VIETNAM NATIONAL UNIVERSITY – HOCHIMINH CITY INTERNATIONAL UNIVERSITY SCHOOL OF BUSINESS
TESTING THE EFFECTIVENESS OF SOME TECHNICAL ANALYSIS METHODS ON HOSE
In Partial Fulfillment of the Requirements of the Degree of BACHELOR OF ARTS in BUSINESS ADMINISTRATION
Student: DANG THI THANH THUY (BAIU09049) Advisor: DUONG NHU HUNG, Ph.D
HoChiMinh city, Vietnam 2013
TESTING THE EFFECTIVENESS OF SOME TECHNICAL ANALYSIS METHODS ON HOSE
APPROVED BY: Advisor
APPROVED BY: Committee,
________________________ Duong Nhu Hung , Ph.D
___________________________________ Nguyen Phuong Anh, Ph.D., Chair
___________________________________ Hoang Thi Anh Ngoc, MBA., Secretary
___________________________________ Duong Nhu Hung, Ph.D. THESIS COMMITTEE
Acknowledgement
I have not been able to finish the thesis without the guidance of my advisor, helps from my friends and support from my family. First and foremost, I would like to express my deepest gratitude to my advisor, Mr. Duong Nhu Hung for the continuous support for my dissertation from finding the appropriate subject to complete the process of the study step by step. From him, I learn not just how to do the research to meet the graduation requirement, but also how to face the problems and solve them intelligently. Without his patience, motivation, enthusiasm, and immense knowledge, it is impossible for me to complete the paper. Once again, I would like to thank you from the bottom of my heart for all guidance and motivation you had given to me. In addition, special thanks to all my friends with my greatest appreciation, especially Mr. Nguyen Do An Ninh, Mr. Mai Thanh Ba Huy and Ms. Duong Thi Thu Van. Although they are busy with their own works and studies, they are always a pleasure to give me a hand when I need. Thanks them for questioning about my ideals, helping me solve it reasonably and hearing when I am depressed. Thanks for teaching me how to use Matlab and write the codes also. Last but not least, I would like to express my eternal gratitude to my parents who brought me to this colorful life and were always encouraging me spiritually even though they probably could not get what the thesis is and what I do for it. Thanks for Gods because he made them to be my parents. Thanks to all!!!
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ABSTRACT
The main purpose of this paper is to investigate the efficiency of some common Technical
Analysis
method
including
Moving
Average,
Moving
Average
Convergence/Divergence and Relative Strength Index on Ho Chi Minh Stock exchange. Inspection period was eight years, between 2005 and 2012. All accumulated returns were used to identify Technical Analysis performance compared to Buy and Hold strategy. These indicators were applied on VNINDEX and portfolios which were classified by industry and firm size as well as individual stocks. About industry and firm size based portfolios, the paper conducted portfolios‟ index which were depended on Equal-Weight theory. This paper found some evidence that the investigated methods perform better in stable markets rather than emerging and fluctuating market. However, the findings strongly support MA strategies which generate the most profit, pass over trading cost challenge and market fluctuation. Moreover, it also found that shorten the computation period is more favorable. The other finding is that investors should invest in stocks regarding to the Construction and Its Materials, Basic Materials and Consumer goods and service industries or small market capitalization firms in an effort to capture excess return.
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Table of Contents Acknowledgement ............................................................................................................... i ABSTRACT ........................................................................................................................ ii LIST OF FIGURE............................................................................................................... v LIST OF TABLES ............................................................................................................ vii LIST OF ABBREVIATIONS .......................................................................................... viii CHAPTER I ........................................................................................................................ 1 INTRODUCTION .............................................................................................................. 1 1.1 Research Background ................................................................................................ 1 1.2 Rationales .................................................................................................................. 2 1.3 Objectives .................................................................................................................. 3 1.4 Scope and limitation .................................................................................................. 3 1.5 Significant and implication of the study ................................................................... 4 1.6 Organization of the Research .................................................................................... 4 CHAPTER 2 ....................................................................................................................... 5 LITERATURE REVIEW ................................................................................................... 5 2.1 Introduction to Technical Analysis ........................................................................... 5 2.1.1 Definition ............................................................................................................ 5 2.1.2 Theories related to technical analysis ................................................................. 6 2.1.3 Three premises of the technical analysis ............................................................ 8 2.2 Three Technical Analysis Methods ........................................................................... 9 2.2.1 Moving Average (MA) ....................................................................................... 9 2.2.2 Moving Average Convergence/Divergence (MACD) ...................................... 11 2.2.3 Relative Strength Index (RSI) .......................................................................... 13 2.3 Fundamental Theories ............................................................................................. 14 CHAPTER 3 ..................................................................................................................... 15
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METHODOLOGY ........................................................................................................... 15 3.1 Research approach and software application .......................................................... 15 3.2 Data collection......................................................................................................... 15 3.3 Portfolio classification............................................................................................. 16 3.3.1 Classification by industries ............................................................................... 16 3.3.2 Classification by Firm size (Market capitalization) ......................................... 17 3.4 Testing method and algorithms ............................................................................... 18 CHAPTER IV ................................................................................................................... 21 EMPIRICAL RESULTS ................................................................................................... 21 4.1 Buy and Hold Strategy ............................................................................................ 21 4.2 Effectiveness of Technical Analysis methods based on VNINDEX ...................... 24 4.2.1 Overview of the return ...................................................................................... 24 4.2.2 Technical Analysis performance ...................................................................... 25 4.3 Effectiveness of Technical Analysis methods based on portfolios classified by industry .......................................................................................................................... 38 4.4 Effectiveness of Technical Analysis methods based on portfolios classified by firm size................................................................................................................................. 41 4.4.1 Period of 2005-2008 ......................................................................................... 42 4.4.2 Period of 2009 to 2012 ..................................................................................... 44 4.4.3 Period of 2005-2012 ......................................................................................... 46 CHAPTER V .................................................................................................................... 49 CONCLUSION AND RECOMMENDATION ................................................................ 49 5.1 Conclusion............................................................................................................... 49 5.2 Recommendation ..................................................................................................... 50 LIST OF REFERENCES .................................................................................................. 52 APPENDIX ....................................................................................................................... 55
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LIST OF FIGURES Figure 1 Moving Average Buying and Selling signals ..................................................... 10 Figure 2 Moving Average Convergence/Divergence Buying and selling signals (MACD) ........................................................................................................................................... 12 Figure 3 Relative Strength Index Buying and Selling Signals ......................................... 13 Figure 4: Process of Testing the Efficiency of Some Technical Analysis Methods ......... 20 Figure 5: Accumulated Return on Investment using Buy & Hold Strategy without Considering Transaction Cost ........................................................................................... 22 Figure 6: Accumulated Return on Investment using Buy & Hold Strategy with Considering Transaction Cost (T= 0.25%) ....................................................................... 23 Figure 7: MA-5 Performance in Comparison with Buy and Hold Strategy without Considering Transaction Cost ........................................................................................... 26 Figure 8: MA-5 Performance in Comparison with Buy and Hold Strategy with Considering Trading Cost ................................................................................................. 27 Figure 9: MA-10 Performance in Comparison with Buy and Hold Strategy without Considering Transaction Cost ........................................................................................... 29 Figure 10: MA-10 Performance in Comparison with Buy and Hold Strategy with Considering Transaction Cost (T=0.25%) ........................................................................ 30 Figure 11: MA-15 Performance in Comparison with Buy and Hold Strategy without Considering Transaction Cost ........................................................................................... 31
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Figure 12: MA-15 Performance in Comparison with Buy and Hold Strategy with Considering Transaction Cost (T=0.25%) ........................................................................ 32 Figure 13: Results of MACD Investment Strategies versus Buy and Hold Strategy without Considering Transaction Cost.............................................................................. 33 Figure 14: Results of MACD Investment Strategies versus Buy and Hold Strategy with Transaction Cost (T=0.25%)............................................................................................. 34 Figure 15: Results of RSI Investment Strategies versus Buy and Hold Strategy without Transaction Cost ............................................................................................................... 35 Figure 16: Results of RSI Investment Strategies versus Buy and Hold Strategy With Considering Transaction Cost (T=0.25%) ........................................................................ 37 Figure 17: Results of MA and RSI Strategies of Firm Size Portfolios (2005-2008) ........ 44 Figure 18: Results of MA and RSI Strategies of Firm Size Portfolios (2009-2012) ........ 46 Figure 19: Results of MA and RSI Strategies of Firm Size Portfolios (2009-2012) ........ 48
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LIST OF TABLES Table 1: Industries Based Portfolios ................................................................................. 17 Table 2: The Accumulated Return of Trading in All Strategies ....................................... 24 Table 3: Results of MACD and RSI investment strategies on 9 industries portfolios ..... 38 Table 4: The result of MA investment strategies on 9 industry portfolios ....................... 40 Table 5: Results of MACD and Buy and Hold of Firm size portfolios (2005-2008) ....... 43 Table 6: Results of MACD and Buy and Hold of Firm size portfolios (2009-2012) ....... 45 Table 7: Results of MACD investment strategies of Firm size portfolios (2005-2012) .. 47
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LIST OF ABBREVIATIONS
MA
Moving Average
MACD
Moving Average Convergence/Divergence
RSI
Relative Strength Index
SMA
Simple Moving Average
EMA
Exponential Moving Average
EMH
Efficiency market hypothesis
ROR
Rate of return
S1
Firm size portfolio 1
S2
Firm size portfolio 2
S3
Firm size portfolio 3
S4
Firm size portfolio 4
S5
Firm size portfolio 5
HOSE
Ho Chi Minh Stock Exchange
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CHAPTER I
INTRODUCTION
1.1 Research Background As consciousness of capital needs‟ significance that affects to development on industrialization and modernization in Vietnam, Ho Chi Minh Stock Exchange (HOSE) was established in July 2000 at Ho Chi Minh City as indispensable tendency to achieve in international integration process. Truthfully, trading in stock market brings the country numerous benefits not only in trade but also in whole of society. In generally speaking, our stock market has been considered as grow up positively, contributing to attract foreign investors and encouraging domestic ones, who expect to earned high profits. However, high expected return is constantly accompanied with high risk even though which kind of investments they choose. Investing in stock market is huge risk because of its big profits which not only come from receiving stock dividends but also buying and selling stock. It looks like a gamble that makes you able to become a billionaire or leave with an empty purse. VNINDEX has passed through 1100 points in the first quarter„s 2007 and dropped significantly below 300 in 2009 as an illustration. Correspondingly, securities analysis, an examination and assessment of economic and market trends, earnings prospects, earnings ratios, and various other indicators and factors to determine suitable investment strategies, is a prerequisite for accomplishment. This awareness is as
1
well reasonable to apply for Vietnam stock market; I hopefully could be testing its effectiveness of some technical analysis methods, some of the most popular security analysis methods in HOSE.
1.2 Rationales Up to now, the term of securities analysis is not peculiar in our stock market, especially after period of 2007- 2008, time of global financial crisis. This period gave the appeal to open the eyes of investors who did not have cautious preparation as well as carefully securities analysis, and enhance perception on importance of securities analysis in order to reduce risk as low as reasonably practicable. Fundamental analysis method is a widely used tool of evaluating securities to determine intrinsic value of shares in the market. Basing on analyzing overall economy, industry, and company private information including financial statement, business activities, and valuation of brand name, micro and macroeconomic factors which have a huge influence on stock price movement, thus investor can have potential investing decision to earn profit and reduce rate of risk. Besides, technical analysis is also a widespread method likewise efficient tool that provides investor information about directions of stock price and trends in future through charts and technical indicators by using historical data. This method brings to users numerous advantages because of some reasons. Firstly, its data requirement including stock price and volume is much less than fundamental analysis, easier to approach, and has higher degree of preciseness. Furthermore, its directions give traders market trend or when they need buy or sell to capture profits, not intrinsic value. Eventually, technical analysis is still not dissemination of intelligence in Vietnam and is considered as inappropriateness with the emerged, imperfectly and dramatically fluctuated market as in
2
Vietnam. That is the reason to conduct the research “Testing the effectiveness of some technical analysis methods in HOSE”.
1.3 Objectives The main aim of the research is to analyze and discuss some common technical analysis methods when applying in HOSE. The investigated methods in the research are Moving Average (MA), Moving Average Convergence/Divergence (MACD), and Relative Strength Index (RSI), momentum indicators in that kind of investment analysis. Moreover, the research is carried out in order to determine the most profitable methods for investors in HOSE (if any).
1.4 Scope and limitation Firstly, the research focuses only on data of VNINDEX and 265 stocks that listed on HOSE, not whole Vietnam stock market. Moreover, technical analysis is a large field in the financial industry and has many methods. Due to the limit of time for the research, only three popular technical analysis methods including Moving Average, Moving Average Convergence/Divergence, and Relative Strength Index are analyzed and tested. Finally, the study tests the profitability of some technical methods for VNINDEX and companies listed before 2012. However, data are collected from 2005 to 2012 since the characteristic of the technical analysis approach – basing on stock volume traded in HOSE.
3
1.5 Significant and implications of the study This study will be a significant endeavor for the enhancement of the investment strategies by using technical analysis. The study will suppose knowledge to determine its strengths and weaknesses and use these as instruments to find out the profitable methods imposed within HOSE. Likewise, the investors might take advantages of the findings to understand market trend as well as propose a feasible investing plan to satisfy expected return. Eventually, another significance of this paper is to bring us an ideal chance so as to review theories and learn more about the financial investing.
1.6 Organization of the Research The research is divided into 5 chapters: Chapter I: Introduction exhibits the background and rationales to conduct the research. The scopes and limitations are also mentioned in the chapter. Chapter II: Literature review summaries the theories that related to the study subject, technical analysis method, and some relevant study that have been conducted before. Chapter III: Methodology describes models and process to complete the research such as which methods have been used, how to collect data and construct portfolios… to reach research objectives Chapter IV: Result of study illustrates the findings of the paper. Chapter V: Conclusion and Recommendation summaries the results and recommend to who stick on this topic and would like to develop in further research.
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CHAPTER 2
LITERATURE REVIEW
2.1 Introduction to Technical Analysis 2.1.1 Definition Technical analysis (TA) is a method of evaluating securities by analyzing the statistics generated by market activity, such as past prices and volume. Technical analysts do not attempt to measure a securities‟ intrinsic value as the fundamental, but instead use charts and other tools to identify patterns that can suggest future activity. Technical analysts think that future price of stock could be forecasted accurately by analyzing historical prices and other trading variables. They believe that market psychological factor influences trading volume as stock price movement. Brown and Jennings (1989) found that technical analysis has value in a model in which prices are not fully revealing and traders have rational conjectures about the relation between prices and signals. Brock, Lakonishok and LeBaron (1992) analyzed 26 technical trading rules during 90 years (1897-1986) of daily stock prices from the Dow
5
Jones Industrial Average and found that they earned money on the stock market. Lo, Mamaysky and Wang (2000) examined the effectiveness of technical analysis on US stocks from 1962 to 1996 and proved that over the 31-year sample period, several technical indicators supported to investment strategy. Furthermore, Korn (1996), Neely and Weller (1998) and Cooper (1999) conducted many study that proved the profitability of technical analysis. In the textbook “Technical analysis explained”, Pring (2002) provided a specific definition: “The technical approach to investment is essentially a reflection of the idea that prices move in trends that are determined by the changing attitudes of investors toward a variety of economic, monetary, political, and psychological forces. The art of technical analysis, for it is an art, is to identify a trend reversal at a relatively early stage and ride on that trend until the weight of the evidence shows or proves that the trend has reversed.” (p. 2) In generally speaking, the method can be applied in any type of securities including stock, future commodity, bond, foreign currency trading… that have history trading data. However, the thesis focuses on technical analysis on Vietnam stock market, especially on Ho Chi Minh Stock Exchange. Readers who really interested in technical analysis method should reference in textbooks written by Murphy (1986), Pring (1991), or Elder (1993).
2.1.2 Theories related to technical analysis 2.1.2.1 Dow Theory Any attempt to trace the origins of technical analysis would inevitably lead to Dow Theory. While more than 100 years old, Dow (1851- 1902) is known as the founding father of what we know today as technical analysis and company “Dow Jones financial
6
information service” that found out ability to predict the future by past prices. The basic theory was written on “Wall Street Journal” with his belief that the behavior of the averages reflected in the entire market. After his death, in 1902, Hamilton has continued to develop the theory. In 1922, his ideal was developed by publishing a book named “The Stock Market Barometer”. However, technical analysis that is used nowadays that was improved by Rhea with a book titled “Dow Theory”.
2.1.2.2 Efficiency market hypothesis (EMH) and Random Walk Efficiency market hypothesis has been introduced by Fama (1965) is considered as one of the most important theories of finance. An investment theory that states it is impossible to "beat the market" because stock market efficiency causes existing share prices to always incorporate and reflect all relevant information. To put it another way, all information may affected price immediately by efficiency market. In EMH point of view, stocks always trade at their fair value on stock exchanges, no potential chances for investors to either purchase undervalued stocks or sell stocks for inflated prices. Therefore, it can be impossible to outstanding the overall market through expert in stock selection or market timing, and a sole way to reach the higher return is by preferring to riskier investments. Levels of market efficiency according to the different response rate that price is affected by information, include three categories: weak, semi-strong and strong. The weak form of EMH argues that investor could not capture the potential profit above buy and hold strategy using any trading rule which just based on historical information. It also means that technical analysis is inefficient and worthless. Jensen (1978) said that efficient market in aspect of information could not generate economic profits basing on information trading rule. However, Brock et al. (1992), in an extensive survey of the literature, reports studies which shows other phenomena inconsistent with the efficient markets hypothesis
7
(hereafter EMH). Guidi and Gupta (2011) found that the stock markets of Indonesia, Malaysia, the Philippines and especially Vietnam are not weak form EMH based on their statistical analysis and advices to reject it for further studies. Fama (1965) also introduced Random Walk hypothesis as a part of EMH. The theory states that the market is efficient and stock price is out of control of historical date. It means that stock price can move any way when they started.
2.1.3 Three premises of the technical analysis Neely mentioned about three premises that guide the market behavior that is outlined by Murphy (1986) and Pring (1991) in his research “Technical Analysis in the Foreign Exchange Market” 1. The Market Discounts Everything In the first premise, the central view of technical analysis concentrates on the price fluctuations, not factors regarding to fundamental analysis of the company. Because, the method assumes that, at any given time, price reflects its value, related to company information involving fundamental factors. Technical analysts believe that company activities, along with other economic factors as well as market psychology, have been transformed into the stock price. In another words, the study of price action is all that is required (Murphy, 1999). 2. Price Moves in Trends This premise is trends created by price fluctuation. This is an essential factor in technical analysis approach because of predictive capability in order to earn money by buying low and selling high when trend has a reversal signal. In other words, historical price created
8
a trend that future price seems to be in the same direction instead of the others. The second assumption can be seen through almost strategies approaching technical analysis
3. History Tends To Repeat Itself The final premise is that history will always repeat itself, as the form of stock price movement. It can be explained that mass psychology stimulates the fluctuation. When takes part in investing, investors would have different ways to adapt the market with other situations. However, standing in contrast way, at the same market status, they will tend to repeats consistently as in the past. Pring (1991) pointed out that it is able to examine some human characteristics that can help investor recognize how market reacts to realistic situations.
2.2 Three Technical Analysis Methods 2.2.1 Moving Average (MA) Moving average (MA) based trading systems are the simplest and most popular trendfollowing systems among practitioners (Taylor and Allen 1992; Lui and Mole 1998). MA is an indicator that shows trend of stock price, not price direction, over given period time. MA is calculated by past price; hence, moving averages and securities are going up and down belong to stock price, but with a lower speed. It probably causes that someone call MA with another name, trend follower. There is a serious problem associated with MA is that it could be faced with a numerous of trading signals because of its sensitivity regarding to short time computation period. On contrary, if length of computing period is too long, MA will be insignificant due to
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react slowly, called “lag effects”. A MA lags and time of periods is inversely proportional to the sensitivity and accuracy. In other words, increasing time of period increase lag effects, along with decreasing sensitivity and predictable ability. Many researchers suggest using filter rule in other to solve the problem. Fama and Blume (1966), and Sweeney (1988) shows that investors could capture identified excess opportunities using filter rule. The figure 1 below shows the way to determine signals. Buying signals is generated whenever stock price increases above moving average and selling signals is generated in opposite site, when stock price decreases below moving average. It can be seen clearly that MA in long term period fluctuate slightly and generate less trading signals than short term one.
Figure 1 Moving Average Buying and Selling signals
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There are five popular types of moving averages: simple (also referred to as arithmetic), exponential, triangular, variable, and weighted. Simple Moving Average (SMA) and the Exponential Moving Average (EMA) are the most popular ones.
Simple Moving Average (SMA) ∑
Where: n = number of periods in MA
Exponential moving average (EMA) EMA = ((Pt- EMA t-1) * k) + EMAt-1 Where:
2.2.2 Moving Average Convergence/Divergence (MACD) One
of
the
most
popular
indicators
nowadays,
the
Moving
Average
Convergence/Divergence (MACD) is originated by Appel in 1979 as a trend following momentum indicator that shows the relationship between two indicators that generated by exponential moving averages (EMA). The first indicator is lag indicator that is calculated by subtracting the slow EMA from the fast one in which slow means long period and fast stands for shorter time. Appel recommends to an EMH 12 as the fast one and the remainder is 26 that means slow. Then take the results of (EMA12-EMA26) is the first curve called MACD. A positive MACD indicates that the 12-day EMA is greater than the 26-day EMA; vice versa a negative MACD value indicates that the 12-day EMA value is smaller than the 26-day EMA value. The second curve is called signal line (the trigger line) that results are absolutely
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based on MACD because signal line is once again applying EMA-9 on MACD data, instead of historical stock price. Similar to MA method, MACD is also impacted by characteristics of lag indicators, which means that trading signals are created after upward or downward trend in price fluctuation. It means the traders could buy or sell stock, but after that it goes in opposite side of their expectation. In finance theory, it is called “problem of whipsawing”. Figure 2 shows us how to determine buying and selling signals by MACD as an illustration. The points of crossover between the lag indicators and signal line create the buy and sell signals. When the MACD is an increase and crosses above the signal line it is interpreted as a buying signal. A selling signal appears when the MACD decreases and crosses below the signal line.
Figure 2 Moving Average Convergence/Divergence Buying and selling signals (MACD)
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2.2.3 Relative Strength Index (RSI) The Relative Strength Index (RSI) is a popular oscillator and developed by J. Welles Wilder (1978) and published in his book titled “News Concepts in Technical Trading System”. In RSI point of view, investor can use it to determine stock status is overbought or oversold by comparing current stock price and its own previous price movement. The values of RSI have range of 0 to 100. As Wilder suggested, investment decisions is usually considered overbought if RSI rises above 70 and oversold when it drop below 30. Besides, alternative ways to analysis data can use the values of 20 or 40 for overbought status and 60 or 80 for oversold. (Murphy, 1986) Another element that influence directly to the success of the method is length of computation periods. Signals that are generated by RSI become more sensitive and aggressive when the period is shortened. However, shortening the computation period will generate more number of signals, which will increase the possibility of false signals also. On other hand, the fluctuation will more slightly when we increase length of computation period. RSI-14 is the most common computation period that is also suggested by Wilder.
Figure 3 Relative Strength Index Buying and Selling Signals
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When Wilder introduced the RSI, he recommended using a 14-day RSI as figure 3, selling when RSI is above 70 and buy when it is below 30. Since then, the 9-day and 25day RSIs have been proved its predictive power by many previous researchers. Therefore, I suggest that you should investigate with various computation periods to find the period that works best for your market status. According to Wilder, the value of RSI is calculated as the formula below:
Where: U= an average of upward price change D= an average of downward price change 2.3 Fundamental Theories + Stock return is profitability from investing on stock market ROR = (P current –P buying)/ P buying + Firm size (market capitalization) Firm size = number of share outstanding * P current + Portfolio index is used to estimate its performance in general. There are 2 ways to get this index including value-weighted and equal-weighted. In this paper, we assume to stocks‟ weight is equally that is proved its outperformance by Plyaha, Uppal and Vilkov (2012). Portfolio index = Average rate of return * Index (t-1) Note: The assumption of all portfolio index at t= 0 is 100.
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CHAPTER 3
METHODOLOGY
3.1 Research approach and software application The process of the study will substantially establish on quantitative method to investigate the profitability of technical analysis methods, in which all analysis will be based on HOSE historical data such as stock price and VNINDEX that available on the internet. However, to ensure the study‟s robustness as its result, information must be considered cautiously. The study uses MATLAB programming software, and Microsoft Excel to check buying and selling signals, imitate transaction, constructs portfolio and work out rate of return by algorithms. Return of portfolios corresponding to each method is based on the returns of the stocks in the portfolio.
3.2 Data collection The study will collect VNIDEX and closing price of 265 public‟s companies listed on HOSE will be collected and considered carefully. Moreover, the thesis will take relevant
15
data as addition companies from 2005 to 2012 to do the analysis. Before 2004, just a few stocks are listed on HOSE, the noise on the stage is large and affected to price movement could make wrong analysis. In the period of 2012, the study will collect data from companies which listed on HOSE before 2012 so as to warrant sufficiently large trading volume. However, data have just been collected in the next year after listed on HOSE except that is listed at the begin of a year. All data collected is used for analyzing in the period of 2005 to 2012.
3.3 Portfolio classification VNINDEX is conducted by all stocks listed on HOSE. It also means that it is a large portfolio and quite otherwise in realistic investments. Obviously, nobody is willing to hold stocks of all listed firms in order to gain profits. Therefore, based on investors‟ point of view, they will consistently consider many factors that directly impact on rate of return to capture economic profits. Far from realistic situations, this thesis will conduct portfolios which base on 2 factors involving industry, market capitalization to reach objectives.
3.3.1 Classification by industries Portfolios analysis based on industries is a familiar concept with financial investors. Some researchers argue that it belongs to fundamental analysis rather than the technical. However, as mentioned in previous sections, technical analysis is conditional upon to investor psychology, and industries are one of impact factors. There are no standard of classification by industries and still exist companies that do business on many industries. Hence, the study will restructure portfolios based on this criterion: divided into small industries if it is too large and merged the similar industries in order to reduce gaps
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between each group. The results showed in table 1. (List of companies in each industry is shown in Appendix C)
Table 1: Industries Based Portfolios
Industry Industry
Number of company
ID 1
Real Estate
42
2
Finance, banking, and oil
17
3
Construction and Its Materials
31
4
Transportation
20
5
Industry & Technology
26
6
Foods and Beverages
36
7
Consumer Goods and Service
36
8
Basic Material
31
9
Others (Public service, Healthcare, Medicine and others)
26
3.3.2 Classification by Firm size (Market capitalization) Companies are classified according to market capitalization in which influences directly on stock return. The study will calculate these values at beginning of each year, rearrange to portfolios until 2012, finishing analysis process and sorted in increasing its value order in 5 portfolios in terms of S1, S2, S3, S4, and S5.
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3.4 Testing method and algorithms Respecting to investigate each technical analysis method, Matlab software is used to reproduce trading transactions whenever buying or selling signals appear and measure daily accumulated rate of return of all listed stock on HOSE. Each of these outcomes from finding return on VNINDEX and portfolios is compared with Buy and Hold strategy – long term and passive investment plan that hold all of stocks until the end – in order to examine technical analysis performance on HOSE. The first test will estimate rate of return on VNINDEX using three methods, MA, MACD and RSI in both cases of ignoring transaction cost and imposing a fix rate of 0.25%. Continuously, the study will also test with the similar methods to test its profitability in each portfolio. Because of stimulating realistic circumstances, the study is only considering trading fee (transaction cost) is 0.25% for the whole. When testing period started, technical methods would operate upon the first signal, different with Buy and Hold- buying shares instantaneously. Based on the signals, Matlab programing will stimulate trading activities to get the accumulated return. For simplicity, all of investment money and gains would be reinvested in the next trading until the end.
MA: In technical analysis, Moving Average is considered as the simplest investment strategy. Because of the thesis limitation, there is only simple moving average that is measured in this section. Initially, the process will be started with different computation periods which were 5 days, 10 days, and 15 days. With each MA, they are classified into 3 small groups in which filters equal 0%, 1.5% and 3% respectively. That means all in all, 9 different investment strategies including MA-5-0, MA-5-1.5, MA-5-3, MA-10-0, MA-10-1.5, MA-10-3, MA-150, MA-15-1.5 and MA-15-3 were observed to discover which profitable trading methods are.
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MACD: MACD is the second technical indicator that is analyzed and discussed. Except for using standard setting periods – 12-26 days, 8-17 and 18-35 will be applied to get the optimized MACD on HOSE. The paper believe it is the worth carrying out in sample testing by Matlab software before trading in real life.
RSI: Finally, RSI would be analyzed and discussed in this part. RSI strategies will have three computation periods in terms of RSI-9, RSI-14 and RSI-25. For simplicity, the paper used only one level of RSI which are 30-70. RSI return which is stimulated by MATLAB software is compared with Buy and Hold strategy to test its performance.
CALCULATION PROCESS: Step 1: Determine buying and selling signal in which method is considering Step 2: when buying signal appears, buying stock at P0 at t0 by initial investment in money account, A=VND 1. Therefore, money available in A is 0 and stock account B = A/ P0. Stocks return ROR 0 = Pt *B -1 Step 3: When selling signal appears at Pn, MATLAB will imitate selling process as getting money. B = 0 and A = B * Pt Stock return ROR n= A – 1 Step 4: step 2 and step 3 will act again until period of analysis. In case of including trading fee T, + Buying signals: ROR n = B*(1– T)/P n + Selling signals: ROR n = A* P n * (1 – T)
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Algorithmic programming
Raw data
Calculation
False Next trading day
Checking buying and selling signals
No signal: Calculate return or risk
Buying signal: Calculate return or risk
Selling signal: Calculate return or risk
Finished time of analysis
True Rate of return with no transaction cost
Rate of return with transaction cost T= 0.25% Figure 4: Process of Testing the Efficiency of Some Technical Analysis Methods
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CHAPTER IV
EMPIRICAL RESULTS
4.1 Buy and Hold Strategy Buy and Hold is defined by Warren Buffet as passive investment strategy in which an investor buys undervalued stocks and hold for an extended period of time, without considering fluctuation in the market. This section will calculate the return on investment based on assumption in which stock is bought stock immediately at the beginning and sold at each later trading day, while technical analysis methods based on trading signals. Therefore, in the study, the presence of trading cost would deduct T = 0.25% from initial investment and the return as well. Consequently, the results are employed as a comparable benchmark that will support to critically verify of the performance of trading results in later sections. The figure 5 exhibits return on investment with VND 1 initial investments by using Buy and Hold strategy without considering transaction cost during investment timeline, from 2005 to 2012. During the period from January 2005 to end of 2007, there was a dramatically increase approximately fivefold compared with the beginning. Nevertheless, globe financial crisis has significantly affected Vietnam financial market as well as Ho Chi Minh stock exchange. Moreover, Vietnam stock market has just been emerged,
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imperfectly and erratically fluctuated. It had a horrible impact on stock price and took almost profit away. To put it another way, investor placed the money during four year and it did not gain anything. The World economy has come into its rehabilitate stage from the crisis, Vietnam stock market has been more mature and stable in addition, that is supposedly cause of increasing rapidly in 2009 and earned half as much again the beginning. However, after being dramatic increase, there was a steady downward trend and end with about 74% rate of return. 6 VND 5 4 3
Buy&Hold with T=0%
2 1 -
Figure 5: Accumulated Return on Investment using Buy & Hold Strategy without Considering Transaction Cost
In addition, Accumulated return based on Buy and Hold strategy in case of considering 0.25% trading fee is indicated in figure 6 of the study in order to enhance its reliability and practicability. The trend of the graph is quite similar to the previous one. The difference due to trading fee induced the investor to hold fewer shares than the other. In comparison with the strategy in figure 1, this strategy earns less profit when stock price goes up, whereas when the price fell, it losses less money and compensate for the less
22
stocks holding. The gaps between 2 figures are a little because the trader spent money on trading cost only 2 times, at beginning and selling stocks.
VND
6 5 4 Buy&Hold with T=0.25%
3 2 1 -
Figure 6: Accumulated Return on Investment using Buy & Hold Strategy with Considering Transaction Cost (T= 0.25%)
Overall, these figures represented accumulated return on investor‟s account during investment timeline ranged from approximately VND 1 to VND 5. In my personal point of view, despite of level of earning, the Buy and Hold strategy is deeply hazardous because of its fluctuation and likely descending gradually in the future. The thesis will utilize its returns to compare with the orders technical methods for evaluating theirs profitability. The benchmarking comparison will accommodate as the main appliance in a step by step process of examining TA methods in which they can beat the market or not.
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4.2 Effectiveness of Technical Analysis methods based on VNINDEX 4.2.1 Overview of the return In general, all of return that generated by signals of 15 technical analysis methods contribute to higher amount than initial investment, VND 1. MA-5-0 was the most profitability if trading cost was ignored. On other hand, with imposing trading cost, MA10-0 was superior to the others. The accumulated return of each strategy compared to Buy and Hold can be seen evidently in table 2, in which 25 of 30 testing methods outperformed the Buy and Hold strategy in both situations. Other noticed point is influence of trading cost to investment outcomes. To illustrate, the return generated on MA-5-0 strategy declined about 182% when trading cost is imposed. Table 2: The Accumulated Return of Trading in All Strategies NO TRADING FEE
TRADING FEE= 0.25%
ACCUMULATED RETURN
RANKING
ACCUMULATED RETURN
RANKING
MA-5-0
27.28
1
9.03
7
MA-5-1.5
14.32
4
9.79
6
MA-5-3
5.55
9
4.69
9
MA-10-0
21.99
2
11.05
1
MA-10-1.5
12.89
5
9.81
5
MA-10-3
12.51
7
10.83
2
MA-15-0
18.27
3
10.45
3
MA-15-1.5
12.53
6
10.08
4
MA-15-3
10.08
8
8.78
8
MACD-8-17
2.34
12
1.22
12
MACD-12-26
0.71
15
0.28
15
MACD-18-35
0.37
16
0.06
16
RSI-9
5.04
10
1.97
10
RSI-14
3.18
11
1.44
11
RSI-25
1.12
13
0.52
14
Buy and Hold
0.74
14
0.74
13
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4.2.2 Technical Analysis performance 4.2.2.1 Moving Average strategies Analysis of MA method for 5 days average MA-5 is the shortest computation period and the most sensitivity against MA-10 and MA-15. The first comparison of MA method is MA 5 day average in case of no transaction cost with various filters, 0%, 1.5% and 3% respectively. Figure 7 shows that, without trading cost, it can be seen apparently that theirs performance bear quite resemble movements to Buy and Hold in first three year, excluding pre-financial crisis period in 2007. At that time, the strategy without the filter seemed to have a bit higher in return than the others. Then, while the market is impacted negatively by financial crisis, investing by these methods makes a big profit to investor instead of a dramatic decrease as Buy and Hold strategy (Figure 5). This period looks like an influential turning point for varying each small group return in next periods. From 2009 onwards, the return based on these MA methods rose significantly and earned numerous times as much as the benchmark. It is not too difficult to distinguish that increasing in filter proportion is cause of descending on the return to investor. The descending is because proportion of filter reduced the number of trading stocks.
25
VND
30 25 20 MA-5-0%
15
MA-5-1.5%
10
MA-5-3% Buy & Hold
5 0 -5
Figure 7: MA-5 Performance in Comparison with Buy and Hold Strategy without Considering Transaction Cost
When considering the trading fee, the return of all three methods has dropped down significantly (Figure 8). The most declined was MA-5-0% that cut off two-third accumulated return against case of non-trading fee above. Indeed, trading cost is a real challenge of investors to reach expectation. A deep analysis of Figure 8, in the period of 2005 to 2008, all three investment strategies are quite same with case of non-trading fee, but they were inferior to Buy and Hold, particularly when the passive strategies rocketed considerably in the first half of 2007. In next 2 years, MA with 3% filter had not perform as well as the others, even it brought stock trader profit more than fourfold compared with Buy & Hold. The return based on two others methods have surged approximately, especially filter 1.5% strategy. From 2011 onwards, filter 1.5% had continuing grew and reach the highest return. At the end, the strategies with filter 1.5% outperformed about nine-fold as much as Buy and Hold.
26
14 VND 12 10 8
MA-5-0% MA-5-1.5%
6
MA-5-3% 4
Buy and Hold
2 0 -2
Figure 8: MA-5 Performance in Comparison with Buy and Hold Strategy with Considering Trading Cost (T= 0.25%)
The finding is probably feasible for investors who do not believe that the appropriate level of filter could generate a good performance, filter 1.5% and 3% are typical instances. Based on the theory, filter can be expressed as the percentage that divides difference of stock price and MA generates over a given period of time by the current price. Therefore, dealer could be faced a great number of trading signals, even though some of them were probably wrong or its profits were not adequate to make up for trading cost if the filter is much too small or equals zero, and whereas it can cause of skipping chances to trading efficiently when applied high level of filter. It can be thought out that searching for the approximate level of filter and number of days to compute average are equally important.
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In sum up, all of 6 MA strategies outperformed the Buy and Hold strategy almost investment process. When not considering transaction cost, MA method for 5 days with 0% filter is superior to filter 1.5% or 3%. Besides, it shows that with imposing trading cost, MA method with filter 1.5% reached its highest point. Another point has noticed that trading cost was deducted the compounding return considerably, while reducing in Buy and Hold was merely a little.
Analysis of MA method for 10 days average In this section, MA method will be analyzed by increasing to 10 days average. As the previous section, no considering transaction cost will be mentioned first. Figure 9 represents how the return is accumulated during investment timeline. Throughout the figure, all three investment strategies are indicated that they have increased during the time. From 2005 to 2008, they went up gently and seemed to overlap each other. After moving together for four years, MA with 0% filter had a dramatic increase and separated from 2 others even though they were raised well. These trends have continued until end of 2012. This result shows that MA for 10 days with applied 0% filter captures the most profit rather than 1.5% and 3% filter in spite of the fact that they had, in general, better results than Buy and Hold.
28
30 VND 25 20 MA-10-0%
15
MA-10-1.5%
10
MA-10-3% Buy and Hold
5 0 -5
Figure 9: MA-10 Performance in Comparison with Buy and Hold Strategy without Considering Transaction Cost
Figure 10 demonstrates the return of MA-10 strategies and Buy and Hold benchmark when investment is imposed trading fees. In the early stage, applying filter 3% seemed to bring out the best execution. However, everything changed after financial crisis in 2008. All of strategies have increased moderately for next 2 years after the crisis, particularly competition to reach a peak of filter 0% and 3% strategies. The competition had continued until end of investment process, and filter 0% strategies seemed to take more advantages than the others and end up as the most profitable strategies. To remark the disturbance of superior strategy in each stage, the study is based on Buy and Hold figure that represents the process of developing as well as the actual status of Vietnam stock market to interpret. The testing timeline is long that included many economy situations as developing, crisis, recovering…Because of emerged, imperfectly and erratically fluctuated characteristics, in early period, VNINDEX has increased considerably, and then it has declined rapidly and returned to its real value. Filter 3% strategy required high level of trading condition, 3% difference of MA and current stock
29
price. For this reason, trader had chances to hold stock longer and sell with higher price. Afterwards, the market has gone into rehabilitation period and more mature. Big differences in price had rarely been happened because of slightly fluctuations. Therefore, filter 3% was not maintained its superior against filter 0% even if it accumulated more return, but a little, in previous stage. 14 12 10 8
MA-10-0%
6
MA-10-1.5% MA-10-3%
4
Buy and Hold
2 0 -2
Figure 10: MA-10 Performance in Comparison with Buy and Hold Strategy with Considering Transaction Cost (T=0.25%)
In brief, without considering the trading fee, MA-10-0 is the optimal parameter that created more trading activities. With imposing a trading cost, optimal strategy had changed at each stage of the process. Therefore, we could not conclude in consistently what superior strategy was. However, all of 6 strategies outperformed the Buy and Hold in general.
30
Analysis of MA method for 15 days average In final part, MA method increases number of periods to 15 days in order to compare with the benchmark. Figure 11 illustrates return of MA-15 investment strategies and Buy and Hold without considering transaction cost on the same testing period. In general speaking, all three strategies outperformed Buy and Hold in the whole time. For four first years of investing process, both filter 0% and 1.5% strategies have been performed better than filter 3%. After escaping crisis and stepping in stable period, all of three strategies‟ returns have gone up significantly. The trend is quite similar with case MA for 5 days without considering a transaction cost, increasing in filter proportion is cause of reducing return on investment. On other words, the returns of 0%, 1.5% and 3% was descended in other. 25 VND 20
15
MA-15-0% MA-15-1.5%
10
MA-15-3% Buy and Hold
5
0
-5
Figure 11: MA-15 Performance in Comparison with Buy and Hold Strategy without Considering Transaction Cost
Similar to the last figure, Figure 12 indicates a rate of return using MA-15 strategies and Buy and Hold, but in the case of considering the trading cost. Overall, all three strategies
31
of MA for 15 days average (Figure 12) have significantly outperformed Buy and Hold. However, the figure has changed distinctively before and after 2009. From 2009 backwards, filter 1.5% performed as the most profitable strategy; filter 3% was ranked second; filter 0% was the other. After that, filter 0% was jumped suddenly and came over both filter 1.5% and 3% strategies respectively. It can be concluded that applied appropriate filter brings more benefits to investors in the period of fluctuated and instable market. 14 VND 12 10 8
MA-15-0%
6
MA-15-1.5% MA-15-3%
4
Buy and Hold
2 0 -2
Figure 12: MA-15 Performance in Comparison with Buy and Hold Strategy with Considering Transaction Cost (T=0.25%)
In short, their results indicate that MA-15-X strategies outperformed the Buy and Hold strategy. In accordance with both cases, considering trading cost and not, MA-15-0% seemed to the most profitable methods in this section.
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4.2.2.2 Moving Average Convergence/Divergence strategies Figure 13 exhibits the accumulated returns in terms of all three MACD strategies and Buy and Hold. Overall, all of three parameters strategies including 8-17, 12-26 and 18-35 lead to positive returns, particularly MACD-8-17 which accumulated the highest rate of return ( about 234%). However, the accumulative return against the Buy and Hold strategy can be seen that was not really feasible. From 2005 to 2008, Buy and Hold strategy outperformed considerably the MACDs particularly in 2007. During the period, MA-8-17 was better than 2 others even though the differences were not big. After the period of instability and fluctuate market, the returns have been going up gradually, but MA-8-17 rose faster to apart from others as the optimized strategy. Its advantage has been maintained up to the end of the investment with about VND 3.5 on hand. Therefore, the study can be based on these evidences to prove that MA-8-17 is the optimized MACD parameter. Nonetheless, we cannot be reasoned to conclude that the MACD outperformed Buy and Hold. 5 VND 4 3
MACD-8-17 MACD-12-26
2
MACD-18-35 1
Buy and Hold
0 -1
Figure 13: Results of MACD Investment Strategies versus Buy and Hold Strategy without Considering Transaction Cost
33
Similar to MA method testing, MACD also imposed trading cost in order pass over real challenge (Figure 14). Because MACD is not applied filter to put out the “noise” and reduce transactions, all the returns have been dropped significantly against absence trading cost case. Obviously, MACD strategies had not outperformed Buy and Hold almost the time. On other words, only MACD-7-18 surpassed Buy and Hold about only 2 last year investment. 5 VND 4 3
MACD-8-17 MACD-12-26
2
MACD-18-35 Buy and Hold
1 0 -1
Figure 14: Results of MACD Investment Strategies versus Buy and Hold Strategy with Transaction Cost (T=0.25%)
In summary, MACD strategies could likely generate profit, but they were obviously underperformed Buy and Hold. Moreover, shorten computation period had more opportunities than long period. As we have seen, MA-8-17 was absolutely superior to the others even though the stock market was stable or fluctuated.
34
4.2.2.3 Relative Strength Index strategy Initially, Figure 15 shows the results of RSI investment strategies and Buy and Hold strategy. All in all, RSI strategies are superior to Buy and Hold excluding the period of 2007. It can understand without a doubt because Buy and Hold increased dramatically in the stage. Before 2009, long computation period in term of RSI-25 had performed as the most profitable method. After that, strategies based on short and mediate computation periods had increased significantly with the greatly compounding returns. Finally, RSI-9 achieved the highest position at the end of 2012 with about 5 times over initial investment. 6 VND 5
4 RSI-9
3
RSI-14 RSI-25
2
Buyand Hold 1
0
-1
Figure 15: Results of RSI Investment Strategies versus Buy and Hold Strategy without Transaction Cost
In case of considering trading cost, the return based on RSI strategies and Buy and Hold which were described in Figure 16 dropped considerably in whole period. Similar to the
35
above figure, long computation period in term of RSI-25 generated more profit than 2 others, RSI-9 and RSI-14 in the first stage. It pointed out that RSI applied long term period had fewer trades, longer time between each buying and selling activities and take more advantages when stock price goes up significantly. Otherwise, when market stepped into stable period, stock price frequently goes up and down together, long computation period could skip good chances to trade in order to earn profits. Hence, short and medium periods in term of RSI-9 and RSI-14 are superior during the rest of investment process. From the highest position, RSI-25 decreased steadily and underperformed Buy and Hold. In my personal point of view, it could not be debated consistently which the most profitability method is because of some reasons. Firstly, there is no strategy that takes the most profit in almost the time. Second, all of six results are not outperform Buy and Hold in a half of investing period. Especially, RSI-25-0.25% just outperformed Buy and Hold strategy in a short period of time, end of 2008 to next midyear. Therefore, it must be considered status of the market as well as computation period carefully before making any decision. In brief, all RSI strategies were feasible in stable market rather than its fluctuation. It also proved that a strategy can consider as successful when it has possibility to face with the presence of trading. To put it another way, investors must examine the real rate of trading cost, not only as fixed rate applied in the paper.
36
5 VND RSI-9-0.25
4
RSI-14-0.25 3
RSI-25-0.25 Buyand Hold
2
1
0
-1
Figure 16: Results of RSI Investment Strategies versus Buy and Hold Strategy With Considering Transaction Cost (T=0.25%)
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4.3 Effectiveness of Technical Analysis methods based on portfolios classified by industry The study investigates 9 industry portfolios of 265 observed stocks listed on HOSE. (Appendix C). Overall, 15 TA methods outperformed Buy and Hold strategy of all portfolios with a great number of accumulated return excluding portfolio of public service, healthcare, medicine and others stocks. Construction and its materials, Basic Materials and Consumer goods and service in terms of ID 3, 7 and 8, were 3 best executive portfolios. Over 8-years period, these portfolios accumulated financial gain of 15 to 35 times over initial investment. (Appendix E) The other remarked point is, in general, 9 MA strategies give the better outcome than RSI and MACD respectively as the whole. Besides, the table 3 shows that total 54 outcomes that were generated by testing 9 portfolios of MACD and RSI strategies in which 32 results underperformed Buy and Hold strategy. In order words, it also meant that there was only 40.9% chances in order to earn more than Buy and Hold, its ratio is too low. Table 3: Results of MACD and RSI investment strategies on 9 industries portfolios Buy ID
MACD-8-17
MACD-12-26
MACD-18-35
RSI-9
RSI-14
RSI-25
& Hold
1
55%
15%
44%
354%
221%
27%
47%
2
37%
58%
65%
55%
19%
21%
46%
3
63%
17%
-3%
503%
598%
320%
121%
4
70%
63%
-5%
392%
122%
149%
65%
5
80%
37%
9%
132%
248%
208%
340%
6
64%
57%
6%
561%
502%
161%
231%
7
47%
28%
18%
385%
302%
349%
263%
8
46%
62%
-7%
775%
546%
212%
207%
9
27%
-2%
20%
32%
17%
77%
76%
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In details, MACD had poor performance in all of three scenarios. Only portfolio 1 and 4 which applied MACD-8-17 and portfolio 2 using the others MACD strategies are superior to Buy and Hold, but just a little bit higher. Even though there were some MACD parameters that captured to higher return, they derived more losing results than underperformed ones. Alternatively, in comparison to Buy and Hold, RSI was likely more favorable than it with higher rate of return. Under these strategies, the return is accumulated a numerous times over initial investment. However, they have been existed some unbelievable choices for who are really favorable to portfolios 2 and 9. Nevertheless, surprisingly, both MACD and RSI bring out an interesting finding for investors who want to trade with these strategies based on industry portfolios. From the authors‟ points of view, RSI-14 and MACD-12-26 are the standard parameters and are believed outstanding than the rest. But the fact that, in the period, short-term computation period in terms of RSI-9 and MACD-8-17 were apparently more favorable. About MA strategies, they outperformed Buy and Hold strategy even though the study clarified number of computation days and level of filter applied. The finding here means that despite if which MA parameters was chosen, the opportunity that trading based on MA indicators outperformed Buy and Hold is extremely high. Table 3 indicates the performance of the investment based on MA strategies. Basically, 0% and 1.5% level of filter was surpassing filter 3% in almost outcomes. Furthermore, it can be seen obviously that filter 1.5% was superior to filter 3%. Returns based on short and medium computation period in terms of MA-5 and MA-10 which applied filter 1.5% was higher than filter 3% in the whole of portfolios. And excluding portfolio 5 and 6, standing for Industry & Technology and Food and Beverages Industries, the other portfolios‟ return went in the same direction when increasing computation period to 15. It is a conclusive proof that high ratio of filter regarding to 3% is too high, it expanded
39
gaps between current stock price and MA value that caused of raising degree of trading condition. It rejected plenty of trading signals which are invaluable chances to collect the profit as much as possible when our stock market does not fluctuate dramatically as the expectation. From that, it can be seen that determining an appropriate level of filter affects significantly the success of investment. Table 4: The result of MA investment strategies on 9 industry portfolios
ID
MA-5
MA-10
MA-15
Filter
Filter
Filter
Filter
Filter
Filter
Filter
Filter
Filter
0%
1.50%
3%
0%
1.50%
3%
0%
1.50%
3%
1
1298%
1163%
383%
1140%
701%
456%
904%
747%
500%
2
719%
843%
411%
870%
581%
430%
701%
550%
328%
3
1757%
2663%
1539%
1818%
3551%
1956%
3280%
2984%
2354%
4
1290%
1535%
494%
1394%
1532%
1088%
1456%
1687%
794%
5
953%
1188%
913%
1558%
2016%
1533%
1591%
1544%
1602%
6
1046%
1095%
618%
1381%
1526%
1217%
1635%
1389%
1511%
7
1539%
1410%
482%
2425%
1837%
1378%
2224%
1699%
1488%
8
2583%
3026%
1098%
2419%
1976%
1904%
2123%
2474%
1623%
9
203%
299%
121%
356%
308%
129%
354%
362%
188%
From the performance showed in table 4 and Appendix E, all strategies based on short, medium or long computation period have advantages that subordinate to in which industry is applied. However, in my personal point of view, medium term is quite better
40
when the amount of compounding return were higher or equivalent to short and long period. In sum up, in case of investing upon industry classification, MA and RSI were proved that they had power of predictable return while MACD were not good enough in order to outperform Buy and Hold.
4.4 Effectiveness of Technical Analysis methods based on portfolios classified by firm size
In definition, market capitalization or firm size refers to how big these companies are. Primarily, investors usually rely on the characteristic so as to bring out decisions. Large capital stocks and blue chips are high liquidity, lower risk but lower return. On the other hand, small capital stocks are low liquidity, high risk and high return consistently. In the fact of that there is no standard to list out and divide listed stocks in these portfolios. For simplicity and fair competition, we will conduct 5 portfolios in terms of S1, S2, S3, S4 and S5 which increase its value of market capitalization respectively. Therefore, these stocks probably have been changed their position among portfolios. (Appendix D) In 2 previous sections, the study tested the profitability of 3 TA methods in the whole time, between 2005 and 2012. However, I would like to break the timeline into 2 periods in order to see TA performance when they started and ended at different points. Observed period were 2005-2009, 2009-2012 and the whole time because of some reasons. First, 8 years is a long testing period with many complicated situations and enormous effects, such as the dramatic increase of the market in pre-financial crisis stage or financial system collapse in 2008…Therefore, there may be exist some strategies that its failures is concealed by the outperform in the past or conversely, it has potential to increase rate of return, but poor performance reduced amount of investment. Besides, the way of dividing
41
investment periods is believed to find out the favorable strategies in different market statuses and its feasibility for short or long term investment strategy. A final reason is also to explain why this kind of analysis was not conducted in the tests of VNINDEX and industry portfolios. The reason is because VNINDEX and industry portfolios do not have ordering disorders as firm size portfolios. Firm size value which is calculated by multiplies number of shares outstanding by current stock price have been changed during the process.
4.4.1 Period of 2005-2008 Table 5 shows the Buy Hold performance in the period of 2005 to 2008 regarding to 5 firm size portfolios. Here, there is one thing must be expressed that S3 and S4 in terms of mid cap stocks had a negative rate of returns, losing about one-third initial investment. Hence, investors who believe in the passive strategy should be careful with their investment decisions. The other portfolios were superior, S1 is especially outstanding that has accumulated 90% rate of return. Basically, return on investment based on MACD strategies was so much lower than MA and RSI, particularly the existence of a few strategies that underperformed Buy and Hold. To put it another way, all three parameters of MACD were outperform just when applied to S3 and S4, but just a little. Therefore, it could be debated that MACD strategies is not really efficient in this case and analysis the other strategies deeply (Table 5 and Appendix F)
42
Table 5: Results of MACD and Buy and Hold of Firm size portfolios (2005-2008)
S1
S2
S3
S4
S5
Buy and Hold
91%
46%
-25%
-32%
64%
MACD-8-17
35%
51%
15%
-4%
17%
MACD-12-26
15%
24%
19%
23%
40%
MACD-18-35
19%
4%
12%
23%
-2%
According to figure 17, MA and RSI strategies accumulated abundance of money and was better than Buy and Hold at all. In comparison about how each portfolio performed, S1, S2 and S5 were higher than S3 and S4 obviously even if which strategy is applied to imitate the trading activities. It is understandable; because these technical methods were significant and helped investors improve and magnify the profits against Buy and Hold strategy. As mentioned before, the market fluctuated complicatedly, upward and downward trends were dramatically in the period. The results are quite similar to investment theories, small cap companies with the high level of risks bring out huge profits. Hence, we do not have any evidence to point out that investing in small companies is untrustworthy. However, it could not be explained profitability of large cap companies if we just focus on the theories. On basically, large cap and blue chip stocks usually have lower returns, balancing with lower risks. Surprisingly, S5 regarding to the large size of firms took many advantages and especially was superior to others in some strategies. In my personal point of view, the instable period that causes investor to be reserved about risky in the investment process. To be in safety, they chose stocks of big companies and waited to appropriate periods instead of facing high risks. So, S3 and S4 were unfavorable and inferior to the others.
43
900% 800% 700% 600% S1
500%
S2
400%
S3
300%
S4
200%
S5
100% 0%
Figure 17: Results of MA and RSI Strategies of Firm Size Portfolios (2005-2008)
4.4.2 Period of 2009 to 2012 Table 6 indicates MACD and Buy and Hold performance in the period of 2009- 2012 in terms of 5 firm size portfolios. About Buy and Hold strategy, outcomes of the portfolios were not too different, excluding S4 portfolio with a negative result. In contrast with Buy and Hold in the previous stage, S3 raised remarkably, in company with S1 and S2, were to be most profitable portfolios. On the contrary, return on S5 declined to 19% and was not credible. It also means that small cap stocks had potential to generate more profits than the medium and large cap.
44
In the period, MACD strategies were still out of the competition with MA and RSI, even they had a poor performance rather than Buy and Hold by almost case. From the results of both periods, we surmise that MACD strategies could be insignificant during the time. Table 6: Results of MACD and Buy and Hold of Firm size portfolios (2009-2012)
S1
S2
S3
S4
S5
Buy and Hold
86%
48%
40%
-5%
19%
MACD-8-17
26%
9%
11%
44%
36%
MACD-12-26
-2%
2%
-7%
11%
31%
MACD-18-35
13%
13%
16%
4%
20%
According to figure 18 and Buy and Hold performance in table 6, all of MA and RSI strategies had good performance which enlarged accumulated return as manyfolds against Buy and Hold. It can be seen that MA and RSI were obviously efficient with the portfolios classified by market capitalization. However, differently from the period of 2005-2008, S5 could not maintain its advantage longer, instead of , S3 was in great process to be one of three most benificial portfolios, together with S1 and S2. The finding could be illustrated absolutely based on the theories that was in brief mentioning before, companies with small cap deal with high rates of return and inverse. Buy and Hold strategy on VNINDEX is the description how the Vietnam stock market has been performed. Between 2009 to 2012, the market stepped into stable stage, and developed quite solid, the risk of investment fell against the last period. Therefore, investors with a higher rate of return on expectation that was being quite willing to invest in risky assets as S1, S2 and S3. Stock investment, in general is not able to out of the law of supply and demand. And MA and RSI as efficient indicators that helped investors reduce to the lowest level of risk and increase potential profits as well. In an obvious
45
manner, the investment is an art of analysis and difficult to make decisions as to which stocks should be invested in, when starts investment and which feasible strategies are. 700% 600% 500% S1
400%
S2 300%
S3 S4
200%
S5
100% 0%
Figure 18: Results of MA and RSI Strategies of Firm Size Portfolios (2009-2012)
4.4.3 Period of 2005-2012 Note that in table 7, Buy and Hold performance of firm size portfolios from 2005 to 2012, keep in hand S3 and S4 were extremely dangerous, one ended with only 6% and the others was negative outcome -35%. And it will more serious if the paper discuss on time value of money matter. S1, S2 and S5 had better performances; S1 was especially outstanding with two and a half over initial investment. Actually, the results were predicted, MACD with any parameter could not outperform Buy and Hold regularly. There was any period that return based on MACD jumped sharply to compensate for another poor performance. The superior to benchmarks of S3 and S4 seemed to not enough evidences to convince traders of its predictive power.
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Similar to 2 previous sections, MACD was out of competition, we can focus analyze and discussed on MA and RSI strategies Table 7: Results of MACD investment strategies of Firm size portfolios (2005-2012)
S1
S2
S3
S4
S5
Buy and Hold
254%
115%
6%
-35%
94%
MACD-8-17
70%
64%
30%
39%
59%
MACD-12-26
1%
26%
12%
41%
83%
MACD-18-35
38%
17%
28%
27%
19%
The result showed in figure 19 can be completely predictable because we have been analyzed and discussed by dividing the time period into 2 halves. Throughout the figure, all 12 investment strategies based on MA and RSI methods accumulated a huge rate of return and earned many-fold as much as total return of 2 divided periods. That is because these technical methods had been outperformed in whole time and generated more capital to reinvest. Overall, ranking of S1, S2 and S4 had not been changed, while S1 and S2 had good performance during the time, S4 did not make any great advance to improve position and profits as well. On the other hand, in the period of 2005 to 2008, S5 created opportunities; hence with an abundant amount of money, investment was still maintained advantages even if its performance was inferior from 2009 to 2012. S3 was in opposite side, the result of the dramatic increase after 2008 helped improving its poor performance in the past.
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6000% 5000% 4000% S1 3000%
S2 S3
2000%
S4 1000%
S5
0%
Figure 19: Results of MA and RSI Strategies of Firm Size Portfolios (2009-2012)
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CHAPTER V
CONCLUSION AND RECOMMENDATION
5.1 Conclusion The main purpose of the thesis was to investigate the profitability of popular technical methods in terms of MA, MACD and RSI on Ho Chi Minh Stock Exchange. 15 investment strategies were conducted in order to analyze and discuss about predictive power throughout the 8-years period between 2005 and 2012. Matlab and Microsoft Excel were used to imitate trading activities and examine the performance compared to Buy and Hold benchmark. Overall, many meaningful results were discovered. The findings from the tests on VNINDEX are in support of all 9 MA investment strategies, because that the strategies virtually outperformed Buy and Hold strategy. However, the results of MACD and RSI were contrary. It indicated that these strategies could not be superior to Buy and Hold consistently; especially during the period of 2007 when the passive strategy generated a large amount of compounding returns. Therefore, the results would probably better the benchmark on the condition that investigation period would be started at 2008. Moreover,
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another noticed point that short and medium term of computation periods seems to more favorable than long periods. Another important test gives the results of portfolios performance based on industries and market capitalization. All in all, all 15 investment strategies generated positive outcomes. However, 3 MACD strategies seemed not to outperform Buy and Hold consistently. The others strategies were superior to Buy and Hold. MA strategies turned to be the most profitable. One of the most significant results found portfolios which had potential to generate abundant profits. Construction and its materials, Basic Materials and Consumer goods and service seem to perform best against the other industries. In comparison to firm size based portfolios, stocks referred to small cap companies captured to high rate of return.
5.2 Recommendation In general speaking, all three investigated technical analysis methods have possibility to capture excess returns in comparison with Buy and Hold, but it absolutely depends on current market status. MA method seems to be the most efficient, captures most profit, no matter how fluctuant market is. MACD and RSI are quite different; they are really efficient when the market fluctuates gradually. Other advice for investors is that shorten the computation period can be more beneficial for investors. When investors make decisions upon industry, Construction and Its Materials, Basic Materials and Consumer goods and service are strongly supported by this paper. On the other hand, in scope of firm size, small cap stocks will be more favorable. All of above advices are meaningful findings that are discovered from the empirical study. The inspection period lasted 8 years; it was a quite extended period of time. However, this paper just calculated rate of return basically, and assumed absence of time value of money. Besides, this paper did not mention trading volume problem, which influence
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directly to the practicability of technical analysis signals. To put it another way, when trading signals appear, it is hardly probable that investors can buy or sell stocks because of small trading volume. Finally, excepting firm size and industry, Beta and Price to Book value factors also affect to rate of return. All of the above-mentioned are the thesis limitations that need to be improved by further researches.. The thesis examined the effectiveness of three most popular methods on HOSE. Hence, researchers can investigate other technical indicators in order to find out the optimal one. Moreover, this subject can be extended throughout investigating all stock exchanges: Ha Noi Stock exchange, Up-com Stock exchange and Ho Chi Minh Stock exchange. Testing on all stock exchanges will give a general view of the whole Vietnam stock market.
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Plykha, Y., Uppal, R. and Vilkov, G. (2012). Why Does an Equal-Weighted Portfolio Outperform Value- and Price-Weighted Portfolios.
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http://www.mathworks.com
http://www.cophieu68.com/
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http://www.hsx.vn/hsx/Default.aspx
http://itrade.vn/home/index
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http://www.vcsc.com.vn/
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APPENDIX APPENDIX A MATLAB CODE Moving Average Algorithm % moving average function to calculate accumulated return % price historical stock price % n n-day sample of closing price % f level of difference of MA and stock price to reduce lag effects % t transaction cost function r_ma=r_ma1(input,num,f,t) r_ma=[]; price=xlsread(input); price=price(:,1); p1 = 1; p2 = 0; buy = true; sell = false; nestedfun; function
nestedfun
f=f/100; t=t/100; ma=tsmovavg(price,'s',num,1);
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for x=1:size(price,1) if (buy) && ((price(x)-ma(x))> price(x)*f) p2 = p1*(1-t)/price(x); p1 = 0; r_ma(x) = (p2*price(x)-1); buy = false; sell = true; elseif (sell) && ((ma(x)-price(x))> price(x)*f) p1 = p2*price(x)*(1-t); p2 = 0; r_ma(x) = (p1-1); buy = true; sell = false; elseif buy r_ma(x) = (p1-1); elseif sell r_ma(x) = (p2*price(x)*(1-t)-1); end end end end
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Moving Average Convergence/Divergence Algorithm % moving average convergence and divergence function to calculate accumulated return % price historical stock price % n n-day sample of closing price % t transaction cost function r_macd=r_macd(input,mas,mal,t) r_macd=[]; [price]=xlsread(input); price=price(:,1); p1 = 1; p2 = 0; buy = true; sell = false; nestedfun; function
nestedfun
t=t/100; macdshort=tsmovavg(price,'e',mas,1); macdlong=tsmovavg(price,'e',mal,1); macdvec=macdshort-macdlong; nineperma = tsmovavg(macdvec(mal:end),'e',9,1); buy = true; sell = false; for x = mal:size(price,1)-1 if buy && macdvec(x)nineperma(x+1mal+1)
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p2=(p1*(1-t)/price(x)); p1=0; r_macd(x)=(p2*price(x)-1); buy=false; sell=true; elseif sell && macdvec(x)>nineperma(x-mal+1) && macdvec(x+1)>nineperma(x+1-mal+1) p1=p2*price(x)*(1-t); p2=0; r_macd(x)=(p1-1); sell=false; buy=true; elseif buy r_macd(x)=(p1-1); else r_macd(x)=(p2*price(x)*(1-t)-1); end end end end
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Relative Strength Index Algorithm % Relative strength index function to calculate accumulated return % price historical stock price % n n-day sample of closing price % t transaction cost function r_rsi=r_rsi1(input,n,t) rsi=[]; [price]=xlsread(input); price=price(:,1); p1 = 1; p2 = 0; buy = true; sell = false; nestedfun; function
nestedfun
t=t/100; rsi=rsindex(price,n); buy = true; sell = false; for x=2:size(price,1) if buy && ( (rsi(x)>=70) || (rsi(x-1) 30))
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p2 = (p1*(1-t)/price(x)); p1 = 0; r_rsi(x) = (p2*price(x)-1); buy = false; sell = true; elseif sell &&((rsi(x)= 70 && rsi(x)
