Technical
Analysis of Banking Sector in India
K. Raj Kumar*
Academic Consultant,
University College, Palamuru University, Mahabubnagar.
*Corresponding Author E-mail: rajunsc@gmail.com
ABSTRACT:
Banking Sector in
India is one of the growing sectors with great dynamics. There are various
factors which affect the share prices of Banking Companies. It is very
difficult quantify the impact of various factors on these companies. Technical
analysis is the study of the market action, primarily through the use of
charts, for the purpose of forecasting future price trends. It assumes that the
market is efficient and the price has already taken into consideration along
with the factors related to the company and the industry.
The study made to know the trends and trend reversal pattern and
identify the buying and selling points based on banking sector market price
movements. The study is based on secondary data from 2008 to 2010. The
statistical tools like Simple moving average, Moving Average Convergence and
Divergence, Relative strength Index and Rate of change method are used.
It is found that share prices of the entire
banks shown decreasing trend in the year 2008 and later there is an Increasing
trend in the share prices. The Moving average convergence and divergence
clearly indicated the buy and sell signals at various levels. When Relative strength index values are
decreasing or increasing to peak level it generates buy and sell signals. The resulted ROC
value indicates Oversold and Overbought regions it generates Buy and Sell
signals. It concluded that technical analysis is an effective tool for the
investors to invest in short term.
Banking industry is said to be the mirror of
an economy’s health. A sound banking system serves as a significant trade
enabler to the country. During the recent global crisis, Indian banking
industry came out with flying colors on the back of stringent stipulations laid
down by the Central Bank.
Technical analysis is the study of the market action, primarily through the use of charts, for the purpose of forecasting future price trends. It is a method of evaluating securities by
analyzing the statistics generated by the market activity, such as past prices
and volume. It mainly seeks to predict the short term price levels. It is an
important criteria for an individual to invest in a particular company. It also
provides the base for decision-making in investment. It is one of the most
frequently used yardstick to check and analyze underlying price progress. For
that matter a variety of tools was considered.
This refers to the study of market generated data like prices and
volume to determine the future direction of prices movements.
Technical
analysis involves the use of various methods for charting, calculating,
interpreting graph and chart to assess the performances and status of the
price. It is the tool of financial analysis, which not only studies but also
reflects the numerical and graphical relationship between the important
financial factors.
In fact, the decision made on the basis of technical
analysis is done only after inferring the market trends and judging the future
movement of the stock on the basis of the market trend. It assumes that the
market is efficient and the price has already taken into consideration along
with the factors related to the company and the industry. It is because of this
assumption that many financial experts considers technical analysis is an
efficient tool, which is effective for the short-term investment.
REVIEW OF LITERATURE:
Brown and Jennings (1989)1 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.
Neftci (1991)2 showed that a few of the rules used in
technical analysis generate well-defined techniques 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)3 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.
Brock, Lakonishok and LeBaron (1992)4 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)5 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.
Lui and Mole (1998)6 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)7 reconciles the fact that using technical trading rules
to trade against US intervention in foreign exchange markets can be profitable,
yet, long term, the intervention tends to be profitable.
LeBaron
(1999)8 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)9 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)10 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)11 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)12 make an
extensive simulation comparison of popular dynamic strategies of asset
allocation and find that technical analysis only performs well in Pacific
markets.
Kavajecz
and Odders-White (2004)13 show that support and resistance levels
coincide with peaks in depth on the limit order book and moving average
forecasts reveal information about the relative position of depth.
The above review
of literature it points out that they focused on effectiveness of technical
analysis of stocks in various countries. The domestic stock markets are in a sideway movement over the last couple of weeks. The current
volatility in the stock markets can be attributed to negative sentiments due to
a fall in global markets, profit booking by foreign institutional investors
(FII), uncertainty over the US sub-prime crisis and high crude oil prices. It
is very difficult to quantify the impact of these factors on various sectors.
The investors made better returns by identifying and investing in the right
sectors, based on market conditions. Most of the traders will focus on using
technical indicators to find and place their trades.
Therefore, it is
important for investors to look for the sectoral
trends in the market in order to get good returns on their investments.
NEED FOR THE STUDY:
The last decade has seen many positive
developments in the banking sector, with the opening up of the sector in early
Nineties by the government. The industry has received a significant boost by
the emergence of the private sector banks which increased competitiveness and
enhanced the level of banking facilities to a top notch level. However, during
the recent global recession, even the lagging public sector banks have made a
big come back on the back of large up gradations to suit the hi-tech services
provided by the private sector and foreign banks.
For a sustained economic growth for the
country, unmatched banking and financial services is a must in order to
facilitate the increasing need of swift and hassle-free transactions. Banking
sector is an enabler to the economic growth. In this context, there is a need
to study the past or historical price and volume movements of banking stocks to
predict the future stock price behavior.
OBJECTIVES OF THE STUDY:
·
To know
the trends and trend reversal pattern in banking
sector stock prices.
·
To
identify the buying and selling points based on banking sector market price movements.
SOURCES OF THE DATA:
The study mainly based on both the primary
data and secondary data. The primary data collected through personal interviews
with the investors, brokers and executive directors of brokerage firms. The
secondary is collected from magazines, journals, newspapers and websites.
PERIOD OF THE STUDY:
The study covers a period of 3 years from
January-2008 to December-2010.
Sample
size:
The banking index has grown at a compounded
annual rate of over 51 percent since April 2001 as compared to a 27 per cent
growth in the market index for the same period. The BSE bank index consists of
18 banks, out of which 4 banks are selected based on their market
capitalization which constitutes State Bank of India (SBI), ICICI, HDFC and
Axis Bank.
Statistical
Techniques:
The data analyzed with the help of
statistical tools like Simple moving average, Moving Average Convergence and
Divergence, Relative strength Index and Rate of change method.
DATA ANALYSIS:
State
Bank of India:
Most chart
patterns show a lot of variation in price movement. This can make it
difficult for traders to get an idea of a security's overall trend. Once the
day-to-day fluctuations are removed, traders are better able to identify the
true trend and increase the probability that it will work in their favor. The below graph reveals the movement of stock
price using various technical tools (Graph-1).
Simple
Moving Average (SMA):
A moving
average is the average price of a security over a set amount of time. By
plotting a security's average price, the price movement is smoothed out.
It simply takes the sum of all of the past closing prices over the time period
and divides the result by the number of prices used in the calculation. For
example, in a 10-day moving average, the last 10 closing prices are added
together and then divided by 10.
It is
identified that there is downward trend and wide fluctuations took place in
stock price movement in the beginning of the year 2008, later there was upward
trend in stock price movement.
Moving Average Convergence and Divergence
(MACD):
A moving
average represents the underlying trend in the share price movement. It can be
used to quickly identify whether a security is moving in an uptrend or a
downtrend depending on the direction of the moving average.
The scrip
price is rising, the short term average would be above the long term average.
The short term average intersects the long term average from below indicating a
further rise in price, gives a buy signal. When the scrip price is falling and
if the short term average intersects the long term moving average form above
and falls below it, the sell signal is generated.
Relative Strength Index (RSI):
RSI helps to
signal overbought and oversold conditions in a security. The indicator is
plotted in a range between zero and 100. A reading above 70 is used to suggest
that a security is overbought, while a reading below 30 is used to suggest that
it is oversold. This indicator helps traders to identify whether a security’s
price has been unreasonably pushed to current levels and whether a reversal may
be on the way.
Graph-1- State Bank of India
When the RSI falls below thirty it is time
to pick up the scrip. It found that share prices falling and RSI is rising in
the oversold zone, it would indicate that share prices will increase in future
so buy the stock. When RSI is more than seventy indicates that Share price in
overbought zone, it would indicate the downfall of the price in future so sell
the stock.
Rate
of Change (ROC):
Rate of change (ROC) are simple technical analysis indicators showing
the difference between today's closing price and the close N days ago. It helps
to find out the overbought and oversold positions in a scrip and useful to
identify the trend reversal.
It found that at end of the year 2008, ROC
values are in negative zone. It indicates that oversold so buy the stock. The
values are moving upward movement and reached to overbought zone it indicates
sell the stock.
ICICI
Bank: (Graph-2)
Simple
Moving Average (SMA):
It observed that there was increasing trend
in the year 2008, later there was fluctuations in share price and decreased to
certain level and started upward trend in the share price.
Moving Average Convergence and Divergence
(MACD):
It found the short term moving average below
the long term moving average curves means there is fall in share price which
indicates to sell the stock. When the short term moving average above the long
term moving average indicates an increasing share price so buy the stock.
Relative Strength Index (RSI):
When the RSI
values are less than thirty it is better to buy the stock, if the RSI values
are more than seventy indicates that to sell the stock.
Rate
of Change (ROC):
ROC values are in negative zone, which
indicates that oversold so buy the stock. When the values are positive it means
shares are in overbought zone it indicates sell the stock.
HDFC
Bank: (Graph-3)
Simple
Moving Average (SMA):
The share prices are downtrend and wide
fluctuations in share movement but in the long run the share prices are in
increasing trend.
Moving Average Convergence and Divergence
(MACD):
The share
short term moving average curve crossed the long term moving average curve it
indicates to buy the stock. When the short term moving average curve below the
long term moving average curve it indicates to sell the stock.
Relative Strength Index (RSI):
The RSI values
are less than thirty, it indicates the share prices will increases in future so
buy the stock. When the RSI values are more than seventy the stock is in
overbought zone, so it is better to sell the stock.
Rate
of Change (ROC):
It shows that when ROC values are negative
it is in oversold zone, it indicates to Buy the stock. If the values are
positive indicates overbought region, it gives to Sell the stock.
Graph-2- ICICI Bank
Graph-3- HDFC Bank
Graph-4- AXIS Bank\
AXIS
Bank: (Graph-4)
Simple
Moving Average (SMA):
It found that there is downward trend and
wide fluctuations took place in stock price movement in the beginning of the
year 2008, later there was upward trend in stock price movement.
Moving Average Convergence and Divergence
(MACD):
The short term
average intersects the long term average from below indicating a further rise
in price, gives a buy signal. When the scrip price is falling and if the short
term average intersects the long term moving average form above and falls below
it, the sell signal is generated.
Relative Strength Index (RSI):
The RSI values
are rising in the overbought zone, it would indicate the downfall of the share
price. It gives a clear signal of Sell. When the RSI is in the oversold region,
it generates the Buy signal.
Rate
of Change (ROC):
It shows that ROC values oscillate across
the Zero line. The ROC line is below the zero line the price is falling
indicates oversold region, it gives a buy signal. When the ROC line is above
the zero line price is raising leads to overbought region, it generates the Sell
signal.
CONCLUSIONS:
The current spot prices of traded assets
provide information about future spot prices when market participants are
heterogeneously informed. However, spot prices generally are imperfect
aggregators of private information. If the current spot price depends on the
unobserved current supply of the good as well as on the private information of
market participants, then it is not a sufficient statistic for the private
information. As a result, historical prices together with the current prices
allow more accurate inferences about past and present signals than do current
prices alone. Because current spot prices are not fully revealing, past price,
that is, technical analysis, provide information to agents forming their
demands.
Using the Simple average model, it found
that all banks share prices decreasing trend in the year 2008 and after that
there is an Increasing trend in the share prices. When the share prices are falling Short term
moving average curve is intercepting the Long term moving average curve from
below, it indicates the buy signal. When the share prices are raising the Short
term moving average curve is above the Long term moving average curve it gives
the Sell signal.
It observed that when the share prices are
fall the RSI values are decreasing, it is considered oversold. When price moves up very
rapidly, at some point it is considered overbought. The level of the RSI is a measure of the
stock's recent trading strength.
When the RSI values are greater than the 70 level are
considered to be in overbought territory, and RSI values lower than the 30
level are considered to be in oversold territory. In between the 30 and 70
level is considered neutral, with the 50 level a sign of no trend. When it is
over bought it gives Sell signal, if it is over sold region, it generates the
buy signal.
The ROC values are always may positive, negative or
Zero. All the banks ROC values are oscillate across the zero line. The ROC
value reaches the historic high values, the scrip is in the overbought region
and a fall in the value can be anticipated. If the ROC value reaches historic
low value, the scrip is in the oversold region, a rise price can be
anticipated. Investor can sell the scrip in the overbought region and buy it in
the oversold region.
REFERENCES:
1.
BROWN, D. P., and R. H. JENNINGS, 1989. On Technical
Analysis. The Review of Financial Studies, 2(4), 527–551.
2. NEFTCI, Salih N., 1991. Naive Trading Rules in Financial Markets
and Wiener-Kolmogorov Prediction Theory: A Study of
“Technical Analysis”. The Journal of Business, 64(4), 549–571.
3.
TAYLOR, Mark P., and Helen ALLEN, 1992. The Use of
Technical Analysis in the Foreign Exchange Market. Journal of International
Money and Finance, 11(3), 304–314.
4.
BROCK, William, Josef LAKONISHOK, and Blake LEBARON,
1992. Simple Technical Trading Rules and the Stochastic Properties of Stock
Returns. The Journal of Finance, 47(5), 1731–1764.
5.
BLUME, Lawrence, David EASLEY, and Maureen O’HARA,
1994. Market Statistics and Technical Analysis: The Role of Volume. The Journal
of Finance, 49(1), 153–181.
6. LUI, Yu-Hon, and
David MOLE, 1998. The Use of Fundamental and Technical Analyses by Foreign
Exchange Dealers: Hong Kong Evidence. Journal of International Money and
Finance, 17(3), 535–545.
7. NEELY, Christopher
J., 1998. Technical Analysis and the Profitability of U.S. Foreign Exchange
Intervention. Federal Reserve Bank of St. Louis Review, 80(4), 3–18.
8. LEBARON, Blake,
1999. Technical trading rule profitability and foreign exchange intervention.
Journal of International Economics, 49(1), 125–143.
9. LO, Andrew W.,
Harry MAMAYSKY, and Jiang WANG, 2000. Foundations of Technical Analysis:
Computational Algorithms, Statistical Inference, and Empirical Implementation.
The Journal of Finance, 55(4), 1705–1765.
10.
FERN´ANDEZ-RODR´IGUEZ, Fernando, Christian
GONZ´ALEZ-MARTEL, and Sim´on SOSVILLA-RIVERO, 2000.
On the Profitability of Technical Trading Rules Based on Artificial Neural
Networks: Evidence from the Madrid Stock Market. Economics Letters, 69(1),
89–94.
11. LEE, Charles M. C.,
and Bhaskaran SWAMINATHAN, 2000. Price Momentum and
Trading Volume. The Journal of Finance, 55(5), 2017–2069.
12.
CESARI, R., and D. CREMONINI, 2003. Benchmarking,
Portfolio Insurance and Technical Analysis: A Monte Carlo Comparison of Dynamic
Strategies of Asset Allocation. Journal of Economic Dynamics and Control,
27(6), 987–1011.
13. KAVAJECZ, Kenneth
A., and Elizabeth R. ODDERS-WHITE, 2004. Technical Analysis and Liquidity
Provision. The Review of Financial Studies, 17(4), 1043–1071.
BOOKS:
1. Technical analysis
of the Financial markets by Jhon J. Murphy, Newyork Institute of Finance, 1999.
2. Essential Technical
Analysis Tools and
Techniques to Spot Market Trends by Leigh Stevens, John Wiley and Sons, Inc, 2002
3. Security Analysis
and Portfolio Management by Punivathy Pandian, Vikas Publishing House,
New Delhi, 2001.
4. Security Analysis
and Portfolio Management by S.Kevin, Prentice Hall of
India, New Delhi, 2001.
WEBSITES:
www.bseindia.com
www.investopedia.com
www.wikipedia.com
www.rbi.org
www.technicalanalysis.com
Received on 10.03.2011 Accepted on 10.05.2011
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Asian J. Management 2(3): July-Sept., 2011 page 98-103