In finance, technical analysis is an analysis methodology for analysing and forecasting the direction of prices through the study of past market data, primarily price and volume. As a type of active management, it stands in contradiction to much of modern portfolio theory. The efficacy of technical analysis is disputed by the efficient-market hypothesis, which states that stock market prices are essentially unpredictable, and research on whether technical analysis offers any benefit has produced mixed results. 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. In Asia, technical analysis is said to be a method developed by Homma Munehisa during the early 18th century which evolved into the use of candlestick techniques, and is today a technical analysis charting tool.

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. He believed patterns and business cycles could possibly be found in this data, a concept later known as "Dow theory". However, Dow himself never advocated using his ideas as a stock trading strategy.

In the 1920s and 1930s, Richard W. Schabacker published several books which continued the work of Charles Dow and William Peter Hamilton in their books Stock Market Theory and Practice and Technical Market Analysis. 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. It is exclusively concerned with trend analysis and chart patterns and remains in use to the present. Early technical analysis was almost exclusively the analysis of charts because the processing power of computers was not available for the modern degree of statistical analysis. Charles Dow reportedly originated a form of point and figure chart analysis. With the emergence of behavioral finance as a separate discipline in economics, Paul V. Azzopardi combined technical analysis with behavioral finance and coined the term "Behavioral Technical Analysis".

Other pioneers of analysis techniques include Ralph Nelson Elliott, William Delbert Gann, and Richard Wyckoff who developed their respective techniques in the early 20th century.

General description

Fundamental analysts examine earnings, dividends, assets, quality, ratios, new products, research and the like. Technicians employ many methods, tools and techniques, one of which is the use of charts. Using charts, technical analysts seek to identify price patterns and market trends in financial markets and attempt to exploit those patterns.

Technicians using charts search for archetypal price chart patterns, such as the well-known head and shoulders or double top/bottom reversal patterns, study technical indicators, moving averages and look for forms such as lines of support, resistance, channels and more obscure formations such as flags, pennants, balance days and cup and handle patterns.

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. These indicators are used to help assess whether an asset is trending, and if it is, the probability of its direction and of continuation. Technicians also look for relationships between price/volume indices and market indicators. Examples include the moving average, relative strength index and MACD. Other avenues of study include correlations between changes in Options (implied volatility) and put/call ratios with price. Also important are sentiment indicators such as Put/Call ratios, bull/bear ratios, short interest, Implied Volatility, etc.

There are many techniques in technical analysis. Adherents of different techniques (for example: Candlestick analysis, the oldest form of technical analysis developed by a Japanese grain trader; Harmonics; Dow theory; and Elliott wave theory) may ignore the other approaches, yet many traders combine elements from more than one technique. Some technical analysts use subjective judgment to decide which pattern(s) a particular instrument reflects at a given time and what the interpretation of that pattern should be. Others employ a strictly mechanical or systematic approach to pattern identification and interpretation.

Comparison with fundamental analysis

Contrasting with technical analysis is fundamental analysis: the study of economic

and other underlying factors that influence the way investors price financial markets. This may include regular corporate metrics like a company's recent EBITDA figures, the estimated impact of recent staffing changes to the board of directors, geopolitical considerations, and even scientific factors like the estimated future effects of global warming. Pure forms of technical analysis can hold that prices already reflect all the underlying fundamental factors. Uncovering future trends is what technical indicators are designed to do, although neither technical nor fundamental indicators are perfect. Some traders use technical or fundamental analysis exclusively, while others use both types to make trading decisions.

Comparison with quantitative analysis

The contrast against quantitative analysis is less clear cut than the distinction with fundamental analysis. Some sources treat technical and quantitative analysis as more or less synonymous, while others draw a sharp distinction. For example, quantitative analysis expert Paul Wilmott suggests technical analysis is little more than 'charting' (making forecasts based on extrapolating graphical representations), and that technical analysis rarely has any predictive power.

Market action discounts everything

Based on the premise that all relevant information is already reflected by prices, technical analysts believe it is important to understand what investors think of that information, known and perceived.

Technical analysts believe that prices trend directionally, i.e., up, down, or sideways (flat) or some combination. The basic definition of a price trend was originally put forward by Dow theory. In other words, each time the stock moved lower, it fell below its previous relative low price. Each time the stock moved higher, it could not reach the level of its previous relative high price.

Note that the sequence of lower lows and lower highs did not begin until August. Then AOL makes a low price that does not pierce the relative low set earlier in the month. Later in the same month, the stock makes a relative high equal to the most recent relative high. 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.

History tends to repeat itself

Technical analysts believe that investors collectively repeat the behavior of the investors who preceded them. To a technician, the emotions in the market may be irrational, but they exist. 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. Surveys that show overwhelming bullishness, for example, are evidence that an uptrend may reverse; the premise being that if most investors are bullish they have already bought the market (anticipating higher prices). 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 investing.

Industry

The industry is globally represented by the International Federation of Technical Analysts (IFTA), which is a federation of regional and national organizations. In the United States, the industry is represented by both the CMT Association and the American Association of Professional Technical Analysts (AAPTA). The United States is also represented by the Technical Security Analysts Association of San Francisco (TSAASF). In the United Kingdom, the industry is represented by the Society of Technical Analysts (STA). The STA was a founding member of IFTA, has recently celebrated its 50th anniversary and certifies analysts with the Diploma in Technical Analysis. In Canada the industry is represented by the Canadian Society of Technical Analysts. In Australia, the industry is represented by the Australian Technical Analysts Association (ATAA), (which is affiliated to IFTA) and the Australian Professional Technical Analysts (APTA) Inc.

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. The CMT Association has published a body of knowledge, which is the structure for the Chartered Market Technician (CMT) exam.

Software

Technical analysis software automates the charting, analysis and reporting functions that support technical analysts in their review and prediction of financial markets (e.g. the stock market).

In addition to installable desktop-based software packages in the traditional sense, the industry has seen an emergence of cloud-based applications and application programming interfaces (APIs) that deliver technical indicators (e.g., MACD, Bollinger Bands) via RESTful HTTP or intranet protocols.

Modern technical analysis software is often available as a web or a smartphone application, without the need to download and install a software package. Some of them even offer an integrated programming language and automatic backtesting tools.

Systematic trading

Neural networks

Since the early 1990s 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, meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.

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.

Backtesting/Hindcasting

200px|thumb|right|Temporal representation of hindcasting

Systematic trading is most often employed after testing an investment strategy on historic data. This is known as backtesting (or hindcasting). Backtesting is most often performed for technical indicators combined with volatility but can be applied to most investment strategies (e.g. fundamental analysis). 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, and are unsuitable for computer processing. Only technical indicators which are entirely algorithmic can be programmed for computerized automated backtesting.

Combination with other market forecast methods

John Murphy states that the principal sources of information available to technicians are price, volume and open interest. Another such approach, fusion analysis, overlays fundamental analysis with technical, in an attempt to improve portfolio manager performance.

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.

Empirical evidence

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. Of 95 modern studies, 56 concluded that technical analysis had positive results, although data-snooping bias and other problems make the analysis difficult. Nonlinear prediction using neural networks occasionally produces statistically significant prediction results. A Federal Reserve working paper

An influential 1992 study by Brock et al. appeared to find support for technical trading rules. Sullivan and Timmerman tested the 1992 study for data snooping and other problems in 1999; they determined the sample covered by Brock et al. was robust to data snooping.

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." Transaction costs are particularly applicable to "momentum strategies"; a comprehensive 1996 review of the data and studies concluded that even small transaction costs would lead to an inability to capture any excess from such strategies.

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: