School of Management SUMMER INTERNSHIP PROJECT on Technical Analysis On Indian Stock Market A Research Project Submitted to Add Value in the Degree of Masters of Business Administration (2009-2011) Faculty Guide Company Guide Prity Dawer Pradip Agrawal Head-Corporate Relations SIP Coordinators
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
School of Management
SUMMER INTERNSHIP PROJECT on
Technical Analysis On Indian Stock Market
A Research Project Submitted to Add Value in the Degree of Masters of Business Administration
(2009-2011)
Faculty Guide Company GuidePrity Dawer Pradip Agrawal
Head-Corporate Relations SIP CoordinatorsMr. James Pal Prof. Latika Rochlani & Prof. Hartej Khera
Chairperson DirectorProf. Gaurav Singh Dr. Prashant Gupta
Submitted by:Apoorva Murdiya MBA(III Sem)
CERTIFICATE FROM COMPANY GUIDE
This is to certify that “Apoorva Murdiya” of MBA (Full Time) Semester III in
Sanghvi Institute of Management and Science, Indore has carried out a Summer
Internship Project titled “Technical Analysis On Indian Stock Market”. The work done
by him/her is genuine and authentic.
The work carried out by the student was found satisfactory. We wish him/her all the
success in career.
Signature
Pradip AgrawalCentre Manager- Capital
CERTIFICATE FROM FACULTY GUIDE
This is to certify that “Apoorva Murdiya” of MBA (Full Time) Semester III in Sanghvi Institute of Management and Science, Indore has carried out a Summer Internship Project titled “Technical Analysis On Indian Stock Market”. The work done by him/her is genuine and authentic.
The work carried out by the student was found satisfactory. We wish him/her all the
success in career.
Signature
Prity Dawer
DECLARATION
I, “Apoorva Murdiya”, a student of School of Management, Sanghvi Institute of
Management & Science, Indore, hereby declare that the work done by me to do the
Summer Internship Project titled “Technical Analysis On Indian Stock Market” is
genuine and authentic.
Name & Signature of the Student
ACKNOWLEDGEMENT Note: It is just a specimen acknowledgement to refer; students may adopt any other
standard pattern to express their indebtedness.
I sincerely and religiously devote this folio to all the gem of persons who have openly
or silently left an ineradicable mark on this research so that they may be brought into
consideration and given their share of credit, which they genuinely and outstandingly
deserve.
This expedition of research encountered many trials, troubles and tortures along the
way. I am essentially indebted to my guides “Pradip Agrawal and Prity Dawer” for this
sweating learning experience. They overlooked my faults and follies, constantly inspired
and mentored via the proficient direction. It was a privilege to work under their sincere
guidance.
I express my thanks to Dr. Prashant Gupta, Director (M.B.A. / P.G.D.M.), Sanghvi
Institute of Management and Science, Indore for his considerate support whenever and
wherever needed. I honestly acknowledge the sincere guidance provided by the Head-
Corporate Relations, Mr. James Pal, Mr. Ashutosh Bakshi, Training & Placement
Officer, the Chairperson Prof. Gaurav Singh, Coordinators, Prof. Latika Rochlani &
Prof. Hartej Khera. I express my indebtedness to the management of Sanghvi Institute of
Management and Science, for inspiring us to grab and utilize this opportunity.
With profound sense of gratitude, I would like to truthfully thank a recognizable
number of individuals whom I have not mentioned here, but who have visibly or invisibly
facilitated in transforming this research into a success saga.
Above all, I would like to conscientiously thank the Omnipotent, Omnipresent and
Omniscient God for His priceless blessings!
Name and Signature of Student
EXECUTIVE SUMMARY
Technical Analysis is one of the most popular techniques used to make better
investment decision nowadays. The very fact that it is used by professional hands and so
informed decision is taken before buying or selling equities and or bonds encourages
many investors to venture in to equity market segment.
The title of the project is Technical analysis of stocks. This project is divided into two
stages:
A study of Technical analysis and
To analyze Nifty movements with technical analysis indicators
The first stage of this project dealt with comprehending the various aspects of
Technical Analysis with respect to Historical and current market movements of S&P
CNX Nifty. This stage mainly dealt with the analysis of secondary data and helped a lot
to build conceptual framework for the further analysis of current market situation.
The Second Stage mainly dealt with the Technical analysis of current markets based
on primary data pertaining to Nifty Index.
Chapter 1
INTRODUCTION
Major investment instruments to be used in the market are Commodities and Equities
where return is highest in market. As an investor, everyone needs to know their behavior
and pattern before investing in to any Equity or Commodity. So Stock markets become
important benchmark to follow the condition of economy and to devise the investment
strategies for short term and long term. This study mainly tries to capture the
effectiveness of Technical Analysis while formulating investment strategies by analyzing
Secondary as well as primary data available on nifty index.
1.1 IMPORTANCE AND RELEVANCE OF STUDY
This study comprises of analytical work based on both primary real time data as well
as historical secondary data using different graphs mainly Candlesticks. So it will be of
great help in formulating various investment strategies for future keeping in perspective
both short term and long term goals.
1.2 LITERATURE REVIEW
The use of market timing has long been the subject of much discussion. Several
researchers question the usefulness of such techniques, arguing that such techniques
usually cannot produce better returns than a buy-and-hold (B-H) strategy. Many filter
rules were tested on the US stock market, with most of them concluding that filter rules
do not generate superior returns to the B-H strategy. If the cost of transactions were
considered, the returns could even be negative (Fama and Blume, 1966; Jensen and
Benington 1970). These results are consistent with the efficient markets hypothesis. This
hypothesis implies that technical analysis is without merit. In an efficient market, the
current price reflects all available information including the past history of prices and
trading volume. As investors compete to exploit their common knowledge of a stock’s
price history, they necessarily drive stock prices to levels where expected rate of return
are exactly commensurate with risk. At those levels one cannot expect abnormal returns
(see Fama, 1970).
Although technicians recognize the value of information on future economic
prospects of the firm, their position is that such information is not mandatory for a
successful trading strategy. The reason is that whatever the fundamental reason for a
change in the stock price, if the stock price is sluggish to adjust, the analyst should be
able to identify a trend that could be exploited during the adjustment period.
Consequently, the key to successful technical analysis is a lazy response of stock prices
to fundamental supply-and-demand phenomena. Note that this prerequisite is
diametrically opposite to the notion of an efficient market.Practitioners’ reliance on
technical analysis is well documented.Frankel and Froot (1990a) noted that market
professionals tend to include technical analysis in forecasting the market.
There is also a shift away from the fundamentals to technical analysis in the 1980s,
according to a survey done by Euromoney (Frankel and Froot, 1990a). On a market level,
the prevalence of technical analysis is demonstrated by the fact that most real time
financial information services, like Reuters and Telerate, provide detailed, comprehensive
and up-to-date technical analysis information. It is obvious that the frequent upgrading of
technical analysis services is a response to the demand for technical analysis services and
competition among the financial information service providers. The guiding principle of
technical analysis is to identify and go along with the trend. When there is a trend,
whether started by random or fundamental factors, technical methods will tend to
generate signals in the same direction. This reinforces the original trend, especially when
many investors rely on the technical indicators. Thus, even if the original trend were a
random occurrence, the subsequent prediction made by the technical indicator could be
self-fulfilling. This self-fulfilling nature leads to the formation of speculative bubbles
(Froot et al.,1992).
Conrad and Kaul (1988) found that weekly returns were positively auto correlated,
particularly for portfolios of small stocks.
Frankel and Froot (1990b) suggested that the overpricing of the US dollar in the
1980s with respect to the underlying economic fundamentals could be due to the
influence of technical analysis.
Shiller (1984, 1987) found that irrational investor behaviour resulted in excess bond
and stock market volatility. He also suggested that the October 1987 world-wide stock
market crash could be due largely to technical analysis.
Fama and French (1988) proposed a mean reverting model to explain stock price
movements. They also found that autocorrelation of returns become strongly negative for
a 3–5 year horizon.
DeBondt and Thaler (1985, 1987) found that stocks that were extreme losers over a
3–5 year period tend to have strong returns relative to the market during the following
years. Conversely, extreme winners tend to have weaker returns in subsequent years.
Sy (1990) had argued against Sharpe’s (1975) conclusion, saying that there was no
need for the predictive accuracy to be as high as 70% for the gains to be large. In
addition, he demonstrated that market timing would be increasingly rewarding when the
difference in returns between cash and stocks were narrowed and when market volatility
increased.
Balvers et al. (1990) show empirically that stock returns could be predicted based on
national aggregate output.
Other studies have shown that some fundamental data like price earnings ratio,
dividend yields, business conditions and economic variables can predict to a large degree
the returns on stocks (Campbell, 1987; Campbell and Shiller, 1988a, 1988b; Fama and
French, 1989; Breen et al., 1990, among others). For further innovations, see Wong
(1993, 1994) and Wong et al. (2001).
Brown and Jennings (1989) showed that technical analysis has value in a model in
which prices are not fully revealing and traders have rational conjectures about the
relation between prices and signals.
Frankel and Froot (1990) showed evidence for the rising importance of chartists.
Neftci (1991) showed that a few of the rules used in technical analysis generate well-
defined echniques of forecasting, but even well-defined rules were shown to be useless in
prediction if the economic time series is Gaussian. However, if the processes under
consideration are non-linear, then the rules might capture some information. Tests
showed that this may indeed be the case for the moving average rule.
Taylor and Allen (1992) report the results of a survey among chief foreign exchange
dealers based in London in November 1988 and found that at least 90 per cent of
respondents placed some weight on technical analysis, and that there was a skew towards
using technical, rather than fundamental, analysis at shorter time horizons.
In a comprehensive and influential study Brock, Lakonishok and LeBaron (1992)
analysed 26 technical trading rules using 90 years of daily stock prices from the Dow
Jones Industrial Average up to 1987 and found that they all outperformed the market.
Blume, Easley and O’Hara (1994) show that volume provides information on
information quality that cannot be deduced from the price. They also show that traders
who use information contained in market statistics do better than traders who do not.
Neely (1997) explains and reviews technical analysis in the foreign exchange market.
Neely, Weller and Dittmar (1997) use genetic programming to find technical trading
rules in foreign exchange markets. The rules generated economically significant out-of-
sample excess returns for each of six exchange rates, over the period 1981–1995.
Lui and Mole (1998) report the results of a questionnaire survey conducted in
February 1995 on the use by foreign exchange dealers in Hong Kong of fundamental and
technical analyses. They found that over 85% of respondents rely on both methods and,
again, technical analysis was more popular at shorter time horizons.
Neely (1998) reconciles the fact that using technical trading rules to trade against US
intervention in foreign exchange markets can be profitable, yet, longterm, the
intervention tends to be profitable.
LeBaron (1999) shows that, when using technical analysis in the foreign exchange
market, after removing periods in which the Federal Reserve is active, exchange rate
predictability is dramatically reduced.
Lo, Mamaysky andWang (2000) examines the effectiveness of technical analysis on
US stocks from 1962 to 1996 and finds that over the 31-year sample period, several
technical indicators do provide incremental information and may have some practical
value.
Fern´andez-Rodr´ıguez, Gonz´alez-Martel and Sosvilla-Rivero (2000) apply an
artificial neural network to the Madrid Stock Market and find that, in the absence of
trading costs, the technical trading rule is always superior to a buy and hold strategy for
both ‘bear’ market and ‘stable’ market episodes, but not in a ‘bull’ market. One criticism
I have is that beating the market in the absence of costs seems of little significance unless
one is interested in finding a signal which will later be incorporated into a full system.
Secondly, it is perhaps naïve to work on the premise that ‘bull’ and ‘bear’ markets exist.
Lee and Swaminathan (2000) demonstrate the importance of past trading volume.
Neely and Weller (2001) use genetic programming to show that technical trading
rules can be profitable during US foreign exchange intervention.
Cesari and Cremonini (2003) make an extensive simulation comparison of popular
dynamic strategies of asset allocation and find that technical analysis only performs well
in Pacific markets.
Cheol-Ho Park and Scott H. Irwin wrote ‘The profitability of technical analysis: A
review’ Park and Irwin (2004), an excellent review paper on technical analysis.
Kavajecz and Odders-White (2004) show that support and resistance levels coincide
with peaks in depth on the limit order book 1 and moving average forecasts reveal
information about the relative position of depth on the book.They also show that these
relationships stem from technical rules locating depth already in place on the limit order
book.
More recently, Lo et al. (2000) examined the prevalence of various technical patterns
in American share prices over the period 1962–1996 and found the patterns to be
unusually recurrent.The study does not prove that the patterns are predictable enough to
make sufficient profit to justify the risk,but the authors conclude that this is likely.
1.3 OBJECTIVE
To study the applicability of Technical analysis to stock markets using Nifty
To identify and sort out simple Technical analysis tool relevant for the
formulation of various investment strategies.
Analysis of Stock Market movements during various cyclic events.
To forecast market scenario for near future based on Technical analysis.
CHAPTER 2
CONCEPTUAL FRAMEWORK ON TECHNICAL
ANALYSIS
The methods used to analyze securities and make investment decisions fall into two very
broad categories: fundamental analysis and technical analysis. Fundamental analysis
involves analyzing the characteristics of a company in order to estimate its value.
Technical analysis takes a completely different approach; it doesn't care one bit about the
"value" of a company or a commodity. Technicians or chartists are only interested in the
price movements in the market.
Despite all the fancy and exotic tools it employs, technical analysis really just studies
supply and demand in a market in an attempt to determine what direction, or trend, will
continue in the future. In other words, technical analysis attempts to understand the
emotions in the market by studying the market itself, as opposed to its components
Technical analysis is a method of evaluating securities by analyzing the statistics
generated by market activity, such as past prices and volume. Technical analysts do not
attempt to measure a security's intrinsic value, but instead use charts and other tools to
identify patterns that can suggest future activity. Just as there are many investment styles
on the fundamental side, there are also many different types of technical traders. Some
rely on chart patterns; others use technical indicators and oscillators, and most use some
combination of the two. In any case, technical analysts' exclusive use of historical price