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...

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…