Effectiveness of investment strategies based on technical indicators

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 September 2014 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 September 2014 Under the guidance and approval of the committee, and approved by all its members, this thesis has been accepted in partial fulfillment of the requirements for the degree. Approved: ---------------------------------------------Chairperson --------------------------------------------Committee member ---------------------------------------------Committee member --------------------------------------------Committee member ---------------------------------------------Committee member --------------------------------------------Committee member 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. -i- 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. ii 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 iii 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 iv Chapter Five – Discussion .................................................................................................40 Chapter Six – Conclusion and Recommendation ..............................................................43 1. Conclusion ..............................................................................................................43 2. Recommendation ....................................................................................................44 3. Limitation ................................................................................................................45 References ..........................................................................................................................48 Appendixes ........................................................................................................................51 v 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 vi 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 vii 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. viii ix 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. 1 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 2 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. 3 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 4 exchange market as well as applications of some technical strategy for Vietnamese investors. 5. Research Structure Introduction Literature Review Data & Methodology Results Conclusion 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. 5  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. 6 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. 7 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). 8 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. 1.1.2 Passive Strategy Two common followed strategies by passive investors are: buy and hold strategy and indexing strategy (Andre & William, 1995):  Buy and hold strategy: an investor who follows buy and hold strategy selects a portfolio and stays with it. Obviously, such an investor chooses a portfolio that promises to meet his investment objectives and spend time and effort in his initial selection.  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 9 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 10 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 11 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 12 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 13 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. 14 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) 15  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: 16  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 17 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: 18 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 19 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 trendfollowing 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. 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. 20 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 50day 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 21 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. 22 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 23 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: 24 25 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. Collecting data Portfolio Formation Identify Trading Signals Based on Indicators Return and Standard Deviation Estimation Significant T-Test for Means 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 26 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. 27 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). 28 If the trading signals lead to the conflict with rule T+3, all of trading decision must wait until the assets are transferred. By the law, the trading action will be done in the next day. For example, if a “sell” signal occurs but the T+3 rule is still in effect (it means the stocks that we bought are not transferred yet), then the trading action could not be performed. But the real market, investors and brokers learn many ways to avoid this difficulty. The bank can help investors to eliminate the effect of T+3 rule. For example, in May Bank Kim Eng Securities Joint Stock Company, investors might borrow stocks or cash from the bank to make trading action even when T+3 rule still in effect. It means the bank will lend investors’ money to trade and then they will get money back when it is transferred into investors’ account few days later. Besides, Securities Company could lend investors stocks and then they can get stocks back when they are transferred into investors’ account few days later. 2.2 Trading signals The Microsoft Office Excel software is used to draw the indicator and give exactly the trading signal. According to the concept of MACD indicator, the “buy” signal occurs when the MACD line raise above the signal line (9-day period) and the “sell” signal occurs when the MACD line fell below the signal line. A very similar method could be used for MA. The “buy” signal occur when MA (30 days) line cross over the MA (120 days) line. RSI indicator use timeframe 14-day period, the “buy” signal occurs when the RSI line fell below 30 and the “sell” signal occurs when the RSI line raise above 70. If these signals satisfy all of general assumption and trading rules, the trading actions will be 29 done. These trading action results will be gathered and calculated automatically by Microsoft Office Excel Add-in application. 3. Measurement of strategy effectiveness In case of market efficiency, all transaction fees, taxes, T+3 rules … will be excluded from the test and data implementing process. The results from those data will be used to measure stocks’ return without inefficiency aspects. In the other hand, when the inefficiency aspects take place, few assumptions will be made to ensure the feasibility of the research. First, all of transactions are assumed to be done by an account in May-Bank Kim Eng Securities Company. So, transaction fees will be calculated based on policy of this company. According to transaction fee table quoted on the official website of MayBank Kim Eng Securities Company, the transaction fee for total transaction value under VND 100 million/due is 0.35% total transaction value. Hence, the returns are calculated automatically including capital gain (or loss) from buy and sell stocks and dividend received (if any). According to the Article 1 of Circular No. 12/8/2009 dated 160/2009/TT-BTC of the Ministry of Finance guides income tax exemption in 2009 by Resolution No. 32/2009/QH12 dated 19.06.2009 of the National Association that individual transferable securities are exempt from personal income tax from 01/01/2009 to end on 31/12/2009. Since 01/01/2010, personal income from transfer of securities shall pay personal income tax as stipulated in the Law on Personal Income Tax and Circular No. 84/2008/TT-BTC dated 30/9/2008 of the Ministry of Finance guiding perform the Circular No. 100/2008/ND-CP dated 09/08/2008 of the Government regulating in details some articles of the Law on Personal Income Tax and 42/BTC-TCT Dispatch on 02/01/2009 Ministry 30 of Finance guides income tax deduction for stock transfer. The personal tax applied for share ownership transferring is 0.1% on sell price or 20% on capital gain for one trading cycle. In this research, 0.1% on sell price personal tax is applied. The gain or loss of each transaction is calculated by the following formula: S–B+D G= x 100% B Where: o G – Gain (loss) per transaction (%) o S = (Close price of the stock at the time the “sell” action is done) – (the transaction fee and personal tax) o B = (Close price of the stock at the time the “buy” action is done) + (the transaction fee) o D – dividend received relate to the trading cycle (if any) Whenever the capital take break in the account (means the investors hold cash instead of stocks), they got interest on that cash. In Vietnam market, each securities company signed a contract with the bank; the investors will get the interest at rate for Non-term Deposit Balance from that bank. In this research, all trading actions are done by the account opened at Kim Eng May Bank Securities Company. So, the interest rate for Non-term Deposit Balance of Eximbank will be applied. It is 3.6% p.a, equivalent to 0.01% per day. So, returns of each stock for the whole investment period are calculated as the following formula: R = ∑G + (N – n) x R 31 Where o R – Average daily gain (loss) for the whole investment period (%) o G – Gain (loss) per transaction (%) o N – 730 days o n – Total non-trading days in investment period o R – 0.01% 32 Chapter Four – Results This section will show the result of trading stocks following exactly the trading rules and assumptions which were set up the methodology. These results include average stock returns, standard deviation, and return distribution; those data will be displayed under using bar chart and box plot. At first, all inefficiency aspects such as dividend, trading fees, taxes, T+3 rules … will be excluded from the test. Secondly, those inefficiency aspects will take place. The results of data with and without inefficiency aspects will be compared to each other in order to test the effect of those inefficiency aspects into the test results. 1. Descriptive Analysis 1.1 Data without inefficiency aspects 250% 200% 150% Return Standard Deviation 100% 50% 0% RSI MACD MA Passive Figure 3: Average Return & Standard Deviation of data without inefficiency aspects from 2009 to 2012 33 The figure above will show the return of stocks in the whole three years from 2009 to 2012. This figure illustrated the domination in average return of technical indicators over passive strategy. The average returns of stocks during three years were 174%, 58%, 42% and 3% corresponding to RSI, MACD, MA and passive strategy. Besides, standard deviation of MACD, MA, and passive strategy were almost the same around 90% while the standard deviation of RSI indicator peaked into nearly 240%. Secondly, the return of stocks during this three years period is broken down into 3 different separate period of time which represent for up-trend, down-trend and sideways market situation. The figure above shows box plot of all three indicators and passive trading in three different market stages (up-trend, sideway and down-trend). Passive - Down MA - Down MACD - Down RSI - Down Passive - Side MA - Side MACD - Side RSI - Side Passive - Up MA - Up MACD - Up RSI - Up -200% 0% 200% 400% 600% 800% 1000% 1200% Figure 4: Box Plot of Return in all three market trends 34 During down-trend and sideway market stage, technical indicator (include RSI, MACD and MA) showed no big differences compared to passive trading method. The mean of stock returns during those two market stages from both technical indicators and passive trading method fluctuated around 0. The situation was much better in up-trend market stage, as the figure below, RSI indicator gained a significant higher average return compared to passive trading method and the other two indicators. RSI got around 180% of average return while MACD and passive trading method stood around 80% of average return. MA indicator only got nearly 40% on average return during this up-trend period. 1.2 Data with inefficiency aspects In overall, the data with inefficiency aspects was almost the same with the data without those inefficiency aspects. The figure below shows the average stock return of the same sample with the test of data without inefficiency aspects in the period of three years from 2009 to 2012. 250% 200% 150% Return 100% Standard Deviation 50% 0% RSI MACD MA Passive -50% Figure 5: Average Return & Standard Deviation of data with inefficiency aspects from 2009 to 2012 35 The average return of all three technical indicators and passive strategy reduce a little bit due to the cost from inefficiency aspects. The average returns of RSI, MACD and MA were corresponding to 163%, 47% and 31% while the average return of passive strategy fall down below 0%. The standard deviation stood almost the same. RSI indicator still got the highest standard deviation (double the others). The standard deviation of MACD, MA and passive strategy were almost nearly to each other (around 90%). 2. T-Test Two Sample for Mean 2.1 Data without inefficiency aspects The table below shows the T-Test result between the stock return of strategies using three technical indicators and passive strategy for the period of three years from 2009 to 2012. First, all three technical indicators confirm the significant different in average return in the comparison with passive strategy. Besides, there is no significant different in average return between MACD and MA since those two indicators were built on almost the same basic while RSI indicator confirm a significant different in the comparison with the other two technical indicators. Table 1: T-Test results of data without inefficiency aspects from 2009 to 2012 Total (2009-2012) RSI MACD MA Passive RSI Sig Sig Sig MACD No Sig MA Sig Sig: Significant Different No: No significant different The table below shows the T-Test p-value of the comparisons between the average return of technical indicators with passive trading method and among those technical 36 indicators for three separate period of time corresponding to three market stages (uptrend, sideway, and down-trend). Table 2: T-Test Results in all three market trends of data without inefficiency aspects Up-trend (2009-2010) RSI RSI MACD MA Passive Sig Sig Sig Sig No MACD MA Sideway (2010-2011) RSI MACD MA Passive No No Sig No Sig Sig Sig: Significant Different Down-trend (2011-2012) RSI Sig MACD MA Passive Sig Sig Sig Sig Sig Sig No: No significant different In overall, the technical indicators brought back a significant different average return compared to passive trading method. During sideway and down-trend period, all TTest results compared means of stock return between technical indicators and passive trading method show a significant different results. During up-trend period, only RSI and MA indicator show a significant different result while T-Test did not confirm that for MACD indicator. Among technical indicators, when the market has formed a trend, the results of those three indicators will be significant different. But when the market fall in a sideway situation, the result between those three indicators will not confirm any significant different. 2.2 Data with inefficiency aspects Data with inefficiency aspects still keeps the same T-Test results compared to the T-Test results of data without inefficiency aspects. The table below shows the T-Test results of the comparison between all three technical indicators with passive strategy and among those three technical indicators during three different period of time which represent for up-trend, down-trend and sideways market situation. 37 Table 3: T-Test results of data with inefficiency aspect Up-trend (2008-2009) MACD MA Passive RSI RSI Sig Sig Sig MACD Sig No MA Sig Down-trend (2010-2011) RSI MACD MA Passive RSI RSI Sig Sig Sig MACD Sig Sig MA Sig Sig: Significant Different No: No significant different RSI Sideway (2009-2010) MACD MA Passive No No Sig No Sig Sig Total (2009-2012) MACD MA Passive Sig Sig Sig No Sig Sig All three technical indicators still confirm the significant different in average return in the comparison with passive strategy. And there is no significant different in average return between MACD and MA while RSI indicator still confirm a significant different in the comparison n with the other two technical indicators. 2.3 Compare between data with and without inefficiency aspects The table below shows the T-Test results of the comparison between the data without inefficiency aspects and data with inefficiency aspects. The data will be compared in pair of each technical indicators corresponding to each market situation (uptrend, down-trend and sideways). The table below shows that there are no significant different between average return of stocks using the same technical indicators in the condition within and without the inefficiency aspects. The results of all three technical indicators were completely the same in case of inefficiency aspects included and excluded. 38 Table 4: T-Test results compare between data with and without inefficiency aspects RSI inefficiency MACD inefficiency MA inefficiency Up-trend (2008-2009) RSI MACD MA Passive No No No No No Down-trend (2010-2011) RSI MACD MA Passive No No No RSI inefficiency MACD inefficiency MA inefficiency Sig: Significant Different No No Sideway (2009-2010) RSI MACD MA Passive No No No No No Total (2009-2012) RSI MACD MA Passive No No No No No No: No significant different No No No No 39 Chapter Five – Discussion In overall, the findings in descriptive analysis using box plot and column chart suggested that the technical indicators was outperformed the passive trading method in most cases. During the up-trend market, only RSI indicator show a significant higher return compared to passive trading method. MACD got nearly the same result with passive trading method while the return of MA indicator even lower than passive trading method. For sideway and down-trend market, the technical indicators still brought a significant higher return but the returns were not high. The results from T-Test two samples for mean also confirm the significant differences among those technical indicators. When the market has formed its trend (either up-trend or down-trend) the return from technical indicators will be differently. Otherwise, the differences will not be considered significantly. Especially, RSI indicator tends to get higher return but higher risk too. The average return and standard deviation of RSI indicator were much higher than the other indicators. The reason could come from the nature of this indicator. RSI is a leading indicator while MACD and MA have more characteristics of a lagging indicator. As in the finding, the profit from “MA” indicator and “MACD” indicator were lower than “RSI” indicator. The profit of active trading strategy using these technical indicators comes from the fluctuation of stock prices. But these technical indicators were formed by different factors and they got some different characteristics. “MA” indicator is a lagging indicator which normally state trading signal later than leading indicator like “RSI” while “MACD” stand in the middle. So, in a sideways movement of stock prices, 40 these 3 technical indicators are almost the same. But when the market trend has been confirmed, the different between lagging and leading technical indicators will be significant. Leading indicator like RSI will have the tendency to move forward the market and promise higher return (if the market going up) and higher level of risk. Lagging indicators tend to react a little bit later then the market since the data of those kinds of indicator has been formed from the moving average. Thus, lagging indicator might miss some potential opportunities by its late react. The word “lagging” in “lagging indicator” could be understood as “slower”. The “MA” indicator in this study might represent for all of lagging technical indicators. These indicators almost got the same movement which is slower than the average market. So, some opportunities might be missed cause of this late trading signal. In contrast, leading indicator like “RSI” usually states trading signal before the average market. Thanks for this fast respond leading indicator could take a lot of opportunities but also cause noises means false trading signal. Trading signal of technical indicator might be false sometimes cause of quick change in stock price trend. For example, a new trend of stock price has been formed. Leading indicator like “RSI” will state trading signal before lagging indicator like “MA” did. But if the trend of stock price changes instantly right after that, the trading signal stated by “RSI” indicator might lead the investors to a false decision. This is called noise. Noise usually occurs in leading indicator cases. It seems to be that lagging indicator is a better choice. But if we take a closer look at the data, lagging indicator like “MA” usually has a lower number of transactions than leading indicator like “RSI”. Besides, lagging indicator states trading signal later, and then some profit will be missed. 41 According to the data, technical indicator returns dominate passive trading method. In the up-trend market, this is very obvious to see. In sideways and down-trend market, the returns of technical indicators still higher than passive trading method but all of the returns were very low around zero. In the aspect of risk taking, “RSI” indicator was a special case compared to the others. This indicator sometimes brought back a lot of return but, of course, the threat high risk comes along with that. For example, during the uptrend period, the return RSI indicator was more than 3 times of the others. But the standard deviation of this group was more than 5 times of the others. Transaction fees could be a concern for investors who follow active trading strategy. Transaction costs are important to investors because they are one of the key determinants of net returns. Transaction costs diminish returns, and over time, high transaction costs can mean thousands of dollars lost from not just the costs themselves but because the costs reduce the amount of capital available to invest. The number of transaction cost move together with the number of transaction that we made. Since the transaction fees are calculated according to the value of that transaction. Besides, active strategy trades stocks actively overtime and then, the number of transaction would be a lot higher. Otherwise, the data shows that inefficiency aspects such as trading fees, dividend and taxes did not affect much on the results. 42 Chapter Six – Conclusion and Recommendation 1. Conclusion This study serves the purpose of investigating the effectiveness of investment strategies using technical against passive trading method. The data is from Ho Chi Minh stock exchange market only (Hanoi and UPCOM market are excluded) for the investment period from 01/01/2009 to 01/01/2012. The collected data were included 140 stocks in total. All funds and preferred stocks were excluded and some needed assumptions about data and liquidity were set up before we started the trading recoding process. The study focus on 3 most popular technical indicators in Vietnam market which are Moving Average (MA), Moving Average Convergence and Divergence (MACD) and Relative Strength Index (RSI). The performances of these technical indicators were confirmed by the previous studies in both Vietnam market and other countries. In this research, active strategy was used to maximize the performance of those technical indicators. The active investors used the support from technical analysis to trading stocks actively during the investment period. All of transactions were tracked and recorded in a excel file for each stock. The transaction cost and dividend were included in the transaction recording process. Data with and without inefficiency aspects such as trading costs, dividend, T+3 rules will be compared to each other. Descriptive statistic and T-Test will be used to make the comparison of the means of these groups. Standard deviation will be used as a tool to measure the level of risk for each group’s data. Descriptive analysis was confirmed that technical indicators show different results across market stages (up-trend, sideways and down-trend). In overall, the performances of technical indicators were better than passive trading method. Technical indicators 43 could maximize the returns during up-trend period and minimize the lost when the market declines. But minimizing the lost does not mean that it performs well. The returns during sideways and down-trend market of technical indicators and passive strategy were nearly zero. T-Test statistic results illustrate a significant different between technical indicators and passive trading method in sideways and down-trend market while only RSI indicator stated a significant better result in up-trend market. “MA” indicator or lagging technical indicator seems to be more stable compared to leading technical indicator (for example: RSI). Return from “MA” indicator was lower than “MACD” or “RSI” indicator but it also faces lower level of risk. “RSI” indicator raise higher return but it needs to bear a very higher level of risk. Besides, technical indicators always have a higher number of transactions since they trade stocks actively while passive trading method only has 1 transaction only. Furthermore, the effect of inefficiency aspects like dividend, trading cost, and taxes on final return was not high. The results with and without inefficiency aspects were almost the same by the confirmation of T-Test results. 2. Recommendation Since the big differences across market stages, using leading technical indicators like RSI is recommended for investors during the up-trend market to maximize the profit. But if the market seems to be confused or declines, holding stocks during this time is not a good choice for Vietnam current trading regulations. Although that the return of technical indicators still higher than passive trading method but they only could reduce the lost. Thus, investors should consider to use technical indicators to get more return when the market perform well but they should not invest much in stock market during 44 crisis time even with the support from technical indicators. When the up-trend market is confirmed, this study strongly recommends investors to use RSI indicator to increase the profit. The study also states that the performance of technical indicators might be different. Each technical indicator will show a different result based on its own characteristic. Investors’ risk tolerance is a very important factor to choose a specific technical indicator since there are some differences between their risk and return. Leading indicators could bring back higher return but the risk comes along. Risk taken investors will prefer to use leading indicator and the risk adverse investors will prefer to use lagging indicator. But as the results of this study, using lagging indicators only get almost the same result with passive trading method. This type of technical indicators might not a good choice for investors. This study states that the transaction cost and dividend paid out were not an important factors for technician in Ho Chi Minh market. Active strategies normally take higher transaction cost and personal tax but a random amount of dividend paid out. Active investor need to accept the higher cost of transaction fee and tax. Especially for the case of MACD, this indicator shows a significant higher number of transactions compared to the other indicators. But those numbers did not affect much on the return; investors don’t need to pay attention on those aspects. 3. Limitation There appear to be some limitations generated during the time carrying out this study, which should be considered in future research. Firstly, Vietnam stock exchange market started running about 13 years ago – at the time this research was accomplished. 45 Due to the limit of time length of the investment period, only 3 years data of stock trading might not reflect all of market characteristics. The selection of time horizontal might affect to the return stocks. Stock market usually follows a specific trend in a specific period of time. Choosing a specific time length might reflect only the trend which the market followed during that time only. Some assumptions related data collecting and liquidity needs to be sated up in order to ensure that the process of input data recording was conductible. These assumptions might not valid in the real market and must be consider more careful in further study. Furthermore, the companies with bigger amount of capital and higher liquidity might attract more investors than the small one. It affects to the active strategies more than passive strategy because trading stocks actively require higher level of liquidity. In the real market, some trading signals may not able to implement because there is no matching order. In consequently, capital firm size effect and liquidity effect must be conducted in further study so that the comparison between the performances of active strategies and passive strategy can be carried out more efficiently. In addition, different markets have some different characteristics which might lead to some different conclusions and affect much to the results. The research only focus on Ho Chi Minh market only, Hanoi market and Up-com market are excluded from the test. These markets have many different characteristics compared to Ho Chi Minh market. These differences could lead us to some different results. Further research must have some comparison between these markets in order to get a bigger overview. Finally, the amount of money which is invested during the investment period was not mentioned. This problem is related to the capital firm size effect. The bigger 46 companies usually got higher stock price compared to the other smaller companies. First, investors can buy only a short amount of big stocks but a lot amount of small stocks. For the big one, there is no big deal, but this inflow of cash will affect directly to that stock price. So, the balance between supply and demand of that stock could change dramatically and then, monopoly affect occurs. This phenomenon was not mentioned in the research too. Any further study must take this in to account very seriously. 47 References Achelis, S. B. (1997). Technique analysis from A to Z (2 ed.). New York. Andre F. P. & William F. S. (1995). Dynamic strategies for asset Allocation. Financial Analyst Journal: Jan-Feb (149). Appel, G. (1999). Technical Analysis Power Tools for Active Investors. ISBN 0-13147902-4. Ben R. 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New Concepts in Technical Trading Systems: ISBN 0-89459-027-8. 50 Appendixes Appendix 1: List of company in the test Symbol 1 ABT 2 ACL 3 4 5 AGF ALP ANV 6 7 8 9 10 11 12 13 ASP BAS BBC BHS BMC BMI BMP BT6 14 15 CII CLC 16 17 18 CNT COM CYC 19 20 DCC DCL 21 22 23 24 DCT DDM DHA DHG 25 DIC 26 DMC 27 28 29 30 31 DPM DPR DQC DRC DTT 32 DXV 33 FBT Company name Công ty Cổ phần Xuất nhập khẩu Thủy sản Bến Tre Công ty cổ phần Xuất nhập khẩu Thủy sản Cửu Long An Giang Công ty Cổ phần Xuất nhập khẩu Thủy sản An Giang Công ty Cổ phần Đầu tư Alphanam Công ty Cổ phần Nam Việt Công ty Cổ phần Tập đoàn Dầu khí An Pha Công ty Cổ phần Basa Công ty Cổ Phần Bibica Công ty Cổ phần Đường Biên Hoà Công ty cổ phần Khoáng sản Bình Định Tổng Công ty Cổ phần Bảo Minh Công ty Cổ phần Nhựa Bình Minh Công ty Cổ phần Beton 6 Công ty Cổ phần Đầu tư Hạ tầng Kỹ thuật Tp.Hồ Chí Minh Công ty Cổ phần Cát Lợi Công ty Cổ phần Xây dựng và Kinh doanh Vật tư Công ty Cổ phần Vật tư - Xăng dầu Công ty Cổ phần Gạch men Chang Yih Công ty Cổ phần Xây dựng Công nghiệp DESCON Công ty Cổ phần Dược phẩm Cửu Long Công ty Cổ phần Tấm lợp Vật liệu xây dựng Đồng Nai Công ty Cổ phần Hàng hải Đông Đô Công ty Cổ phần Hoá An Công ty Cổ phần Dược Hậu Giang Công ty Cổ phần Đầu tư và Thương mại DIC Công ty Cổ phần Xuất nhập khẩu Y tế Domesco Tổng Công ty Phân bón và Hóa chất Dầu khí-CTCP Công ty Cổ phần Cao Su Đồng Phú Công ty Cổ phần Bóng đèn Điện Quang Công ty Cổ phần Cao su Đà Nẵng Công ty Cổ phần Kỹ nghệ Đô Thành Công ty Cổ phần VICEM Vật liệu Xây dựng Đà Nẵng Công ty Cổ phần Xuất nhập khẩu Lâm Thủy sản Bến Tre Issue Date # of Stocks Cap 25/12/2006 11.007.207 429,28 09/05/2007 18.150.000 190,58 05/02/2002 18/12/2007 12/07/2007 12.859.288 192.484.413 65.605.250 507,94 635,2 459,24 15/02/2008 11/11/2008 19/12/2001 20/12/2006 28/12/2006 21/04/2008 07/11/2006 18/04/2002 22.829.996 9.600.000 15.420.782 31.497.459 12.392.630 75.500.000 45.371.300 32.993.550 123,28 14,4 400,94 434,66 663,01 921,1 3221,36 191,36 18/05/2006 16/11/2006 112.801.500 13.103.830 2028,27 275,18 28/07/2008 08/07/2006 31/07/2006 9.915.069 13.752.368 9.046.425 49,58 357,56 26,23 12/12/2007 17/09/2008 10.300.000 9.913.692 97,85 193,32 10/10/2006 22/07/2008 14/04/2004 21/12/2006 27.223.647 12.244.495 15.061.213 65.366.299 51,72 7,35 108,44 6994,19 28/12/2006 16.446.069 90,45 25/12/2006 17.809.336 653,6 11/05/2007 30/11/2007 21/02/2008 29/12/2006 22/12/2006 379.934.260 43.000.000 21.982.279 83.073.849 8.151.820 15045,4 1750,1 509,99 3239,88 60,32 26/02/2008 9.900.000 24,75 14/01/2008 11.265.000 52,95 51 34 35 FMC FPT 36 GIL 37 38 39 40 41 42 GMC GMD GTA HAG HAP HAS 43 HAX 44 HBC 45 46 HDC HLA 47 48 49 50 HMC HPG HRC HSG 51 52 53 HSI HT1 HTV 54 55 ICF IFS 56 IMP 57 58 ITA KDC 59 60 61 KHA KHP KMR 62 63 KSH L10 64 LAF 65 66 67 68 69 LBM LCG LGC LSS MCP 70 MCV Công ty Cổ phần Thực phẩm Sao Ta Công ty Cổ phần FPT Công ty Cổ phần Sản xuất Kinh doanh Xuất nhập khẩu Bình Thạnh Công ty Cổ phần Sản xuất Thương mại May Sài Gòn Công ty Cổ phần Gemadept Công ty Cổ phần Chế biến Gỗ Thuận An Công ty Cổ phần Hoàng Anh Gia Lai Công ty Cổ phần Tập Đoàn Hapaco Công ty Cổ phần HACISCO Công ty Cổ phần Dịch vụ Ô tô Hàng Xanh Công ty cổ phần Xây dựng và Kinh doanh Địa ốc Hoà Bình Công ty Cổ phần Phát triển nhà Bà RịaVũng Tàu Công ty Cổ phần Hữu Liên Á Châu Công ty Cổ phần Kim khí Thành phố Hồ Chí Minh Công ty cổ phần Tập đoàn Hòa Phát Công ty Cổ phần Cao su Hòa Bình Công ty Cổ phần Tập đoàn Hoa Sen Công ty Cổ Phần Vật tư tổng hợp và Phân bón Hóa sinh Công ty Cổ phần Xi Măng Hà Tiên 1 Công ty Cổ phần Vận tải Hà Tiên Công ty Cổ phần Đầu tư Thương mại Thủy Sản Công ty Cổ phần Thực phẩm Quốc tế Công ty Cổ phần Dược phẩm IMEXPHARM Công ty Cổ phần Đầu tư và Công nghiệp Tân Tạo Công ty Cổ phần Kinh Đô Công ty Cổ phần Xuất nhập khẩu Khánh Hội Công ty Cổ phần Điện lực Khánh Hòa Công ty Cổ phần Mirae Công ty Cổ phần Tập đoàn Khoáng sản Hamico Công ty cổ phần Lilama 10 Công ty Cổ phần Chế biến Hàng xuất khẩu Long An Công ty Cổ phần Khoáng sản và Vật liệu xây dựng Lâm Đồng Công ty Cổ phần LICOGI 16 Công ty Cổ phần Cơ khí - Điện Lữ Gia Công ty Cổ phần Mía đường Lam Sơn Công ty Cổ phần In và Bao bì Mỹ Châu Công ty Cổ phần Cavico Việt Nam Khai thác Mỏ và Xây dựng 12/07/2006 13/12/2006 12.222.630 275.119.729 140,56 11610,05 01/02/2002 13.234.588 338,81 22/12/2006 22/04/2002 23/07/2007 22/12/2008 08/04/2000 19/12/2002 10.583.173 114.421.669 9.830.000 718.156.322 27.919.791 7.800.000 243,41 2826,22 78,64 14794,02 150,77 35,88 26/12/2006 11.116.169 42,24 27/12/2006 47.333.644 766,81 10/08/2007 30/10/2008 26.908.212 34.442.993 384,79 185,99 21/12/2006 15/11/2007 26/12/2006 12/05/2008 21.000.000 419.053.329 17.260.976 96.931.578 151,2 12781,13 809,54 3634,93 21/12/2007 13/11/2007 01/05/2006 9.885.590 197.952.000 93617.790 46,46 1049,15 125,99 18/12/2007 17/10/2006 12.807.000 29.140.992 35,86 276,84 12/04/2006 16.405.950 580,77 15/11/2006 12/12/2005 618.478.153 166.136.014 3587,17 8024,37 19/08/2002 27/12/2006 30/06/2008 12.768.439 40.051.296 34.397.626 159,61 496,64 85,99 11/12/2008 25/12/2007 11.690.000 8.900.000 79,49 81,88 15/12/2000 14.728.019 70,69 20/12/2006 18/11/2008 27/12/2006 01/09/2008 28/12/2006 8.157.500 56.249.956 8.283.561 50.000.000 9.831.162 104,42 303,75 127,57 675 131,74 12/11/2006 12.092.161 43,53 52 71 MHC 72 73 MPC NAV 74 NKD 75 NSC 76 77 78 NTL OPC PAC 79 80 PET PGC 81 PIT 82 83 84 PJT PNC PPC 85 PVD 86 87 PVF PVT 88 89 90 RAL REE RIC 91 SAM 92 93 94 SAV SBT SC5 95 96 97 SCD SFC SFI 98 99 SGT SJD 100 SJS 101 SMC 102 103 104 105 SSC SSI ST8 STB Công ty Cổ phần Hàng hải Hà Nội Công ty Cổ phần Tập đoàn Thủy sản Minh Phú Công ty Cổ phần Nam Việt Công ty Cổ phần Chế biến Thực phẩm Kinh Đô Miền Bắc Công ty cổ phần Giống cây trồng Trung ương Công ty Cổ phần Phát triển Đô thị Từ Liêm Công ty Cổ phần Dược phẩm OPC Công ty Cổ phần Pin Ắc quy Miền Nam Tổng Công ty Cổ phần Dịch vụ Tổng hợp Dầu khí Tổng Công ty Gas Petrolimex-CTCP Công ty Cổ phần Xuất nhập khẩu Petrolimex Công ty Cổ phần Vận tải Xăng dầu Đường thủy Petrolimex Công ty Cổ phần Văn hóa Phương Nam Công ty Cổ phần Nhiệt điện Phả Lại Tổng Công ty Cổ phần Khoan và Dịch vụ Khoan Dầu khí Tổng Công ty Tài chính Cổ phần Dầu khí Việt Nam Tổng công ty Cổ phần Vận tải Dầu khí Công ty Cổ phần Bóng đèn Phích nước Rạng Đông Công ty Cổ phần Cơ điện lạnh Công ty Cổ phần Quốc tế Hoàng Gia Công ty Cổ phần Đầu tư và Phát triển Sacom Công ty Cổ phần Hợp tác kinh tế và Xuất nhập khẩu SAVIMEX Công ty Cổ phần Bourbon Tây Ninh Công ty Cổ phần Xây dựng số 5 Công ty Cổ phần Nước giải khát Chương Dương Công ty Cổ phần Nhiên liệu Sài Gòn Công ty Cổ phần Đại lý Vận tải SAFI Công ty Cổ phần Công nghệ Viễn thông Sài Gòn Công ty Cổ phần Thủy điện Cần Đơn Công ty Cổ phần Đầu tư Phát triển Đô thị và Khu Công nghiệp Sông Đà Công ty Cổ phần Ðầu tư Thương mại SMC Công ty Cổ phần Giống cây trồng Miền Nam Công ty cổ phần Chứng khoán Sài Gòn Công ty Cổ phần Siêu Thanh Ngân hàng Thương mại Cổ phần Sài 21/03/2005 13.555.514 43,38 20/12/2007 22/12/2006 69.369.440 8.000.000 1650,99 46,4 15/12/2004 15.122.638 623,05 21/12/2006 10.031.028 797,47 21/12/2007 30/10/2008 12/12/2006 60.989.950 12.856.860 26.625.031 725,78 893,55 471,26 09/12/2007 24/11/2006 69.842.000 50.287.503 1480,65 477,7 24/01/2008 11.842.878 69,87 28/12/2006 07/11/2005 26/01/2007 9.660.000 10.799.351 318.154.614 72,45 54 7285,74 12/05/2006 247.469.635 12373,48 11/03/2008 12/10/2007 600.000.000 232.600.000 4020 1302,56 12/06/2006 28/07/2000 31/07/2007 11.500.000 244.640.638 70.369.564 518,65 6164,94 415,18 28/07/2000 130.798.432 915,59 05/09/2002 25/02/2008 18/10/2007 9.626.650 139.504.610 14.984.550 78,94 1883,31 247,25 25/12/2006 21/09/2004 29/12/2006 8.477.640 11.234.819 8.704.480 139,88 213,46 177,57 18/01/2008 25/12/2006 74.001.604 35.879.150 162,8 631,47 07/06/2006 99.041.940 1257,83 30/10/2006 29.520.126 366,05 03/01/2005 29/10/2007 18/12/2007 07/12/2006 14.979.417 350.748.034 11.896.902 1.142.511.590 657,6 5752,27 160,61 19536,95 53 106 SZL 107 TAC 108 TCM 109 TCR 110 111 TDH TMS 112 113 114 115 116 117 118 TNA TNC TPC TRA TRC TRI TS4 119 TSC 120 121 TTF TTP 122 TYA 123 124 125 UIC VFC VHC 126 127 VHG VIC 128 VID 129 130 131 132 VIP VIS VKP VNA 133 134 135 136 VNE VNM VNS VPK 137 VSC 138 139 VSH VTB 140 VTO Gòn Thương Tín Công ty cổ phần Sonadezi Long Thành Công ty Cổ phần Dầu Thực vật Tường An Công ty Cổ phần Dệt may - Đầu tư Thương mại Thành Công Công ty Cổ phần Công nghiệp Gốm sứ Taicera Công ty Cổ phần Phát triển Nhà Thủ Đức Công ty Cổ phần Transimex-Saigon Công ty Cổ phần Thương mại Xuất nhập khẩu Thiên Nam Công ty Cổ Phần Cao su Thống Nhất Công ty Cổ phần Nhựa Tân Đại Hưng Công ty Cổ phần TRAPHACO Công ty Cổ Phần Cao Su Tây Ninh Công ty Cổ phần Nước giải khát Sài Gòn Công ty Cổ phần Thủy sản Số 4 Công ty Cổ phần Vật tư kỹ thuật Nông nghiệp Cần Thơ Công ty Cổ phần Tập đoàn Kỹ nghệ Gỗ Trường Thành Công ty Cổ phần Bao bì Nhựa Tân Tiến Công ty Cổ phần Dây và Cáp điện Taya Việt Nam Công ty Cổ phần Đầu tư Phát triển Nhà và Đô thị Idico Công ty Cổ phần VINAFCO Công ty Cổ phần Vĩnh Hoàn Công ty Cổ phần Đầu tư và Sản xuất Việt - Hàn Tập đoàn Vingroup - Công ty Cổ phần Công ty Cổ phần Đầu tư Phát triển Thương mại Viễn Đông Công ty Cổ phần Vận tải Xăng dầu VIPCO Công ty Cổ phần Thép Việt Ý Công ty Cổ phần Nhựa Tân Hóa Công ty Cổ phần Vận tải Biển Vinaship Tổng công ty Cổ phần Xây dựng điện Việt Nam Công ty Cổ phần Sữa Việt Nam Công ty Cổ phần Ánh Dương Việt Nam Công ty Cổ phần Bao bì dầu thực vật Công ty cổ phần Tập đoàn Container Việt Nam Công ty Cổ phần Thủy điện Vĩnh Sơn – Sông Hinh Công ty Cổ phần Viettronics Tân Bình Công ty Cổ phần Vận tải Xăng dầu VITACO 09/09/2008 20.000.000 244 26/12/2006 18.980.200 882,58 15/10/2007 49.100.739 657,95 29/12/2006 44.534.533 138,06 14/12/2006 08/04/2000 37.913.039 23.073.824 477,7 634,53 20/07/2005 22/08/2007 28/11/2007 26/11/2008 24/07/2007 28/12/2001 08/08/2002 8.000.000 19.250.000 21.268.956 24.673.911 29.125.000 27.548.360 23.920.990 169,6 259,88 189,29 2072,61 1121,31 49,59 205,72 10/04/2007 8.012.915 46,47 18/02/2008 12/05/2006 59.059.713 14.999.998 301,2 405 15/02/2006 27.892.014 100,41 11/12/2007 24/07/2006 24/12/2007 8.000.000 33.801.062 60.206.163 84,8 162,25 1384,74 28/01/2008 19/09/2007 25.000.000 928.806.879 147,5 57586,03 25/12/2006 25.522.767 94,43 21/12/2006 25/12/2006 19/06/2008 09/09/2008 59.323.395 49.220.262 8.000.000 20.000.000 433,06 531,58 7,2 50 08/09/2007 19/01/2006 29/07/2008 21/12/2006 62.122.141 833.490.871 40.499.818 7.999.368 254,7 118355,7 1862,9 244,78 01/09/2008 28.646.050 1051,31 18/07/2006 27/12/2006 206.241.246 10.804.520 2804,88 109,13 10/09/2007 78.866.666 323,35 54 Appendix 2: T-Test 2 samples for means compare among 3 technical indicators and passive strategy without inefficiency aspects in case of up-trend market situation. t-Test: Paired Two Sample for Means Mean Variance Observations Pearson Correlation Hypothesized Mean Difference df t Stat P(T[...]... 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... 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... 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... 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 9 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... 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. .. 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... 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... 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... 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... 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 .. .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... recent period of time Research Objectives This study aims at exploring the effectiveness of investment strategies which are based on technical indicators, namely MACD (Moving Average Convergence... 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