Technical trading rules and market efficiency in the Colombia stock market

Technical Analysis of Columbia Financial Market

Advocates of market efficiency generally believe that it is impossible for technical analysts to predict recurring-price pattern and technicians could not beat buy-and-hold strategy using technical trading rules. However, technical traders believe that there is some form of market efficiencies and technical traders can use price patterns strategy to beat buy-and-hold strategy.

Objective of this paper is to investigate Columbia Stock Market and attempt to find technical trading rules to predict changes in the Columbia Stock Market Index. The paper identifies trading strategy to be used to beat buy-and-hold strategy to enhance efficiency of Columbia stock market.

Within the past few decades, the stock markets in the emerging economies have experienced rapid growth, which have attracted investors across the globe. With the increase in the price movements in the emerging economies, greater importance has been given to market efficiency. While technical trading rules have been implemented to test market efficiencies in the emerging countries such as BRIC (Brazil, Russia, India and China), there is a paucity of research that confirms that technical trading rules could be applied in the Columbia stock market.

This study evaluates different technical trading rules on Columbia stock market and determines whether changes in the Columbia market index could be predicted using technical analysis. The remainder of the paper is structured as follows: Section II of the paper discusses literature review, while section III discusses the data and methodology, while section IV provides the empirical results on technical trading rules. Section V provides buy-and-hold strategy and section VI provides the conclusions.

II. Literature Review

Dominant theme in financial market since the last few decades is the concept of efficient market systems. Fama (1970) defines efficient market system as the market, which its security price always reflects available information and any new information simultaneously and quickly reflect in prices. Moreover, news of any company always unpredictably arrive randomly leading to price changes unpredictably or follow a random walk. According to Fama perspectives, there are three forms of EMH (Efficient Market Hypothesis):

a) the weak form,

b) the semi-strong form, and c) the strong form.

Supporters of the weak-form market efficiency show that investors will not be able to drive up profits above buy -- and hold strategy using trading rules, which depend solely on past market information such as volume or price, and the implying technical trading, will be useless.

More than three decades of research shows that financial economists, academic researchers and practitioners have not yet reached a consensus whether technical trading rules could yield profitable trading results. However, overwhelming number of economists and financial analysts support the "weak-form" efficient market hypothesis. While the earlier research strongly supported the random walk hypothesis, however, the semi-strong form of Efficient Market Hypothesis has been the basis of most empirical research.

Early results from the literatures reveal that profitability derived from the technical trading was overwhelmingly negative. For example, Fama and Blume (1966), Alexander (1964), Larson (1960), Granger and Morgenstern (1963), Van Horn and Parker (1967), Jensen and Benington (1970), Mandelbrot (1963), Fama (1965), and Osborne (1962) all supported the weak form of market efficiency.

Since the beginning of the1990s, financial analysts have used technical trading rules to evaluate financial market performances. In 1990s, technical trading rules have enjoyed a renaissance in both academic circles and Wall Street. Several papers deliver evidences that simple predicting trading rules are very effective in predicting stock returns. Contrary to fundamentalists who use balance sheet or income statements of a company to predict stock returns, technical analysis is based on the assumption that past volume, prices and many other indicators could be used to detect future price movements. The art of technical analysis is to identify changes in prices and maintain investment postures using the trade signals. (Pring, 1991).

Murphy (1999) points out that technical analysis is the study of market actions such as volume and price to forecast future price actions. To test power of technical trading rules, stock technicians use the WFEMH (Weak-Form Efficient Market Hypotheses) using past returns to determine random walk testing. Following the test, technicians use various trading rules to predict profits using buy-and-hold strategy.

While many studies have investigated whether technical rules could be used to provide a superior investing performance, however, the most comprehensive recent study of Brock, Lakonishok, and LeBaron (1992) (BLL) using 90 years of daily stock prices discovered 26 technical rules that had been applied to Dow Jones Industrial Average and significantly outperformed a holding cash benchmark. BBL analyzed moving and Dow John Index from 1897 to 1985 using various short and long run moving average of prices in order to generate buy and sell signals. The authors tested 50, 150,200 days long moving averages and short moving averages of 1,2,5 days. Their findings showed that stock market is not efficient when using the weak form efficient market hypothesis and presented findings to show that technical rules have predictive power. The researcher such as Bessembinder and Chan et al. (2006) also used BBL's moving average to investigate whether it is possible to predict stock market indices using some simple form of technical analysis. Bessembinder et al. (1995) conclude that BBL technical rules are very effective in predicting stock price movement in South Korea, Japan, Hong Kong, Taiwan and Thailand.

Moreover, Ergul, Holmes and Priestley (1997) use the prices of 63 stocks traded daily on the Istanbul Stock Exchange to evaluate the technical trading rules. The authors conclude that technical analysis can be used to predict stock returns, which cannot be predicted with previous analysis. Pruitt and White (1998) also use the University of Chicago's Research in Security Prices (CRSP) daily data tapes between 1976 and 1985, and their findings reveal that technical trading rules are very effective in outperforming a simple buy-and-hold strategy after accounting for transaction costs. Bessembinder and Chan (1998) also confirm the basic BLL results by pointing out that the BLL findings lie parallel with the concepts of market efficiency even after accounting for the transaction costs.

Ratner and Leal (1999) also support the predictive power of stock return of technical trading rules. (Gencay 1998a, 1998b). Kwon and Kish (2002) in their own case apply three popular technical trading rules to NYSE index of between 1962 and 1996. The authors conclude that the "technical trading rules have the potential to capture profit opportunities over various models when compared to buy and hold strategy." (Metghalchi. Glasure, and Garza-Gomez, 2011. p.4). However, Ready (2002) points out in his recent study that the apparent success of the BLL moving average rules is due to a spurious result of data snooping and the issue should not persist in the future. Researchers have also applied the technical trading rules to foreign exchange markets. Taylor and Allen (1992) point out that technical advice could be self-fulfilling for foreign exchange markets. Mengoli (2004) also collects data of the listed securities from the Italian Stock exchanges over the period of 1950- 1995 and conclude that technical trading rules are very effective to determine market profitability.

Neely, and Weller, (2011) argues that 30% of the traders in the foreign exchanges markets in the United States are being implemented by the technical trading rules and there have been continuous used of technical trading rules for foreign exchange market.

However, some studies do not support technical trading rules. Szakmary, Davidson, and Schwarz (1999) apply technical trading rules on stocks listed in Nasdaq index and conclude that individual stock perform poorly using technical trading rules strategies, however, the Nasdaq index earn satisfactory abnormal returns. The author believes that abnormal returns can still be insignificant since high level of transactional costs is generally associated to NASDAQ trading. Sullivan, Timmermann and White (1999) find no evident of profitability after applying the technical strategies to the S & P. And Dow Jones Industrial Average. Coutts and Cheung (2000) apply technical strategies on the Hang Seng returns over a period of 1985 -- 1997 and conclude that both the trading breakout rules and moving average fail to provide net transaction costs and positive abnormal returns.

III Data and Methodology

The study collects data from Datastream's daily index prices of the Columbia stock market from October 12, 2001 to October 12, 2011, and computes daily returns based on the changes in the stock index level. To determine daily interest rate, the study uses DataStream's daily Columbian interbank one month and Columbia Treasury three-month rates.

Technical analysis is based on the assumption that prices move in trends and determined by the traders' changing attitude following the political, economics, and psychological forces. In other words, technical analysis uses the past behavior of market data to assist in making trading decision in financial markets. The historical price data generally assist in making these decisions. As being put forwards by Pring (1991), the technical analysis is a reflection that prices move in trends and generally being determined by changing attitude of investors. Typically, technical approach is based on the idea historical data can be used to forecast future price movement based on the monetary, economic, political and psychological forces. Pring (1991) further argues that the goal of technical analysis is to identify changes in trends and maintain investment posture until there is a reversal in trends. Major Trend-Determining Techniques is based on buy (sell) signals emitted when the price of index moves below or above the local minimum or maximum.

This study uses both short-term moving and long-term moving average. The short-term moving average is 9,and 12 and 26 and long-term moving average is 50, 100, and 200 day's local minimum or maximum and the strategy to employed in this study is as follows:

We are in the market when a buy signal is emitted and when the price level has broken out above the local maximum,

We out of the market and exit the long when a sell signal is emitted and the price level has moved below the local minimum.

Based on the technical trading rules, the study can be "either in the market (buy days) or out of the market (sell days)." (Metghalchi. et al., nd. p.4).The methodology reveals that the study follows the break out rules, which assists in observing the price movement in other to make a trading decision. If we are out of the market and our closing price is above the local maximum, then trader should in the market the following day by buying the index at today's closing price (Next must be the day to buy). Thus, the next day's return is going to be difference between the logarithms of today's closing price and the log of the closing price of next day. Once we are in the market, the position is a buy day until the closing price is below the minimum of the local price. In that day, the next action is to sell the price index at the closing price and be out of the market the next day, which is the sell day. Meanwhile, we continue staying out of the market as long as price is not above the local maximum. Thus, the study defines mean sell, X(s), mean buy, X (b), and returns as revealed in the following equations:

The equation 1 defines the moving average.

MAt (M)= 1 M-1 (1)

M ? Pt-i i=0

1

X (b) = N (b) ?Rb (2)

In the equation 2, when the system generates sell signal we are out of the market however, we are in the market when the system generate the buy signal. Thu, some days we are in the market and some days we are out of the market. In the equation 2, we define the mean of buying days and average returns of the days in the market.

1

X(s) = N(s) ?Rs (3)

The equation 3 defines the average returns of sell days.

As being revealed in the equation 1 and 2, the N (b) are total number of buy days and N(s) are total number of buy sell days. Moreover, Rb are daily returns of buy days and Rs are daily returns of sell days. The study "tests whether the returns of any trading breakout rules are greater than a buy and hold strategy and whether the mean buy is different than the mean sell." (Metghalchi, et al., nd, p. 5) specifically based on the following hypothesis:

H0: "X (b) - X (h) =0, X(s)-X (h) = 0, X (b) -- X(s) =0"

HA: "X (b) -- X (h) ? 0, X(s) -- X (h) ? 0, X (b) -- X(s) ? 0"

"Where X (h) is the mean return for the buy-and-hold strategy. The test statistic for the mean buy returns over the mean buy -- and hold strategy is": (Metghalchi, et al., nd, p. 5)

Equation 4 is the test statistics of two mean that is comparing means to reveal whether the mean of buy and mean of sell different from each other.

X (b)- X (h)

t=

( 4)

Var (b) / Nb+ Var (h) / N

Where Var (b) is the variance of buy and Var (h) is buy-and-hold returns. The formula presented above is used to test the mean sell returns and the mean buy-and-hold strategy. The formula is also used to test the mean of buy returns over the total mean of sell returns, and this is done by replacing appropriate variables in the t-statistic formula.

Moreover, the study uses the Exponential Moving Average (MVA) and MACD (moving average convergence/divergence) for our methodology. The MACD is employed to spot changes direction, strength, duration and momentum of the stock prices; however, EVA is similar to the simple moving average except that more weight is given to the latest data.

V Trading Strategies

The study provides a degree by which traders could use the technical trading rules to earn trading profits, which could be used to beat buy-and-hold strategy. Based on the fact that the mean buy is greater that the mean sell and the profitability of the technical analysis is based on the trading strategies and the position that traders should take when the trading rules emit sell signals. Typically, the traders are not to invest when the system emits sell signal, the trader will earn zero profits based on the mean return of (Nb/N)*X (b) + (Ns/N) *0. To be beat buy-hold-strategy, the study suggests a simple strategy by which the traders will be in the stock market when the system emits emit buy signals and in the money market when the trading rule emits sell signal. The new trading strategy will assist the traders to be in the market all the time and making returns. To get daily returns, the strategy is to estimate the daily returns and deduct it from the daily return of buy-and-hold. In the strategy, we present the null and alternative hypothesis to test whether the average daily difference value is greater than zero and the test is as follows:

H0: ddif = 0

HA: ddif ? 0

The t-statistic used for the above test is:

X (ddif)

t=

( 5)

Var (ddif) / N

Where X (ddif) is the average difference value of the daily difference of returns of each of the strategy of the buy-and-hold strategy.

On the other hand, Var (ddif) is the value of the variance of daily difference returns while N. is the total number of days. Thus, Table 2 presents the results of the proposed strategy.

The total trading return on buy days for leverage/money strategy is

TRt = 2 * Rt -- Mt,

Where Rt is the index return on day t and Mt is the money market return on day t.

Rules

Xddif

SD (ddif)

N

MACD & MA9 (9, 9)

0.002363424

0.001389797

(10.45)*

MACD & MA9 (18, 18)

0.001972145

0.001865112

(7.53)*

RSI>(50)

0.001905781

0.003471573

(7.50)*

VI: Conclusions

Analysis of empirical studies reveals that simple trading rule is very effective in predicting market returns. This study investigates various trading breakouts that could be used for the Columbia stock index between 2001 and 2011. The results provide a strong support for the technical analysis, and the results of all the differences between buy and sell are positive. Moreover, the results of the t-statistics are very significant based on the fact that our results support the alternative hypothesis and reject the null hypothesis of equal with zero. Moreover, the t-statistics for buy and sell days are very significant because the t-values rejects the null hypothesis revealing that sell day returns and buy day returns are equal to the unconditional 1- day return as being revealed in Table 1.

This study supports the technical analysis because the system appreciates that the sale day returns are different from buy day returns. Thus, our results show that technical analysis significantly has a predictive power, which can assist traders to record profitable, trading. Based on the predictive power of trading rules systems, we have designed a trading strategy to surpass the buy-and-hold strategy. Thus, the results of our trading strategy support the null hypothesis that technical trading system has ability to surpass buy-and-hold strategy.

In summary, technical analysis delivers predictive power and is effective in designing a trading strategy that will outperform buy-and-hold strategy in the Columbia capital market. Based on the observation of the technical trading rules, the system does have predictive power in the Columbia stock market. However, this study suggests that traders must be very careful before implementing our strategy for future trading. Traders should estimate the transaction costs before using the technical analysis; however, the technical analysis will be very effective for future trading if the transaction costs are very small.

List of References

Alexander S. (1964).Price Movements in Speculative Markets: Trends or Random Walks," Industrial Management Review, 2, pp. 25-46.

Bessembinder H. And Chan K.(1998), Market Efficiency and Returns to Technical Analysis,"

Financial Management, 27 (2), 5-17.

Brock, W., Lakonishok, J., & Lebaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance, 47(5), 1731 -- 1764

Chang, Y., Metghalchi, M., & Chan, C. (2006). Technical Trading Strategies and Cross-national Information Linkage: The Case of Taiwan Stock Market. Applied Financial Economics, 16, 731-743.

Coutts A., and Cheung K.C., (2000). Trading rules and stock returns: some preliminary short run evidence from the Hang Seng 1985-1997, Applied Financial Economics,, 10. 579-686

Ergul A, Holmes P., and Priestley R.,(1997). Technical Analysis, Trading Volume and Market

Efficiency: Evidence from an Emerging Market, Applied Financial Economics, 7, 361-65.

Fama, E. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25, 383-417.

Fama E. And Blume M.,(1966) Filter Rules and Stock Market Trading Profits, Journal of Business, 39, pp. 226-341.

Gencay R., (1998a) Optimization of Technical trading strategies and profitability in Security Markets, Economics Letters, 59 (2), 249-254.

Gencay R.,(1998b), The Predictability of Security Returns with Simple Technical Trading Rules, Journal of Empirical Finance, 5 (4), 347-359.

Gupta, R, & Yang, J. (2011). Testing Weak form Efficiency in the Indian Capital Market. International Research Journal of Finance and Economics, 75, 108-119.

Granger C. And Morgenstern O., (1963). Spectral Analysis of New York Stock Market Prices, Kyklos, 16, pp. 1-27.

Kwon K. And Kish R., (2002).Technical Trading Strategies and Return Predictability: NYSE, Applied Financial Economics, 12, . 639-53.

Metghalchi, M., Chang, Y., & Marcucci, Y. (2008). Is the Swedish Stock Market Efficient? Evidence from some Simple Trading Rules. International Review of Financial Analysis, 17(3), 475-490.

Mandelbrot B.,(1963). The Variation of Certain Speculative Prices, Journal of Business, 36,

394-419.

Mengoli S.,(2004). On the Source of Contrarian and Momentum Strategies in the Italian Equity Market, International Review of Financial Analysis, 13 (3).301-331

Murphy, J. (1999). Technical Analysis of the Financial Markets. New York, NY: Institute of Finance.

Pring, M. (1991). Technical Analysis: Explained. New York, NY: McGraw-Hill.

Pruitt S. And White R., (1998). The CRISMA Trading System: Who Says Technical Analysis Can't Beat the Market? Journal of Portfolio Management, 14 (3), pp. 55-58.

Neely, C.J. & Weller, P.A. (2011). Technical Analysis in the Foreign Exchange Market. Federal Reserve Bank of St. Louis, the Federal Reserve System.

Ratner M. And Leal R.P.C.,(1999). Test of Technical Trading Strategies in the Emerging Equity Markets of Latin America and Asia," Journal of Banking and Finance, 23 (1), 1887-1905.

Ready M., (2002).Profits from Technical Trading Rules, Financial Management, Autumn, 43-61.

Szakmary A., Davidson W.N., and Schwarz T.V., (1999), Filter Tests in Nasdaq Stocks, The

Financial Review, 34, 45-70.

Sullivan R., Timmermann A. And White H., (1999). Data-Snooping, Technical Trading Rule

Performance, and the Bootstrap, Journal of Finance, 54 (5), 1647-1691.

Van Horn J.C. And Parker G.C.,(1967) .The Random Walk Theory: an Empirical Test, Financial Analyst Journal, 23, pp. 87-92.