Fundamental analysis is performed on historical and present data, but with the goal of making financial forecasts. There are several possible objectives:

• to conduct a company stock valuation and predict its probable price evolution,
• to make a projection on its business performance,
• to evaluate its management and make internal business decisions,
• to calculate its credit risk.

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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 0.50 percent.” Nauzer J. Balsara, Gary Chen and Lin Zheng The Chinese Stock Market: An Examination of the Random Walk Model and Technical Trading Rules

Critics of technical analysis include well-known fundamental analysts. For example, Peter Lynch once commented, “Charts are great for predicting the past.” Warren Buffett has said, “I realized technical analysis didn’t work when I turned the charts upside down and didn’t get a different answer” and “If past history was all there was to the game, the richest people would be librarians.”

An influential 1992 study by Brock et al. which appeared to find support for technical trading rules was tested for data snooping and other problems in 1999; 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 paper published in the Journal of Finance Dr. Andrew W. Lo, director MIT Laboratory for Financial Engineering, 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. 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.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution—conditioned on specific technical indicators such as head-and-shoulders or double-bottoms—we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.” In that same paper Dr. Lo wrote that “several academic studies suggest that…technical analysis may well be an effective means for extracting useful information from market prices.”

Efficient market hypothesis

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. 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 1970, and said “In short, the evidence in support of the efficient markets model is extensive, and (somewhat uniquely in economics) contradictory evidence is sparse.” EMH advocates say that if prices quickly reflect all relevant information, no method (including technical analysis) can “beat the market.” Developments which influence prices occur randomly and are unknowable in advance. The vast majority of academic papers find that technical trading rules, after consideration for trading costs, are not profitable.

Technicians say that EMH ignores the way markets work, in that many investors base their expectations on past earnings or track record, for example. Because future stock prices can be strongly influenced by investor expectations, technicians claim it only follows that past prices influence future prices. They also point to research in the field of behavioral finance, specifically that people are not the rational participants EMH makes them out to be. Technicians have long said that irrational human behavior influences stock prices, and that this behavior leads to predictable outcomes. 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.^[35]

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). Likewise, complete information is reflected in the price because all market participants bring their own individual, but incomplete, knowledge together in the market.

Random walk hypothesis

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: “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.” In a 1999 response to Malkiel, Andrew Lo and Craig McKinlay collected empirical papers that questioned the hypothesis’ applicability 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.

Technicians say the EMH and random walk theories both ignore the realities of markets, in that participants are not completely rational and that current price moves are not independent of previous moves. Critics reply that one can find virtually any chart pattern after the fact, but that this does not prove that such patterns are predictable. Technicians maintain that both theories would also invalidate numerous other trading strategies such as index arbitrage, statistical arbitrage and many other trading systems.

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• OHLC - Open High Low Close charts plot the high and low of the price movement vertically and the open and close horizontally. Used to graph range and outliers.
• Candlestick chart - Similar to OHLC, but open and close are filled. Often Black or Red candles represent a close lower than the open. While White, Green or Blue candles represent a close higher than the open.
• Line chart - Connects each closing interval together on a line

Concepts

• Average true range - averaged daily trading range, adjusted for price gaps
• Chart pattern
• Coppock curve - Edwin Coppock developed the Coppock Indicator to identify the commencement of bull markets
• Dead cat bounce - the phenomenon whereby a spectacular decline in the price of a stock is immediately followed by a moderate and temporary rise before resuming its downward movement
• Elliott wave principle and the golden ratio to calculate successive price movements and retracements
• Hikkake Pattern - pattern for identifying reversals and continuations
• Momentum - the rate of price change
• Point and figure charts - charts based on price without time

Overlays

Overlays are generally superimposed over the main price chart.

• Resistance - an area that brings on increased selling
• Support - an area that brings on increased buying
• Breakout - when a price passes through and stays above an area of support or resistance
• Trend line - a sloping line of support or resistance
• Channel - a pair of parallel trend lines
• Moving average - lags behind the price action but filters out short term movements
• Bollinger bands - a range of price volatility
• Pivot point - derived by calculating the numerical average of a particular currency’s or stock’s high, low and closing prices

Price-based indicators

These indicators are generally shown below or above the main price chart.

• Accumulation/distribution index—based on the close within the day’s range
• Advance decline line — a popular indicator of market breadth
• Average Directional Index — a widely used indicator of trend strength
• Commodity Channel Index - identifies cyclical trends
• MACD - moving average convergence/divergence
• Parabolic SAR - Wilder’s trailing stop based on prices tending to stay within a parabolic curve during a strong trend
• Relative Strength Index (RSI) - oscillator showing price strength
• Stochastic oscillator, close position within recent trading range
• Trix - an oscillator showing the slope of a triple-smoothed exponential moving average, developed in the 1980s by Jack Hutson

Volume based indicators

• Money Flow - the amount of stock traded on days the price went up
• On-balance volume - the momentum of buying and selling stocks
• PAC charts - two-dimensional method for charting volume by price level

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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. However, large-scale application is problematic because of the problem of matching the correct neural topology to the market being studied.

Rule-based trading

Rule-based trading is an approach intended to create trading plans using strict and clear-cut rules. Unlike some other technical methods and the approach of fundamental analysis, it defines a set of rules that determine all trades, leaving minimal discretion. The theory behind this approach is that by following a distinct set of trading rules you will reduce the number of poor decisions, which are often emotion based.

For instance, a trader might make a set of rules stating that he will take a long position whenever the price of a particular instrument closes above its 50-day moving average, and shorting it whenever it drops below.

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The principles of technical analysis derive from the observation of financial markets over hundreds of years. The oldest known example of technical analysis was a method developed by Homma Munehisa during early 18th century which evolved into the use of candlestick techniques, and is today a main charting tool.

Dow Theory is based on the collected writings of Dow Jones co-founder and editor Charles Dow, and inspired the use and development of modern technical analysis from the end of the 19th century. Other pioneers of analysis techniques include Ralph Nelson Elliott and William Delbert Gann who developed their respective techniques in the early 20th century.

Many more technical tools and theories have been developed and enhanced in recent decades, with an increasing emphasis on computer-assisted techniques.

Principles

Technicians say that a market’s price reflects all relevant information, so their analysis looks more at “internals” than at “externals” such as news events. Price action also tends to repeat itself because investors collectively tend toward patterned behavior – hence technicians’ focus on identifiable trends and conditions.

Market action discounts everything

Based on the premise that all relevant information is already reflected by prices, pure technical analysts believe it is redundant to do fundamental analysis – they say news and news events do not significantly influence price, and cite supporting research such as the study by Cutler, Poterba, and Summers titled “What Moves Stock Prices?”

On most of the sizable return days [large market moves]…the information that the press cites as the cause of the market move is not particularly important. Press reports on adjacent days also fail to reveal any convincing accounts of why future profits or discount rates might have changed. Our inability to identify the fundamental shocks that accounted for these significant market moves is difficult to reconcile with the view that such shocks account for most of the variation in stock returns.

Prices move in trends

Technical analysts believe that prices trend. Technicians say that markets trend up, down, or sideways (flat). This basic definition of price trends is the one put forward by Dow Theory.

An example of a security that had an apparent trend is AOL from November 2001 through August 2002. A technical analyst or trend follower recognizing this trend would look for opportunities to sell this security. AOL consistently moves downward in price. Each time the stock rose, sellers would enter the market and sell the stock; hence the “zig-zag” movement in the price. The series of “lower highs” and “lower lows” is a tell tale sign of a stock in a down trend. In other words, each time the stock edged 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 doesn’t 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 that preceded them. “Everyone wants in on the next Microsoft,” “If this stock ever gets to $50 again, I will buy it,” “This company’s technology will revolutionize its industry, therefore this stock will skyrocket” – these are all examples of investor sentiment repeating itself. 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. For example, many technicians monitor surveys of investor sentiment. 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. 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 trading.

Use

Many traders say that trading in the direction of the trend is the most effective means to be profitable in financial or commodities markets. John W. Henry, Larry Hite, Ed Seykota, Richard Dennis, William Eckhardt, Victor Sperandeo, Michael Marcus and Paul Tudor Jones (some of the so-called Market Wizards in the popular book of the same name by Jack D. Schwager) have each amassed massive fortunes via the use of technical analysis and its concepts. George Lane, a technical analyst, coined one of the most popular phrases on Wall Street, “The trend is your friend!”

Many non-arbitrage algorithmic trading systems rely on the idea of trend-following, as do many hedge funds. A relatively recent trend, both in research and industrial practice, has been the development of increasingly sophisticated automated trading strategies. These often rely on underlying technical analysis principles (see algorithmic trading article for an overview).

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Technicians especially search for archetypal patterns, such as the well-known head and shoulders or double top reversal patterns, study indicators such as moving averages, and look for forms such as lines of support, resistance, channels, and more obscure formations such as flags, pennants or balance days.

Technical analysts also extensively use indicators, which are typically mathematical transformations of price or volume. These indicators are used to help determine whether an asset is trending, and if it is, its price direction. Technicians also look for relationships between price, volume and, in the case of futures, open interest. Examples include the relative strength index, and MACD. Other avenues of study include correlations between changes in options (implied volatility) and put/call ratios with price. Other technicians include sentiment indicators, such as Put/Call ratios and Implied Volatility in their analysis.

Technicians seek to forecast price movements such that large gains from successful trades exceed more numerous but smaller losing trades, producing positive returns in the long run through proper risk control and money management.

There are several schools of technical analysis. Adherents of different schools (for example, candlestick charting, Dow Theory, and Elliott wave theory) may ignore the other approaches, yet many traders combine elements from more than one school. Technical analysts use judgment gained from experience to decide which pattern a particular instrument reflects at a given time, and what the interpretation of that pattern should be.

Technical analysis is frequently contrasted with fundamental analysis, the study of economic factors that influence prices in financial markets. Technical analysis holds that prices already reflect all such influences before investors are aware of them, hence the study of price action alone. Some traders use technical or fundamental analysis exclusively, while others use both types to make trading decisions.

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Technical analysis stands in distinction to fundamental analysis. Technical analysis “ignores” the actual nature of the company, market, currency or commodity and is based solely on “the charts,” that is to say price and volume information, whereas fundamental analysis does look at the actual facts of the company, market, currency or commodity. For example, any large brokerage, trading group, or financial institution will typically have both a technical analysis and fundamental analysis team.

Technical analysis is widely used among traders and financial professionals, and is very often used by active day traders, market makers, and pit traders. In the 1960s and 1970s it was widely dismissed by academics. In a recent review, Irwin and Park reported that 56 of 95 modern studies found it produces positive results, but noted that many of the positive results were rendered dubious by issues such as data snooping so that the evidence in support of technical analysis was inconclusive; it is still considered by many academics to be pseudoscience. Academics such as Eugene Fama say the evidence for technical analysis is sparse and is inconsistent with the weak form of the efficient market hypothesis. Users hold that even if technical analysis cannot predict the future, it helps to identify trading opportunities.

In the foreign exchange markets, its use may be more widespread than fundamental analysis. While some isolated studies have indicated that technical trading rules might lead to consistent returns in the period prior to 1987, most academic work has focused on the nature of the anomalous position of the foreign exchange market. It is speculated that this anomaly is due to central bank intervention. Recent research suggests that combining various trading signals into a Combined Signal Approach may be able to increase profitability and reduce dependence on any single rule.

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Total income rose by 24.24 per cent to Rs 3,196.06 crore during the first quarter, Central Bank of India said in a filing to the Bombay Stock Exchange. During the April-June quarter, the bank’s interest income grew 16.22 per cent to Rs 2,802.48 crore, w hile interest expenditure rose by 15 per cent to Rs 2,225.30 crore over the same period last year.

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