Efficient market hypothesis traces its origin back in 1960s by its founders Paul A.Samuelson and Eugene F. Fama who provided perspectives regarding the stock prices of financial securities that the market prices provide all the information that is available. Samuelson came up with the notion that if all market player’s information and expectations are put into consideration then there will be an informational efficient market (Brown, 2012). This would mean that price fluctuations must be unforecastable if they are properly anticipated. On the other hand Fama empirical analysis of efficient markets contributed to the deep focus on test concerning both single factor and multi factor linear asset pricing models, events study and empirical regularities and anomalies in stock, bond, currency and commodity markets. His argument was based on providing a way forward in distinction between technical analysis that involves the use of geometric patterns in price movements and volume charts to predict future price movements of a security and fundamental analysis that sought to determine a security’s fail value with the help of accounting and economic data (Brown, 2012). These concepts proved very helpful in ensuring that significant profits were generated due to the use of statistical properties of stock prices this was characterized by random walk hypothesis. However to develop these methodological and empirical research Fama used digital computes in finance especially in the mathematical formulations.
An efficient market was characterized by competition and a large group of investors aimed at getting huge returns from investment in the market securities by using prediction mechanisms with each potential investor searching for the relevant information that enabled real prices at every point in time represent very good estimates of intrinsic values (Burton, and Shah, 2017).   In addition he went ahead and expounded more on his theory efficient markets and categorized them into distinct levels of strong efficiency, semi-strong and weak efficiency. The three levels were based on the extent of the availability of information whereby for a strong market the prices depict information available to those who form the market or where the investment managers acquire as much information they can get by paying for it. In addition a semi-strong market involves the easily accessible information concerning the public in connection with stock splits and announcements by media sources and other reports (Brown, 2012). The behavior as a results of these announcements are then tested with the help of capital asset pricing model   A weak efficient market however requires any available information which is mirrored by price to be obtained especially one relating to the past and it requires random walk hypothesis. Based on past knowledge of price changes a weak form efficiency tests raises concern that the investor may consistently obtain returns that are of higher level than the normal ones. However the strong market efficiency proved irrational and could not be relied upon due to the fact that the corporate managers had the opportunity to acquire non-public information that they used for their advantage. In addition through payment for privileged information by market makers they were exposed to profit opportunity that served as an open door to their best interests at hand. Those who did not have the means to acquire the public information were left vulnerable and faced high uncertainty on their investments (Guerrien, and Gun, 2011)
Despite the accolades given the efficient market hypothesis, some authors have argued that some inefficiency may exist in the market from time to time. The perfection of market efficiency would render the gathering of information unhelpful since profit is not being reflected. In addition such inefficiencies would mean that investors are willing to dig deeper into their pockets in search of information and trading. This is expected to be reflected in the numerous profit opportunities as a result of those few individuals who pretend to have information to trade but they only get the economic rents. This would mean that an investor my find himself having information concerning market inefficiency and would require a higher return.  However some tests may imply efficiency or inefficiency depending the trend in the price fluctuations and the pattern depicted. It goes without saying that market efficiency cannot be fully denied except a situation where an individual is certain that the right model has been chosen for that market in case of normal returns. It requires therefore appropriate bench marking for market efficiency. It is argued that for abnormal return the information gathering and processing is deemed to be expensive. In addition a joint test of inefficiency and the model that indicates the normal returns in an efficient market is important and termed as of greater impact and relevance forecasting of future returns from past returns.

In modern times the theory of efficient market hypothesis has been widely used by investors due to its consistent dominance in the market and the constant positive feedback on its reliability due to the successive price changes.
The technical analysis date back in the 20th centuries where the behavioral and psychological elements were premised on principle. That information available is relevant in stock market in price changes which move in in trends to enable prediction during trade and also historical patterns in the past stock price changes that replicates over and over again throughout.
Recent technical analysts have adopted majorly the charting and indicator methods in ensuring that past data on price changes is captured for trading decisions. The use of judgmental skills are applied in interpretation of these patterns in charting and in indicator method adopts the mathematical algorithms in analyzing past and present price changes and the use of technical trading rules was fundamental in the trading decisions (Muhammad, and Rahman, 2010).  The bar charts are suitable in representing individual securities with their indication in price changes and the patterns associated with them such as head and shoulder patterns and triangle patters.

Arguably technical analysis rests on the some basis assumption that forces of demand and supply cause the changes in trend, despite the minimal fluctuations in the market the price of security change for a longer period of time and they do so in trends and the interactions of the demand and supply determines the market value caused by both rational and irrational factors and with the use of charting mechanism the shifts in demand and supply can be predicted on time (Burton, and Shah, 2017).
Theorists have provided different perspectives on the trends especially Charles Dow in his theory providing three classification of trends. He examines the primary trends, secondary movements and tertiary moves and suggests the use of line charts to illustrate these types. In addition Elliot wave theory identifies the different cycles that a market moves and is divided into impulse waves and corrective waves.
There are other technical analysis techniques that involve the use of behavior of market participants .such systems analyze the strength market decline or advances in, short sales levels and small investors with small funds and they are well known as contrary opinion theories.
Most importantly is note that with the aid of the above techniques the demand and supply is determined and also information is made available as far as selection of appropriate securities to purchase or sold are concerned (Borges, &Maria, 2010). The assumption that slowly occurring shifts can be detected and this validates the new information that is observed in the security prices. This has amassed wealth to some individuals for being smart enough to apply the techniques in useful timing decisions with the help of fundamental tools on securities to be purchased

Analysts have demonstrated how technical rules have contributed to high returns over a long duration especially in 1970s and 1980s. There has been persistence in the sophisticated and complex rules that have lately been used since 1990s to generate profits associated with the trading (Yalçın, 2010).

A controversial notion was put forward by some new economics discarded the use of information available of stocks that spreads widely pertaining the individual stocks and stock markets. They claimed that in order to earn excess risk the determination of stock prices relied heavily upon predictable future stock prices in the event of past stock price patters .using behavioral and psychological elements (Borges, &Maria, 2010).
The forecasting power of pattern charts evaluated by head and shoulders patterns sought to determine how the stock prices varied with the fluctuating currencies to statistically predict profits suing floating rates in an efficient market. The research sought to combine the technical analysis and efficient market hypothesis in evaluating statistical significant profits. The researchers first identified the head and shoulder patterns and by using the available information to identify the resulting profit values. The last step was to use technical analysis in head and shoulder patters to assess the predictive power of some currencies over others.   The technical analysis was directed to solving the market inefficiencies by using algorithms of comparisons of major currencies and the dollar (Burton, and Shah, 2017). The randomness of price movements and fluctuations in an efficient market is contributed by the informational efficiency. Research has shown that the more an efficient market is the more the randomness and unpredictability. This therefore is very resourceful to market participants since they are able to generate statistically significant profits from this informational efficiency. The self-interest therefore leads to many potential investors adopting the concept that matriangles are followed by price in their trading to reduce on costs due to accurate available information at their disposal which is very advantageous in the stock markets.
In conclusion it is probably seasonable to acknowledge that the adaptation speed of markets continued reduction in profit opportunities deviates with the standard notion of market efficiency (Holton, 2015). The paradigm of the adaptive market hypothesis allows future developments of models. The establishment of profits earned rather than risks incurred is very important especially in devising market inefficiency in the profitability of technical analysis.
The interaction of the technical analysis and market efficiency is envisaged on the emphasis that the decision rules will move from standard rational paradigm including the focus on learning and evolutionary mechanisms that will address financial markets especially in currency market research (Burton, and Shah, 2017).

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Borges, Maria Rosa. “Efficient market hypothesis in European stock markets.” The European Journal of Finance 16, no. 7 (2010): 711-726.
Brown, R., 2012. Analysis of investments & management of portfolios.
Burton, F.E.T. and Shah, S.N., 2017. Efficient Market Hypothesis. CMT Level I 2017: An Introduction to Technical Analysis.
Guerrien, B. and Gun, O., 2011. Efficient Market Hypothesis: What are we talking about?. real-world economics review, 56, pp.19-30.
Holton, G., 2015. Efficient market hypothesis.
Lee, C.C., Lee, J.D. and Lee, C.C., 2010. Stock prices and the efficient market hypothesis: Evidence from a panel stationary test with structural breaks. Japan and the world economy, 22(1), pp.49-58.
Muhammad, N.M.N. and Rahman, N.M.N.A., 2010. Efficient market hypothesis and market anomaly: Evidence from day-of-the week effect of Malaysian exchange. International Journal of Economics and Finance, 2(2), p.35.
Sewell, M., 2011. History of the efficient market hypothesis. RN, 11(04), p.04.
Yalçın, K. C. (2010). Market rationality: Efficient market hypothesis versus market anomalies. European Journal of Economic and Political Studies, 3(2), 23-28.


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