Investigating Market Efficiency In Indian Stock Market

Published Date: 02 Nov 2017

A Project Report submitted in partial fulfillment of the requirements for the Degree of

Master of Business Administration

Under the guidance of:

Dr. Raj S. Dhankar

Submitted by:

Aditya Gupta

F-78, MBA (FT)

Faculty of Management Studies

Executive Summary

From year to year there is a considerable growth in Indian stock market’s indexes.

There is also a continuous establishment of laws which regulate the functioning of stock

market and protect investors from illegal activities. Nowadays, India can be regarded

as a country with rather low share of external debt, in comparison with other countries such as

USA, United Kingdom, Germany, etc. Therefore, Indian stock market can be considered as

valuable instrument for investment’s attraction especially among Emerging Markets. But in order to gainfully invest funds in the stock market, it should be carefully examined. But before that you should decide which tools of analysis to use in order to successfully make this investigation.

This research accurately studies technical analysis which can be assigned to one of the respective schools of stock market analysis. The main aim of the research is to find out, whether we can detect market efficiency or inefficiency in Indian stock market by implementation of technical analysis on group of stocks. The practical part of this research is devoted to modeling and performing trading strategies based on technical analysis on the stocks with high liquidity issues. The demand to liquidity is crucial one for prosperous implementation of technical analysis. The insight to data is provided by account with Sharekhan, one of India’s leading brokerage firms.

Analysis and further discussion revealed that technical trading strategy can be gainfully

implemented in Indian stock market. Therefore, it was concluded that Indian stock market

is not efficient and you can model and perform trading strategy based on technical analysis.

CHAPTER 1: INTRODUCTION

Nowadays, Indian stock market continues its high development which was started

since 2002-2003, when there was an explosive growth in market capitalization and an

increase in trading volume for all liquid stocks. According to growth rates, India was the fastest growing market within BRIC (Brazil, Russia, India and China) group - countries with a very fast economics development and major Index Nifty rising by around 26% in 2012. Analytics forecast that Sensex may hit 25000 in couple of years (Rajen Shah, Chief Investment Officer, Angel Broking, 2010).

Such advantageous progression of stock market in dynamics and significant transition

to perform the function of investments attraction in fact opens opportunity for reasonable

analysis and estimation of future development of Indian stocks. However, the question arise

which tools to use in order to implement a reasonable analysis. Should it be gauges which are

provided by fundamental analysis or it should be devices which have come from technical

analysis. Nowadays in India as elsewhere else there is a high popularization of stock market

among population. Brokers try to attract as much clients as possible, by promotion of

favorable sides in stock market’s participation, offering discounts and free services and by

performing educational classes. They provide you with an introduction to technical analysis,

with reference to technical trading strategies, since at first glance it may be more obvious and

understandable than fundamental especially for people who do not have financial education

and nowadays all these technical tools are available and directly at your disposal and

moreover you do not need to calculate them, since special programs on your PC will make all

necessary calculations for you. However, the reality is much more complex, and technical

analysis should not be regarded as a tool with which you can create automated system and

safely earn high incomes. Thus, this research tries to evaluate whether technical analysis

actually works, so there will be an attempt to analyze its theory with reference to its

performance in the past and demonstrate by testing the impact of using its instruments on

trading results. The author himself is highly interested in technical analysis, due to the fact

that there was a lack of it during financial classes, while fundamental analysis was given first priority. However, he takes a neutral point in the sense that he does not give full preference to

one against another of these respective schools of analysis and considers that a reasonable

combination is possible. He adheres to idea that weaknesses of fundamental analysis such as

ignorance of investor’s irrational behavior or psychological aspect and timing matters, can

be replaced with strengths of technical analysis, but fundamental analysis can give a highly

reliable indication of choosing a right stock. However, the researcher is not going to use

fundamental analysis as a basis for stocks selection. The very first point that we should take

into consideration is to understand the demands which come from technical analysis to

different types of stocks.

So, the general aim of this thesis is to find out, whether it is advantageous to trade stocks in Indian stock market with means of technical analysis. Such statement brings us with formulation of the following research question:

Research question: Whether Indian stock market is inefficient in a sense that we can

model and perform trading strategies based on technical analysis and earn abnormal return or

have more advantageous readings of Sharpe ratio against the performance of holding strategy.

So besides test’s performing, this thesis also aims to evaluate suitable technical gauges which can be reasonably combined in the trading strategy.

There will be following important sections in this research:

Methodology: This part will include the choice of philosophical paradigm, the research design, unit of analysis, details about measures, data collection and analysis. It will also touch such issues as validity, both internal and external, reliability and threats to these points.

Theoretical part: This section brings us with issues regarding theory of technical analysis. It provides us with explanations about core concepts of technical analysis, differentiates technical analysis from fundamental and puts some criticism on that matter. It also describes trend issues, variety of technical gauges, risk control considerations. Moreover, it send us back to history to test whether technical analysis proved its efficiency and make an introduction to different trading strategies.

Practical part: After consideration of theory, we can make a step further in understanding the demands for successful implementation of technical analysis. On this basis this section will allow us to choose necessary stocks for testing, select and combine needful technical gauges, determine the conditions of their execution, and specify risk control considerations. After such modeling and subsequent testing, all important results will be presented and analyzed.

Conclusion: Final considerations regarding attainability of research’s objectives with reference to validity and reliability will be reflected in this part.

There is a famous message from Jessie Livermore, a famous stock trader in the beginning of 20th century. In the book about him "Reminiscences of a Stock Operator", written in 1923 by Edwin Lefèvre, he calls us never listen to anybody - your broker, different analytics, friends, wife, etc. First, test your findings by yourself and then implement if your results proved to be successful. If it proved to be inefficient in reality – at least it will be your mistake, which you can analyze, fix and carry on with improvement.

CHAPTER 2: REVIEW OF LITERATURE AND THEORETICAL FRAMEWORK

Determining the stock market timing, when to buy and sell, is a very difficult problem for humans because of the complexity of the stock market (Lee & Jo, 1999). Weak form efficient market hypothesis excludes predictions of future market movements from historical data and makes the technical analysis of no use. However the technical analysis is still widely used by traders and speculators who steadily refuse to consider the market as a fair game and survive with such believe.

For many years technical analysis (TA) has been a contentious issue between academicians and financial practitioners. While the academic world is still discussing whether charting works, or whether it is nothing more than voodoo finance, practitioners have been using technical analysis for decades (Fock et al, 2005). Technical analysis can be defined as a set of methods for predictions of future prices which is based on "mathematical" rather than economical calculations. There are a huge number of combinations of prices and volumes, which helps in the development

of various technical trading strategies like momentum, oscillators, Donchian channels, stochastic etc.

Technical analysts have long relied on the premise of predicting market returns through identifying patterns in past stock prices. Belief in past price patterns in security movements violates the random walk hypothesis - the weak form of stock market efficiency. According to efficient market theory, technical analysis should not produce abnormal returns. In the past literature more emphasis has been on the efficiency of the developed countries, however little is known about the developing countries (Ratner & Leal, 1999). The current paper aims to test the technical trading rules in the Indian market which is considered as one of the developing countries.

Efficient Market Hypothesis

Introduction

The EMH is one of the most incessant and respected theories in finance, yet it still comes under heavy criticism. EMH has been based on an earlier theory that the market prices follow a random walk, hence they are not predictable. The theory was first introduced in 1960’s, but the theory has its stem from around the start of the century. Since then the theory has been heavily researched upon and majority of papers published have backed the theory. So by the end of 1970’s there

was an overwhelming wealth of research that had confirmed the theories of EMH. But recently there have been a number of distinguished economists who have suggested that the economic world accepted the theory too quickly, and that in effect the theory doesn’t hold up in the real world. There have been numerous market anomalies too that jeopardise the implications of EMH. There is a separate section which explains what these anomalies are and how they have weakened the implications of EMH.

The topic has therefore been opened up again, and is currently being analysed by many leading economists. However there are a number of rules which suggest that predictions of the future prices can be made like Brock et al (1992), Mitra et al (2002), but on the other hand there is still a wealth of research which has proved that EMH holds, and that prices do follow a random walk. Financial markets are the largest markets in the world, and consequently the possible gains and the losses from the markets are huge. The theory of EMH therefore becomes vital for the investors to be aware of. If the EMH does not hold true then that would mean that highly acquainted investors would be able to beat the market and secure abnormal returns. This makes the theory of EMH of a great interest.

In this study I wish to perform technical analysis on NSE Nifty, which is seen as a reliable benchmark for the Indian stock market, to see whether or not a significant amount of predictability was generated or not.

There are many individuals and companies which claim that they have developed methods through which they are able to predict the market movements effectively in order to generate abnormal profits. However these are not any new developments. Technical analysis and trading rules in particular have been around for many years, and a lot of investors make their investment decision on the basis of these methods.

The Beginnings

The EMH has stems from around the beginnings of the century. Louis Bachelier (1900) a French mathematician whose PhD. Dissertation titled "The theory of Speculation" was the first author to propose the idea of market efficiency in his dissertation (Investor Home, 1999). He suggested that the "past, present and even discounted future events are reflected in the market price, but often show no apparent relation to price changes"(Dimson and Massuvian, 1998 Pp.91). He concluded that commodity prices fluctuate randomly, and later studies conducted by (Working, 1934) and (Cowles and Jones, 1937) went on to show that US stocks price and other ‘economic series’ also fluctuated randomly. In the early part of the twentieth century these were the only major studies conducted on the theory put forwarded by Bachelier. The theory got glossed over and wasn’t appreciated initially, until in 1960’s when Eugene Fama put forward the theory of EMH. Fama persuasively made the argument that in an active market that includes many well-informed and intelligent investors, securities will be appropriately priced and reflect all available information. If a market is efficient, no information or analysis can be expected to result in the out performance of an appropriate benchmark (Investor Home, 1999).

The ‘random walk’ notation was introduced in 1905 by Pearson, but the theory as it is known was generally referred to as a ‘fair game’. The fair game notation states that no one had an advantage over other people when investing in the stock market. In other words there is an equally likely chance that the stock price will fall or it will rise, so the person investing in the stock has as equal chance of seeing the stock prices fall as seeing the stock price rise.

The Random Walk Theory

Random walk theory assumes that the past stock process do not follow a particular pattern or a trend, so basically past price movements cannot be used to predict the future price movements as they are independent of each other. It may be argued that if prices are bid immediately to fair levels, given all available information, it must be that they increase or decrease only in response to new information. New information, by definition, must be unpredictable; if it could be predicted, then the prediction would be part of today’s information. Thus the change in the stock prices in response to new information also must move unpredictably and the past price changes and the future price movements are independent. So the stock price changes should be random and unpredictable (Malkiel, 1999).

The random walk theory was fairly tested by various researchers in the late 1950’s and 1960’s. Osborne (1959), Robert (1959), Working (1960), Alexander(1961), Treynor (1961), and Cootner(1962) all conducted further research into the random walk theory and seemed to support the ideas that were put forward early in the century (Sewell, 2003). For instance Roberts (1959) showed that a time series generated from a sequence of random numbers shared many of the characteristics of US stock prices. But in contradiction some of the researchers like Workings (1934), Cowles and Jones (1937), and Kendall (1953) had reported "occasional instances of anomalous price behaviour, where certain series appeared to follow predictable paths" (Dimson and Massuvian, 1998 Pp.93). So the theories suggested so far were neither assertively verified nor rejected.

As stated earlier Fama stated in his research about the random walk theory and the EMH. In Fama’s (1965) opening paragraphs he gives definitions concerning the chartist theories and the random walk. Chartist theories suggest that the past behaviour of a security’s price is rich in information concerning its future behaviour. History repeats in that "patterns" of past price behaviour will tend to recur in the future. Thus if careful analysis of price charts one develops an understanding of these "patterns", this can be used to predict the future behaviour of prices and in this way increased expected gains. By contrast the theory of random walk says that the future path of price level of a security is no more predictable than the path of a series of a cumulated random numbers. Most simply this implies that the series of price changes has no memory, that is, the past cannot be used to predict the future in any meaningful way (Fama, 1965 Pp.34). Further he suggests that the random walk theory can be split into two separate hypotheses:

"Successive price changes are independent"; and

"The price changes conform to some probability distribution."

Fama performed tests on varied and extensive data sets to show that both of these hypotheses actually hold true. In his conclusions he states, "It seems safe to say that this paper has presented strong and voluminous evidence in favour of the random walk hypotheses" (Fama, 1965 Pp.98). Therefore "That charts reading, though an interesting pastime is of no real value to the stock market investor" (Fama, 1965 Pp.34). The theory was an important breakthrough which fully supported the ideas of random walk theory and stated that chart reading and technical analysis is practically of no use to the investors in real markets. This theory was widely accepted, and the random walk theory was deemed to be a fair conclusion of how markets move.

Types of EMH

The investigation pertaining to the efficiency of stock markets has been an important preoccupation of empirical financial economics and much of the literature has focussed on the efficiency of the stock markets (Groenewold et al, 2003). Markets may be studied under three forms of Efficient Market Hypothesis (EMH). They are weak form EMH, semi strong-form EMH and strong-form EMH. Each differs from others in terms of its assumptions, nature and characteristics about the amount and kind of information that comes to the marketplace and the rate at which it is reflected in price (Sharpe, 1964). The more quickly information comes to the marketplace and the more rapidly it is reflected in the market prices, the more highly efficient is the market. Market efficiency rests on the conditions such as, absence of transaction costs for trading securities, market participants have costless access to available information, and past prices are serially uncorrelated and future price depends upon, information at future date. A major problem of testing the efficient market hypothesis is that of determining what constitute normal or expected returns (Bodie et al, 2005)

Weak form of market efficiency : states that stock prices already reflect all information that can be derived by examining trading data such as history of past prices, trading volume, or short interest. This version of the hypothesis implies that trend analysis or technical analysis is fruitless. Past stock price data are publicly available and virtually costless to obtain so if such data ever conveyed reliable signals about future performance, all investors already would have learned to exploit the signals. Ultimately the signals lose their value as they become widely known because a buy signal, for instance, would result in an immediate purchase.

Semi strong form hypothesis: states that all publicly available information regarding the prospects of the firm must be reflected in the stock price. This indicates that even the fundamental analysis is of no use and won’t help the investor in earning abnormal profits.

Practically no markets are considered in the strong form efficient because to restrict the insider information is highly difficult task and even with the existence of strict laws against insider information, it is a common practise in companies. Like the crash of the two largest companies of US, Enron and WorldCom is clear evidence that the companies still speculate their share prices to make money and the markets are not strong form efficient.

Market Efficiency

The weak form of market efficiency has been exhaustively tested. Although EMH is still heavily preached by many authors it has come under a considerable amount of scrutiny. Recently technical analysis has been re-examined by the academic community (Brock et al (1992), Hudson et al (1996), Suvilian et al (1999), Sarla & Anubhai (2005)) given that considerable resources are allocated to this activity for apparently little return. This in itself suggests that the academic community has failed to appreciate some of the subtleties of technical analysis or that value is being created within some of the subsets of the overall activity (Marshall et al, 2006). Past studies of technical analysis, including Fama and Blume (1966) and Jensen and Bennington (1970), conclude that technical analysis is not useful. In contrast a more recent study by Brock, Lakonishok, and Le Baron (1992) demonstrates that a relatively simple set of technical trading rules possess significant forecast power for changes in the Dow Jones Industrial Average over a long sample period, Bessembinder and Chan (1995) report that the same rules are useful for forecasting index returns for a group of Asian stock markets. Sharpe, Alexander, and Bailey (1995) summarize the response of some observers to the recent evidence, stating that "the apparent success of these technical strategies offer a challenge to those who contend that the stock market if highly efficient" (Bessembeinder & Chan, 1998). "As also stated by Campbell, Lo and Mac Kinlay (1997, Pp.80) that "recent econometric advances and empirical evidence seem to suggest that financial returns are predictable to some degree. Thirty years ago this would have been a tantamount to an outright rejection of market efficiency. However, modern financial economics teaches us that other, perfectly rational, factors may account for such predictability. Time - varying expected returns due to changing business conditions can generate predictability. A certain degree of predictability may be necessary to reward investors for bearing certain dynamic risks."(Barnett & Serletis, 1998, Pp.7)

As stated earlier for efficient market hypothesis, it is observed that the share price changes, following a random walk over time. It is noted here that share price that follows a random walk over time, means that each observation is independent of the previous observations. The test of randomness in stock price changes is a method of measuring their auto-correlation, i.e., the correlation between price changes in one period and changes for the same stock in another period. If the auto-correlations are close to zero, the price changes are said to be serially independent (Alexander, 1961). If there is a very little correlation of stock changes over time, it is difficult to understand how historical prices can be of much use in predicting future price changes. There are many mechanical trading rules based on historical prices to forecast future prices.

However, in the weak-form of efficient market hypothesis only the historical prices of securities are considered as base for fixing the current prices of the securities and the investors would not be able to consistently earn abnormal profit by simply observing such historical prices of the securities. Roberts (1959) and Osborne (1959) in their individual studies argue that stock price changes because of the changes in the perceived earning potential of the issuing firms or changes in the returns expected from alternative investments(Akhter & Misir, 2005).

Efficiency of Indian Stock market

India is an emerging economy and there has not been a substantial amount of study done by the researchers on the efficiency of the Indian stock market as well as on the applicability of trading techniques to predict the market. But due to an increasing investor interest in emerging markets has motivated a great deal of research aimed at understanding the return and risk characteristics of stock prices in these markets. (Poshakwale, 2002) examines the random walk hypothesis in the emerging Indian stock market by investigating daily returns calculated from an equally weighted portfolio of 100 stocks and a sample of 38 most actively traded stocks in the Bombay stock Exchange. By applying statistical tests he rejected the random walk model of efficient price formation for the Indian market, and suggested that further research should be carried out whereby other plausible models should be analysed to confirm the empirical evidence presented.

In a previous study Sunil Poshakwale [1996] provided empirical evidence on weak form of efficiency and the day of the week effect in Bombay Stock Exchange over a period of 1987-1994. The results provide evidence of day of the week effect and that the stock market is not weak form efficient. He wrote: "The results of Runs test and serial correlation coefficients tests indicate non-random nature of the series and, therefore, violation of weak form of efficiency in the BSE."

In yet another study conducted by (Pandey, 2003) presented the evidence of the inefficient form of the Indian stock market. The study was based on autocorrelation analyses and runs test which identified that the series of stock indices in the Indian stock market are biased random time series. The autocorrelation analysis indicates that the behaviour of share prices does not confirm the applicability of the random walk model in the Indian stock market. However (Sharma & Kennedy, 1977) compared the behaviour of stock indices of the Bombay, London and New York Stock Exchanges during 1963-73 using run test and spectral analysis. Both run tests and spectral analysis confirmed the random movement of stock indices for all the three stock exchanges. They concluded that stock on BSE follow a random walk and are equivalent in the markets of advanced industrialized countries.

Kulkarni (1978) investigated the weekly RBI stock price indices for Bombay, Calcutta, Delhi, Madras and Ahmedabad stock exchanges and monthly indices of six different industries by using spectral method. He concluded that there is a repeated cycle of four weeks for weekly prices and seasonality in monthly prices. This study has thus rejected the hypothesis that stock price changes were random. Some of the recent studies conducted in the same area include Bhattacharya & Mukhopadhyay (2005), and Bhole & Patnaik (2002). The paper of Bhattacharya & Mukhopadhyay (2005) considers nonlinear dependence in returns and envisages appropriate specification of both the conditional first- and second-order moments, so that final conclusions are free from any probable statistical consequences of misspecification. It was found that the Indian stock market is predictable, and the observed lack of efficiency is due to serial correlation, nonlinear dependence, day-of-the week effects, parameter instability, conditional heteroskedasticity (GARCH), daily-level seasonality in volatility, the short-term interest rate (in some sub periods of some indices), and some dynamics in the higher-order moments. Bhole & Patnaik (2002), found that the Indian stock markets are still speculative, volatile and riddled with certain drawbacks. They also found that the share price behaviour in India, particularly short-term, cannot be explained in terms of economic fundamentals.

The empirical research findings demonstrate largely that the emerging markets are inefficient. India is still considered as an emerging market is on its way towards becoming an efficient market with strong regulations being imposed by the SEBI and also with the information becoming widely and less costly available to the investors. Due to the inefficiency of the stock markets most of the financial advisors and stock broking companies use technical analysis to help provide their clients abnormal returns over the market. But to the contradiction Brock et al. (1992) found technical trading rules to have predictive ability with regards to the Dow Jones Index. The paper of (Hudson et al, 1996) also considers whether investors could earn excess returns from technical analysis in a costly trading environment. Their paper concludes that although the technical trading rules examined do have predictive ability in terms of UK data, their use would not allow investors to make excess returns in the presence of costly trading.

Technical Analysis

Definition

Pring J. (1991, Pp.3) defines "The Technical approach to investment is just a reflection of an the idea that prices move in trends that are determined by the changing attitudes of investors toward a variety of economic, political, monetary and psychological forces. The art of technical analysis, for it is an art, is to identify a trend reversal, at an early stage and ride on that trend until the weight of the evidence shows or proves that the trend has reversed". Technical trading rules, while many and varied, aim in general to identify the initiation of new trends. Some of the simpler rules include filter rules, trading range breaks, and moving average intersections. For each rule the analyst chooses the time horizon over which troughs and peaks are to be identified and moving averages calculated, as well as the threshold before a decision is made (Beckley et al. 2000, Pp.9). "Forecasting techniques which use only past prices to forecast future prices are called forecast techniques. They can be classified under two categories: Chartism and mechanical system. Chartism is based on the assumption that trends and patterns in charts reflect not only the available information but the psychology of the investors as well. Analysts who use charts look for geographical cycles and repetition of patterns to discern trends. The rules derived from the analysis of charts are often subjective and, as such, Chartism is considered more of an art than a science." (Acar and Sathcell, 1997, Pp.165). Therefore it presents the weakness of Chartism as it is not considered to be scientific; the accuracy of Chartism can be flawed.

There are three premises on which the technical approach is based (Murphy, 1986, pp. 2-4):

Market action discounts everything

Prices move in trends

History repeats itself

Major Technical indicators and oscillators

To the technical analyst, the chart is the place to find clues regarding the future price direction of an asset. Technical analysts use several different charts, including bar charts, point-and-figure charts and candlestick charts. All these charts are based on historical prices. Thus, the users of these charts obviously do not believe in weak-form market efficiency. Charts typically are analyzed using moving averages and relative strength indicators. In addition, technical analysis implements technical indicators to interpret trends. Technical indicators typically draw from additional historical market-related data, such as volume of trading. Several technical indicators are believed to be leading indicators of future security price movements. The users of these technical indicators apparently do not believe in semi-strong-form market efficiency, because the inputs to the analysis are publicly available information.

Moving Averages:

A moving average is simply the arithmetic average of a given number of past closing prices of a stock or index over a fixed interval of time. For instance, a 50-day moving average is the average of the past 50 days of closing prices. For each new trading day, the oldest price is dropped and the most recent price is added to compute the average.

The example of 50 days simple moving average is presented in a graph below for Nifty

Figure 2.1: Example of 50 days SMA

C:\Users\Sony\Desktop\FMS\Class Notes - 4th Sem\Dissertation\Images\50 Day Average.jpg

Most technical analysts use moving averages for longer time periods. For example, one popular approach is that if the Dow Jones Industrial Average is above its 200-day moving average, then security prices should rise, and if it is below its 200-day moving average, then security prices should fall.

The length of moving average should fit the market cycle you wish to follow. For example, if you determine that a security has a 40-day peak to peak cycle, the ideal moving average length would be 21 days calculated using the following formula:

Ideal Moving Average Length = (Cycle Length/2) +1;

Here is the table, which suggests optimal length of moving average for different trend’s lengths:

Table 2.1: Optimal length of moving average

Trend

Moving Average

Very Short Term

5-13 days

Short Term

14-25 days

Minor Intermediate

26-49 days

Intermediate

50-100 days

Long Term

100-200 days

The purpose of moving average is to identify or signal that a new trend has begun or that an old trend has ended or reversed. Many technical analysts use moving averages in an attempt to identify the primary, intermediate and short-term trends.

MACD (also can be used as oscillator):

One indicator for trend continuation or reversal is known as moving average convergence -divergence (MACD). Generally credited to Gerald Appel as a stock market indicator originally, MACD shows the relationship between two moving averages of prices. The MACD is the difference between a 2 exponential moving averages.

There are following basic concepts concerning MACD:

MACD represents the difference of the short-term exponential moving average minus the long-term exponential moving average.

When market trends are improving, short-term averages will rise more quickly than long-term averages. MACD lines will turn up.

When market trends are losing strength, shorter-term averages will tend to flatten, and ultimately falling below longer-term averages if declines continue. MACD lines will fall below 0.

Weakening trends are reflected in changes of direction of MACD readings, but clear trend reversals are not usually considered as confirmed until other indications take place.

During the course of price movements, short-term moving averages will move apart (diverge) and move together (converge) with longer-term moving averages - hence, the indicator name moving average convergence-divergence.

The example of MACD is presented below:

Figure 2.2: Example of MACD 12-26-9

C:\Users\Sony\Desktop\FMS\Class Notes - 4th Sem\Dissertation\Images\MACD.jpg

There are three popular ways to use MACD: crossovers, overbought/oversold conditions, and divergences:

Crossovers: The basic trading rule concerning MACD is to sell when the MACD falls below its signal line. Similarly, a buy signal occurs when the MACD rises above its signal line. It is also popular to buy/sell when the MACD goes above/below zero.

Overbought/Oversold Conditions: The MACD is also useful as an overbought/oversold indicator. When the shorter moving average pulls away dramatically from the longer moving average (i.e., the MACD rises) it is likely that the security price is overextending and will soon return to more realistic levels. MACD overbought and oversold conditions vary from security to security.

A bearish divergence occurs when the MACD is making new lows while prices fail to each new low. A bullish divergence occurs when the MACD is making new highs while prices fail to reach new highs. Both of these divergences are most significant when they occur at relatively overbought/oversold levels.

Bollinger Bands

Developed by their namesake, John Bollinger, a well-known trader and portfolio manager, Bollinger Bands are generally overlaid directly on a chart’s price bars/candles. These bands consist of simple moving average (SMA) with two additional lines: one that is a certain number of standard deviations above the SMA and the other that is the same number of standard deviations below the SMA. This tool is usually set with a 20-period SMA along with outer lines, each at 2 standard deviations away from the SMA, one above and the other below. However, these settings can be changed readily.

The example of Bollinger band is presented below:

Figure 2.3: Example of Bollinger bands

C:\Users\Sony\Desktop\FMS\Class Notes - 4th Sem\Dissertation\Images\Bollinger.jpg

The basic interpretation of Bollinger Bands is that prices tend to stay within the upper and lower band. The distinctive characteristic of Bollinger Bands is that the spacing between the bands varies based on the volatility of the prices. During periods of extreme price changes (i.e., high volatility), the bands widen to become more forgiving. During periods of stagnant pricing (i.e., low volatility), the bands narrow to contain prices

Following are the characteristics of Bollinger Bands:

Sharp price changes tend to occur after the bands tighten, as volatility lessens;

When prices move outside the bands, a continuation of the current trend is implied;

Bottoms and tops made outside the bands followed by bottoms and tops made inside the bands call for reversal in the trend;

A move that originates at one band tends to go all the way to the other band. This observation is useful when projecting price targets.

The average directional index (ADX)

It was developed by a prolific developer of indicators, J. Welles Wilder. The ADX (sometimes you can meet such indicator’s name as DMI or Directional Movement Index, ADX is an improved addition to DMI to allow for volatile, extreme periods) resides vertically either above or below the bar or candle price chart. Its purpose is to measure the strength, lack of strength, of the current trend as well as whether the trend is increasing or decreasing in strength. Strong trends have high ADX readings, while non trending markets have low ADX readings. An increasing ADX reading is a sign that the trend may be increasing in strength, while a decreasing ADX reading is a sign that the trend may be decreasing in strength or moving toward consolidation phase. Many traders who use ADX will place a horizontal line of demarcation on the indicator to mark the general boundary between trending and non trending. A popular location to place this line is at the 30 level. Another well-popular border is 20. Once this level is set, the trader may then institute a guideline that if the ADX moves above the 30 level, it is moving into strong trending mode. Conversely, if the ADX moves below the 30 level, it is approaching a non trending, consolidating phase. This is an important distinction that can help the trader determine when to use trending techniques and when to turn to range-trading techniques. It is also important to note that ADX by itself does not provide any indication as to the direction of the trend. This function is fulfilled by two other indicators that are closely related to and frequently used with ADX: DI+ and DI-, where "DI" stands for directional indicator. These two indicators provide the directional component to the ADX. When DI+ is moving up and DI- is moving down, it means that price is bullish, or going up. When DI- is moving up and DI+ is moving down, it means that price is bearish, or going down. Some traders will look for crossovers of the DI+ and DI- to provide directional trading signals. ADX, in conjunction with DI+ and DI-, can give vital clues to aid in the important task of determining market trend strength. This system also involves an extreme point rule: on the day of crossover, the extreme price that day is a reverse point for a stop. If long, it is the low of the crossover day; if short, it is the high.

The example of ADX is presented below:

Figure 2.4: Example of ADX:

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The parabolic stop and reverse (SAR)

It is an indicator that excels at providing a sensible trailing stop and reverse methodology. Trailing stops are useful elements of an overall stop-loss and risk management strategy. This indicator was originated by J. Welles Wilder in his pioneering work on chart indicators. The parabolic SAR is usually comprised of dots that follow price in such a way that if a dot is below a price bar, the trade should be long with a dynamic stop loss at the dot. Conversely, if the dot is above the price bar, the trade should be short with dynamic stop-loss at the dot. This indicator, therefore, provides the trader with built-in trading system for being long or short, with the added bonus of convenient locations for stop-losses. Like many other indicators, however, the parabolic SAR can be prone to vicious whipsaws where the trading signals result in a string of losses due to the lack of a strong trend. But the fact that this indicator stresses a logical use of the trailing stop-loss concept makes it a valuable tool for any technical trader.

The example of SAR is presented below:

Figure 2.5: Example of SAR

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The Volume of trading

Volume is secondary in importance and is used primarily as confirming indicator. The thorough technical analyst should include this figure in his or her checklist of things to watch, and always be on the alert for those situations where important messages are being alert.

On one hand, if a stock makes an unexpected move to the upside and there is no news on the company, a large increase in trading volume indicates something significant has probably happened, but has not yet been reported. This price movement prior to a news report is a common occurrence because information about a major event within a company is often leaked before the official news release is published. On the other hand, a price move with a small or no increase in trading volume is more likely to develop from temporary imbalance in the supply and demand relationships.

Key oscillators

There is a special subset of indicators that are called oscillators. These indicators fulfil a special role in that they generally concentrate on market momentum and excel at providing readings of price overextension that are normally referred to as overbought and oversold.

Generally, when an oscillator reading is above a certain overbought threshold during a trading range, the indication is that upward momentum may soon be exhausted and that an impending downward turn may potentially occur. On the other side, when an oscillator reading is below a certain oversold threshold during a trading range, the indication is that downward momentum may soon be exhausted and that an impending upward turn may occur. Also, there is another important key function of chart oscillators. It is in their use in providing divergence signals. Divergences can provide important clues as to the possible direction of near-future momentum.

The most important technical concept for confirmation of a trend is named a divergence. As long as an indicator - especially one that measures the rate of change of price or other data (called momentum) - corresponds with the price trend, the indicator is said to "confirm" the price trend. But, if an indicator or oscillator fails to confirm the trend, it is called a negative divergence or positive divergence. It depends on whether peaks or bottoms fail to confirm price peaks or bottoms. A divergence is an early warning of a potential trend change. It means that the analyst must keep an eye on price data more closely than when the indicators and oscillators are confirming new highs and lows. Divergence analysis is used between almost all indicators and prices; a divergence can occur more than one time before a price reversal.

It is important to note here that there can be two types of divergences that any technical trader should be aware of: regular divergence and hidden divergence. Regular divergence is the most popular type, and it is what most traders mean when they refer to the general concept of divergence. The signal is manifested in an uptrend when price makes a higher high while the oscillator makes a lower high. This is called bearish regular divergence, and warns of a potential reversal and possible subsequent move to the downside. The opposite is called bullish regular divergence and occurs during downtrends. In a bullish divergence, price makes a lower low while oscillator makes a higher low. In both cases, bearish and bullish, the oscillator diverges from the price, giving an indication that price momentum in the currently prevailing direction may be warning.

In contrast to regular divergence, the second type of divergence, called hidden divergence, can be considered the polar opposite. This signal is also a technical imbalance between price movement and oscillator movement. But instead of signalling a potential reversal, hidden divergence is used primarily to signal a potential continuation in the prevailing trend. Bearish hidden divergence usually occurs during a downtrend and is characterized by price making a lower high while the oscillator makes a higher high. In this case, price and the oscillator are diverging in their signals, but the overriding signal that should be taken from an occurrence of bearish hidden divergence is a potential continuation of the lower highs in price, which is the equivalent of a potential continuation in the prevailing downtrend. Bullish hidden divergence usually occurs during an uptrend and is characterized by price making a higher low while the oscillator makes a lower low. In this case, price and the oscillator are diverging in their signals, but the overriding signal that should be taken from an occurrence of bullish hidden divergence is a potential continuation of the higher lows in price, which is the equivalent of a potential continuation in the prevailing uptrend.

1) The rate of change indicator (ROC):

One quick is an indicator called the rate of change indicator (ROC). It is merely a plot of the ratio or difference between today’s closing price and the closing price at some specified time in the past, such as 20 days. When the market or stock is hitting a new high and the 20-day ROC is hitting a new high, we have a confirmation of the price action. If the ROC is not hitting a new high at the same time as the market or stock, then we have a negative divergence. It is a warning that the upward momentum in price is slowing down. An example of Momentum (graphically has the same representation and interpretation as ROC, however it is not the ratio, but the difference between today’s closing price and the closing price at some specified time in the past) is figured below:

Figure 2.6: Example of Momentum

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2) The relative strength index (RSI):

The relative strength index is a popular indicator introduced by J. Welles Wilder in his 1978 book, "New Concepts in Technical Trading Systems". This oscillator is among the most widely used by technical traders in all financial markets. Some innovative traders have developed this tool far beyond its original purposes, to serve as a primary trading tool almost as important as price action itself. Other traders use RSI as a key confirmation tool on the bottom of their charts. During horizontal ranging markets, RSI is a classic oscillator that excels at providing a measure of price momentum as well as providing overbought and oversold indications. In this way, it uses the same concept of reversion to the mean that is behind the linear regression indicator. In order to derive it mathematically, then RSI is a simply a comparison of magnitude of recent gains to recent losses, with formula that looks in the following way: 100/ (1+RS) where RS=average of x periods closes up divided by the average of x periods closes down.

The x number of periods is the primary RSI setting, which is usually set at a default of 14 periods but it can be changed. Also by default, the overbought and oversold boundaries are usually set at 70 and 30, respectively. A cross above the 70 level is considered an indication of price being potentially overbought, while a cross below the 30 level is considered an indication of price being potentially oversold. Some technical traders use RSI as a trade signalling and confirmation tool in this manner. Potential long trades in a ranging market would be confirmed on a cross of the RSI from the oversold region above the 30 level. Potential short trades in a ranging market would be confirmed on a cross of the RSI from the overbought region below the 70 level.

RSI is figured in a picture below:

Figure 2.7: Example of RSI

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3) Stochastics

The Stochastics oscillator was introduced in the 1950s by George Lane, a trader and pioneering technical analyst, and is therefore often referred to as Lane’s Stochastics. The stochastic oscillator compares where a security’s price closed relative to its price range over a given time period. Generally two different varieties of Stochastics can be found on most charting software: %K (fast) and %D (Slow). The main line is called %K. The second line, called %D, is a moving average of %K (which is usually set at a three-period moving average). These lines travel between the extremes of 0 and 100, where the default oversold and overbought levels are generally set at 20 and 80, respectively. The calculation of %K is: 100*[(C-Ln)/ (Hn-Ln)] where C=most recent closing price, Ln=low of the last n days, Hn=high of the last n days.

Stochastics is pictured below:

Figure 2.8: Example of Stochastics

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There are several ways to interpret a stochastic oscillator. Three popular methods include:

Buy, when the Oscillator (either %K or %D) falls below a specific level (e.g., 20) and then rises above that level. Sell when the Oscillator rises above a specific level (e.g., 80) and then falls below that level.

Buy when the %K line rises above the %D line and sell when the %K line falls below the %D line.

Look for divergences, for example, where prices are making a series of new highs and the stochastic oscillator is failing to surpass its previous highs.

As far as Stochastics has two lines as opposed to RSI‟s one line, Stochastics can give off an additional signal that results when the %K line crosses the %D line, much in the same way that trading signals are derived from moving average crossovers.

4) Williams %R

Williams %R is a momentum indicator that measures overbought/oversold levels. Williams %R was developed by Larry Williams. The interpretation of this indicator is very similar to that of the stochastic oscillator, except that %R is plotted upside-down and the Stochastic Oscillator has internal smoothing. While %K in Stochastics compares the closing price with the lowest low for n periods, %R (the name comes from percent of range) compares the close with the highest high. Instead of reading from 0 percent at the bottom to 100 percent at the top, the index scale for %R is flipped upside down so an overbought condition occurs when the indicator is less than 20 percent or 30 percent and the oversold area is above 70 percent or 80 percent (we can derive %R quickly by taking 100 minus %K. Or we can find %K by taking 100 minus %R). In addition, the time period for %R traditionally is 10 periods vs. 5 for %K, and %R usually does not show the three-day moving average (%D). There is an interesting phenomenon of the %R indicator in its uncanny ability to anticipate a reversal in the underlying security’s price. The indicator always forms a peak and turns down a few days before the security’s price peaks and turns down. Likewise, %R usually creates a trough and turns up a few days before the security’s price turns up.

%R is presented below:

Figure 2.9: Example of CCI

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As with all overbought/oversold indicators, it is best to wait for the security’s price to change direction before placing your trades. For example, if an overbought/oversold indicator (such as the Stochastic Oscillator or Williams %R) is showing an overbought condition, it is wise to wait for the security’s price to turn down before selling the security. It is not unusual for overbought/oversold indicators to remain in an overbought/oversold condition for a long time period as the security’s price continues to climb/fall. Selling simply because the security appears overbought may take you out of the security long before its price shows signs of deterioration.

Introduction to Various Technical Trading Strategies

To be successful, it is important not to let results depend on coincidence, but to have a plan. This is very true for stock trading. When you invest in the financial markets, it is crucial to plan as much as possible, put this into a strategy, and then follow that strategy closely with a positive and objective attitude. It is very important not to let fluctuating emotions influence your plan.

Table 2 below explains various technical trading strategies:

Table 2.2: Technical trading strategies

Types of Strategy

Description

Technical Tools in use

Moving Average

When the price crosses above a certain moving average that is a signal to buy, or go long. Conversely, averages crossovers when price crosses below that same moving average, (MA) that is a signal to sell short. Several MA can be used.

Decisions are made again upon crossovers of MA.

Moving Average (MA)

Breakout Trading

Support and resistance levels should be respected. Thus, it is assumed that you will be rewarded (not always) in participating in breakout opportunity, if important support and resistance levels will be broken.

Dynamic and Static Support and Resistance Levels (Trendlines) MA

Trend trading

It is assumed to exploit natural directional bias of a given financial market. You trade in the direction of current trend.

Trendlines, MA

Range trading

Exploiting the ups and downs in a sideways, ranging market.

Trendlines, MA, Bollinger Bands

Price- Oscillator divergences

Divergences between prices and oscillators direction are considered as important signals to enter a trade. However, divergences alone should be confirmed by other technical factors, thus their reliability much greater, when they are used in conjunction.

RSI, Stochastics, ROC, MACD, Williams %R

Oscillator trading

Trading is based in concentrating on the movements of certain oscillators (overbought/oversold conditions or signals from crossovers, which can serve as entry/exit decisions), even to the exclusion of the price bars.

RSI, Stochastics, ROC, MACD, Williams %R

Fibonacci Trading

It is concerned primarily to determine potential retracements within trends. This allows traders to estimate price regions where price may retrace during dips and rallies.

Fibonacci retracement percentages