| It is difficult to overstate the importance of a stock market for a developing country. The primary benefits of a well functioning stock market are, mobilization of savings, fund term matching with efficient allocation of investment resources, and acceleration of economic growth. Share markets facilitate investors with excess funds to invest them in a profitable way as well as for companies to raise their capital requirements through shares, debentures and any other type of securities. At present Colombo Stock Exchange (CSE) is dealing with shares of 242 companies and debentures of 15 companies. Inauguration of the CSE dates back to the nineteenth century, while the Share Brokers Association (SBA) controlled it at its outset. In 1984 Colombo Brokers association established a public trading floor and commenced trading on "open outcry system". From 3rd of June 1997 onwards, Colombo stock exchange trading system converted from an open outcry system to an automated trading floor along with the installation of a screen based trading system. In recent decades emerging stock markets showed remarkable potential to attract the attention of global investors. Being an emerging stock market, CSE became the best bourse in 2010 and its potentiality to growth is exceptional.This study investigates the efficiency of the CSE by utilizing moving average and trading range breakout strategies. Further it examine whether these strategies can outperform the unconditional buy-and-hold strategy to forecast stock price movements and earn excess returns after adjusting for transaction costs. Technical analysis is considered as one of the preliminary methods of investment analysis, because stock prices and volume figures have been publicly available prior to any other types of financial information. The technicians or the chartists always believed that history is bound to repeat in the market. With that presumption in mind, technicians used historical prices and volume figures to predict about the market. They employed a number of charts, and diagrams to explain the market situations and market trends. Technical analysis is being extensively used by brokering firms, foreign exchange dealers, investors, and commodity traders. The school of thought that the price of stock is determined not only by previous prices and volume data, but also with the help of firm specific financial and macro level data, led to the development of Fundamental analysis to explain the stock prices. But still technicians emphasize the value of Technical analysis because of Accounting irregularities and scandals.This study is composed of two main sections. Focus of the first section is on the overall market based on the price data of All Share Price Index (ASPI), which works as a composite index. The second section is based on evaluations of sector price indexes in telecommunication industry, diversified holdings industry, information technology industry, and stores and supplies industry. Selection of industry sectors were based on the market capitalization of each sector, thus the first two industries represent high market capitalized industries and other two industries represent low market capitalized industries. This study selected All Share Price Index (ASPI) data from 1985 to 2010 and the period considered for selected industry sectors spans from 2003 to 2010. Sixteen variable length moving average (VMA) rules, sixteen fixed length moving average (FMA) rules, and four trading range breakout rules are tested on ASPI and other selected four industry sectors. The short moving average used in the study is the previous day's closing price and the long moving average varies in length from 50 to 200 days. In addition to that each rule is tested with 1% band to avoid whiplash signals generated from moving average strategies. The results of the technical trading strategies are tested for transaction cost by obtaining breakeven cost through "double or out" strategy.The empirical findings related to ASPI data shows that the trading rules have stronger predictive power in explaining the stock market prices. The results revealed that the mean return subsequent to a buy signals is positive for all VMA rules while mean return is negative succeeding a sell signal. Further it shows that the exercise of technical trading strategies generate excess return when compared to unconditional buy-and-hold strategy before transaction costs adjust. The mean return following buy signals are all positive for FMA rules while it is negative for all sell signals except (1,50,0) and (1,50,0.01) rules. Even the FMA strategy also shows high predictive ability and excess return before adjusting the transaction costs. The TRB strategy also shows positive returns for all buy signals and negative returns for all sell signals but they are not as significant as the returns generated from moving average signals. Anyway TRB strategy generates excess returns when compared to unconditional buy-and-hold strategy before making adjustments for transaction costs.The VMA rules for other four industry sectors do not provide statistically significant results. There is not much difference between the results of high market capitalized sector and low market capitalized sector. However the results related with these elected sectors shows a huge deviation from the ASPI, which represent the overall market. It provides an indirect idea though the high market capitalized sector accounted for more than 40% of the overall market capitalization, its behavior is not truly represented from the composite market index.According to the empirical findings there is no evidence to confirm the profitability of technical trading strategies over the unconditional buy-and-hold strategy, after considering transaction costs. None of the strategies; VMA, FMA, or TRB generate profit for ASPI, telecommunication, diversified holdings, information technology, or stores and supplies sectors, after considering transaction costs. The results show some of the strategies generating negative breakeven costs, while it indicates that even those who can trade at zero costs would have earned less return by trading on the technical trading strategies than from the unconditional buy-and-hold strategy.When transaction costs are introduced the technical trading strategies does not generate a return different from the unconditional buy-and-hold strategy. This is true for all technical trading strategies tested in this study. This implies that investors cannot outperform the market for any abnormal return by utilizing the tested trading strategies. The fact that at least some forecasting power is recognized, it is not leading to a violation of efficient market hypothesis. The inclusion of transaction costs causes that equality, between the return from trading strategies and the return obtained from unconditional buy-and-hold strategy. Thus the weak-form efficiency cannot be rejected for Colombo Stock Exchange.
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