Standard Deviation as a Risk Indicator for Investment Purposes Essay

Standard Deviation as a Risk Indicator for Investment Purposes - Essay Example41, 2003). However, all over the years, many experts and researchers have also tried to point fingers at this approach trying to highlight its serious shortcomings. This paper is an attempt to capture a discern of that debate and critically analyze the use of ensample deviation as a fortune indicator for investment purposes. Discussion Standard deviation, in finance, is unmatchable of the widely used indicators of risk associated with any given security such as bonds, stocks, properties, commodities and others. Standard deviation allows the investors to predict and anticipate the behaviour of the security in the near future (Bhansali, pp. 34-35, 2010). Simply, streamer deviation, which is square of the variance, tells the investors that how much they can expect the price of the security to deviate from its mean returns (Brase & Brase, pp. 10-12, 2011). Therefore, securities with high measure are more (prenominal) likely to state violate behaviour but the ones with low standard deviation are more likely to show consistent behaviour. Quite understandably, the former(prenominal) type of securities will have a great risk and later would be less risky (Wander & DVari, pp. 36, 2003). Investors are interested in the set of standard deviation because that helps greatly in the process of portfolio construction and management. A risk adverse investor will only select a handful of securities high standard deviation in terms of its returns and mix that up with securities having lower standard deviation in order to offset the impact of risk and enjoy persistent returns (Gravetter & Wallnau, pp. 22, 2010). First, the biggest and the most important shortcoming of standard deviation as the measure of investment risk is rooted in the fact that it assumes normal distribution of values and they are poor measures of risk when it comes to asymmetric distribution. In normal distribution, the val ues are distributed equally to both sides of the graph however, in any asymmetrical distribution one tail of the graph, either positive or negative side has greater concentration of values (Brase & Brase, pp. 10-12, 2011). Therefore, standard deviation fails to give an exact enter of the possible variation in the values. Even the father of the concept of financial engineering, Harry Markowitz has admitted, Downside variance is more accurate than standard deviation when it comes to financial risk analysis. This is true because not only many investing portfolios have asymmetrical distribution but their distribution is skewed positively as well(p) (Haslett, pp. 264, 2010 Connor, Goldberg & Korajczyk, pp. 88-89, 2010). Second, like many other statistical measures of risk computation in finance, standard deviation relies heavily on historical data and there is no guarantee that historical trends will continue in the future as well (Brase & Brase, pp. 10-12, 2011). Furthermore, the peri od undertaken to calculate standard deviation is also of great importance. For example, the standard deviation of stocks for the period of 2002-2006 may show lower standard deviations for most of the stocks, however, the standard deviation computed over the last five years will show higher standard deviation for many of the stocks (Gravetter & Wallnau, pp. 22, 2010 Brigham & Houston, pp. 74-75, 2009). Therefore, it