, the channel breakout rule, and the Bollinger band trading rule, after accounting for transaction costs of 0.

An influential study by Brock et al. Subsequently, a comprehensive study of the question by Amsterdam economist Gerwin Griffioen concludes that: 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. In a paper published in the Journal of Finance , Dr.

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. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression , and apply this method to a large number of U. In that same paper Dr.

Lo wrote that "several academic studies suggest that Thomas DeMark 's indicators enjoy a remarkable endorsement in the financial industry. Market entry signals have been tested by comparing conditional returns i. For the period from Jan. The efficient-market hypothesis EMH contradicts the basic tenets of technical analysis by stating that past prices cannot be used to profitably predict future prices.

Far from Random : Using Investor Behavior and Trend Analysis to Forecast Market Movement

Thus it holds that technical analysis cannot be effective. Economist Eugene Fama published the seminal paper on the EMH in the Journal of Finance in , and said "In short, the evidence in support of the efficient markets model is extensive, and somewhat uniquely in economics contradictory evidence is sparse. Technicians say [ who? Because future stock prices can be strongly influenced by investor expectations, technicians claim it only follows that past prices influence future prices. Technicians have long said that irrational human behavior influences stock prices, and that this behavior leads to predictable outcomes.

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 EMH advocates reply that while individual market participants do not always act rationally or have complete information , their aggregate decisions balance each other, resulting in a rational outcome optimists who buy stock and bid the price higher are countered by pessimists who sell their stock, which keeps the price in equilibrium.

The random walk hypothesis may be derived from the weak-form efficient markets hypothesis, which is based on the assumption that market participants take full account of any information contained in past price movements but not necessarily other public information. 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: Malkiel has compared technical analysis to " astrology ".

In the late s, professors Andrew Lo and Craig McKinlay published a paper which cast doubt on the random walk hypothesis. In a response to Malkiel, Lo and McKinlay collected empirical papers that questioned the hypothesis' applicability [60] 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.

Technical analysis

In a paper, Andrew Lo back-analyzed data from U. The random walk index attempts to determine when the market is in a strong uptrend or downtrend by measuring price ranges over N and how it differs from what would be expected by a random walk randomly going up or down. The greater the range suggests a stronger trend.

Caginalp and Balenovich in [64] used their asset-flow differential equations model to show that the major patterns of technical analysis could be generated with some basic assumptions. Some of the patterns such as a triangle continuation or reversal pattern can be generated with the assumption of two distinct groups of investors with different assessments of valuation. The major assumptions of the models are that the finiteness of assets and the use of trend as well as valuation in decision making.

Many of the patterns follow as mathematically logical consequences of these assumptions. One of the problems with conventional technical analysis has been the difficulty of specifying the patterns in a manner that permits objective testing. Japanese candlestick patterns involve patterns of a few days that are within an uptrend or downtrend.

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Caginalp and Laurent [65] were the first to perform a successful large scale test of patterns. A mathematically precise set of criteria were tested by first using a definition of a short term trend by smoothing the data and allowing for one deviation in the smoothed trend. They then considered eight major three-day candlestick reversal patterns in a non-parametric manner and defined the patterns as a set of inequalities. Among the most basic ideas of conventional technical analysis is that a trend, once established, tends to continue. However, testing for this trend has often led researchers to conclude that stocks are a random walk.

One study, performed by Poterba and Summers, [66] found a small trend effect that was too small to be of trading value. As Fisher Black noted, [67] "noise" in trading price data makes it difficult to test hypotheses. One method for avoiding this noise was discovered in by Caginalp and Constantine [68] who used a ratio of two essentially identical closed-end funds to eliminate any changes in valuation. A closed-end fund unlike an open-end fund trades independently of its net asset value and its shares cannot be redeemed, but only traded among investors as any other stock on the exchanges.

In this study, the authors found that the best estimate of tomorrow's price is not yesterday's price as the efficient-market hypothesis would indicate , nor is it the pure momentum price namely, the same relative price change from yesterday to today continues from today to tomorrow. But rather it is almost exactly halfway between the two. Starting from the characterization of the past time evolution of market prices in terms of price velocity and price acceleration, an attempt towards a general framework for technical analysis has been developed, with the goal of establishing a principled classification of the possible patterns characterizing the deviation or defects from the random walk market state and its time translational invariant properties.

Trend-following and contrarian patterns are found to coexist and depend on the dimensionless time horizon. Using a renormalisation group approach, the probabilistic based scenario approach exhibits statistically signifificant predictive power in essentially all tested market phases. A survey of modern studies by Park and Irwin [70] showed that most found a positive result from technical analysis.

In , Caginalp and DeSantis [71] 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.

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Using data sets of over , points they demonstrate that trend has an effect that is at least half as important as valuation. The effects of volume and volatility, which are smaller, are also evident and statistically significant. An important aspect of their work involves the nonlinear effect of trend. Positive trends that occur within approximately 3. For stronger uptrends, there is a negative effect on returns, suggesting that profit taking occurs as the magnitude of the uptrend increases. For downtrends the situation is similar except that the "buying on dips" does not take place until the downtrend is a 4.

These methods can be used to examine investor behavior and compare the underlying strategies among different asset classes. In , 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.

These past studies had not taken the human trader into consideration as no real-world trader would mechanically adopt signals from any technical analysis method. Therefore, to unveil the truth of technical analysis, we should get back to understand the performance between experienced and novice traders. If the market really walks randomly, there will be no difference between these two kinds of traders.

However, it is found by experiment that traders who are more knowledgeable on technical analysis significantly outperform those who are less knowledgeable.

Far from Random : Using Investor Behavior and Trend Analysis to Forecast Market Movement

Until the mids, tape reading was a popular form of technical analysis. 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. Market data was sent to brokerage houses and to the homes and offices of the most active speculators. 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.

Another form of technical analysis used so far was via interpretation of stock market data contained in quotation boards, that in the times before electronic screens , were huge chalkboards located in the stock exchanges, with data of the main financial assets listed on exchanges for analysis of their movements. This analysis tool was used both, on the spot, mainly by market professionals for day trading and scalping , 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.

Despite to continue appearing in print in newspapers, as well as computerized versions in some websites, analysis via quotation board is another form of technical analysis that has fallen into disuse by the majority. From Wikipedia, the free encyclopedia. Foreign exchange Currency Exchange rate.

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