Efficient Market Hypothesis - Essay Example

?Efficient market Hypothesis Efficient market hypothesis presumes that market can function exceptionally well in allocating resources. It is a situation where no investor in the money markets can achieve excess profits based on risk-adjustment, if information on the investment is in public domain at the time when making the investment. Efficient market hypothesis stipulates that the prices of stocks in the money markets represent summation of all probabilities of all future consequences. The information available in the public domain is assumed to reflect stock prices in the money markets. The efficient market theory assumes that there are no transaction costs, money market is not segmented and it is easy to enter the money markets. Efficient market hypothesis is explained in three ways. First, there is weak form efficiency. Weak form efficiency stipulates that all past information that is available in public domain is a reflection of stock prices. The prices are considered unbiased and best estimation of security value. It presumes that it is impossible to predict future prices using past information through technical analysis (Pompian, 2006). Therefore, an investor cannot use technical analysis to predict future prices that are likely to give excess profits (returns). Secondly, there is Semi-strong form efficiency. This form of efficiency stipulates that all publicly available information reflects prices of stock. It further states that prices adjust instantly as new information is made available. Fundamental analysis cannot be relied upon to generate excess returns to the investor. Thirdly, there is strong form efficiency. According to this form of efficiency, prices are reflected by both private (insider) and public information. This means that all investors irrespective of whether they have insider information or not, make equal profits on their investments. It further assumes that insider trading laws are usually enforced. This means that uninformed investors who purchase a diversified portfolio are likely to make same profits as those made by industry experts. Efficient market hypothesis is associated with ‘random walk’. Therefore, if information flow is not hampered and travels immediately in any investment especially stock pricing, the current price reflects current news (Boatright, 2010). Therefore, current prices depend on current news and not yesterday’s news. However, news is usually unpredictable and thus price changes of investments are also likely to be unpredictable and random. According to the efficient market hypothesis, news spread quickly and new information is quickly incorporated into the prices of investment in stocks without delay. This shows that there is no need for technical analysis from past price movements to predict movement of prices. Lee (2009) explained that efficient market hypothesis presumes that large number of profit maximizing investors exists. It also provides that new information must enter the market randomly and independently over time. Efficient market hypothesis has been challenged by economists who believe that there are psychological and behavioral factors that predict returns on investment. According to Malkiel (2003), the new breed of financial economists believes that prices are wholly or partially predictable based on behavioral patterns of individual investors and fundamental valuation metrics. They also argued that predictability of future stock prices enable investors to earn excess profits on their investments. A number of economists, statisticians and other experts have stated that Efficient Market Hypothesis (EMH) is to blame for the global financial crisis that occurred in 2007-2010. This is because of a number of reasons advanced by number of people. First, according to Jeremy Grantham, people had a lot of faith in efficient market hypothesis. This made them to throw caution in the air and underestimate the risk of assets bubbles because they believed that asset market was able to adjust itself accordingly (Nocera, 2009). The investors, government officials and everyone else believed in efficient market hypothesis. It was evident that rating agencies threw caution to the wind and failed to exercise their duty of providing accurate rating of the mortgage backed securities (MBS). The rating agencies gave investment grade rating to mortgage backed securities that were bought by investors to finance housing boom. Bank gave loans based on recommendations of rating agencies. If mortgage backed securities was not given AAA rating, the banks would have given less loans than they actually gave. Financial institution automated loan approvals, making it easier to issue loan to customers without appropriate review and documentation. Following the loans that were given in disregard to credit worthiness, in accurate ratings and poor documentation, there were over $523 billion bank losses and write-offs in 2008. The conditions that were necessary before loans were awarded were relaxed significantly and individuals as well as business entities borrowed loans beyond their means. This was because customers were induced by low interest rates in the market. Second mortgage loans were offered based on perceived future price increase of properties. This was dangerous because no one was sure as to what property prices were to be in future. When property prices nosedived, mortgage financing became impossible to most customers and lender began foreclosures. Each foreclosure was estimated to be about $50,000. Consequently, the investors, government and its regulatory agencies sat idly and did not intervene to save the situation. In fact, some government agencies such as Security and Exchange Commission took practical steps to relax net capital rules, which encouraged largest five investment banks in the United States of America to strikingly increase their debts and issue mortgage-backed securities aggressively. In 2004, the federal Bureau of Investigation warned the governments and industry players of the emerging mortgage fraud. But their warning was disdained and business in the money markets was usual. For Richard Thaler, complexity in the asset market and herd behavior of investors contributed greatly to global financial crisis that lasted for over four years beginning 2007. Efficient market hypothesis do not apply technical analysis. This is a serious issue because without analysis, it is impossible to predict future prices and problems in the money markets. According to Colby (2003), successful traders and investors attribute their profits as a direct reflection of their skills and not random outcomes as stated by efficient market hypothesis. The theory assumes that relevant information concerning stocks is available to all for free. It presumes that technical analysis is of little or no value. The efficient market hypothesis requires economic agents to act rationally and adjust rationally with emergence of new information. Furthermore, the theory claims the population is correct on average. The above assumptions are not always true. This is because information is sold currently and most individual are influenced by group psychology. To sum it all, efficient market hypothesis do not examine real market functioning and is seriously flawed because it neglected human nature. Shaffer (2010) explained that efficient market hypothesis stipulates that stock markets trade effectively because prices of assets are reflective of all information available in the market. This means that stock markets are self regulating. Therefore, any problems in the market place are corrected by the invisible hand. The theory assumes that prices change instantly to reflect new information in the market place and no person can be able to influence prices single handedly. Thus, due to unwavering believe on the efficient market hypothesis, government and private programs were ineffective. Responses to critics of efficient market hypothesis The proponents of efficient market hypothesis stated that the theory does not rule out future uncertainty, claiming that the theory worked for most individuals in the past. The theory allows for investors’ overreaction or under-reaction. However, the reactions are random and follow a pattern of normal distribution so that the net result on market prices leads to normal profits. Bruce (2010) said that risk clustering and herding behavior on the part of investors contribute to anomalies in the stock market. Risk clustering and herding behaviour make investors perceive financial system as unable to sustain a huge withdrawal of money and may cause panic in the market. Furthermore, lack of transparency give rise to information asymmetry and uncertainty. According to Nocera (2009), Richard Thaler asserts that herd behaviour and complexity caused the 2008 global financial crisis. Thus, global financial crisis of 2007-2010 was attributed to overreactions, overconfidence, human errors as well as representation and information bias, which could have been explained by behavioral finance. President Barack Obama in his speech in June 2009 indicated that lenders and borrowers acted irresponsibly in one way or another. In technical terms, many large lenders did not have enough capital to absorb the losses they made or support normal transactions yet they continued borrowing. According to Hens and Rieger (2010), behavioral finance refers to the study of the influence of psychology (cognitive or emotional differences) on the behaviour of individual or collective financial practitioners, investors and subsequent consequences on markets. Fuller (2000) asserts that behavioral finance attempts to explain causes of anomalies observed and reported. It studies how investors systematically make errors in judgment or mental mistakes. Differences in emotional and cognitive perspectives of individual or collective persons or institutions create anomalies in stock prices. Cognitive preconceptions include overconfidence and observer-expectancy effect, illusion control and clustering as well as hindsight bias and gambler fallacy. The global financial crisis was attributed to lack of accountability and unethical behaviors of both financial practitioners and investors. Behavioral finance has the ability to identify and explain behaviors of both investors and investment companies. This will help to predict the effect of human and institutional behaviors in stock pricing as well as future profitability of stocks (Noyes and Bloomfield, 2010). Predicting behaviour of players in the capital market could have helped in forecasting the possible outcome in the money markets thus help formulate appropriate policies to respond to destructive human behaviors. In behavioral finance, analysts use hyperbolic discounting. This attracts arbitrage and eliminates individual biases. Behavioral finance also allows application of both technical and fundamental analysis, which makes it possible to track price changes and well as discover anomalies in money markets. Therefore, behavioral finance could have been more helpful in predicting financial crisis in 2007-2010. References Boatright, JR 2010, Finance Ethics: Critical Issues in Theory and Practice, John Wiley and Sons, New Jersey. Bruce, B 2010, Handbook of Behavioral Finance, Edward Elgar Publishing, Cheltenham, UK. Colby, RW 2003, The encyclopedia of technical market indicators, 2nd edn, McGraw-Hill Professional, London. Fuller, RL 2000, Behavioral Finance and the Sources of Alpha, San Mateo, CA, viewed 27 June 2010, http://www.fullerthaler.com/downloads/bfsoa.pdf Hens, T & Rieger, MO 2010, Financial Economics, Springer, Germany. Lee, AC, Lee, JC & Lee, FC 2009, Financial analysis, planning & forecasting: theory and application, 2nd edn, World Scientific, 2009 5 Toh Tuck Link, Singapore. Malkiel, GB 2003, The Efficient Market Hypothesis and Its Critics, CEPS Working Paper no. 91, Princeton University, viewed 27 June 2010, http://www.princeton.edu/~ceps/workingpapers/91malkiel.pdf Nocera, J 2009, ‘Poking Holes in a Theory on Markets’, New York Times, 5 June, viewed 27 June 2010, http://www.nytimes.com/2009/06/06/business/06nocera.html Noyes, HN & Bloomfield, R 2010, Forthcoming in Behavioral Finance, John Wiley and Sons. Cornell University, viewed 27 June 2010, http://fasri.net/wp-content/uploads/2009/10/traditional-vs-behavioral-finance-chapter-_wiley_.pdf Pompian, MM 2006, Behavioral finance and wealth management: how to build optimal portfolios that account for investor biases, John Wiley and Sons, New Jersey. Shaffer, DS 2010, Profiting in Economic Storms: A Historic Guide To Surviving Depression, Deflation, Hyper-Inflation, and Market Bubbles, John Wiley and Sons, New Jersey. Read More