Effectiveness of investment strategies based on technical indicators

This paper, with a purpose to explore market inefficiencies, aims to investigating the effectiveness of investment strategies using 3 most popular technical indicators MA, MACD, and RSI

EFFECTIVENESS OF INVESTMENT STRATEGIES BASED ON TECHNICAL INDICATORS

In Partial Fulfillment of the Requirements of the Degree of

MASTER OF BUSINESS ADMINISTRATION

EFFECTIVENESS OF INVESTMENT STRATEGIES BASED ON TECHNICAL INDICATORS

In Partial Fulfillment of the Requirements of the Degree of

MASTER OF BUSINESS ADMINISTRATION

In Finance

by Mr: Phan Huy Tam ID: MBA05038 International University - Vietnam National University HCMC

Acknowledge

First of all, I would like to express our deepest gratitude to my advisor, Mrs Nguyen Thu Hien, who always pay attention and help me in every steps of my research I would also like to thank the Student Security Fund (SFF) who was the first one to respond to our call for support SFF’s enthusiasm and generosity have been so encouraging for us to keep on knocking doors until budget and facility requirement was complete

Finally, I would like to dedicate my concluding words to all friends and fellows of mine and of everyone involved in this special project The experiences they have given to me

or been with me through, the inspirations they have created and we look up to, the critiques they have made for me to improve, and the trust they put in me have lead me to the ultimate courage to carry out this difficult yet so meaningful work

Plagiarism Statements

I would like to declare that, apart from the acknowledged references, this thesis either does not use language, ideas, or other original material from anyone; or has not been previously submitted to any other educational and research programs or institutions

I fully understand that any writings in this thesis contradicted to the above statement will automatically lead to the rejection from the MBA program at the International University – Vietnam National University Ho Chi Minh City

Copyright Statement

This copy of the thesis has been supplied on condition that anyone who consults it

is understood to recognize that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without the author’s prior consent

© Phan Huy Tam/ MBA05038/ 2013

Table of Contents

List of Tables vi

List of Figures vii

Abstract viii

Chapter One - Introduction 1

1 Statement of the Problem 1

2 Research Objectives 3

3 Research Scope 3

4 Research Significance 4

5 Research Structure 5

Chapter Two: Literature Review 7

1 Investment and technical analysis 7

1.1 Investment Strategy 8

1.2 Technical Analysis 9

1.3 Previous Researches on technical analysis 21

2 Effectiveness of investment strategy 23

3 T-Test two-sample for mean 24

Chapter Three – Methodology 26

1 Study Population and Data Collection 27

2 Trading Rules 27

2.1 General trading rules 28

2.2 Trading signals 29

3 Measurement of strategy effectiveness 30

Chapter Four – Results 33

1 Descriptive Analysis 33

1.1 Data without inefficiency aspects 33

1.2 Data with inefficiency aspects 35

2 T-Test Two Sample for Mean 36

2.1 Data without inefficiency aspects 36

2.2 Data with inefficiency aspects 37

2.3 Compare between data with and without inefficiency aspects 38

Chapter Five – Discussion 40

Chapter Six – Conclusion and Recommendation 43

1 Conclusion 43

2 Recommendation 44

3 Limitation 45

References 48

Appendixes 51

List of Tables

Table 1: T-Test results of data without inefficiency aspects from 2009 to 2012 36 Table 2: T-Test Results in all three market trends of data without inefficiency aspects 37 Table 3: T-Test results of data with inefficiency aspect 38 Table 4: T-Test results compare between data with and without inefficiency aspects 39

List of Figures

Figure 1: Research Structure 5 Figure 2: Data Interpreting Process 26 Figure 3: Average Return & Standard Deviation of data without inefficiency aspects from

2009 to 2012 33 Figure 4: Box Plot of Return in all three market trends 34 Figure 5: Average Return & Standard Deviation of data with inefficiency aspects from

2009 to 2012 35

Abstract

There have been many studies done around the world considering the effectiveness of technical analysis To name a few, Sahli N Nefli (1991); Brock, Lakonishok, and LeBaron (1992); Neely, Christopher, Weller, and Dittmar (1997); and Salih N Nefli and Polinaco (1984), etc provided evidence that technical analysis can predict price movements or developed models of market in which investors benefit from conditioning of historical information In Vietnam, only a few empirical studies about technical analysis have been implemented This paper, with a purpose to explore market inefficiencies, aims to investigating the effectiveness of investment strategies using 3 most popular technical indicators (MA, MACD, and RSI) taking into account market conditions, with and without trading costs and transaction fees With this approach, the study enhances conclusions that could be applicable for both market efficient conditions and market inefficient conditions, which is suitable for a young and dynamic market like Vietnam

In order to test the performance of the technical analysis in different market condition, the data in this study will be taken from Ho Chi Minh stock exchange market for the investment period from 01/01/2009 to 01/01/2012 During this time, investors had experienced 3 different market conditions which are up-trend, down-trend and sideways The collected data included 140 stocks Funds and preferred stocks were excluded and some needed assumptions about data and liquidity were set up before the trading recoding process take place

Chapter One - Introduction

1 Statement of the Problem

Within stock market, there are two approaches for stock price evolution analysis

In one hand it is the fundamental analysis, which takes into account future prospects of firms through accounting and financial information of the company beside other data about firms’ operations Fundamental analysis usually aims to developing company value

The technical analysis stands in the other hand This kind of analysis will try to forecast future stock trend through technical indicators In order to develop an automated system to stock market prediction and analysis, the most common solution is to turn to technical analysis, as information needed is limited to stock price history of the value to

be studied While fundamental analysis requires a deeper training and wider data set which include a lot of hard measuring variables

As a study of Pring (1980) shows that technical approach to investment is essentially a reflection of the idea that the stock market moves in trends which are determined by changing attitudes of investors to a variety of economic, monetary, political and psychological forces The art of technical analysis is to identify changes in such trends at an early stage and to maintain an investment posture until a reversal of that trend is indicated By studying the nature of previous market turning points, it is possible

to develop some characteristics which can help identify major market tops and bottoms Technical analysis is therefore based on the assumption that the history will repeat, it means that people will continue to make the same mistakes that they made in the past

Refer to this conflict between fundamental analyst and technical analyst, many studies had been conducted Some have found results consistent with the practitioner’s view by providing evidence that technical analysis can predict price movements or by developing models of market in which investors benefit from conditioning of historical information For example, Sahli N Nefli (1991); Brock, Lakonishok, and LeBaron (1992); Neely, Christopher, Weller, and Dittmar (1997); and Salih N Nefli and Polinaco (1984) cited by Sewell (2008) tests different trading rules and find evidence consistent that technical analysis provide incremental information beyond that already incorporated into the current price In theory, Sorensen (1984) and Brown and Jennings (1989) examine settings in which privately informed investors use past prices to determine whether their information has been revealed to the market or to learn about the private signals of other traders, respectively Similarly, Blume, Lawrence, Easley, and O’Hara (1994) demonstrate that volume may provide relevant information if prices do not react immediately to new information Furthermore, there is a growing literature examining the possibility that common biases in human judgment lead to market inefficiencies

Meanwhile, according to Murphy (1999), technical analysts believe that investors collectively repeat the behavior of the investors that preceded them To a technician, the emotions in the market may be irrational, but they exist Because investor behavior repeats itself so often, technicians believe that recognizable (and predictable) price patterns will develop on a chart

Technical analysis provides analyst a series of indicators as a main tool in analyzing stock movement According to Achelis (1997), an indicator is a mathematical calculation that is applied to security’s price and/or volume fields Price data includes any

combination of the open, high, low or close over a period of time Some indicators might use only the closing prices, while others incorporate volume and open interest into their formulas Indicators use those price data to produce some specific points or lines Those results could be used to anticipate changes in price

Most empirical studies of technical analysis, include E Fama and Blume (1966), conclude that technical analysis is not useful for improving returns In contrast, a more recent study demonstrates that a relatively simple set of technical trading rules possess significant forecast power for changes in markets for the long sample period This paper will test the performance of technical analysis by using a set of trading rules which are based on some popular technical indicators in Vietnam market in a recent period of time

2 Research Objectives

This study aims at exploring the effectiveness of investment strategies which are based on technical indicators, namely MACD (Moving Average Convergence and Divergence), RSI (Relative Strength Index) and MA (Moving Average)

3 Research Scope

This research will analyze the performance of three technical indicators: MACD (Moving Average Convergence Divergence), RSI (Relative Strength Index) and MA (Moving Average) The test will be applied for data on listed stocks on Ho Chi Minh stock exchange market (UPCOM stock exchange market and Hanoi stock exchange market are excluded) Then, the index in this paper will be understood as the index in Ho Chi Minh stock exchange market only Only joint stock companies are included in the test, all funds are excluded This research focuses on common stock only All of preferred stocks are excluded

The collected data are the daily close price, daily trade volume, and firm’s market capitalization in three years from 01/01/2009 to 01/01/2012 Only companies which classified the time-length data are included in the test All of the other companies will be excluded So, there are 140 classified stocks during this time length that satisfy the condition of the test

4 Research Significance

This research brings a better understanding of using technical analysis and indicators in Ho Chi Minh stock exchange market It also provides a better insight into the stock investment Besides that, this research provides more information about Ho Chi Minh stock exchange market, helps to get deeper understanding about some specific aspects and characteristics of the market, and of course this could be a good guideline for stock investment

The study of technical indicators is very important, especially in the business fields of learning It is very significant for understanding stock behavior, the factors that affect capital return, and it helps us realize investment opportunity We learn to understand why stocks tend to act in their ways; hence, we learn how to meddle with them The research gives us patterns wherein we can predict whether our securities would

be having generated return or not

The results of this research can be a reference for the functional agencies to make trading decision In addition, investors could have a better understanding about the significance of technical analysis and its potential impact on their portfolio return, helping them on choosing stocks and making decisions Furthermore, this research also provides concepts and discussion, enriching more understandings about our stock

Figure 1: Research Structure

This is the research structure of this study These 5 steps are expressed on 6 chapters:

 Chapter 1: Introduction: to introduce generally about the study and to explain the reason to conduct the research, state the background and research motivation, introducing the general scope and objectives of the research as well as its practical significance and the overall structure of the research

 Chapter 2: The literature review or context of the study: the purpose of this chapter is to show that this research fits into the overall context of research in field To do this, this chapter will: describe the current state of research in stock exchange area; identify a gap where further research is needed; and explain how this paper plans to attend to that particular research gap This can lead logically into a clear statement of the research question(s) or problem(s) In addition to the research context, there may be other relevant contexts to present

 Chapter 3: Data and methodology: In these chapters a straightforward description

is required of how I conducted the research, describe particular equipment, processes, or materials precisely and how I used them

 Chapter 4 & 5: Results and Discussion: this chapter will present outcomes throughout data processing and analysis I also appreciate the limitations of research, and how these may affect the validity or usefulness of the findings and report on the implications of the findings for theory, research, and practice

 Chapter 6: Conclusions: this chapter tends to be much shorter than the Discussion

It is not a mere ‘summary’ of research, but needs to be ‘conclusions’ as to the main points that have emerged and what they mean for stock exchange market

Chapter Two: Literature Review

1 Investment and technical analysis

The philosophy behind technical analysis in sharp contrast to the efficient market hypothesis, which contends that past performance, has no influence on future performance or market values Technical analysis also is different from principles of fundamental analysis, which involves making investment decisions based on the examination of the economic environment and firm activities to arrive at intrinsic value

of an asset Different from efficient market hypothesis or fundamental analysis, technical analysis involves the examination of historical market data such as prices and trading volume to help estimate the future price trends and therefore investment decision Having said this, it is admitted that in making investment decisions, buying or selling stocks, technical analysts also use economic data that are usually separated from the stock or bond market Therefore, technical analysis is an alternative method of making investment decision (Reilly and Brown, 2006)

Numerous empirical studies have tested the profitability of various technical trading systems, and many of them included implications about market efficiency According to Park and Irwin (2004), more than 130 empirical studies have examined the profitability of technical trading rules over the four decades back For example, Brock et

al (1992) found support for technical trading rules on the Dow Jones Index Following their study the interest in testing the profitability of technical trading rules has grown considerably Several authors have presented supportive evidence in emerging markets about profitability of technical trading rules

1.1 Investment Strategy

1.1.1 Active strategy

Active management refers to a portfolio management strategy where the manager makes specific investments with the goal of outperforming an investment benchmark index While passive management will often invest in an index fund since they expect a return that closely replicates the investment weighting and returns of a benchmark index

Ideally, the active manager exploits market inefficiencies by purchasing stocks that are undervalued or by short selling securities that are overvalued Either of these methods may be used alone or in combination Depending on the goals of the specific investment portfolio, hedge fund or mutual fund, active management may also serve to create less volatility (or risk) than the benchmark index The reduction of risk may be the goal of creating an investment return greater than the benchmark (Malkiel, 1996)

Active portfolio managers may use a variety of factors and strategies to construct their portfolio(s) These include quantitative measures such as price–earnings ratios and PEG ratios that attempt to anticipate long-term macroeconomic trends (such as

a focus on energy or housing stocks), and purchasing stocks of companies that are temporarily out-of-favor or selling at a discount to their intrinsic value Some actively managed funds also pursue strategies such as risk arbitrage, short positions, and asset allocation Indicator-led trading strategies are used as a tool to decide trading points for active traders as technical indicators based on statistics of historical prices and volume to decide the market trend and therefore the price directions of stocks The effectiveness of

an actively managed investment portfolio obviously depends on the skill of the manager and research staff but also on how the term active is defined (Hebner, 2007)

In this study, active strategy will be applied using technical indicators which are

MA, MACD and RSI Specific trading rules will be set up based on those three technical indicators Stocks will be traded actively over investment period using trading signals which are stated by those three technical indicators by following trading rules strictly

 Indexing strategy: if the capital market is efficient, effort to find underpriced securities or to time the market may be futile The philosophy is that they assume that most investors are unlikely to outperform the market Hence, they may build a portfolio that mirrors a well-known index

This research considers the passive buy and hold strategy as the benchmark to evaluate the performance of active strategy The indexing strategy is not concerned in this research In this paper, passive strategy will be understood as buy and hold strategy, which means that portfolios follow passive buy and hold strategy will make only 1 buy decision at the beginning and 1 sell decision at the end of investment period

1.2 Technical Analysis

Technical analysis is the study of prices, with charts being the primary tool The technical approach to investment is essentially a reflection of the idea that prices move in

trends which are determined by the changing attitudes of investors toward a variety of economic monetary, political and psychological forces… Since the technical approach is based on the theory that the price is a reflection of mass psychology (“the crowd”) in action, it attempts to forecast future price movement on the assumption that crowd psychology moves between panic, fear and pessimism on one hand and confidence, excessive optimism, and greed on the other (Pring, 1980)

The roots of modern-day technical analysis stem from the Dow Theory, developed around 1900 by Charles of Dow, founder Wall Street Journal Brock et al (1992), and Salih N Nefli and Polinaco (1984) claim in their research that the first illustration of technical analysis is the discussion of Dow theory in Rhea Stemming either directly or indirectly from the Dow Theory, these roots include such principles as the trending nature

of prices, prices discounting all known information, confirmation and divergence, volume mirroring changes in price, and support/resistance (Achelis, 1997)

Any discussion of technical analysis using price and volume data should begin with consideration of Dow Theory because it was among the earliest work on this topic and remain the basis for many technical indicators (Glickstein & Rolf, 1983 cited by Reilly & Brown, 2006) Dow described stock price as moving in trend analogous to the movement of water He postulated three types of price movement over time: (1) major trends that are like tides of the ocean, (2) intermediate that resemble waves, (3) short-run movement that like ripples Follower of the Dow Theory attempt to detect the direction of the major trend (tide), recognizing that intermediate movement (waves) may occasionally move in opposite direction They recognize that major market advance does not go

straight up but, rather, includes small price declines as some investors decide to take profits

Pring (1980) outline three principles that guide the behavior of technical analysts Those three principals were mainly based on the famous Dow Theory The first is that market action (prices and transactions volume) discounts everything The second is that asset prices move in trend And the third is that history repeats itself (J Neely & Weller,

2011 cited by Sewell (2008))

Technical analysts believe that the intrinsic value of a firm or its estimated earnings-potential, and the demand and supply information, is insufficient to predict its future prices Instead, they rely on statistical methods and collective psychology techniques, and use data charts and computer programs, to study past movements in prices and trading volumes to detect current and future trends Most technical analysis is short- or intermediate-term, and is based on three major tenets: history repeats itself what goes around comes around, prices move in trends and usually follow known patterns, and current market price of a stock or commodity reflects the effect of all available information about it (Achelis, 1997)

A fundamental principle of technical analysis is that a market's price reflects all relevant information, so their analysis looks at the history of a security's trading pattern rather than external drivers such as economic, fundamental and news events Therefore, price action tends to repeat itself due to investors collectively tending toward patterned behavior – hence technical analysis focuses on identifiable trends and conditions

Technical analysis is not limited to charting, but it always considers price trends For example, many technicians monitor surveys of investor sentiment These

surveys gauge the attitude of market participants, specifically whether they are bearish or bullish Technicians use these surveys to help determine whether a trend will continue or if a reversal could develop; they are most likely to anticipate a change when the surveys report extreme investor sentiment Surveys that show overwhelming bullishness, for example, are evidence that an uptrend may reverse; the premise being that if most investors are bullish they have already bought the market (anticipating higher prices) And because most investors are bullish and invested, one assumes that few buyers remain This leaves more potential sellers than buyers, despite the bullish sentiment This suggests that prices will trend down, and is an example of contrarian trading (Kirkpatrick & Dahlquist, 2006)

1.2.1 Dow Theory

The Dow Theory on stock price movement is a form of technical analysis that includes some aspects of sector rotation Dow theory has six basic tenets: (1) the market has three movements, (2) market trends have three phases, (3) the stock market discounts all news, (4) stock market averages must confirm each other, (5) trends are confirmed by volume, and (6) trends exist until definitive signals prove that they have ended (Glickstein & Rolf, 1983 cited by Reilly & Brown, 2006)

 The basis of Dow theory is based on:

The market has three movements: the "main movement", primary movement or major trend may last from less than a year to several years It can be bullish or bearish

(2) The "medium swing", secondary reaction or intermediate reaction may last from ten

days to three months and generally retraces from 33% to 66% of the primary price

change since the previous medium swing or start of the main movement (3) The "short

swing" or minor movement varies with opinion from hours to a month or more The three movements may be simultaneous, for instance, a daily minor movement in a bearish secondary reaction in a bullish primary movement

Market trends have three phases: Dow Theory asserts that major market trends are composed of three phases: an accumulation phase, a public participation (or absorption)

phase, and a distribution phase The accumulation phase (phase 1) is a period when

investors "in the know" are actively buying (selling) stock against the general opinion of the market During this phase, the stock price does not change much because these investors are in the minority demanding (absorbing) stock that the market at large is supplying (releasing) Eventually, the market catches on to these astute investors and a

rapid price change occurs (phase 2) This occurs when trend followers and other

technically oriented investors participate This phase continues until rampant speculation occurs At this point, the astute investors begin to distribute their holdings to the market

(phase 3)

The stock market discounts all news: stock prices quickly incorporate new information as soon as it becomes available Once news is released, stock prices will change to reflect this new information On this point, Dow Theory agrees with one of the premises of the efficient market hypothesis

Stock market averages must confirm each other: in Dow's time, the US was a growing industrial power The US had population centers but factories were scattered throughout the country Factories had to ship their goods to market, usually by rail Dow's first stock averages were an index of industrial (manufacturing) companies and rail companies To Dow, a bull market in industrials could not occur unless the railway

average rallied as well, usually first According to this logic, if manufacturers' profits are rising, it follows that they are producing more If they produce more, then they have to ship more goods to consumers Hence, if an investor is looking for signs of health in manufacturers, he or she should look at the performance of the companies that ship the output of them to market, the railroads The two averages should be moving in the same direction When the performance of the averages diverges, it is a warning that change is

in the air

Trends are confirmed by volume: Dow believed that volume confirmed price trends When prices move on low volume, there could be many different explanations An overly aggressive seller could be present for example But when price movements are accompanied by high volume, Dow believed this represented the "true" market view If many participants are active in a particular security, and the price moves significantly in one direction, Dow maintained that this was the direction in which the market anticipated continued movement To him, it was a signal that a trend is developing

Trends exist until definitive signals prove that they have ended: Dow believed that trends existed despite "market noise" Markets might temporarily move in the direction opposite to the trend, but they will soon resume the prior move The trend should be given the benefit of the doubt during these reversals Determining whether a reversal is the start of a new trend or a temporary movement in the current trend is not easy Dow Theorists often disagree in this determination Technical analysis tools attempt to clarify this but they can be interpreted differently by different investors

1.2.2 Technical Indicators

Technical indicators are distinguished by the fact that they do not analyze any part

of the fundamental business, like earnings, revenue and profit margins Technical indicators are used most extensively by active traders in the market, as they are designed primarily for analyzing short-term price movements and making trading decisions, picking stocks, buying and selling decisions To a long-term investor, most technical indicators are of little value, as they do nothing to shed light on the underlying business (Caginalp & Laurent, 1998)

1.2.2.1 Moving average convergence divergence (MACD)

MACD is a technical analysis indicator created by Gerald Appel in the late 1970s, has become one of the most popular of technical tools, used by short- and longer-term investors in the stock, bond, and other investment markets According to Appel (1999), technical analysis is about the best stock-market timing tools Appel smoothed out the noise of shorter-term price fluctuations by moving average so as to more readily be able

to identify and define significant underlying trends

The formula of exponential moving average is as below:

EMA (day i) = WEIGHT (current) x DATA (day i) + WEIGHT (moving average) x

Moving Average (day i-1) Where

 WEIGHT(current): is the weight of current day’s data in the exponential moving average and its calculation as follows:

WEIGHT (current) = 2/ (number of day in moving average + 1)

 WEIGHT(moving average) : is the weight given to the moving average in the calculation of the exponential moving average and its determined formula is as follows:

WEIGHT (moving average) = 100% - WEIGHT (current)

In his paper, Appel (1999) introduces the formula of MACD as below:

MACD = EMA (12) – EMA (26)

Where

 MACD: is Moving Average Convergence/Divergence value

 EMA(12): current value of the shorter exponential moving average (12-day)

 EMA(26): current value of the longer exponential moving average (26-day) The period for the moving averages on which an MACD is based can vary, but the most commonly used parameters involve a faster EMA of 12 days, a slower EMA of 26 days, and the signal line as a 9 day EMA of the difference between the two It is written in the form, MACD (faster, slower, and signal) or MACD (12, 26, and 9)

The build of MACD "oscillator" or "indicator" include three signals, calculated from historical price data, most often the closing price The indicator is represented by three lines These three signal lines are: the MACD line, the signal line (or average line), and the difference (or divergence) The term "MACD" may be used to refer to the indicator as a whole, or specifically to the MACD line itself The first line, called the

"MACD line", is calculated by the difference between a "fast" (short period) exponential moving average (EMA), and a "slow" (longer period) EMA Those two lines are usually called the MACD line and signal line MACD indicator was formed based on those basic Concepts:

 MACD represents the difference of the short-term exponential moving average minus the long-term exponential average When market trends are improving, short-term averages will rise more quickly than long-term averages MACD lines will turn up above 0

 When market trends turned down, shorter-term averages will tend to fall below longer-term averages and then the MACD lines will fall below 0

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

 Short-term moving averages will move apart (diverge) and move together (converge) with longer-term moving averages Hence, the indicator name moving average convergence-divergence

Since the MACD is based on moving averages, it is inherently a lagging indicator However, in this regard the MACD does not lag as much as a basic moving average crossing indicator, since the signal cross can be anticipated by noting the convergence far

in advance of the actual crossing As a metric of price trends, the MACD is less useful for

stocks that are not trending (trading in a range) or are trading with erratic price action

The MACD is only as useful as the context in which it is applied An analyst might apply the MACD to a weekly scale before looking at a daily scale, in order to avoid making short term trades against the direction of the intermediate trend

Signal–line crossovers are the primary cues provided by the MACD The standard interpretation is to buy when the MACD line crosses up through the signal line, or sell when it crosses down through the signal line The upwards move is called a bullish

crossover and the downwards move a bearish crossover Respectively, they indicate that the trend in the stock is about to accelerate in the direction of the crossover

1.2.2.2 Relative Strength Index (RSI)

The Relative Strength Index, introduced by Wilder (1988), is one of the most well-known momentum oscillator systems Momentum oscillator techniques derive their name from the fact that trading signals are obtained from values which “oscillate” above and below a neutral point, usually given a zero value In a simple form, the momentum oscillator compares today’s price with the price of n-days ago (Wilder, 1988)

The upward change U or downward change D for each trading period is calculated Up periods are characterized by the close being higher than the previous close:

Conversely, a down period is characterized by the close being lower than the previous period's (note that D is nonetheless a positive number),

If the last close is the same as the previous, both “U” and “D” are zero The average U and D are calculated using an n-period exponential moving average (EMA) but with an equal-weighted moving average in Wilder's original version The ratio of these averages is the relative strength or relative strength factor:

If the average of D values is zero, then according to the equation, the RS value

will approach infinity, so that the resulting RSI, as computed below, will approach 100 The relative strength factor is then converted to a relative strength index between 0 and

100

The exponential moving averages should be appropriately initialized with a

simple average using the first n values in the price series As Wilder (1988), the

momentum oscillator measures the velocity of directional price movement When the price moves up very rapidly, as some point it is considered to be overbought; when it moves down very rapidly, at some point it is considered to be oversold In either case, a reaction or reversal is imminent Momentum values are similar to standard moving averages, in that they can be regarded as smoothed price movements However, since the momentum values generally decrease before a reverse in trend has taken place, momentum oscillators may identify a change in trend in advance, while moving averages usually cannot

The RSI is most typically used on a 14 day timeframe, measured on a scale from

0 to 100, with high and low levels marked at 70 and 30, respectively Shorter or longer timeframes are used for alternately shorter or longer outlooks Wilder believed that tops and lowest are indicated when RSI goes above 70 or drops below 30 Traditionally, RSI readings greater than the 70 level is considered to be in overbought territory, and RSI

readings 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 In this study, 14 day timeframe will be used to calculate and draw the RSI indicator

1.2.2.3 Dual Moving Average Crossover (MA)

Moving average based trading systems are the simplest and most popular following systems among practitioners (Lui and Mole (1998) cited by Sewell (2008)) According to Sahli N Nefli (1991), the (dual) moving average method is one of the few technical trading procedures that is statistically well defined When the short-term trend rises above or below the long-term trend, the Dual Moving Average Crossover system generates trading signals

trend-Moving Average is an indicator that shows the average value of a security's price over a period of time When calculating a moving average, a mathematical analysis of the security's average value over a predetermined time period is made Average price moves

up or down as the securities price changes A buy signal is generated when the security's price rises above its moving average and a sell signal is generated when the security's price falls below its moving average

The study of Sewell (2008) states that the critical element in a moving average is the number of time periods used in calculating the average Sewell found that the optimum number of months in the preceding chart would have been 43 The key is to find

a moving average that will be consistently profitable The most popular moving average

is the 39-week (or 200-day) moving average This moving average has an excellent track

record in timing the major (long-term) market cycles

Tushar (1992) states in his study that Moving averages are a powerful tool for analyzing the trend in a security They provide useful support and resistance points and are very easy to use The most common time frames that are used when creating moving averages are the 200-day, 100-day, 50-day, 20-day and 10-day The 200-day average is thought to be a good measure of a trading year, a 100-day average of a half a year, a 50-day average of a quarter of a year, a 20-day average of a month and 10-day average of two weeks Moving averages help technical traders smooth out some of the noise that is found in day-to-day price movements, giving traders a clearer view of the price trend So far we have been focused on price movement, through charts and averages In the next section, we'll look at some other techniques used to confirm price movement and patterns In this study, the moving average indicator will be defined as the dual cross of moving average 200 day (slow line) and the moving average 30 day (fast line)

1.3 Previous Researches on technical analysis

Numerous empirical studies have tested the profitability of various technical trading systems, and many of them included implications about market efficiency According to Park and Irwin (2004), more than 130 empirical studies have examined the profitability of technical trading rules over the last four decades For example, Brock et

al (1992) found support for technical trading rules on the Dow Jones Index Following their study the interest in testing the profitability of technical trading rules has grown considerably Several authors have presented supportive evidence in emerging markets \

The research of Ben, Rochester & Jared (2006) cited that Chaudhur and Wu (2003) find that the technical trading rules could earn high profit since the random walk hypothesis might not work in many emerging markets Parisi and Vasquez (2000) show

huge profits to technical trading rules in the Chilean stock market Bessembinder and Chan (1998) find that the profit of technical trading rules could be exceed the transaction costs in the emerging markets of Malaysia, Thailand, and Taiwan Ito (1999) also tests technical trading rules and finds profitability beyond transaction costs in Indonesian, Mexican and Taiwanese equity indices Finally, Ratner and Leal (1999) conduct a test in markets of India, Korea, Malaysia, Philippines, Taiwan, Thailand, Argentina, Brazil, Chile, and Mexico, and find some evidence of profit from technical trading rules in Taiwan and Thailand markets

Neely, Christopher, Weller, and Dittmar (1997) cited in their research that "Osler and Chang (1995) construct a computer algorithm to identify head and shoulders pattern, and look at the returns to this rule in several currencies over the period 1973-1994 With bootstrap methodology they find evidence of significant profits for the mark and yen, but not for the pound sterling, Canadian dollar, French franc or Swiss franc”

Blanco, Sagi, Soltero, and Hidalgo (2004) test an application of technical trading rules on Moving Average Convergence Divergence (MACD) from 2000 to 2005 of Dow Jones Industrial Average (DJIA) and compare it with passive buy and hold strategy at the same period of time They prove that parameters of technical indicators can be improved with Evolutionary Algorithms

In Vietnam, Sang (2010) did a test and concluded that technical analysis earns more return but also more profit These studies provide a strong confirmation for using technical analysis, especially in Vietnam stock exchange market Furthermore, these studies also state the different between indicators during the test For example, Sang found that the results of RSI and MACD are significantly different for the same portfolio

Hung, H Nguyen, & Yang Zhaojun (2013) conduct a paper considering whether the moving average rules can forecast stock price movements and outperform a simple buy-and-hold strategy over the period from July 2000 to March 2011 on Vietnamese data Hung and Yang concluded that the technical trading rules examined have strongly predictive ability in term of Vietnamese data The rules have greater forecasting power for Vietnamese than those for some other Asian markets The profitability of short-term technical trading rules is better than that of longer-term ones This study also confirm that the (1,10,0) rule, (1,20,0) rule, and (1,50,0) rule are determined to be very effective in Vietnamese stock market because they allow investors to make a large excess returns before trading cost Specially, Hung proves that the technical trading rules are profitable, even after adjusting for trading costs

2 Effectiveness of investment strategy

Effectiveness is always a critical concern of any investment In financial sector, especially in stock exchange market, effectiveness of a strategy will be measure by the return of stocks which might include capital gain, dividend collected and any other form

of inflow by stock trading Effectiveness measurement will be difficult due to various aspects such as special market characteristic, micro and macro-economic situation, other affects from other related markets

In this paper, effectiveness of stock trading rules will be measure by simply accumulate all of possible return from stocks trading in a specific investment period To

be more specify, the effectiveness of a trading rule will be measure by the average return from trading a set of stock portfolios using that trading rule in a period of investment Different trading rules could be compared to the others by using the average return of the

same portfolio in the same investment period For example, effectiveness of indicator MACD will be measure by the average return from trading a stock portfolio in 3 years from 01/01/2009 to 01/01/2012 The effectiveness of indicator MACD could be compared to the effectiveness of MA or RSI indicator by simply compare the average return from the same stock portfolio trading in the investment period

The return of stocks is calculated simply by the formula below:

R = (P1 – P0)/P0

Where

o R is the return of stock in a period of time

o P1 is the sell price

o P0 is the buy price

3 T-Test two-sample for mean

After implementing the descriptive statistics, the 2 sample T-Test for means are used to compare the performance of each stock group to the others respectively The Microsoft Office Excel Add-in software is used to give the result of T-Test with confidence level of 95% Results of the test will be calculated automatically by SPSS software to check the hypothesis 1 and 2 in order Unequal (or equal) sample sizes, unequal variances T-Test formula will be applied as following:

Where

With degree of freedom as following:

Chapter Three – Methodology

This chapter presents the process of data collection and analysis method Secondary data are collected from the Internet through websites www.vndirect.com.vn and www.cophieu68.com Microsoft Office Excel software was used to analyze the collected data

Figure 2: Data Interpreting Process

Figure number 2 describes the process of collecting and interpreting data in this research All of data will be collected and handled through these 5 steps First, data will

be collected according to investigate the effectiveness of active indicators strategies in Ho Chi Minh market and scope as detailed in chapter 1 – Introduction

Second, stock portfolio will be formed by technical indicators In this research,

we focus on 3 different stage of the market which are up-trend, down-trend and sideway

So, there are 4 portfolios based on 3 separate technical indicators and passive strategy for each stage In third step, detail rules and principle will identify “buy” and “sell” signal based on 3 technical indicators And then, the next step will focus on estimating portfolio

return and standard deviation Finally, these portfolios will be compared across each other using T-Test 2 samples for means

1 Study Population and Data Collection

The collected data are daily prices which include open price, close price, highest price, and lowest price on daily basis and trade volumes of each stock in 3 years from 01/01/2009 to 01/01/2012 During those years, the market was experienced 3 different trends Stocks which do not satisfy this time length will be excluded So, there are 140 stocks included in the test

At first, open price, close price, highest price, and lowest price on daily basis and trade volumes of all classified stock was collected from website www.vndirect.com.vn for the period of 3 years form 01/01/2009 to 01/01/2012 (including 749 daily price observations)

Secondly, “buy” and “sell” signals are identified on the historical price graph according to the collected data in the first step to calculate the return for each strategy Returns of each stock will be calculated and gathered into groups All of the factors that might affect to the return of trading stocks (such as dividend, interest, T+3 rules …etc.) will be taken into account in the calculation of step 2

2 Trading Rules

For active strategy, stocks are traded actively over time, “buy” and “sell” decisions are made continuously overtime based on “sell” and “buy” signals of technical indicators (meaning MACD, RSI, and MA) Each indicator has its own principles In order to ensure the validity of the test, trading rules are stipulated in advance for each indicator and applied strictly during the whole investment period

2.1 General trading rules

If the indicator states a “buy” signal or “sell” signal, then “buy” decision or “sell” decision will be made with the price of the transaction dates Stocks could be traded by

“trading cycle” Each trading cycle begins with the “buy” decision and end with the

“sell” decision So, there is no short-selling transaction during the tested period In case there are more than one “sell” signal is seen, only the first “sell” signal that follows the

“buy” signal is taken into account, the following “sell” signals will be skipped until we have the next “buy” signal Similarly, for the last “buy” signal toward the end of the investment period, and there is no “sell” signal before the end of the investment period (it means this trading cycle is not closed yet), then the stocks will be sold at the price of the last trading day of the investment period to close this trading cycle to ensure the validity

of the test

Furthermore, one trading cycle can begin when the previous one has not been finished yet In some cases, some “buy” signals might occur continuously or vice versa for “sell” signal, and then trading cycles can be intersected each other’s For example, if a

“buy” signal occurs and then 1 trading cycle begins with buy decision, it means we hold stocks in account Some days later, we expect for a “sell” signal to sell these stocks and end up this trading cycle, but one more “buy” signal occurs and another trading cycle begins It means another buy decision is made even when we did not sell the stocks that

we bought in the first trading cycle, and we got some more stocks in account And then, if the indicator state a “sell” signal, all of stocks in account will be sold and both these two trading cycles will be closed at the same time (Sang, 2010)