Technical Analysis Versus The Efficient Market Hypothesis: An Empirical Study On The Vietnamese Stock Market

Transcription

1 Technical Analysis Versus The Efficient Market Hypothesis: An Empirical Study On The Vietnamese Stock Market By Duy Tan Vo April 2012 First supervisor Drs. L.J. van de Leur Second supervisor Dr. J.E. Ligterink Master Business Economics Finance Track Faculty of Economics and Business University of Amsterdam Abstract: This thesis finds strong evidence that the Vietnamese stock index does not follow a random walk from January 2006 till December The weak form of the efficient market hypothesis has been examined with the autocorrelation test, runs test and variance ratio test. In addition, the technical trading strategy based upon the slope of the exponential moving average also finds evidence that it is possible to earn abnormal returns during this period. These findings imply that the efficient market hypothesis has been violated on the Vietnamese stock index.

2 Table of Content 1. Introduction p Literature review p The efficient market hypothesis p The random walk model p Fundamental analysis p Technical analysis p Overview of previous empirical studies concerning technical analysis p Behavioral models that explain anomalies in the market p Summary p Methodology p Data p Autocorrelation test p Runs test p Variance ratio test p The exponential moving average trading strategy p Empirical results p Empirical results of tests in regards to the random walk p Empirical results of the exponential moving average trading strategy p Discussion p Limitations p Conclusion p. 34 References p. 35 Appendices p. 37 Appendix A. Summary statistics of the VN-Index p. 37 Appendix B. Results of the autocorrelation and variance ratio test p. 38 Appendix C. Rules and the results of the trading strategy p. 42

3 Chapter 1 Introduction Jesse Livermore, John Templeton, Peter Lynch, Warren Buffet and George Soros are few of many respected individuals that demonstrated their ability to outperform the stock market. Many practitioners try to imitate their strategies and pursue their dream to become a millionaire. However, it is known that most of the market participants lose money in the financial markets. Nevertheless, speculators are still convinced that it is possible to outperform the market. Due to the advance of the Internet and computers, financial markets became more accessible. This resulted in an explosive gain in the competition in the financial markets. Empirical studies showed evidence that specific trading strategies were able to yield significant profits throughout history. With this in mind, is it still possible to earn significant profits in the current financial market while taken into account the competitive environment and the self-destructive nature of trading strategies? This thesis will examine the weak form of the efficient market hypothesis on the Vietnamese stock index (VN-Index). The weak form of the efficient market hypothesis dictates that one cannot use historical information to predict stock prices (Fama, 1970). This theory created a lot of debate, because market participants use technical analysis to determine their market timing (Taylor and Allen, 1992). Technical analysis is a technique to predict price movements with historical prices, volumes and open interest (Park and Irwin, 2004). Due to the skepticism of academics and the overconfidence of market participants regarding their ability to predict stock prices, a lot empirical studies have been conducted the last decades. The aim of this thesis is to examine the weak form of the VN-Index from the 3 rd of January 2006 till the 31 st of December The VN-Index will be examined, because no research has been reported that examined the VN-Index on market efficiency as from the 3 rd of January The examination of Truong (2006) is the most recent examination that examined the VN-Index, based on data from the 28 th of July 2000 till the 31 st of December In addition, a small number of empirical studies in regards to technical analysis on emergent markets have been reported. The verdict of this thesis will therefore have an added value to the current financial paradigm. Translated to a hypothesis, this thesis will test the following: H 0 : The Vietnamese stock index is weak efficient H 1 : The Vietnamese stock index is not weak efficient The weak form of the efficient market hypothesis will be determined with the autocorrelation test, variance ratio test and runs test. These tests will be conducted in order to examine if the VN-Index has a random walk process at a significance level of α = A random walk process implies that the departures of subsequent prices are unpredictable. However, if the autocorrelation test, variance ratio test and runs test jointly reject the presence of a random walk process on the VN-Index, statistical evidence has been found that prices were predictable. The former stated observation indicates that trading strategies that anticipate on trending price movements could take advantage of predictable prices. 2

4 A technical trading strategy will be used to examine the ability to earn excess risk adjusted returns on the VN- Index. The slope of an exponential moving average will be used to generate a long (positive slope) or short (negative slope) signal. This technical trading strategy has been chosen, because no other empirical studies have been reported that examine the performance of a strategy that uses the slope of an exponential moving average. Moreover, traditional empirical studies focus on moving average crossovers. Therefore, examining a trading strategy that uses the slope of an exponential moving average as an entry signal will enrich the paradigm of technical analysis. The returns of the technical trading strategy will be tested with use of a one-tailed t-test, because this strategy is only concerned with the over performance compared to the VN-Index. The Jensen s α test, which will be estimated with the capital asset pricing model, will be used to examine the excess risk adjusted returns. The exponential moving average with a period of observations from 1 till 50 preceding days will be tested. In addition, these strategies will be performed with commissions of respectively 0%, 0.10%, 0.25%, 0.50% and 1.00% (Griffioen, 2003). The tests regarding the random walk process and the profitability of the technical trading strategies will be performed on the full sample and two subsamples. The first subsample is equal to two-third of the full sample and the second subsample is equal to the remaining one-third of the full sample. The subsamples are separated systematically relatively to the full sample, as proposed by Gençay (1998). Many empirical studies separate the subsamples in bull and bear markets. Distinguishing subsamples in such manner will produce biased results, because a predetermination of the subsamples on its trend will affect the performance of technical trading strategies that rely solely on trending price movements. The data and number of observed returns are illustrated below: / (N = 1.242) / (N = 839) / (N = 403) The rationale of separating the full sample in two subsamples is to determine the performance of the tests and trading strategies in more detail. In addition, it reduces the probability of incorrectly rejecting H 0 if the results of different samples jointly reject the efficient market hypothesis. The findings of this thesis indicate that the VN-Index did not possess a random walk process from the 3 rd of January 2006 till the 31 st of December The weak form of the efficient market hypothesis is therefore rejected. The autocorrelation test indicates that the first lag coefficient of the full sample, sample one and sample two are different from zero, at a significance level of 1%. Furthermore, the runs test rejects the null hypothesis of a random walk process on the VN-Index at a significance level of 1%. The z-statistics of the full sample, sample one and sample two are -7.42, and respectively. In addition, the results of the variance ratio test are consistent with the autocorrelation and runs test. The null hypothesis of a linear relationship between the variance ratios is rejected. The results of all samples show that the variance ratios with an interval up to four days are greater than one at a significance level of 1%. In contrast to the results of the tests concerning the random walk process, the results of the exponential moving average strategy are not always in favor of the predictive power of technical analysis. The Jensen s α test and t-test show promising results for the full sample and sample one. In case of commissions of 0.00%, 0.10%, 3

5 0.25% and 0.50%, the number of significant α and t-tests amount at least to 49 at each transaction cost case for the full sample and sample one. On the other hand, the exponential moving average strategy performed poor in sample two. Out of 250 trading strategies, only 24 were able to generate significant excess risk adjusted returns and 5 were able to generate a significant higher average return compared to the VN-Index. The cause of the poor performance is due to too many trading signals, transaction costs and a sideways market. This indicates that applying a profitable exponential moving average trading strategies does not guarantee future profits. For example, using a particular exponential moving average trading strategy based on the results of sample one did not result in abnormal returns in sample two in all cases. However, due to the presence of few trading strategies which were able to yield significant profits in all samples, the final verdict states that technical analysis also showed evidence that the VN-Index was not efficient in the weak form. It is worthy to note that the results could be biased and invalid, because sample two is not normal distributed. In order to justify the results, bootstrapping methods should be performed in future research. It should be noted that the exponential moving average strategy should be calibrated before using it in practice. Stated differently, downside risk measures, position sizing and diversification should be taken into account in order to increase the performance. Previously stated measures in regards to the calibration are widely used in reality to reduce drawdowns, i.e., the impact of consecutive losing trades on the trading balance. This thesis includes five chapters and is structured in the following chapters: Introduction, Literature review, Methodology, Empirical results and Conclusion. The literature review describes the efficient market hypothesis, fundamental and technical analysis. Technical analysis will be discussed in detail and includes previous empirical studies concerning profitable trading strategies. In addition, positive and negative evidence regarding technical analysis will be discussed. At last, behavioral models that explain anomalies in the financial market are discussed briefly. The methodology section will elaborate the autocorrelation test, variance ratio test, runs test, t- test, Jensen s α test and the exponential moving average strategy. In addition, the description of the underlying data will be presented. The fourth chapter will present the results of the descriptive statistics of the VN-Index and the significance of the aforementioned tests in detail. Furthermore, the results will be discussed and explained with use of studies that have been conducted by other academics. At last, a verdict will be given based on the findings of this thesis in chapter five. 4

6 Chapter 2 Literature review This chapter will discuss the efficient market hypothesis, fundamental and technical analysis. The rationale behind the efficient market hypothesis and its three forms will be explained briefly. The random walk model, the foundation of the efficient market hypothesis, will also be illustrated. Furthermore, the differences between fundamental and technical analysis will be discussed. Influential studies regarding technical analysis will be elaborated in order to reflect the added value of technical analysis. Despite the added value, deficiencies in technical analysis will also be discussed in this chapter. At last, behavioral models that explain the anomalies in the financial markets will be discussed. 2.1 The efficient market hypothesis Bachelier (1900), the pioneer of the random walk theory, wrote an influential thesis entitled Théorie de la Spéculation. Bachelier formulated the random walk theory in the beginning of the 20 th century and Samuelson (1965) developed this theory into a theoretical framework. The random walk hypothesis has been examined by many academics such as Fama (1965), Osborne (1962) and Cootner (1962). They conclude that stock prices are unpredictable, because their findings imply that changes in stock prices are independent. The combination of Samuelson s (1965) theoretical framework and early statistical studies resulted in the efficient market hypothesis (Fama, 1970). The efficient market hypothesis states that all available information is fully reflected in the prices (Fama, 1970). The efficient market hypothesis is subject to three conditions; 1) no transaction costs, 2) information is free and is publicly available, 3) current prices reflect all available information. However, Fama (1970) argues that a violation of the conditions does not necessarily imply that a market is inefficient due to the competitive environment. The efficient market hypothesis is separated in three forms: the weak, semi-strong and strong form (Fama, 1970). Each form has a different view of the market efficiency with regards to the available information that is reflected in the market. Fama (1970) discusses that it is not possible to consistently earn abnormal returns based on historical information in a weak efficient market, because historical information is already reflected in the current prices. This dictates that technical analysis has no predictive value. Technical analysis is a technique to predict stock prices with use of historical prices, volumes and open interest (Park and Irwin, 2004). The semi-strong form dictates that it is not possible to consistently earn abnormal returns with use of all public information. The semi-strong form implies that one cannot use fundamental analysis and technical analysis to predict stock prices. Fundamental analysis is a technique to determine the intrinsic value of a stock with use of firm specific information such as data from financial statements. At last, the strong form of the efficient market hypothesis dictates that stock prices reflect public and private information. The strong form therefore states that insider information, fundamental analysis and technical analysis cannot be used to earn abnormal returns. 5

7 2.2 The random walk model As mentioned previously, a stock market is efficient when the stock price includes all available information. Therefore, stock prices should reflect the intrinsic value of a stock. The intrinsic value of a security can be measured with use of dividend discount models or discounted cash flow models, i.e., fundamental analysis. Fama (1965, p. 56) states In an efficient market, on the average, competition will cause the full effects of new information on intrinsic values to be reflected instantaneously in actual prices. Since information becomes public in a random manner, stock prices should therefore also move randomly. However, Fama (1965, p. 56) argues that a random walk does not imply that stock prices are always at their intrinsic value, because uncertainty concerning intrinsic values will remain and actual prices of securities will wander randomly about their intrinsic values. In order to illustrate the random walk process, its equation is expressed as follows (Park and Irwin, 2004): P t = P t-1 + ε t ε t ~ IID(0,σ 2 ) where P t : stock price on time t; P t-1 : stock price on time t-1; ε t : random error term on time t; The equation indicates that the change between P t and P t-1 is due to the random error term. In addition, the equation indicates that the error term is independent and identical distributed with mean 0 and variance σ 2. The rationale of empirical studies concerning the random walk process is to determine the independency of successive price changes. Park and Irwin (2004) elaborate that the random walk model may be regarded as a martingale model. In addition, Park and Irwin (2004, p.7) state that the martingale model suggest that the correlation coefficient between successive price changes will be zero, given information about today s prices and past prices. A martingale process is expressed in the following equation:,, ) = 0 This equation implies that the future expected price changes, conditional on current and past prices, are equal to zero. Therefore, trading strategies that use historical prices should not be able to yield excess expected returns. Fama (1965, p. 57) argues that if a security has a random process, no mechanical trading rules and chartist techniques should be able to consistently earn abnormal returns compared to a buy and hold strategy. However, technical analysts do not consider the tests regarding the random process adequate, according to Fama (1965, p. 57). Fama, (1965, p. 57) states, the simple linear relationships that underlie the serial correlation model are much too unsophisticated to pick up the complicated patterns that the chartist sees in stock prices. The skepticism of technical analysts and academics results in ongoing debates and empirical studies. The next section will illustrate the methods how market participants approach the market. 6

8 2.3 Fundamental analysis As stated in section 2.1, the semi-strong form of the efficient market hypothesis dictates that one cannot consistently earn abnormal returns with use of fundamental analysis and technical analysis. Technical analysts try to predict the direction of the market with use of historical information, while fundamental analysts attempt to estimate the value of a firm. Fundamental analysis is based on the firm-foundation theory. The firmfoundation theory postulates that a stock has an intrinsic value. Fundamental analysts estimate the intrinsic value with use of data of a financial statements and focuses on economic factors that affect the actual and future performance of the firm. One who uses this approach assumes that the market corrects a discrepancy over time. Therefore, the main purpose of fundamental analysts is to determine whether a firm is over- or undervalued at the current market price (Malkiel, 1996). In practice, using fundamental analysis is challenging due to the complexity and the quantity of factors that could affect the performance of the firm. Quantitative metrics such as the discounted dividend model are performed to estimate the intrinsic value of a firm. Malkiel (1996, p.30) adds the following regarding the firm-foundation theory: The theory stresses that a stock's value ought to be based on the stream of earnings a firm will be able to distribute in the future in the form of dividends. It stands to reason that the greater the present dividends and their rate of increase, the greater the value of the stock; thus, differences in growth rates are a major factor in stock valuation. A downside of this approach is that financial statement analysis relies heavily on assumptions with regards to future expectations. Moreover, Malkiel (1996, p. 30) argues that it is a slippery little factor to make assumptions about the future growth and its duration. Therefore, Malkiel (1996) concludes that the firmfoundation theory might be less reliable than claimed. Fama (1965) argues that fundamental analysis is only beneficial when one is able to predict future events that affect the intrinsic value. In addition, one is required to anticipate quicker to news announcements than their competitors (Fama, 1965). Also, financial statements are in general published two to four times per year. Therefore, fundamental analysts are subject to few opportunities to take advantage of discrepancies. In general fundamental analysis is used for long-term investments and technical analysis is used for short-term trading (Park and Irwin, 2004). 2.4 Technical analysis Nowadays almost all market participants are familiar with technical analysis. Technical analysis is a technique to predict stock prices with use of historical prices, volumes and open interest (Park and Irwin, 2004). Technical analysts believe that shifts in the equilibrium of the demand and supply could be observed in the prices (Brock et al, 1992). In general, technical analysts use a set of mechanical trading rules to determine buy and sell signals. Technical analysts also use charts of historical price movements to predict subsequent price movements. Popular technical analysis techniques include chart pattern analysis (Carginalp and Laurent, 1998) and technical trading systems (Brock et al, 1992). Technical analysis is based on the following premises (Murphy, 1999): 7

9 History repeats itself; Price move in trends; Technical analysts tend to believe that past profitable price patterns will have the same outcome in the future. These patterns are visually and mathematically explained by chart patterns and by indicators such as the Relative Strength Index (Wilder, 1978) and the Alexander s Filter Rule (Alexander, 1964). Indicators are used to give information about the momentum of the trend. The main purpose of technical analysis is to detect a trend or trend reversal (Pring, 2002). The Dow theory popularizes the rationale of trending price movements (Rhea, 1932). It assumes that there are three types of trends: primary, secondary and tertiary upward and downward movements. In addition, the Dow theory is based on the premise that all information is reflected in the averages. Detecting a trend could be very subjective in case of a chart pattern analysis, because a chart pattern analysis is subject to one s interpretation. For example, individual A and individual B could observe different chart patterns based on the same chart. Therefore, academic studies on technical analysis are habitually limited to mathematical strategies such as the moving average crossovers strategy and the channel breakout strategy (Park and Irwin, 2004). Most academics believe that technical analysis is a self-fulfilling prophecy due to the optimization of the parameters of a trading strategy. Academics also believe that technical analysis has no added value, because they tend to believe that markets are informational efficient, as discussed in section 2.1. Another disadvantage of technical analysis is that it is not able to predict the magnitude and the duration of trending price movements (Pring, 2002). Many academics have done research on technical analysis in the last century. Empirical studies are characterized by examinations based upon the random walk model, simple and advanced technical trading strategies. Simple technical trading strategies use a set of predetermined rules to generate buy and sell signals. Whilst advanced technical trading strategies, which are discussed in the next section, rely on genetic programming, reality check and non-linear studies. Exhaustively tested trading strategies include moving averages, support and resistance, chart patterns and a momentum oscillator (Park and Irwin, 2004). These components are illustrated in figure 2.1 and are explained below. Support and resistance: these levels show a possible trend reversal if the price is unable to penetrate these levels. However, it could result in a trend continuation if the price is able to penetrate a support or resistance. Technical analysts use the local maximum and minimum of the prices in order to determine the resistance and support; Moving average: a moving average refers to the average price of a security over a certain period. The average will roll over to the next period as time progresses; Momentum oscillator: this indicator is used to identify oversold and overbought conditions of the trend in the short term. They are also called predicting indicators, because they may identify changes in the trend in advance (Park and Irwin, 2004); Chart pattern analysis: this is a combination of various price formations. Chart patterns are used to identify trend reversals and trend continuations. Popular price formations are the head and shoulder and double top formation; 8

10 Figure 2.1 Graphical plot of the S&P500. The diagonal black lines illustrate a price channel with its support and resistance. The blue line illustrates a price channel that is based on the local maximum and minimum of preceding 20 days. The red line represents the moving average based on the closing prices of 50 preceding days. A momentum oscillator (RSI) is shown under the chart. (Source: freestockcharts.com) Overview of previous empirical studies concerning technical analysis The rationale of statistical studies is to determine the predictability of price movements. However, technical analysts argue that linear statistical models such as the autocorrelation test are not adequate to test the predictability of stock prices. Technical analysis gained popularity as of Park and Irwin (2004) distinguish two periods in which empirical studies were conducted, i.e., early empirical studies ( ) and modern empirical studies ( ). Early empirical studies are characterized by simple trading strategies and statistical studies such as the autocorrelation test and the runs test. Trading strategies based on a price channel breakout, moving average crossover and Alexander s filter rules were examined during this period. It should be noted that early empirical studies did not test the significance of the returns. They used the average return as a measure to determine the profitability, without taking into account the variability of the returns. Donchian (1960) introduced a trading system based upon the idea that a trend continues when the price penetrates a support or resistance level. Donchian (1960) used a price channel based on the local maximum and minimum of the prices of the preceding two weeks. This trading strategy generated a positive gain and was the foundation for similar range breakout studies conducted by other academics. However, this empirical study did not take into account commissions. Therefore, the results lack the evidence of an inefficient market. Alexander (1961) introduced a trading system that anticipates on a trend reversal. This trading strategy generates sell signals if the price decreases by x% from previous high; it generates buy signals if the price increases by x% from previous lows. Alexander (1961) examined the DJIA and S&P Industrials in the period of and respectively. He concluded that his strategy earned higher returns than a buy-andhold strategy. However, profits disappear when transaction costs are taken into account. Therefore, Alexander 9

11 (1964) suggested that market participants should use other strategies in order to outperform the buy-and-hold strategy. Fama and Blume (1966) also examined the Alexander s filter rule on the daily data of 30 stocks that were quoted on the DJIA between 1956 and They showed that the Alexander s filter rule lacked the consistency of outperforming the buy-and-hold strategy. In fact, in case the buy and sell signals were divided in two portfolios, Fama and Blume (1966) concluded that trading upon short signals is unprofitable and could result in a wipe of the trading account. James (1968) was the pioneer on trading strategies that included moving average crossovers. James (1968) used two moving averages to generate buy and sell signals. A moving average crossover trading strategy generates buy signals when a shorter moving average crosses a longer moving average from below; a sell signal will be generated otherwise. He applied this strategy on monthly data of the stocks that were traded on the NYSE between 1926 and James (1968) concluded that a moving average crossover strategy did not yield abnormal returns. However, his examination did not include daily and weekly data. Early empirical studies lacked the evidence to reject the efficient market hypothesis. Therefore, the efficient market hypothesis was rooted within the financial paradigm till the late 1980s. However, technical analysis gained popularity again as of 1988 due to the advance of the computers and their power to calculate numerous and more complex strategies. Modern empirical studies are characterized by comprehensively statistical analysis regarding the results of the trading strategies. In addition, modern empirical studies have been improved compared to early empirical studies with regards to the treatment of transaction costs, risk, parameter optimization, out-of-sample tests, and data snooping problems, as discussed by Park and Irwin (2004, p.24). Park and Irwin (2004) distinguish modern empirical studies in six groups 1 : Standard studies: include parameter optimization, risk factors and subsample testing (Lukac et al, 1988); Chart patterns: include algorithms to identify chart patterns (Chang and Osler, 1999); Model-based bootstrap: Bootstrapping involves resampling the original data. The rationale of applying a trading strategy on different resampled data is to examine the consistency of the performance of a trading strategy (Brock et al, 1992); Genetic programming: trading strategies that include an ability to learn and to be optimized as time progresses. Genetic programming strategies are faced to the survival of the fittest principle. Therefore, only viable trading strategies are reproduced while failures will be rejected (Koza, 1992); Reality check: this model picks the best in sample trading strategy and compares it to a benchmark strategy. This model is used to test whether a significant result of a trading strategy is due to data snooping (White, 2000); Nonlinear studies: trading strategies that generate signals based upon feed forward models and a nearest neighbor regression (Gençay, 1998). A feed foward model uses a set of interconnected variables (nodes) to predict prices. Moreover, the independent variables in a feed forward model are dependent on other independent variables, which also could be dependent on other independent 1 See Park and Irwin (2004) for a detailed explanation where examples of the groups are elaborated in detail. 10

12 variables. In addition, the nearest neighbor regression predicts prices with use of matching the observation at period t with the nearest similar match in the look back period [t --1, t -2, t -n ]; An overview of modern empirical studies will be limited to influential standard studies in this section due to the complexity of many modern empirical studies and their scope beyond this thesis. Standard studies are examinations of trading systems that include commissions, risk factors, parameter optimization, and subsamples. Subsamples are used to examine the consistency of the results. In addition, t-tests are conducted to determine the statistical significance of the average returns, while the Jensen s α examines whether the risk-adjusted returns of a trading strategy are significant higher than a buy-and-hold strategy. Lukac et al (1988) reported the first modern empirical standard study that examined various exchanges in the period of They applied 12 trading strategies that included moving averages, price channels, oscillators and stop-losses. The Jensen s α test observed significant risk adjusted returns and the out of sample returns confirmed that the trading strategies were most profitable on the sugar and corn markets. Brock et al (1992), perhaps the most influential empirical study, examined moving average and trend breakout trading strategies. They tested the profitability of 26 trading strategies on the closing prices of the DJIA in the period of 1897 till Brock et al (1992) used the t-test to determine the significance of the returns for each trading strategy. They concluded that all trading strategies performed significantly better than a buy-andhold strategy. However, Brock et al (1992) ignored transaction costs and they were aware of the data snoop problem, i.e., the performance of a trading strategy could be the result of luck or over-optimization of the parameters. They tried to reduce the data snoop problem by using a long data set with overlapping subsamples. In addition, they also recognized that the results of the t-test could be invalid when the underlying did not exhibit a stationary and time independent distribution. In order to justify the profitability of the trading strategies, they used bootstrap techniques to determine the consistency of the trading strategies on resampled time series. Based on those results, they concluded that the results of the t-test were strongly valid. The results of Brock et al (1992) and their statistical approach resulted in growing skepticism towards the efficient market hypothesis. However, Brock et al (1992) acknowledged that their results are biased, because they did not include transaction costs. The empirical study of Brock et al (1992) gained popularity and many empirical studies replicated their study on different markets such as foreign exchange markets (Levich and Thomas, 1993; Lee and Mathur, 1996), exchanges that are located in Asia (Bessembinder and Chan, 1995) and exchanges that are located in Latin America (Ratner and Leal, 1999). Levich and Thomas (1993) examined the significance and the profitability of technical trading strategies in the foreign exchange market. They examined the prices of the futures of five currencies (DM, BP, CD, JY and SF) and documented that the profitability of technical trading strategies was significant from 1976 till The data was separated in three subsamples and they observed that the profitability declined in the most recent sub period. In addition, they argued that the relationship between profitable trading strategies and serial dependency in the data is still an open question, because the profitability of the trading strategies was observed in data with and without significant autocorrelation (Levich and Thomas, 1993). In addition, they added the following explanation of the persistence of profitable trading strategies: The profitability of trend following rules may instead be the result of excessive private speculation that causes prices to follow, at least temporarily, a 11

13 speculative bubble path away from their fundamental equilibrium values (Levich and Thomas, 1993, p.469). Lee and Mathur (1996) conducted a research on six spot cross-rates (JY/BP, DM/BP, JY/DM, SF/DM, SF/BP, and JY/SF) from 1988 till They showed that technical trading rules were not significantly profitable in general. They only reported two cross rates (JY/DM and JY/SF) that could be exploited with use of technical trading rules. The results of Lee and Mathur (1996) are in strong contrast with the results of Levich and Thomas (1993). However, they argued that it is an open question whether it is possible to earn significant profits on intraday returns, because trends may exist only for very short periods, possibly less than a day (Lee and Mathur, 1996, pp. 961). Bessembinder and Chan (1995) examined the significance and the profitability of technical trading strategies on Asian stock indices (Hong Kong, Japan, Korea, Malaysia, Thailand and Taiwan). Daily returns in the period from 1975 through 1989 were examined. They found evidence that technical trading strategies were able to predict price movements on Asian stock indices. Moreover, Malaysia, Thailand and Taiwan showed the strongest results. Therefore, they concluded that Asian markets were inefficient during the sample period. In addition, they observed that markets are cross-correlated. Signals emitted by technical trading strategies on data of the U.S. market showed power to predict price movements on Asian markets. Due to little information regarding market efficiency on emergent markets, Ratner and Leal (1999) also examined 10 emergent markets that are located in Latin America (Argentina, Brazil, Chile and Mexico) and Asia (India, Korea, Malaysia, Philippines, Taiwan and Thailand). The predictability of the emergent markets was examined with use of ten moving average models in the period from 1982 till They found evidence that moving average models were significantly profitable. However, they suggested that the relationship between the profitability of technical trading strategies and different microstructures (liquidity, commission, bid-ask spread, legislation etc.) of stock exchanges should be examined in future research. Because technical analysts argue whether linear statistical models are adequate to determine the predictability of stock prices, recent studies expanded their statistical analysis models. Besides the autocorrelation test and the runs test, many empirical studies include the variance ratio test to determine the weak form of the efficient market on stock markets. In essence, the autocorrelation test determines the correlation of a time series with itself at different lag lengths, while the runs test determines if the sequence of positive and negative returns are independently distributed. Lo and MacKinlay (1988) introduced the variance ratio test to determine a random walk process. The null hypothesis of this model states that the relationship between the variance of the 1 st difference of the natural logarithmic prices should be q times smaller than the variance of the q th difference of natural logarithmic prices, in case of a random walk. This will be elaborated in section 3.4. Urrutia (1995) performed the variance ratio test and the runs test to determine the random walk on four emergent markets in Latin America (Argentina, Brazil, Chile and Mexico) in the period of 1975 to Urrutia (1995) rejected the weak form of the efficient market hypothesis for all four indices based on the variance ratio test. However, the runs test was not able to reject the weak form of the efficient market hypothesis. Contradicting results leave open questions which models are more appropriate to determine the presence of a random walk process. Fawson et al (1996) examined the monthly returns of the Taiwan Stock Exchange while performing the autocorrelation and the runs test. Both the autocorrelation and the runs test indicate that the Taiwan Stock Exchange was weak efficient during the period of 1967 till 1993 at a significance level of 5%. 12

14 2.4.2 Behavioral models that explain anomalies in the market Technical trading strategies depend on their ability to anticipate on trend reversals and trend continuations. Behavioral models such as herding and positive feedback trading might explain trending price movements. Technical analysis is based on human behavior and it is also known as heuristic representativeness (Tversky and Kahneman, 1974). Heuristic representativeness is called when events are judged being similar when they share the same characteristics. For example, technical analysts tend to believe that past profitable price patterns will also be profitable in the future. Hence, technical analysts imply that history repeats itself. Other psychological explanations that explain the popularity of technical analysis are: communal reinforcement, conformation bias and self-deception (Sewell, 2008). Communal reinforcement: a strong belief is formed based on repeatedly claims rather than on empirical evidence; Conformation bias: one tends to search for information that is in line with favorable evidence; Self-deception: by ignoring the evidence that contradict one s beliefs, they are misleading themselves by accepting something that could be invalid; Herding refers to replicating the positions of a financial professional. For example, if a reputable investment analyst discloses his new positions, the public replicates his positions and rides along his expertise. Positive feedback trading involves buying winners and selling losers. Both herding and positive feedback trading could have a big impact in stock prices if it occurs on a large scale. For example, a buying pressure of all market participants will result in higher prices as the demand exceeds the supply. A trending price movement will occur when the buying pressure continues for a period. Lakonishok et al (1991) acknowledged these views and performed an empirical study to determine herding and feedback trading among pension funds. Lakonishok et al (1991) argue that institutional investors mainly drive the equity market and their trades may influence stock prices. In addition, they argue that institutions might herd more than individual investors due to the following reasons (Lakonishok et al, 1991): Institutions assume that positions of their competitors are initiated based on proper research. Therefore, replicating those positions will take advantage of the knowledge and the expertise of their competitors; In order to avoid underperformance compared to competitors, money managers have an incentive to replicate the portfolio of top performing competitors; Institutions might react on the same information such as earnings announcements and analysts recommendations; Lakonishok et al (1991) found evidence that herding is more likely to be observed in small stocks, because less information is available for small stocks. They also found evidence that positive feedback trading is present in small stocks (Lakonishok et al, 1991). They argue that this phenomenon is due to window dressing. Window dressing refers to liquidating stocks that yield a negative return in order to increase the relative amount of winners in a portfolio. Poterba and Summers (1986) also performed an examination in regards to feedback 13

15 trading. They conclude that previous winners were positively serial correlated in the short term and negatively serial correlated in the long term. This indicates that stock prices of previous winners tend to move higher in the short term and move lower in the long term. Fama and French (1988) argue that serial correlated stocks exhibit this movement, because stock prices are mean reverting. Mean reversion refers to the premise that stock prices temporarily diverge and tend to move to their average as time progresses. Arbitrageurs are aware that herding and positive feedback trading could result in a divergence of stock prices from their fundamental values. Shleifer and Vishny (1997) show that arbitraging mispriced stocks is not always profitable due to the existence of arbitrage risk, noise traders and transaction costs. Arbitrage risk concerns about similar stocks that are traded on different indices with different prices. In addition, noise traders are irrational individuals who act upon insignificant information. Brown and Jennings (1989) and De Long et al (1991) argue that noise traders are able to cause stock prices to deviate from their intrinsic value. Therefore, an arbitrageur has to take into account that prices could diverge further away from their intrinsic value and decides not always to get involved in mispriced stocks. As a result, discrepancies will not always be immediately traded away. 2.5 Summary Influential modern empirical studies showed the predictive power of technical trading strategies. The profitability of technical analysis increased over time as technical analysis has become more complex (Park and Irwin, 2004). However, conflicting evidence still has been reported the last decades, because trading strategies did not perform consistently. Hence, some trading strategies were only significantly profitable during certain subsamples; were only significantly profitable with unrealistic low transaction costs; or were only significantly profitable on certain markets/exchanges. Therefore, technical analysis is not a technique that guarantees abnormal profits, but its added value should not be discarded. In addition, due to mixed evidence regarding the profitability of technical trading strategies, the efficient market hypothesis is still accepted in the current financial paradigm. 14

16 Chapter 3 Methodology This section will describe which data has been used in this thesis. In order to test if the VN-Index has a random walk, the following tests are performed: the autocorrelation test, the runs test and the variance ratio test. The exponential moving average strategy will also be explained in this section. In order to test whether the returns of the exponential moving average strategy are significant different from the VN-Index, the one-tailed t-test will be used. In addition, the Jensen s α test will be estimated in order to determine the significance of the risk adjusted returns of the exponential moving average trading strategies. All tests are performed at a significance level of 5% (α = 0.05) in order to reject the null hypothesis. 3.1 Data The market efficiency of the Vietnamese stock index (VN-Index) will be tested in this thesis. The VN-Index was founded on the 28 th of July 2000 and is located in Ho Chi Minh City. The data, which has been used in this thesis, is from the 3 rd of January 2006 till the 31 st of December The departures of the prices during this period are illustrated in figure 3.1. This sample consists of 1243 (1242) daily closing prices (returns) and they are retrieved from DataStream via the library of the University of Amsterdam. The HCMNVNE mnemonic has been used in order to access the daily closing prices of the VN-Index. The tests regarding the random walk process and the exponential moving average (EMA) trading strategies will be tested on the full sample and two subsamples. The first sub-sample (sample one) is equal to two-third of the full sample and the second sub-sample (sample two) is equal to the remaining one-third of the full sample. Figure 3.1 VN-Index closing prices Plot of the VN-Index from till , with time on the x-axis and the closing price on the y-axis. Figure 3.2 VN-Index returns Plot of the returns of the VN-Index from till , with time on the x-axis and the returns on the y-axis. The VN-Index will be examined, because no research has been reported that examined the VN-Index on market efficiency as from the 3 rd of January In addition, this thesis will have an added value because only a small number of empirical studies in regards to technical analysis on emergent markets are reported. The daily returns 15

17 are used in order to test the market efficiency on the VN-Index. The daily returns are illustrated in figure 3.2 and are computed as follows: = "# where Log is denoted as the natural logarithm, r t is the return at time t, P t is the price at time t, and P t-1 is the price at time t-1. The returns of the VN-Index will be analyzed based on the mean, standard deviation, skewness, kurtosis and Jarque-Bera. This analysis will be done in Eviews. The Jarque-Bera test determines the normality of the sampling distribution. The null hypothesis states that the population follows a normal distribution. Identification of the normality of the distribution helps to determine whether a parametric or non-parametric test is more useful when the presence of the random walk is tested. The equation of the skewness, kurtosis and Jarque-Bera are expressed in the following equations: = 1 ( 1)/, = 1 ( 1)/, " = where S is the skewness, K is the kurtosis, JB is the Jarque-Bera, is the sample mean of the returns, T is the sample size, and s is the standard deviation. Skewness is a measurement of the symmetry of the dataset about its mean value. Skewness is distinguished in three groups, i.e., no skewness, positively skewed and negatively skewed. A dataset is symmetric when it has zero skewness. A dataset is called positively skewed when it has a long right tail. The opposite is measured for a negative skewed dataset (Brooks, 2006). Kurtosis is a measurement of the peakedness and flatness of the distribution and is used to determine the thickness of the tails of a distribution. The normal distribution has a kurtosis of three (mesokurtic). When the kurtosis is greater than three (leptokurtic), the distribution is peaked and has a fat tail, which indicates that the extreme values have a relative high effect on the variance. The opposite is measured when the kurtosis is less than three (platykurtic) (Brooks, 2006). 3.2 Autocorrelation test The autocorrelation test will be performed in order to determine if a random walk process was present on the VN-Index. The autocorrelation test measures the variation between the stock returns at period t and the stock returns at period t-k. The equation of the autocorrelation test is expressed as follows: ρ = r r r r / r r where ρ is the serial coefficient of lag k, T is the sample size, k is the lag length, r t is the return at time t and r is the sample mean of the stock returns. The aim of the autocorrelation test is to determine if the serial correlation coefficients are zero, with a significance level of α = The 95% confidence interval is determined with the 16

18 following equation: ±1.96/ (T) (Brooks, 2006, p. 232). Serial correlation coefficients that are significant different from zero indicate that prices are dependent and predictable. Hence, significant serial correlation coefficients indicate that the weak form of the efficient market hypothesis should be rejected. The Ljung-Box Q-statistics will be used to determine if ρ 1 to ρ k are simultaneous equal to zero. The null hypothesis of this test is ρ 1 = ρ 2 = = ρ k = 0. The Ljung-Box Q-statistics follows a chi-squared distribution and is expressed as follows: " = + 2 ~ where ρ is the serial correlation of lag k, T is the sample size, m is the maximum lag length and degrees of freedom, and k is the lag length. This thesis will examine an autocorrelation test with a maximum lag of 10. In order to prevent that positive and negative correlation coefficients cancel each other out, the correlation coefficients are squared. The distribution has been used because of the following statement: Since the sum of squares of independent standard normal variates is by itself a variate with degrees of freedom equal to the number of squares in the sum, it can be stated that the Q-statistic is asymptotically as a under the null hypothesis that all m autocorrelation coefficients are zero (Brooks, 2006, p.233). In addition, the null hypothesis is rejected if: " > ; The critical value of the distribution with 10 degrees of freedom at a significance level of 5% is equal to Alternately, the critical value of the distribution with 10 degrees of freedom at a significance level of 1% is equal to Runs test The runs test is used to determine if successive changes in prices are randomly distributed. This is a nonparametric test and is more appropriate for distributions that are not normal distributed. The runs test implies that a sequence is independent over time when the expected number of runs does not deviate significantly from the actual number of runs. A run is described as a sequence of the same characteristics. In the case of stock prices, the same characteristics are positive (+) and negative (-) returns. An example is shown below in order to understand this concept: n 1 = 13, there are 13 occurrences of a positive (+) sign; n 2 = 8, there are 8 occurrences of a negative (-) sign; R (runs) = 6, there are 6 observed runs (sets that are underlined), 17

19 The expected number of runs, the standard error of the expected number of runs and the z-score are calculated based on n1, n2 and R. These are shown in the following equations: = = = where 2 ( 2 ) + ( + 1) ( ± 0.5) x: expected number of runs; n 1: number of positive signs; n 2: number of negative signs; s x: standard deviation of x; R: the number of observed runs; Z: Z-statistics of the standard normal distribution; The runs test will determine the randomness of the runs based on a two-tailed test. The sign of the continuity adjustment will be positive (+0.5) if the actual number of runs is less than the expected number of runs (R x), and negative otherwise (Wallis and Roberts, 1956). A significant Z value indicates that the random walk process is not present on the underlying sample. 3.4 Variance ratio test The variance ratio test states that a random walk should show a linear relationship with regards to the variance of the first difference and the variance of qth difference. Hence, the variance of Pt Pt-1 should be q times smaller than the variance of Pt Pt-q (Lo and MacKinlay, 1988, p. 43). Lo and MacKinlay (1988, p. 62) show that the variance of the first difference follows a Gaussian limiting distribution that is equal to (0, 2 ). In addition, the variance of the qth difference follows a Gaussian limiting distribution that is equal to (0, 2 ). Therefore, they proof that the variance of the qth difference is q times greater than the variance of the first difference. The null hypothesis of the variance ratio test states that the ratio of the variance of the qth difference, which has been scaled down q times, and the variance of the first difference is equal to one. For a sample size of nq + 1 (,,, " ), the equation of the variance ratio is expressed as follows (Lo and MacKinlay, 1988, p. 46): " = "#( ) = "#( ) with = 18

20 = (" + 1)(1 = = " " " ) 1 ( ) " " where VR(q) is the variance ratio of qth difference, σb2(q) is the scaled variance of the qth difference, σa2 is the variance of the first difference, and P is the closing price. Lo and MacKinlay (1988, p. 45) clarify that the estimators, and correspond to the maximum-likelihood estimators of the and. Maximum-likelihood is a technique to estimate parameters with use of minimizing the least squares. Lo and MacKinlay (1988) developed a standard normal test in order to determine the significance of the variance ratios. They introduced two test statistics in order to cope with the assumption of homoscedasticity and heteroscedasticity. Homoscedasticity refers to the assumption that a vector of a random error term has a finite variance, as discussed in section 2.2. Alternately, heteroscedasticity is the absence of homoscedasticity. The standard normal test is expressed as follows: = " 1 () ~(0,1) Under the assumption of homoscedasticity, the following estimator will be used: = ( 1) 3(") Alternately, the following estimator will be used under the assumption of heteroscedasticity: = 2( ) () where " = 3.5 " The exponential moving average trading strategy Trading strategies based upon moving averages are commonly used and have been tested extensively in the last four decades. Moving average strategies are commonly used, because they are easy to understand (Taylor and Allen, 1992) and reduce noise. However, moving averages are called lagging indicators, because they use historical prices. Therefore, current events will be reflected in the moving average with a delay. Moving average strategies are separated in two categories: trend following strategies and countertrend strategies. A trend following model follows the market with a lag. In order to compensate the lag, one could use a moving average 19