Abstract
Any trader or investor gets benefits to analyze the market by using the method of technical analysis. Bollinger Bands is one of the most important indicators of technical analysis. In this study I have discussed easily about technical analysis, various types of indicators especially Bollinger Bands with example so that any new or old trader or investor can get understand about these fact. Then I have focus on practical implications where I have implemented Bollinger Bands as indicator to analyze ten listed companies of DSE. These ten companies are- LankaBangla Finance Ltd., Meghna Petroleum Ltd., United Airways (BD) Ltd., Jamuna Oil Company Ltd., Padma Oil Company Ltd., Active Fine Chemicals Ltd, Bangladesh Submarine Cable Company Ltd., Islami Bank Bangladesh Ltd., Square Pharmaceutical Ltd., & Titas Gas Transmission & Dist. Co. Ltd.
I have also presented a regression analysis for each company. The regressions are done by two variables which are closing price of each company as dependent variable & Index of DSE as independent variable. These regression analyses are done to find dependence of company’s closing price on Index of DSE. Finally, I have presented the Bollinger Bands charts of Index of DSE to highlight the market condition of DSE.
At last I expect this study will be a conducive for any new or old trader or investor to analyze their markets.
The practical implication of Technical analysis with Bollinger Bands on empirical study on Dhaka Stock Exchange (DSE)
1.
Research Paper
On
The practical implication of Technical analysis
with Bollinger Bands on empirical study on
Dhaka Stock Exchange (DSE)
2.
A Report on
The practical implication of Technical analysis with Bollinger Bands
on empirical study on Dhaka Stock Exchange (DSE)
Prepared For
Chairman
Department of Finance & Banking
Faculty of Business Studies
Prepared By
Khairuzzaman Mamun
Student ID: 20113137
EMBA Program, Major in Finance & Banking
Faculty of Business Administration
Jahangirnagar University
Savar, Dhaka-1342
Date of Submission: 24th
August, 2013
3.
24th
August, 2013
The Chairman
Department of Finance & Banking
Faculty of Business Studies
Jahangirnagar University
Savar, Dkaka-1342
Through: Mohammed Sawkat Hossain, Thesis Supervisor
Subject: Submission of the Master Thesis on “The practical implication of Technical analysis with
Bollinger Bands on empirical study on Dhaka Stock Exchange (DSE)”
Dear Sir,
It is my pleasure to submit the paper on “The practical implication of Technical analysis with
Bollinger Bands on empirical study on Dhaka Stock Exchange (DSE)”. The paper is submitted as
part of the partial fulfillment of the MBA program. The main purpose of this study is to verify
the feasibility of Technical Analysis by Bollinger Bands on Dhaka Stock Exchange (DSE). The
research paper is analyzed by the secondary data of the companies which are listed in the Dhaka
Stock Exchange (DSE). In technical methods Bollinger Bands & % b indicators are used in the
research paper.
I am grateful to you for providing me the opportunity to have such an excellent experience.
Sincerely,
Khairuzzaman Mamun
ID: 20113137
EMBA Program, Major in Finance & Banking
i
4.
Acknowledgement
From the core of my heart I would like to convey my earnest admiration, loyalty and reverence
to the almighty Allah, the most merciful, for keeping everything in order and enabling me to
complete this thesis successfully.
First let me express my thanks to my research supervisor Md.Sawkat Hossain, Department of
Finance & Banking, Jahangirnagar University. He gives me my first introduction to the world
about Technical analysis. It would be impossible for me to finish this research without his
direction and encouragement. It has been a pleasure to have worked with him. His support,
supervision and assistance will always be greatly appreciated.
I would like to especially thank Md. Tarikul Islam, Chairman, Department of Finance &
Banking, Jahangirnagar University; Without his kind guidance, this thesis would have been
impossible.
I am grateful to the Dean of the Faculty of Business Studies, Jahangirnagar University, for
allowing me to work on “The practical implication of Technical analysis with Bollinger
Bands on empirical study on Dhaka Stock Exchange (DSE)”.
I also express my profound gratitude to all other teachers for their kind help and valuable
suggestion. I also thank Jahangirnagar University and generally thank to the office staff for their
collaboration.
I am thankful and grateful to all the employees of Securities and Exchange Commission (SEC)
and Dhaka Stock Exchange (DSE) for their help. I also recall, with gratitude, the patience they
showed during my frequent interruptions in their regular jobs for answering my various queries.
Finally, I stretch out my heartiest love to my beloved mother, father, sister and brother during the
progress of the thesis paper for their direct and indirect co-operation without which this study
would be impossible.
Sincerely,
Khairuzzaman Mamun
ID: 20113137
EMBA Program, Major in Finance & Banking
ii
5.
Abstract
Any trader or investor gets benefits to analyze the market by using the method of technical analysis. Bollinger Bands
is one of the most important indicators of technical analysis. In this study I have discussed easily about technical
analysis, various types of indicators especially Bollinger Bands with example so that any new or old trader or
investor can get understand about these fact. Then I have focus on practical implications where I have implemented
Bollinger Bands as indicator to analyze ten listed companies of DSE. These ten companies are- LankaBangla
Finance Ltd., Meghna Petroleum Ltd., United Airways (BD) Ltd., Jamuna Oil Company Ltd., Padma Oil Company
Ltd., Active Fine Chemicals Ltd, Bangladesh Submarine Cable Company Ltd., Islami Bank Bangladesh Ltd., Square
Pharmaceutical Ltd., & Titas Gas Transmission & Dist. Co. Ltd.
I have also presented a regression analysis for each company. The regressions are done by two variables which are
closing price of each company as dependent variable & Index of DSE as independent variable. These regression
analyses are done to find dependence of company’s closing price on Index of DSE. Finally, I have presented the
Bollinger Bands charts of Index of DSE to highlight the market condition of DSE.
At last I expect this study will be a conducive for any new or old trader or investor to analyze their markets.
iii
6. Table of Contents
Chapter Title Page
Letter of Transmital i
Acknowledgement ii
Abstract iii
Table of Contents iv
List of Tables v
List of charts vi
1 Introduction 01-03
Background 01
Problem Statement 02
Objectives 02
Methodology 02
Limitations 03
Report Layout 03
2 Technical Analysis 04-08
Introduction 04
Principles of Technical Analysis 05
Types of Share Price Movement 05
Types of overall trend under primary movements 05
Indicators of technical analysis 05
Types of indicators 06
Conclusion about Technical Analysis 08
3 Bollinger Bands 09-15
Introduction 09
Bollinger Bands consist of 09
Sharp charts calculation 09
The determinative parameter for BB 10
Estimation of market volatility 11
Classic bounce from Bollinger Bands lines 12
Bollinger Bands squeeze 13
Bandwidth & %b 14
Some additional advice 15
Conclusions about Bollinger Bands 15
4 Implementation & Analysis 16-47
Introduction 16
LankaBangla Finance Ltd. 17
Meghna Petroleum Ltd 20
United Airways (BD) Ltd 23
Jamun Oil Company Ltd 26
Padma Oil Company Ltd 29
Active Fine Chemicals Ltd 32
Bangladesh Submarine Cable Company Ltd 35
Islami Bank Bangladesh Ltd 38
Square Pharmaceuticals Ltd 41
Titas Gas Transmission & Dist. Co. Ltd 44
Dhaka Stock Exchange (DSE) 47
5 Findings & conclusions 50-51
6 References 52
iv
7. List of Tables
Table Title Page
Table#1 Bollinger Bands (20, 2); LankaBangla Finance Ltd 17
Table#2 Model summery, ANOVA, Coefficients; LankaBangla Finance Ltd. 19
Table#3 Bollinger Bands (20, 2); Meghna Petroleum Ltd. 20
Table#4 Model summery, ANOVA, Coefficients; Meghna Petroleum Ltd. 22
Table#5 Bollinger Bands (20,2); United Airways (BD) Ltd. 23
Table#6 Model summery, ANOVA, Coefficients; United Airways (BD) Ltd. 25
Table#7 Bollinger Bands (20,2); Jamun Oil Company Ltd. 26
Table#8 Model summery, ANOVA, Coefficients; Jamun Oil Company Ltd. 28
Table#9 Bollinger Bands (20, 2); Padma Oil Company Ltd. 29
Table#10 Model summery, ANOVA, Coefficients; Padma Oil Company Ltd. 31
Table#11 Bollinger Bands (20,2); Active Fine Chemicals Ltd. 32
Table#12 Model summery, ANOVA, Coefficients; Active Fine Chemicals Ltd. 34
Table#13 Bollinger Bands (20,2); Bangladesh Submarine Cable Company Ltd. 35
Table#14 Model summery, ANOVA, Coefficients; Bangladesh Submarine Cable Co. Ltd. 37
Table#15 Bollinger Bands (20, 2); Islami Bank Bangladesh Ltd. 38
Table#16 Model summery, ANOVA, Coefficients; Islami Bank Bangladesh Ltd. 40
Table#17 Bollinger Bands (20, 2); Square Pharmaceuticals Ltd. 41
Table#18 Model summery, ANOVA, Coefficients; Square Pharmaceuticals Ltd. 43
Table#19 Bollinger Bands (20,2); Titas Gas Transmission & Dist. Co. Ltd. 44
Table#20 Model summery, ANOVA, Coefficients; Titas Gas Transmission & Dist. Co. Ltd. 46
Table#21 Bollinger Bands (20,2); Dhaka Stock Exchange (DSE) 47
v
8. List of Charts
Chart Title Page
Chart # i Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example 10
Chart # ii Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example 11
Chart # iii Closing Price Daily, 20-period/3-deviations Bollinger Bands; Example 12
Chart # iv Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example 12
Chart # v Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example 13
Chart # vi Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example 14
Chart #1 Closing Price Daily, 20-period/2-deviations Bollinger Bands; LankaBangla Finance Ltd 18
Chart #2 Daily Deviation of closing prices; LankaBangla Finance Ltd 18
Chart #3 Daily %b of Bollinger Bands; LankaBangla Finance Ltd 19
Chart #4 Closing Price Daily, 20-period/2-deviations Bollinger Bands; Meghna Petroleum Ltd. 21
Chart #5 Daily Deviation of closing prices; Meghna Petroleum Ltd 21
Chart #6 Daily %b of Bollinger Bands; Meghna Petroleum Ltd 22
Chart #7 Closing Price Daily, 20-period/2-deviations Bollinger Bands; United Airways (BD) Ltd 24
Chart #8 Daily Deviation of closing prices; United Airways (BD) Ltd 24
Chart #9 Daily %b of Bollinger Bands; United Airways (BD) Ltd 25
Chart #10 Closing Price Daily, 20-period/2-deviations Bollinger Bands; Jamun Oil Company Ltd 27
Chart #11 Daily Deviation of closing prices; Jamun Oil Company Ltd 27
Chart #12 Daily %b of Bollinger Bands; Jamun Oil Company Ltd 28
Chart #13 Closing Price Daily, 20-period/2-deviations Bollinger Bands; Padma Oil Company Ltd 30
Chart #14 Daily Deviation of closing prices; Padma Oil Company Ltd 30
Chart #15 Daily %b of Bollinger Bands; Padma Oil Company Ltd 31
Chart #16 Closing Price Daily, 20-period/2-deviations Bollinger Bands; Active Fine Chemicals Ltd 33
Chart #17 Daily Deviation of closing prices; Active Fine Chemicals Ltd 33
Chart #18 Daily %b of Bollinger Bands; Active Fine Chemicals Ltd 34
Chart #19 Closing Price Daily, 20-period/2-deviations Bollinger Bands; Bangladesh Submarine Cable Company Ltd 36
Chart #20 Daily Deviation of closing prices; Bangladesh Submarine Cable Company Ltd 36
Chart #21 Daily %b of Bollinger Bands; Bangladesh Submarine Cable Company Ltd 37
Chart #22 Closing Price Daily, 20-period/2-deviations Bollinger Bands; Islami Bank Bangladesh Ltd 39
Chart #23 Daily Deviation of closing prices; Islami Bank Bangladesh Ltd 39
Chart #24 Daily %b of Bollinger Bands; Islami Bank Bangladesh Ltd 40
Chart #25 Closing Price Daily, 20-period/2-deviations Bollinger Bands; Square Pharmaceuticals Ltd 42
Chart #26 Daily Deviation of closing prices; Square Pharmaceuticals Ltd 42
Chart #27 Daily %b of Bollinger Bands; Square Pharmaceuticals Ltd 43
Chart #28 Closing Price Daily, 20-period/2-deviations Bollinger Bands; Titas Gas Transmission & Dist. Co. Ltd 45
Chart #29 Daily Deviation of closing prices; Titas Gas Transmission & Dist. Co. Ltd 45
Chart #30 Daily %b of Bollinger Bands; Titas Gas Transmission & Dist. Co. Ltd 46
Chart #31 Index of DSE, Daily, 20-period/2-deviations Bollinger Bands; Dhaka Stock Exchange (DSE) 48
Chart #32 Daily Deviation of Index of DSE; Dhaka Stock Exchange (DSE) 48
Chart #33 Daily %b of Bollinger Bands; Dhaka Stock Exchange (DSE) 49
vi
9. 1 | P a g e
Chapter 1
Introduction
Background:
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 (sometimes called 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 and fundamental analysis are the two main schools of thought in the financial
markets. Difference are-
1. A technical analyst approaches a security from the charts, while a fundamental analyst
starts with the financial statements. Fundamental analysis takes a relatively long-term
approach to analyzing the market compared to technical analysis. While technical
analysis can be used on a timeframe of weeks, days or even minutes, fundamental
analysis often looks at data over a number of years.
2. Not only is technical analysis more short term in nature than fundamental analysis, but
the goals of a purchase (or sale) of a stock are usually different for each approach. In
general, technical analysis is used for a trade, whereas fundamental analysis is used to
make an investment. Investors buy assets they believe can increase in value, while traders
buy assets they believe they can sell to somebody else at a greater price. The line between
a trade and an investment can be blurry, but it does characterize a difference between the
two schools.
Although technical analysis and fundamental analysis are seen by many as polar opposites - the
oil and water of investing - many market participants have experienced great success by
combining the two. For example, some fundamental analysts use technical analysis techniques to
figure out the best time to enter into an undervalued security. Oftentimes, this situation occurs
when the security is severely oversold. By timing entry into a security, the gains on the
investment can be greatly improved.
Alternatively, some technical traders might look at fundamentals to add strength to a technical
signal. For example, if a sell signal is given through technical patterns and indicators, a technical
trader might look to reaffirm his or her decision by looking at some key fundamental data.
Oftentimes, having both the fundamentals and technical’s on your side can provide the best-case
scenario for a trade.
10. 2 | P a g e
Problem Statement:
Although Fundamental analysis is important in analyzing any security, I focus on technical
analysis in share price movement of any security. There are many indicators for analyzing the
share price movement of any security. Among them Bollinger Bands is another important
indicator for analyzing any security. In this study I use Bollinger Bands as indicator to analyze
some randomly selected securities of DSE. The main purpose of this study is to verify the
feasibility of Technical Analysis by Bollinger Bands on Dhaka Stock Exchange (DSE) and to
analyze securities price movement by using Bollinger Bands of Technical analysis.
Objectives:
1. To verify the feasibility of Technical Analysis on Dhaka Stock Exchange (DSE)
2. To analyze securities price movement by using Bollinger Bands of Technical analysis.
3. To calculate moving average of price, deviation of price, upper band, & lower band.
4. To show their graphical representation.
5. To identify the upward and downward price movements of selected securities.
6. To calculate the value of %b & to show its graphical representation.
7. To forecast future price range & trend of each security.
Methodology:
Research Design
Now a day’s DSE is very frequent fluctuate market. Here all the data are historical. For this the
given assumptions are built in the base of the historical data. All showed graphs are help to
recognize the recent market. This study is very much authentic and realistic. For this any new
investor or the old investor can take decision from it. So the research is design in the base of
historical data and show the situation of the market. More over the market is now very
unpredictable, so the analysis report is very much important for all.
The study involved a secondary data about DSE, Dhaka, Bangladesh. Also the companies own
website is also helpful for the research.
Sources of Data: The data has been collected from Secondary sources.
Secondary data sources: Secondary data, such as several academic articles and books have
been analyzed to form and clarify the topic of the research, and thus to identify the key variables
related to the response and attribution of the entrepreneur in the incidents of the Technical
Analysis collecting from DSE and company information. The secondary data sources were-
websites, books etc.
11. 3 | P a g e
Data collection procedure: The data was collected from the secondary market of DSE.
Method of Analysis: For implementation & analysis I have taken closing prices of ten listed
company then I have done some calculation with excel to find the moving average, deviation,
upper band & lower band. Using these data I have found three charts for each company such as-
1. Closing Price Daily, 20-period/2-deviations Bollinger Bands
2. Daily Deviation of closing prices
3. Daily %b of Bollinger Bands
I have also presented a regression analysis for each company. The regressions are done by two
variables which are closing price of each company as dependent variable & Index of DSE as
independent variable. These regression analyses are done to find dependence of company’s
closing price on Index of DSE.
Finally I have presented the Bollinger Bands charts of Index of DSE to highlight the market
condition of DSE.
Limitations:
I have taken daily closing price. If I would take longer period, results, interpretation & analysis
may defer.
I have used Bollinger Bands as indicator. If I would take another indicator, then interpretation &
analysis may defer.
Interpretation depends upon perception of chartists based on charts without considering other
socio economical influencing factors.
The technical analysis failed to signal an uptrend or downtrend in time.
Report Layout:
Chapter 1 gives background, methodology, objective and limitations of the report.
Chapter 2 describes the Technical Analysis.
Chapter 3 describes the Bollinger Bands
Chapter 4 gives Implementation & Analysis
Chapter 5 highlights findings & conclusions
Chapter 6 gives reference
12. 4 | P a g e
Chapter 2
Technical Analysis
Introduction:
Technical Analysis is the forecasting of future financial price movements based on an
examination of past price movements. Like weather forecasting, technical analysis does not
result in absolute predictions about the future. Instead, technical analysis can help investors
anticipate what is "likely" to happen to prices over time. 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 and volume data is what separates them from their fundamental
counterparts. Unlike fundamental analysts, technical analysts don't care whether a stock is
undervalued - the only thing that matters is a security's past trading data and what information
this data can provide about where the security might move in the future.
The field of technical analysis is based on three assumptions:
1. The Market Discounts Everything: A major criticism of technical analysis is that it
only considers price movement, ignoring the fundamental factors of the company.
However, technical analysis assumes that, at any given time, a stock's price reflects
everything that has or could affect the company - including fundamental factors.
Technical analysts believe that the company's fundamentals, along with broader
economic factors and market psychology, are all priced into the stock, removing the need
to actually consider these factors separately. This only leaves the analysis of price
movement, which technical theory views as a product of the supply and demand for a
particular stock in the market.
2. Price Moves in Trends: In technical analysis, price movements are believed to follow
trends. This means that after a trend has been established, the future price movement is
more likely to be in the same direction as the trend than to be against it. Most technical
trading strategies are based on this assumption.
3. History Tends To Repeat Itself: Another important idea in technical analysis is that
history tends to repeat itself, mainly in terms of price movement. The repetitive nature of
price movements is attributed to market psychology; in other words, market participants
tend to provide a consistent reaction to similar market stimuli over time. Technical
13. 5 | P a g e
analysis uses chart patterns to analyze market movements and understand trends.
Although many of these charts have been used for more than 100 years, they are still
believed to be relevant because they illustrate patterns in price movements that often
repeat themselves.
Technical analysis is applicable to stocks, indices, commodities, futures or any tradable
instrument where the price is influenced by the forces of supply and demand.
Principles of Technical Analysis:
1. Demand and supply factors
2. Rational and irrational factors
3. Continuous in particular direction
4. Shift of Demand and supply factors
5. Charts representing market action
6. Patterns to forecast forwarded share price
Types of Share Price Movement:
1. Tertiary Movement – [Day to day movement]
2. Secondary Movement – [A number of weeks or month]
3. Primary Movement – [Period of several months or years]
Types of overall trend under primary movements:
There are two types of overall trend under primary movements
1. Bullish Trend
2. Bearish Trend
Bullish Trend: Three phases of a bullish market - revival of confidence, improvement of
corporate earnings, speculation & inflation.
Bearish Trend: Three phases of a bearish market-abandonment of hopes, low profits and
dividends, further distress selling.
Indicators of technical analysis:
Indicators are calculations based on the price and the volume of a security that measure such
things as money flow, trends, volatility and momentum. Indicators are used as a secondary
measure to the actual price movements and add additional information to the analysis of
securities. Indicators are used in two main ways:
1. to confirm price movement and the quality of chart patterns, and
2. To form buy and sell signals.
14. 6 | P a g e
Types of indicators:
There are many types of indicators as follows:
Trend Indicators: Trend indicators reflect three tendencies in price movements: Up moves,
down moves and sideways price moves. These indicators help define prevailing
directions/ trends of the price moves by smoothing price data over a certain period of time. In
simple words, Trend indicators allow us to visualize Trends in the market.
1. Ichimoku Kinko Hyo
2. Advance Decline Line (ADL)
3. Average Directional Index (ADX)
4. Average Directional Movement Index Rating (ADXR)
5. Commodity Selection Index (CSI)
6. Directional Movement Index (DMI)
7. Double Exponential Moving Average (DEMA)
8. Heiken Ashi
9. Moving Average Convergence and Divergence (MACD)
10. Moving Averages: EMA, SMA and WMA
11. Parabolic SAR
12. Percentage Price Oscillator (PPO)
13. Point & Figure
14. Triple Exponential Moving Average (TEMA)
15. Triple Exponential Moving Average (TRIX)
Momentum Indicators: Momentum indicators show the strength of trends by recording the
speed of prices moving over certain time period. At the same time, Momentum indicators track
strength and weakness of a trend as it progresses over a given period of time: the highest
momentum is always registered at the beginning of a trend, the lowest - at its end point.
1. Accumulative Swing Index (ASI)
2. Advance Decline Ratio (ADR)
3. Aroon Indicator
4. Aroon Oscillator
5. Chande Momentum Oscillator
6. Commodity Channel Index (CCI)
7. Intraday Momentum Index
8. Centre of Gravity
9. Linear Regression Slope
10. Mass Index
11. Momentum Indicator
12. Price Oscillator
13. Qstick
14. Random Walk Index
15. Rate of Change (ROC)
16. Relative Momentum Index (RMI)
15. 7 | P a g e
17. Relative Strength Index (RSI)
18. Smoothed Indexed Rate of Change (SIROC)
19. Stochastics
20. Stochastic RSI
21. Stochastic Momentum Index
22. Ultimate Oscillator
23. Williams %R
24. Williams' Accumulation-Distribution
Volatility Indicators: Volatility indicators show the size and the magnitude of price
fluctuations. In any market there are periods of high volatility (high intensity) and low volatility
(low intensity). These periods come in waves: low volatility is replaced by increasing volatility,
while after a period of high volatility there comes a period of low volatility and so on. Volatility
indicators measure the intensity of price fluctuations, providing an insight into the market
activity level.
1. Average True Range (ATR)
2. Bollinger Bands (BB)
3. Chaikin Volatility (CHV)
4. Donchian Channel
5. Keltner Bands
6. McGinley Dynamic indicator (MDI)
7. Moving Average Envelopes
8. Starc Bands
Volume Indicators: Volume indicators are used to determine investors' interest in the market.
High volume, especially near important market levels, suggests a possible start of a new trend,
while low volume suggests trader’s uncertainty and/or no interest in a particular market.
1. Acceleration Bands
2. Market Facilitation Index
3. Chaikin Money Flow (CMF)
4. Chaikin Oscillator
5. Accumulation Distribution Oscillator (ADO)
6. Volume Oscillator (PVO)
7. Demand Index
8. On Balance Volume (OBV)
9. Money Flow
16. 8 | P a g e
Cycle Indicators: A cycle in the market is determined by a series of repeating patterns. These
patterns are, as a rule, dedicated to certain market events, such as seasons, simple day counts,
event-to-event sequence, market theories and formulas and so on.
1. Detrended Price Oscillator (DPO)
2. Elliott waves
3. Schaff Trend Cycle
4. Fibonacci
5. Fourier Transform
Conclusions about Technical analysis:
Technical analysts consider the market to be 80% psychological and 20% logical. Fundamental
analysts consider the market to be 20% psychological and 80% logical. Psychological or logical
may be open for debate, but there is no questioning the current price of a security. Technical
analysis focuses directly on the bottom line: What is the price? Where has it been? Where is it
going?
In this study I use Bollinger Bands as indicator to analyze some our randomly selected securities
of DSE, so I am presenting a brief about this indicator in the next chapter.
17. 9 | P a g e
Chapter 3
Bollinger Bands
Introduction:
Bollinger Bands is a technical analysis tool invented by John Bollinger in the 1980s, and a term
trademarked by him in 2011. Having evolved from the concept of trading bands, Bollinger Bands
and the related indicators %b and bandwidth can be used to measure the "highness" or "lowness"
of the price relative to previous trades. Bollinger Bands are a volatility indicator similar to the
Keltner channel. Bollinger Bands is a versatile tool combining moving averages and standard
deviations and is one of the most popular technical analysis tools available for traders.
Bollinger Bands consist of:
Bollinger Bands consist of a middle band with two outer bands. The middle band is a simple
moving average that is usually set at 20 periods. A simple moving average is used because the
standard deviation formula also uses a simple moving average. The look-back period for the
standard deviation is the same as for the simple moving average. The outer bands are usually set
2 standard deviations above and below the middle band. These are listed below-
1. an N-period moving average (MA)
2. an upper band at K times an N-period standard deviation above the moving average
(MA + Kσ)
3. a lower band at K times an N-period standard deviation below the moving average
(MA − Kσ)
Typical values for N and K are 20 and 2, respectively. The default choice for the average is a
simple moving average, but other types of averages can be employed as needed. Exponential
moving averages are a common second choice. Usually the same period is used for both the
middle band and the calculation of standard deviation.
Sharp Charts Calculation:
1. Middle Band = 20-day simple moving average (SMA)
2. Upper Band = 20-day SMA + (20-day standard deviation of price x 2)
3. Lower Band = 20-day SMA - (20-day standard deviation of price x 2)
18. 10 | P a g e
Chart # i | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example
Bollinger Bands, however, are created by calculating two standard deviations above and below a
20-day simple moving average. Therefore, the price should remain within the bands about 95%
of the time.
The determinative parameter for BB:
In general, the determinative parameter for BB is the period/type of MA, because bands are
derivative lines from the MA parameter. The major value for trading of this indicator is in bands.
Normal distribution suggests that some variable – in our case this is price, will stay in a range of
two standard deviations with a probability ~95%, in range of three standard deviations with a
probability more than 99%.
1. MA – period, its type and type of price – close, high, low, etc.
2. Number of standard deviations. The greater this parameter – the wider band lines, and the
less probable that market will touch it. In most cases there is not much sense to apply
more than 3 standard deviations.
We should keep in mind, that applying 2 deviations tells that price action will be inside the bands
with probability 95%, 3 standard deviations – 99%. So, market will touch the bands very rarely.
If applying of 3 deviations suggests, that price will have to remain between the bands with 99%
probability, and market occasionally touches upper or lower band.
The MA period has the same sense as in a simple MA. The longer the MA-period, the more
smooth and lazy these MA & BB lines. Also they will be wider. In fact, this is the same
sensitivity to most recent price action – if you appoint a short period, then bands of BB will be
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very sensitive to price behavior and looks like kinked curves. If you appoint greater period – the
line will be smoother and bands range will be wider.
Because there is direct correlation between the deviation and the period, Just to keep it simple –
the greater the period, the greater the volatility and deviation. So as deviation is greater – the
distance between bands is also wider.
1. Estimation of market volatility:
In general, when the market is calm and quiet – volatility decreases, hence deviation of price
from its average also becomes less, and… At the same reasoning the distance between bands
becomes greater, when the market turns in sloppy price action or in an impulse and thrusting
move.
Chart # ii | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example
Here you can see – when the market was calm – the distance between the lines was tighter. In
March, market fluctuations became wider, hence, volatility was increased and the distance
between BB bands then expanded. During the collapse in April, the market was really loud – the
bands’ distance has become really wide.
0
10
20
30
40
50
60
70
80
90
100
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
20. 12 | P a g e
Partially it because of just 2 deviations application Look, how it will be, if we apply 3 deviations:
Chart # iii | Closing Price Daily, 20-period/3-deviations Bollinger Bands; Example
I even have had to eliminate some points, where price previously touched the bands.
2. Classic bounce from Bollinger Bands lines:
As we’ve noted already, due to math laws, price tends to return to the middle of the bands.
According to normal distribution, when price reaches any of the bands – it becomes stretched,
like a spring and tends to move right back. So, the bands of BB indicator acts like support and
resistance:
Chart # iv | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example
0
10
20
30
40
50
60
70
80
90
100
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
155.00
160.00
165.00
170.00
175.00
180.00
185.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
21. 13 | P a g e
The whole idea behind the Bollinger bounce is that price tends to return to the middle of the
bands.
The longer the time frame you are in, the stronger these bands tend to be. Many traders have
developed systems that thrive on these bounces and this strategy is best used when the market is
ranging and there is no clear trend.
3. Bollinger Bands squeeze:
This method works better when you give market more room for breathing by appointing just 2
deviations. Why? Because the market has 5 times greater probability to move outside the bands –
5 % against ~ 1% when 3 deviations appointed.
Chart # v | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example
Here we can see that bands squeeze tightly and move almost parallel, range between them is
stable. This is an early caution about potential breakout.
Because, calm and very harmonic movement of bands and a tight range between them tell us,
that volatility extremely stable. This couldn’t hold too long. Tight range between bands tells, in
turn, that volatility is low. This combination tells, that market in extremely calm period - it
couldn’t hold too long. Extreme calming is unnatural for markets and this could lead only to a
single scenario – growth of volatility and breakout.
So, when market shows a close below some band – this is the most probable direction of the
breakout. Especially if it shows a number of closes in this direction. On our chart this is a close
below the lower band – hence, we should expect breakout to the downside:
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
22. 14 | P a g e
Chart # vi | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Example
In general this approach could be used to identify the starting of new strong move as soon as
possible. But, as you understand, you will not see such action very often.
Bandwidth & %b:
In spring of 2010, John Bollinger introduced three new indicators based on Bollinger Bands.
They are BB-Impulse, which measures price change as a function of the bands, percent
bandwidth (%b), which normalizes the width of the bands over time, and bandwidth delta, which
quantifies the changing width of the bands.
%b (pronounced "percent b") is derived from the formula: %b = (Price - Lower Band)/
(Upper Band - Lower Band)
%B quantifies a security's price relative to the upper and lower Bollinger Band. There are six
basic relationship levels:
1. %b equals 1 when price is at the upper band
2. %b equals 0 when price is at the lower band
3. %b is above 1 when price is above the upper band
4. %b is below 0 when price is below the lower band
5. %b is above .50 when price is above the middle band (20-day SMA)
6. %b is below .50 when price is below the middle band (20-day SMA)
Bandwidth tells how wide the Bollinger Bands are on a normalized basis. Writing the same
symbols as before, and middle BB for the moving average, or middle Bollinger Band: Bandwidth
= (upper BB – lower BB) / middle BB.
0
10
20
30
40
50
60
70
80
90
100
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
23. 15 | P a g e
Some additional advice:
1. If the market penetrates BB band and couldn’t reach the MA line – middle of the range
between the bands, then it tells about power of the market in this direction and that the
previous move has a solid odds to continue. Just look at chart # vi – market penetrates
lower band, but was not able to return even to MA line. Then, the move down
continues…;
2. BB bands, especially by applying 2.5-3 deviations and about 20-period could be used as
an indicator of overbought/oversold levels and conditions on the market. When the
market touches the upper band – the market is overbought, when it touches the lower one
– oversold. It is preferable to apply this method not lower than on the daily time frame. A
combination of Oversold+ Fib support or Overbought + Fib Resistance has a greater
probability to stop the market for some time.
3. BB bands could save you from unwelcome areas to enter the market. It’s safer to buy in
the lower half of the range, and Sell in the higher half of the range. Also avoid entering
the market, when it stands at the bands – long at higher band, and short at lower band.
Because, as we’ve said already – there is a high probability of bouncing in the middle of
the range.
4. AS with any other indicator, BB demands fine tuning, by analyzing its application with
different pairs and time frames. You should find parameters that most suitable for your
trading style and pairs that you trade.
You may apply different variations of the tools that I’ve pointed out. For instance – I said “if the
market shows close outside of some band”. You instead could apply not one but 2 or 3 closes.
The akin variations are allowed in other strategies with BB. Be free to experiment with the
parameters you use, confirmation signals and other things! Your task is to find out how to make
more pips on the market. And there is nothing forbidden with any indicator.
Conclusions about Bollinger Bands:
Even though Bollinger Bands can help generate buy and sell signals, they are not designed to
determine the future direction of a security.
Bollinger Bands serve two primary functions:
1. To identify periods of high and low volatility
2. To identify periods when prices are at extreme, and possibly unsustainable, levels.
Remember that buy and sell signals are not given when prices reach the upper or lower bands.
Such levels merely indicate that prices are high or low on a relative basis.
A security can become overbought or oversold for an extended period of time.
Finally, the bands are just bands, not signals. A band of the upper Bollinger Band is NOT a sell
signal. A band of the lower Bollinger Band is NOT a buy signal.
24. 16 | P a g e
Chapter 4
Implementations & Analysis
Introduction:
For implementation & analysis I have taken closing prices of ten listed company then I have
done some calculation with excel to find the moving average, deviation, upper band & lower
band. Using these data I have found three charts for each company such as-
4. Closing Price Daily, 20-period/2-deviations Bollinger Bands
5. Daily Deviation of closing prices
6. Daily %b of Bollinger Bands
Then I have presented some analysis on these charts.
The selected companies are shown below:
1. LankaBangla Finance Ltd.
2. Meghna Petroleum Ltd.
3. United Airways (BD) Ltd.
4. Jamuna Oil Company Ltd.
5. Padma Oil Company Ltd.
6. Active Fine Chemicals Ltd
7. Bangladesh Submarine Cable Company Ltd.
8. Islami Bank Bangladesh Ltd.
9. Square Pharmaceutical Ltd.
10. Titas Gas Transmission & Dist. Co. Ltd.
I have also presented a regression analysis for each company. The regressions are done by two
variables which are closing price of each company as dependent variable & Index of DSE as
independent variable. These regression analyses are done to find dependence of company’s
closing price on Index of DSE.
Finally I have presented the Bollinger Bands charts of Index of DSE to highlight the market
condition of DSE.
26. 18 | P a g e
Chart #1 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; LankaBangla Finance Ltd
Chart #2 | Daily Deviation of closing prices; LankaBangla Finance Ltd
Here we can see from chart#1 & chart#2- In March, market fluctuations became wider, hence,
volatility was increased and the distance between BB bands then expanded. After the 23rd
March
the market was gradually becoming calm. During the collapse in April, the market was really
calm – the bands’ distance between the lines was really tighter. Here we can see that bands
squeeze tightly and move almost parallel, range between them is stable. This is an early caution
about potential breakout. Because, calm and very harmonic movement of bands and a tight range
between them tell us, that volatility extremely stable. This couldn’t hold too long. Extreme
calming is unnatural for markets and this could lead only to a breakout. On 20th
April, it has
occurred and it may continue.
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
27. 19 | P a g e
Chart #3 | Daily %b of Bollinger Bands; LankaBangla Finance Ltd
Since, we know that %b is below 0 when price is below the lower band. So from chart#3 we see
that in 18th
March and 21st
April the price line breakout the lower band and still now the price is
below the lower band.
Regression analysis:
I have taken 34 days price of LankaBangla Finance Ltd and Index of DSE to find the relationship
between them. Then I have run regression by SPSS taking price as dependent variable and Index
as independent variable. The result summery of regression is shown below by Table#2 | Model
summery, ANOVA, Coefficients
Table#2 | Model summery, ANOVA, Coefficients; LankaBangla Finance Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.870 0.756 99.232 0.000 Constant -56.179 9.558 -5.878 0.000
INDEX 0.025 0.003 9.962 0.000
From the table we see that 75.6% of the variation of price of LankaBangla Finance Ltd can be
explained by the Index of DSE and the sensitivity of the price of LankaBangla Finance Ltd to
Index of DSE is 0.025, that is, if the Index of DSE will be increased by Tk. 1/- then price of
LankaBangla Finance Ltd will be increased by Tk. 0.025/-.
‐0.80
‐0.60
‐0.40
‐0.20
0.00
0.20
0.40
0.60
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May %b
29. 21 | P a g e
Chart #4 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Meghna Petroleum Ltd.
Chart #5 | Daily Deviation of closing prices; Meghna Petroleum Ltd
Here we can see from chart#4 & chart#5- prior to the March, market fluctuations became wider,
hence, volatility was increased and the distance between BB bands then expanded. But during
the March & April the market was gradually becoming calm. During the collapse in April, the
market was really calm – the bands’ distance between the lines was really tighter. Here we can
see that bands squeeze tightly and move almost parallel, range between them is stable. This is an
early caution about potential breakout.
Again, from chart# 4 we have noticed that there is a classic bounce from Bollinger Bands lines
and price tends to return to the middle of the bands. So, the bands of BB indicator acts like
support and resistance & it indicates that there is less probability of breakout.
155.00
160.00
165.00
170.00
175.00
180.00
185.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
30. 22 | P a g e
Chart #6 | Daily %b of Bollinger Bands; Meghna Petroleum Ltd
Since, from chart#6 we see that the value of %b is always in between 0.20 to 0.82. So the price
always remains between bands. Again the last value of %b is 0.40 which indicate that the price is
near the meddle line.
Regression analysis:
I have taken 34 days price of Meghna Petroleum Ltd and Index of DSE to find the relationship
between them. Then I have run regression by SPSS taking price as dependent variable and Index
as independent variable. The result summery of regression is shown below by Table#4 | Model
summery, ANOVA, Coefficients.
Table#4 | Model summery, ANOVA, Coefficients; Meghna Petroleum Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.571(a) 0.326 15.470 0.000(a) Constant 126.013 10.498 12.004 0.000
INDEX 0.011 0.003 3.933 0.000
From the table we see that 32.6% of the variation of price of Meghna Petroleum Ltd can be
explained by the Index of DSE and the sensitivity of the price of Meghna Petroleum Ltd to Index
of DSE is 0.011, that is, if the Index of DSE will be increased by Tk. 1/- then price of Meghna
Petroleum Ltd will be increased by Tk. 0.011/-.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
%b
32. 24 | P a g e
Chart #7 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; United Airways (BD) Ltd
Chart #8 | Daily Deviation of closing prices; United Airways (BD) Ltd
Here we can see from chart#7 & chart#8- during the March, volatility was increased and the
distance between BB bands then expanded. But during the April the volatility gradually became
stable and, hence, the bands’ distance between the lines was really tighter. Here we can see that
bands squeeze tightly and move almost parallel, range between them is stable. This is an early
caution about potential breakout.
Again, from chart# 7, in April we have noticed that there occurred a classic bounce from
Bollinger Bands lines and price tends to return to the middle of the bands. So, the bands of BB
indicator acts like support and resistance & it indicates that there is less probability of breakout.
0.00
5.00
10.00
15.00
20.00
25.00
30.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
33. 25 | P a g e
Chart #9 | Daily %b of Bollinger Bands; United Airways (BD) Ltd
Since, we know that %b is below 0 when price is below the lower band. So from chart#9 we see
that there is two breakout of the lower band which occurred prior to the 12th
March & after the
29th
March. Again the last value of %b is 0.20 which indicate that the price is near the lower
band.
Regression analysis
I have taken 34 days price of United Airways (BD) Ltd and Index of DSE to find the relationship
between them. Then I have run regression by SPSS taking price as dependent variable and Index
as independent variable. The result summery of regression is shown below by Table#6 | Model
summery, ANOVA, Coefficients
Table#6 | Model summery, ANOVA, Coefficients; United Airways (BD) Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.718(a) 0.516 34.054 0.000(a) Constant -1.640 3.398 -0.483 0.633
INDEX 0.005 0.001 5.836 0.000
From the table we see that 51.6% of the variation of price of United Airways (BD) Ltd can be
explained by the Index of DSE and the sensitivity of the price of United Airways (BD) Ltd to
Index of DSE is 0.005, that is, if the Index of DSE will be increased by Tk. 1/- then price of
United Airways (BD) Ltd will be increased by Tk. 0.005/-.
‐0.60
‐0.40
‐0.20
0.00
0.20
0.40
0.60
0.80
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
%b
35. 27 | P a g e
Chart #10 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Jamun Oil Company Ltd
Chart #11 | Daily Deviation of closing prices; Jamun Oil Company Ltd
Here we can see from chart#10 & chart#11- the deviation was gradually decreasing and, hence,
the bands’ distance between the lines was gradually decreasing. After 18th
April, we can see that
bands squeeze tightly and move almost parallel, range between them is stable, but prior to the
18th
April bands distance was expanded. This combination tells that market is in extremely calm
period - it couldn’t hold too long. Extreme calming is unnatural for markets and this could lead
only to a single scenario – growth of volatility and breakout.
155.00
160.00
165.00
170.00
175.00
180.00
185.00
190.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
36. 28 | P a g e
Chart #12 | Daily %b of Bollinger Bands; Jamun Oil Company Ltd
Since, from chart#12 we see that the value of %b is always in between 0.00 to 1.00. So the price
always remains between bands & never touches the bands. Again the last value of %b is 0.20
which indicate that the price is near the lower line.
Regression analysis:
I have taken 34 days price of Jamuna oil company Ltd and Index of DSE to find the relationship
between them. Then I have run regression by SPSS taking price as dependent variable and Index
as independent variable. The result summery of regression is shown below by Table#8 | Model
summery, ANOVA, Coefficients
Table#8 | Model summery, ANOVA, Coefficients; Jamun Oil Company Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.030(a) 0.001 0.028 0.867(a) Constant 176.979 18.963 9.333 0.000
INDEX -0.001 0.005 -0.169 0.867
From the table we see that 0.1% of the variation of price of Jamuna oil company Ltd can be
explained by the Index of DSE and the sensitivity of the price of Jamuna oil company Ltd to
Index of DSE is -0.001, that is, if the Index of DSE will be increased by Tk. 1/- then price of
Jamuna oil company Ltd will be increased by Tk. -0.001/-.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
%b
38. 30 | P a g e
Chart #13 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Padma Oil Company Ltd
Chart #14 | Daily Deviation of closing prices; Padma Oil Company Ltd
Here we can see from chart#13 & chart#14- the deviation was gradually decreasing and, hence,
the bands’ distance between the lines was gradually decreasing. Before the 26th
April, bands
squeezed tightly and moved almost parallel, range between them was stable. This was an early
caution about potential breakout and exactly it was occurred on 26th
April.
0.00
50.00
100.00
150.00
200.00
250.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
39. 31 | P a g e
Chart #15 | Daily %b of Bollinger Bands; Padma Oil Company Ltd
Since, 26th
April, the value of %b was below 0.00, so the price breakout the lower band. Again
the last value of %b is -0.90 which indicate that the price is below the lower band.
Regression analysis:
I have taken 34 days price of Padma oil company Ltd and Index of DSE to find the relationship
between them. Then I have run regression by SPSS taking price as dependent variable and Index
as independent variable. The result summery of regression is shown below by Table#8 | Model
summery, ANOVA, Coefficients
Table#10 | Model summery, ANOVA, Coefficients; Padma Oil Company Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.803(a) 0.645 58.230 0.000(a) Constant 76.474 13.074 5.849 0.000
INDEX 0.027 0.003 7.631 0.000
From the table we see that 64.5% of the variation of price of Padma oil company Ltd can be
explained by the Index of DSE and the sensitivity of the price of Padma oil company Ltd to
Index of DSE is 0.027, that is, if the Index of DSE will be increased by Tk. 1/- then price of
Padma oil company Ltd will be increased by Tk. 0.027/-.
‐1.20
‐1.00
‐0.80
‐0.60
‐0.40
‐0.20
0.00
0.20
0.40
0.60
0.80
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May %b
41. 33 | P a g e
Chart #16 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Active Fine Chemicals Ltd
Chart #17 | Daily Deviation of closing prices; Active Fine Chemicals Ltd
We see from chart#16 – there was occurred a huge breakout of the lower band on 27th
March.
Before the 27th
March, bands squeezed tightly and moved almost parallel, range between them
was stable. This was an early caution about potential breakout and exactly it was occurred on
27th
March.
Here we can see from the chart#17- the deviation was rapidly increased after the 2nd
April and,
hence, the bands’ distance between the lines was rapidly expanded.
0
10
20
30
40
50
60
70
80
90
100
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
42. 34 | P a g e
Chart #18 | Daily %b of Bollinger Bands; Active Fine Chemicals Ltd
We see that the value of %b is always near 0.00 and some time it is less than 0.00. So, the price
maximum times remains near the lower band and sometimes it breakout the lower band.
Regression analysis:
I have taken 34 days price of Active Fine Chemicals Ltd and Index of DSE to find the
relationship between them. Then I have run regression by SPSS taking price as dependent
variable and Index as independent variable. The result summery of regression is shown below by
Table#12 | Model summery, ANOVA, Coefficients; Active Fine Chemicals Ltd
Table#12 | Model summery, ANOVA, Coefficients; Active Fine Chemicals Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.913(a) 0.834 161.015 0.000(a) Constant -215.060 21.682 -9.919 0.000
INDEX 0.073 0.006 12.689 0.000
From the table we see that 83.4% of the variation of price of Active Fine Chemicals Ltd can be
explained by the Index of DSE and the sensitivity of the price of Active Fine Chemicals Ltd to
Index of DSE is 0.073, that is, if the Index of DSE will be increased by Tk. 1/- then price of
Active Fine Chemicals Ltd will be increased by Tk. 0.073/-.
‐2.00
‐1.50
‐1.00
‐0.50
0.00
0.50
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
%b
44. 36 | P a g e
Chart #19 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Bangladesh Submarine Cable Company Ltd
Chart #20 | Daily Deviation of closing prices; Bangladesh Submarine Cable Company Ltd
We know, calm and very harmonic movement of bands and a tight range between them tell us,
that volatility extremely stable. This couldn’t hold too long. Tight range between bands tells, in
turn, that volatility is low. This combination tells, that market in extremely calm period - it
couldn’t hold too long. Extreme calming is unnatural for markets and this could lead only to a
single scenario – growth of volatility and breakout.
Here we can see that bands squeeze tightly and move almost parallel, range between them is
stable. This is an early caution about potential breakout.
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
45. 37 | P a g e
Chart #21 | Daily %b of Bollinger Bands; Bangladesh Submarine Cable Company Ltd
Since the probability of breakout is high & the last value of %b is above 0.50 & near 1.00, that
means the last price is between the middle & upper line, so I think breakout will be occurred
with upper band.
Regression analysis:
I have taken 34 days price of Bangladesh Submarine Cable Company Ltd. and Index of DSE to
find the relationship between them. Then I have run regression by SPSS taking price as
dependent variable and Index as independent variable. The result summery of regression is
shown below by Table#14 | Model summery, ANOVA, Coefficients; Bangladesh Submarine
Cable Company Ltd
Table#14 | Model summery, ANOVA, Coefficients; Bangladesh Submarine Cable Company Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.591(a) 0.349 17.151 0.000(a) Constant 62.993 13.549 4.649 0.000
INDEX 0.015 0.004 4.141 0.000
From the table we see that 34.9% of the variation of price of Bangladesh Submarine Cable
Company Ltd. can be explained by the Index of DSE and the sensitivity of the price of
Bangladesh Submarine Cable Company Ltd. to Index of DSE is 0.015, that is, if the Index of
DSE will be increased by Tk. 1/- then price of Bangladesh Submarine Cable Company Ltd. will
be increased by Tk. 0.015/-.
‐0.40
‐0.20
0.00
0.20
0.40
0.60
0.80
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
%b
47. 39 | P a g e
Chart #22 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Islami Bank Bangladesh Ltd
Chart #23 | Daily Deviation of closing prices; Islami Bank Bangladesh Ltd
We see from chart#22 – there was occurred a huge breakout of the lower band on 12th
April.
Before the 12th
April, bands squeezed tightly and moved almost parallel, range between them
was stable. This was an early caution about potential breakout and exactly it was occurred on
12th
April.
Here we can see from the chart#23- the deviation was rapidly increased after the 21st
April and,
hence, the bands’ distance between the lines was rapidly expanded.
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
48. 40 | P a g e
Chart #24 | Daily %b of Bollinger Bands; Islami Bank Bangladesh Ltd
Since, on 13th
April, the value of %b was less than 0.00, so the price breakout the lower band,
again, it return back between the bands on 26th
April. The last value of %b is 0.20 which indicate
that the price is near the lower band.
Regression analysis
I have taken 34 days price of Islami Bank Bangladesh Ltd. and Index of DSE to find the
relationship between them. Then I have run regression by SPSS taking price as dependent
variable and Index as independent variable. The result summery of regression is shown below by
Table#16 | Model summery, ANOVA, Coefficients; Islami Bank Bangladesh Ltd
Table#16 | Model summery, ANOVA, Coefficients; Islami Bank Bangladesh Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.487(a) 0.237 9.937 0.004(a) Constant -19.454 18.739 -1.038 0.307
INDEX 0.016 0.005 3.152 0.004
From the table we see that 23.7% of the variation of price of Islami Bank Bangladesh Ltd. can be
explained by the Index of DSE and the sensitivity of the price of Islami Bank Bangladesh Ltd. to
Index of DSE is 0.016, that is, if the Index of DSE will be increased by Tk. 1/- then price of
Islami Bank Bangladesh Ltd. will be increased by Tk. 0.016/-.
‐2.50
‐2.00
‐1.50
‐1.00
‐0.50
0.00
0.50
1.00
1.50
2.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
%b
50. 42 | P a g e
Chart #25 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Square Pharmaceuticals Ltd
Chart #26 | Daily Deviation of closing prices; Square Pharmaceuticals Ltd
Here we can see from chart#25 & chart#26- the deviation was gradually decreasing and, hence,
the bands’ distance between the lines was gradually decreasing. This combination tells that
market is in extremely calm period - it couldn’t hold too long. Extreme calming is unnatural for
markets and this could lead only to a single scenario – growth of volatility and breakout.
Since the price touches the upper band, so the breakout may occur with the upper band.
168.00
170.00
172.00
174.00
176.00
178.00
180.00
182.00
184.00
186.00
188.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
51. 43 | P a g e
Chart #27 | Daily %b of Bollinger Bands; Square Pharmaceuticals Ltd
We see that the value of %b was more than 1.00 on 24th
April, so there is one breakout of upper
band which was occurred on that day. The last value of %b is 0.98 which indicate that the price
is near the upper band.
Regression analysis:
I have taken 34 days price of Square Pharmaceutical Ltd. and Index of DSE to find the
relationship between them. Then I have run regression by SPSS taking price as dependent
variable and Index as independent variable. The result summery of regression is shown below by
Table#18 | Model summery, ANOVA, Coefficients; Square Pharmaceuticals Ltd
Table#18 | Model summery, ANOVA, Coefficients; Square Pharmaceuticals Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.394(a) 0.155 5.872 0.021(a) Constant 194.714 6.949 28.020 0.000
INDEX -0.004 0.002 -2.423 0.021
From the table we see that 15.5% of the variation of price of Square Pharmaceutical Ltd. can be
explained by the Index of DSE and the sensitivity of the price of Square Pharmaceutical Ltd. to
Index of DSE is -0.004, that is, if the Index of DSE will be increased by Tk. 1/- then price of
Square Pharmaceutical Ltd. will be increased by Tk. -0.004/-.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
%b
53. 45 | P a g e
Chart #28 | Closing Price Daily, 20-period/2-deviations Bollinger Bands; Titas Gas Transmission & Dist. Co. Ltd
Chart #29 | Daily Deviation of closing prices; Titas Gas Transmission & Dist. Co. Ltd
Here we see from chart#28 & chart#29- the deviation was gradually decreasing and, hence, the
bands’ distance between the lines was gradually decreasing & market became in extremely calm
period and this could lead only to a single scenario – growth of volatility and breakout and that
was occurred on 18th
April.
Since deviation is increasing so breakout may occur again with the upper band.
62.00
64.00
66.00
68.00
70.00
72.00
74.00
76.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Price
MB
UB
LB
0.00
0.50
1.00
1.50
2.00
2.50
3.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
54. 46 | P a g e
Chart #30 | Daily %b of Bollinger Bands; Titas Gas Transmission & Dist. Co. Ltd
We see that the value of %b was more than 1.00 on 18th
April, so there is one breakout of upper
band which was occurred on that day. The last value of %b is 0.74 which indicate that the price
is near the upper band.
Regression analysis
I have taken 34 days price of Titas Gas Transmission & Dist. Co. Ltd. and Index of DSE to find
the relationship between them. Then I have run regression by SPSS taking price as dependent
variable and Index as independent variable. The result summery of regression is shown below by
Table#18 | Model summery, ANOVA, Coefficients; Titas Gas Transmission & Dist. Co. Ltd
Table#20 | Model summery, ANOVA, Coefficients; Titas Gas Transmission & Dist. Co. Ltd
Model summery ANOVA Coefficients Table
R R Square F sig Model Coefficients St. error t. stat sig
0.216(a) 0.046 1.559 0.221(a) Constant 81.627 10.335 7.898 0.000
INDEX -0.003 0.003 -1.249 0.221
From the table we see that 4.6% of the variation of price of Titas Gas Transmission & Dist. Co.
Ltd. can be explained by the Index of DSE and the sensitivity of the price of Titas Gas
Transmission & Dist. Co. Ltd. to Index of DSE is -0.003, that is, if the Index of DSE will be
increased by Tk. 1/- then price of Titas Gas Transmission & Dist. Co. Ltd. will be increased by
Tk. -0.003/-.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
%b
56. 48 | P a g e
Chart #31 | Index of DSE, Daily, 20-period/2-deviations Bollinger Bands; Dhaka Stock Exchange (DSE)
Chart #32 | Daily Deviation of Index of DSE; Dhaka Stock Exchange (DSE)
We know, calm and very harmonic movement of bands and a tight range between them tell us,
that volatility extremely stable. This couldn’t hold too long. Tight range between bands tells, in
turn, that volatility is low. This combination tells, that market in extremely calm period - it
couldn’t hold too long. Extreme calming is unnatural for markets and this could lead only to a
single scenario – growth of volatility and breakout.
Here we can see that bands squeeze tightly and move almost parallel, range between them is
stable. This is an early caution about potential breakout.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Index of DSE
MB
UB
LB
0.00
50.00
100.00
150.00
200.00
250.00
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
Deviation
57. 49 | P a g e
Chart #33 | Daily %b of Bollinger Bands; Dhaka Stock Exchange (DSE)
Since the probability of breakout is high & the last value of %b is 0.09, that means- the last
Index of DSE is exactly near the lower band, so I think breakout will be occurred with lower
band.
‐0.60
‐0.50
‐0.40
‐0.30
‐0.20
‐0.10
0.00
0.10
0.20
0.30
0.40
0.50
7‐Mar 17‐Mar 27‐Mar 6‐Apr 16‐Apr 26‐Apr 6‐May
%b
58. 50 | P a g e
Chapter 5
Findings & Conclusions
Findings:
LankaBangla Finance Ltd.
Bollinger Bands: breakout with lower band has occurred and it may continue.
Regression analysis: price depends 75.6% on Index of DSE and the sensitivity of the price to
Index of DSE is 0.025.
Meghna Petroleum Ltd.
Bollinger Bands: Bollinger bounce has occurred. So, less probability of breakout has been
created.
Regression analysis: price depends 32.6% on Index of DSE and the sensitivity of the price to
Index of DSE is 0.011.
United Airways (BD) Ltd.
Bollinger Bands: Bollinger squeeze & bounce has occurred. Bollinger squeeze increases the
probability of breakout; on the other hand Bollinger bounce decreases the probability of breakout.
So it’s so complex.
Regression analysis: price depends 51.6% on Index of DSE and the sensitivity of the price to
Index of DSE is 0.005.
Jamuna Oil Company Ltd.
Bollinger Bands: Bollinger squeeze has occurred. So, strong probability of breakout has been
created.
Regression analysis: price depends 0.1% on Index of DSE and the sensitivity of the price to Index
of DSE is -0.001.
Padma Oil Company Ltd.
Bollinger Bands: Bollinger squeeze has observed; breakout with lower band has occurred and it
may continue.
Regression analysis: price depends 64.5% on Index of DSE and the sensitivity of the price to
Index of DSE is 0.027.
59. 51 | P a g e
Active Fine Chemicals Ltd
Bollinger Bands: it shows downward trend & its deviation is high so it is risky.
Regression analysis: price depends 83.4% on Index of DSE and the sensitivity of the price to
Index of DSE is 0.073.
Bangladesh Submarine Cable Company Ltd.
Bollinger Bands: Bollinger squeeze has occurred. So, strong probability of breakout has been
created.
Regression analysis: price depends 34.9% on Index of DSE and the sensitivity of the price to
Index of DSE is 0.015.
Islami Bank Bangladesh Ltd.
Bollinger Bands: huge breakout with lower band has occurred. Since deviation is increasing so
breakout may occur again with the lower band.
Regression analysis: price depends 23.7% on Index of DSE and the sensitivity of the price to
Index of DSE is 0.016.
Square Pharmaceutical Ltd.
Bollinger Bands: Bollinger squeeze has occurred & the price line is near upper band. So, strong
probability of breakout with upper band has been created.
Regression analysis: price depends 15.5% on Index of DSE and the sensitivity of the price to
Index of DSE is -0.004.
Titas Gas Transmission & Dist. Co. Ltd.
Bollinger Bands: breakout with upper band has occurred. Since deviation is increasing so
breakout may occur again with the upper band.
Regression analysis: price depends 4.6% on Index of DSE and the sensitivity of the price to Index
of DSE is -0.003.
Dhaka Stock Exchange (DSE)
Bollinger Bands: It shows downward trend, & breakout with lower band. Bollinger squeeze has
observed; so breakout with lower band may occur and it may continue.
Conclusions:
Based on this study I see that most of the companies are risky to invest or trade now. Again
Dhaka Stock exchange is also risky market to invest or trade now. Traders or investors should
wait for extreme downfall of index of DSE. After that they should trade or invest.
At last I expect this study will be a conducive for any new or old trader or investor to analyze
their markets.
60. 52 | P a g e
Chapter 6
References
Websites:
Dhaka Stock Exchange (DSE) - www.dsebd.org
www.wikipedia.org
www.investopedia.com
www.stockcharts.com
www.traders.com
www.stockta.com
www.bollingerbands.com
www.incrediblecharts.com
www.onlinetradingconcepts.com
www.istockanalyst.com
Books:
Security Analysis & Portfolio Management - Fishers & Jordon.
Investment analysis and portfolio Management, Chandra, McGraw Hill2009.
Security Analysis & Portfolio Management- V.A.Avadhani.