The Capital Assets Pricing Model is a theory that applies to a number of concepts about market trends and investor behaviour to provide equilibrium situations that necessitate the need for prediction of return of an asset for its level of calculated risk. It was formulated in the sixties by William Sharpe and John Linter. Financial institutions and investors continue to take caution on the emergence of new equity markets around the world. Interest to focus the attention on these issues has mainly been caused by the amazing returns from the markets. As a result, capital pricing model is applied in assessment of exposure of risks to different assets (Kürschner, 2008). Many experiments have been carried out to ascertain the relevance of the model in calculating asset values under various situations. This paper will discuss the model in light to the recent developments in macroeconomics.
The model proposes that high expected returns are normally associated with high amount of risk. Capital assets pricing model has been mainly based on experimental work for the past several decades. Nevertheless, increased research in this field has raised doubts in the capacity of the model to describe the actual progress of asset returns (Harvey & Graham, 2001). The advantage of this model is that it provides great predictions about calculation of risk and the correlation between expected returns and risk.
However, the experimental data of the model is unsatisfactory thereby invalidating the manner in which it is used in various applications. Problems associated with the experimental data of the model could be a result of strong assumptions that underlie it. In addition, it could result from problems in employing legitimate tests of the model. As a matter of fact, the model suggests that the risk of a stock is calculated in relation to an all-inclusive market range (Fama & French, 2002). This takes account of the financial assets in addition to human resource, customer durables and real estates. Therefore, it is important to look at the model from different perspectives so as to make a good inference from it.
The Capital Assets Pricing Model is based on the model of portfolio choice that was formulated by Harry Markowitz in 1959. The model of portfolio choice presumes that investors are risk averse and that they are only concerned about the mean and variance of their single time investment gains (Fama & Macbeth, 1992). It further offers an algebraic condition on asset weights that turns into an examinable forecast about the relation between risk and expected return by recognizing an efficient collection of assets.
Since its inception in the sixties, capital assets pricing model has presented one of the most complicated topics in financial economics studies. Every time a project manager wants to carry out an assignment, his decision is to some extent determined by the concept behind CAPM. This is because the CAPM model offers a way for a company to measure the returns that investors need (Edwards, 1986). It assesses the risk related to cash flow of a great investment project, including the approximate cost and anticipated returns.
CAPM was developed to mark the discrepancies found in the risk premium found in various assets. According to this model, variations are a result of differences in the levels of returns from assets. CAPM model suggests that the amount of risk on an asset is measured by its beta while the premium for every unit of risk is similar across all assets (Zopounidis, 1997). Therefore, the model can predict the anticipated risk premium of a given asset if the risk free rate and beta of an asset are given. Capital asset pricing model has resulted to a lot of debate in the academic and financial economic fields especially on its validity and usefulness. This implies that the empirical data on CAPM aims at evaluating whether the theory should be rejected or not (Zopounidis, 1997). It also offers information that guides decision makers on various economic choices.
One of the initial experimental studies that supported the evidence for CAPM was done by Black and is team in early 1970s. Using information on monthly returns, this team evaluated the linearity of the expected returns. Securities were integrated into the collective assets so that diversification of the specified elements of returns could be achieved. As a result, the precision of beta approximations and the projected rate of returns of the collective securities were enhanced (Lee & Lee, 2010). This strategy helped to check the statistical issues that arise from measurement errors in beta value. The researchers found that the information was in line with suggestions by capital assets pricing model. Therefore, the correlation between average returns and beta is almost linear and the portfolios that have high betas vary directly with the average returns (Harvey & Graham, 2001).
Another experimental study by Fama and Macbeth in 1973 further supported the theory. The two were concerned with finding a positive correlation between the average returns and beta (Fama & Macbeth, 1992). The researchers also enquired whether the squared value of beta and unpredictability of expected returns could explain the remaining discrepancies in average returns across assets that are not catered by beta alone.
The following factors posed as potential challenges to the legitimacy of the theory. Many studies have been done to factor out the elements that capital asset pricing model was missing in identifying and explaining the differences in risk-return (Roll, 1977). The model was challenged through illustrations by people such as Banz in 1981 who focused on the role of firm sizes in explaining the discrepancies that were not explained by the model’s beta. He argued that the size of a company does influence the variation in average returns on a given asset portfolio better than beta.
The researcher further found out a phenomenon known as the size effect. It suggests that the average returns on stocks of small organizations were higher than those of bigger firms. This concept further elaborated other variables that affected the average returns. Following these findings by Banz, it was realized that the model could be missing some features of reality (Banz, 1981). However, even if the findings turned out so, they suggested that the differences were not significant enough to disqualify the theory.
Other studies were still carried out later. For instance in 1992, Fama and French challenged Banz’s findings after they found no correlation between risk and return (Banz, 1981). Their study was also criticized by Shaken and Sloan in 1995 by suggesting that the findings by Fama and French depended on how numerical data was interpreted (Fama & French, 2002). This implied that their argument was not original but instead based on other previous findings.
Despite all the criticism, reaction to results from study by Fama and French is generally centered on substitute asset pricing models. For instance, Jagannathan and Wang argued that the absence of experimental basis for the capital asset pricing model might have been caused by unsuitability of basic assumptions made to aid in experimental analysis. Most of the experiments done presuppose that the returns on a wide stock market indices is a good replacement for the returns on collective assets in the economy (Roll, 1977).
Further evidence from experiments on returns is founded on the premise that the unpredictability of stock is dynamic. Consideration of a time-varying return distribution requires reference to the mean, variance and even covariance that are ever changing depending on the existing data (Lee & Lee, 2010). It is worth noting that all the experiments aim at enhancing the validity of capital assets pricing model. Any modification made cannot be used as a valid proof to support the capital asset pricing model.
Consequently, investors are usually advised not to take any diversifiable risk but only the non-diversifiable ones since they are the ones that are remunerated in this model. This implies that the average return on an asset is associated with the level of risk in a collective setting (Ehrhardt & Brigham, 2010). The following assumptions are included in the capital asset pricing model. First, the model aims at getting the most out of the financial utilities. Assumptions in the model are also taken to be risk averse. They are taken as strategies to manage the risks which are diversified across a wide range of ventures.
Furthermore, there are various problems that are associated with capital assets pricing model as highlighted below. For instance, the model proposes that the variance of average returns is enough to measure risk. This approach could be very satisfactory when used with normally distributed returns. However, it is not applicable when other distributions and risk measurements are considered. As a matter of fact, risk in economic investments is not variance in itself but instead, the likelihood of losing (Ehrhardt & Brigham, 2010). Another issue related to this model is that it demonstrates the direct relationship between anticipated returns from investment and the amount of risk involved. It excludes the possibility of having lower expected returns for higher risk as found in gambling and some stock trading enterprises. Gamblers usually involve themselves in very risky endeavors, which do not guarantee a high return.
Another problem associated with CAPM is that it also rules out the inclusion of taxes and transaction costs. However, the case is different when considering more complicated versions of CAPM. Theoretically, the market portfolio comprises all asset types that are taken as an investment by anyone. This implies that investors choose investments that are majorly a function of their risk-return.
The assumptions that the market betas help to explain the average returns from investments can be assessed using time-series regressions. In this method, the point of intercept provides the difference between the investment’s excess return and the average excess market return (Fama & French, 2002). Therefore, the application of this method is that, in order to test the assumption that market betas are adequate to explain projected returns; the time-series regression for a set of assets is considered.
A common theme is evident in the above arguments. All the relationships comprising of the assets and investments have information about the projected returns that are missed by market betas. It can therefore be affirmed that the contradictions arising from the capital asset pricing model point to the need for a more intricate model. For instance, the presumption that investors are concerned with the mean and variance of portfolio return is severe (Fama & Macbeth, 1992). It is quite reasonable if investors care about how their portfolio returns relates to their revenue and projected investment opportunities. This will help the portfolio return variance to miss vital dimensions of risk.
As a result, the market beta does not give a full description of an asset’s risk. The differences in beta values do not necessarily have to reflect the variations in expected returns. As a matter of fact, more research still lies ahead in explaining the average returns than how it is currently done by beta.
It has been established that the experimental work by Sharpe and Lintner in the sixties has never been a success as far as accounting for all the variables in CAPM is concerned. Years that followed involved considerable amount of research which unearthed other factors such as size and price ratios in line with explanation of average returns given by beta (Zopounidis, 1997). Problems that arose from these suggestions were serious to nullify relevance of CAPM.
Capital asset pricing model has a wide range of application especially in measurement of the performance of most of managed portfolios. Time-series regression for a portfolio are used and helps to make estimations and in calculating the abnormal performance by considering the position of the intercept in Jensen’s approach. According to the information from recent researches in the area, capital assets pricing model lacks the capacity to handle complicated problems in asset pricing (Harvey & Graham, 2001). As a result, it can only be used as introduction to the elemental theory of portfolio and asset pricing models. Therefore, it provides a platform for development of other complicated models in this category.