Focus of technical analysis in the stock market
The efficient Market Hypothesis is a controversial theory in business due to its contradictory nature. Yen and Lee (2008) note that this theory has both proponents and critics. Brooks (2006, p.2) explains that market efficiency guarantees that current stock prices consistently incorporate all relevant information.
The purchase of stock is always based on its fair value in the stock exchange, allowing investors to decide whether to buy undervalued stock and potentially sell it during inflation. Technical analysis examines market prices using historical data, focusing on price and volume. However, this approach is challenged by the efficient market hypothesis, which argues that stock market prices cannot be predicted (Lo and Hasanhodzic, 2013, p. 45). Thomsett (2003) provides a discussion on the support and resistance chart within technical analysis.
The concept
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of strong market efficiency
According to Harder (2010), the prices of assets are greatly influenced by both market information and changes in discount rates. Some investors seek out market loopholes in order to maximize their profits. The Efficient Market Hypothesis (EMH) is a theory in financial economics that asserts that asset prices fully reflect all available information. Rupper (2004, p. 9) classifies market efficiency into three levels: high, semi-strong, and weak. Strong efficiency implies that the market incorporates all public, private, and additional information, whereas weak and semi-strong efficiency suggest that the market only reflects public information.
According to Chan, Benton, and Ming-Shiun (2003), the presence of weak efficiency in the market suggests that there should be no correlation between market prices. The authors contend that investors strive to acquire stocks that are undervalued or sell those tha
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of strong market efficiency
According to Harder (2010), the prices of assets are greatly influenced by both market information and changes in discount rates. Some investors seek out market loopholes in order to maximize their profits. The Efficient Market Hypothesis (EMH) is a theory in financial economics that asserts that asset prices fully reflect all available information. Rupper (2004, p. 9) classifies market efficiency into three levels: high, semi-strong, and weak. Strong efficiency implies that the market incorporates all public, private, and additional information, whereas weak and semi-strong efficiency suggest that the market only reflects public information.
According to Chan, Benton, and Ming-Shiun (2003), the presence of weak efficiency in the market suggests that there should be no correlation between market prices. The authors contend that investors strive to acquire stocks that are undervalued or sell those tha
are overvalued in order to generate abnormally high profits. However, since stock prices are constantly influenced by market information and trade at fair values, it becomes challenging for investors to outperform the overall market. In accordance with the efficient market hypothesis, stock returns are determined by available market information and discount rates at specific points in time (Chan, Benton, and Ming-Shiun, 2003, p.).
39). Eugene (1970) developed the efficient market hypothesis (EMH), which argues that it is impossible for an investor to outperform the stock market because all information related to it has already been incorporated into its stock prices.
The EMH contradicts technical analysis
As mentioned in the research conducted by Chan, Benton, and Ming-Shiun (2003), the EMH challenges technical analysis by asserting that past prices cannot be relied upon to predict future profitability. Eugene provides an extensive explanation of the EMH.
According to EMH, instead of past prices, investors should focus on previous profits. Successfully predicting such trends can result in high profits for investors. This strategy is commonly used by investors who have been able to achieve significant gains. They conduct a thorough market analysis of stock prices and make informed decisions on when to purchase and sell stocks (Murphy, 1999, p.
From the provided graphs, it can be observed that purchasing shares in January and selling them in April typically yields high profits, whereas selling the stocks in September or October results in lower profitability. Therefore, the selection of stocks to buy is not the determining factor.
By observing past prices and using moving averages, one can predict price movements. Murphy (1999) states that the analysis of securities can be
done through technical analysis or fundamental analysis. Fundamental analysis involves studying factors like the economy, company management, financial conditions, and company conditions (Lorenzo, 2013, p. 92).
The financial aspects of a business, such as earnings, assets, liabilities, and expenses, are the focus of fundamental analysis (Schlichting, 2013, p 40). When a company is financially strong, its value and stock value tend to be high, making it appealing for investment. The behavior of a business often creates the initial impression on investors before conducting further analysis. However, there are cases where a company's behavior does not align with its value, resulting in the need for technical analysis (Murphy, 1999,p.
65). The analysis discussed here pertains to market price movements. It is generally accurate or very close to accuracy in its predictions.
Technical analysis of market trends
According to Osler and Chang (1995), the head and shoulder technique is considered the most reliable form of technical analysis in predicting market trends. It serves as a starting point for empirical research and forms the foundation for all predictions.
The use of the head and shoulder technique will always influence the final prediction, regardless of the researcher's chosen method. However, it may not always accurately reflect future market prices for a stock. Unforeseen events can make the market unpredictable, rendering the technique ineffective in estimating stock value. Additionally, different investors have varying perspectives on stock valuation (Chan, Benton, and Ming-Shiu, 2003, p. 102).
Even with the head-to-shoulder technique, it is still difficult to predict market movements and take advantage of the markets because each investor assigns a different value to individual stocks. Technical analysis relies on past
market information to determine possible stock exchange prices, but it must reverse existing trends in order to make accurate predictions.
Conclusion
Technical analysis allows investors to predict market trends and anticipate future financial price movements. It utilizes various charts to display anticipated prices.
The main focus of technical analysis, which is relevant to all investors, is primarily on the price. It takes into account factors such as supply and demand, as well as support and resistance.
Bibliography
- Brooks, J. C. (2006). Mastering technical analysis: using the tools of technical analysis for profitable trading. New York, McGraw-Hill
- Chan, K C, Benton E, C, Ming-Shiun, P (2003). International Stock Market Efficiency and Integration: A Study of Eighteen Nations. Journal of Business Finance & Accounting. 24(6): 803–813
- DI Lorenzo, R. (2013).
Basic technical analysis of financial markets: a modern approach. Milan, Springer.
Harper Business. ISBN 0-06-059899-9.
LO, A. W., & Hasanhodzic, J. (2013). The evolution of technical analysis: financial prediction from babylonian tablets to bloomberg terminals. Hoboken, N.J., Bloomberg Press.
Murphy, J.
J. 1999, Technical analysis of the financial markets: A comprehensive guide to trading methods and applications, New York Institute of Finance, New York
Osler C. L. and Chang P.
H. K., 1995, ‘Head and Shoulders: Not Just a Flaky Pattern’, Federal Reserve Bank of New York, C, Staff Report No. 4
Springer,
New York, NY [u.a.].
(2002). Essential technical analysis: tools and techniques to spot market trends. New York, NY, Wiley.
(2003). Support and resistance simplified. Columbia, Md, Marketplace Books.
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