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FIN 300 Lecture Notes - Lecture 10: Dow Jones Industrial Average, Efficient-Market Hypothesis, Managerial Finance

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School
Ryerson University
Department
Finance
Course
FIN 300
Professor
Laleh Samarbakhsh

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Document Summary

Security prices change without predictable trends or patterns. Stock prices seem to go up or down on any particular day, regardless of what has occurred on previous days. The(cid:396)efo(cid:396)e, o(cid:374)e (cid:272)a(cid:374)"t use past patte(cid:396)(cid:374)s to p(cid:396)edi(cid:272)t futu(cid:396)e p(cid:396)i(cid:272)e (cid:272)ha(cid:374)ges. Random walk is a proof of market efficiency. Since all information is rapidly incorporated into the price of the stock, all stocks are (cid:272)o(cid:396)(cid:396)e(cid:272)tl(cid:455) p(cid:396)i(cid:272)ed a(cid:374)d (cid:374)o i(cid:374)(cid:448)esto(cid:396) o(cid:396) issue(cid:396) (cid:272)a(cid:374) ea(cid:396)(cid:374) (cid:862)e(cid:454)(cid:272)ess(cid:863) (cid:396)etu(cid:396)(cid:374)s. Weak form: market prices reflect all information contained in past market prices. Semi-strong form: market prices reflect all publicly available information. Strong form: market prices reflect all known information. Bay street and wall street often do not agree with the emh. Technical analysts search for patterns in past prices attempt to predict future stock prices. Emh says trading rules should not work. Technical analysis may not work, but technical analysts can help keep the markets efficient.

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Related Questions

Please write a 250 word response to the below discussion. Please provide 2 scholarly sources.

The efficient market hypothesis states that share prices reflect all relevant information, and that it is impossible to beat the market or achieve above-average returns on a sustainable basis. There are many critics of this theory, such as behavioral economists, who believe in inherent market inefficiencies. The Efficient Market Hypothesis (EMH) states that securities are very efficient in reflecting the truth about a stock. In other words, when new information is available about a stock, it is incorporated into the price adjustments of the stock quickly enough to reflect the actual state of the market (Malkiel, 2003). EHM debunks the idea that financial analysis and forecasting are useful tools to shop for stocks. In other words, because securities accurately reflect the status of that stock, it is not possible to use financial analysis to find an undervalued stock or predict trends in the market in order to buy low with the future insight to sell high. There are three levels, or degrees, of the efficient market hypothesis: weak, semi-strong, and strong. The weak form assumes that current stock prices reflect all available information, and that past price performance has no relationship with the future. In other words, this form of the hypothesis says that using technical analysis to achieve exceptional returns is impossible. The semi-strong form says that stock prices have factored in all available public information. Because of this, it's impossible to use fundamental analysis to choose stocks that will beat the market's returns. Finally, the strong form of the efficient market hypothesis says that all information, public as well as private, is incorporated into current stock prices. This form of the efficient market hypothesis essentially assumes a perfect market, and isn't plausible when there are insider trading restrictions (VanBergen, 2011). Yes, I believe that financial statement analysis can provide a significant advantage to an investor. The key is that empirical analysis, specifically statistical models aimed at forecasting future trends based on historical data, are simply another tool to be taken in to account. They are not a single source of the definitive answer. But, through the use of high power computing and mathematically rigorous modeling tools, forecasting is a powerful tool to provide additional insight to decision makers to include the financial industry. In support of my position, (Malkiel 2003) reports the findings of several empirical studies which demonstrate positive statistical correlations in short-run return analysis and the successful prediction of up to 40% of variation in long-term return analysis through time-series statistical models, which are able to take into account seasonal fluctuations (Malkiel 2003). One experimental study demonstrated with statistical significance that the Ohlson model provides predictive ability for future stock price movements (Lee, Chen and Tsai, 2014).