Factors That Can Be Applied For Predicting Asset Prices Essay

Fama and French’s three factor model attempts to explain the variation of stock prices through a multifactor model that includes a size factor and BE/ME factor in addition to the beta risk factor. Fama-French model essentially extended the CAPM (which breaks up cause of variation of stock price into systematic risk which is non-diversifiable and idiosyncratic risk which is diversifiable) by introducing these two additional factors. Fama and French find that stocks with high beta didn’t have consistently higher returns than stocks with low beta and this indicates that beta was not a useful measure under their model. Their model is based on research findings that sensitivity of movements of the size and BE/ME factor constituted risk, and

The extracted data used includes monthly returns from January 1972 to July 2011. The assets are selected so that the portfolio contains the largest, most liquid, and most tradable assets. The choice of such a variety of assets across several markets was used in order to generate a large cross sectional dispersion in average return. It helped to reveal new factor exposure and define a general framework of the correlated value and momentum effects in various asset classes.

“The Benefits of diversification are clear. Portfolio theory has played a crucial role in explaining the relationship between risk and return where more than one investment is held. It also enables us to identify optimal and efficient portfolios.”

Another issue might arise by the determination of explanatory variables. As we follow the facesheet from Morningstar (2011) to choose our explanatory variables as a symbol of the defensive sectors and cyclical sectors, it is possible that the category of sectors is incorrect. Without the support of academic journals, the facesheet might be purely based on analysts ‘opinions instead of facts. By estimating wrong explanatory variables, the findings will become

Harry Markowitz 1991, developed a theory of “Portfolio choice”, that allows the investors to examine the risk as per the expected returns. In modern World, this theory is known as Modern portfolio theory (MPT). It attempts to attain the best portfolio expected return for a predefined portfolio risk, or to minimise the risk for the predefined expected returns, by a careful choice of assets. Though it’s a widely used theory, still has been challenged widely. The critics question the feasibility of theory as a strategy for

In this literate review the most important papers about explaining stock returns from 1952, when Markowitz came up with Modern Portfolio Theory, till around 2011 will be discussed. As stated in Chapter 2, Jack Treynor was one of the first economists that started to work on the CAPM model. When he developed the CAPM in 1961, there was no way yet to fully test it. Because there were no samples large enough or of sufficient quality, the real testing of the CAPM started in 1970. In 1973, the world was shown the famous Black and Scholes options pricing model. One of the first studies that gave a different answer than the CAPM was the research by Basu (1977). While he agrees with the Efficient Market Hypothesis, Basu reaches another

Systematic risk results from factors that affect all stocks. The risk of a project from the viewpoint of a well-diversified shareholder. This measure takes into account that some of the project’s risk will be diversified away as the project is combined with the firm’s other projects, and, in addition, some of the remaining risk will be diversified away by shareholders as they combine this stock with other stocks in their portfolios.” (Keown PG, 510)

The goal of this paper is to explain why CCM’s aggressive program is a good alternative to any investor looking to diversify its portfolio. The paper will be divided into three distinct parts: the operational analysis, the quantitative analysis and a comparison against its peers (including the impact of CCM in a traditional portfolio).

The correlation between the market portfolio and HML and the correlation between intercept and HML is -0.335 and -0.070, which indicates a moderate negative relationship between market portfolio and HML, and weak negative relationship between intercept and HML. Also, the correlations between the market portfolio and SMB, and between the SMB and HML are 0.348 and 0.191 respectively, which means that there are some positive relationships between them.

We believe, however, that returns response uses only partial market information about stocks. The Traditional Capital Asset Pricing Model (CAPM) holds the opinion that systematic risk would be the only factor that influences asset prices since idiosyncratic risk could be eliminated away by holding a well-diversified investment portfolio. However, recent empirical studies have found that the risk intensity of stocks is mainly due to the idiosyncratic risk of individual stocks. This conclusion was different from CAPM’s argument that only systematic risk would have an effect on returns. Volatility, in many studies, has also been found to carry current and future information. A recent study that attempted to break through the traditional method of calculating returns was performed by Campbell, Lettau, Malkiel and Xu (2001). They successfully separated the volatility of stock returns into market, industry and firms’ idiosyncratic volatility by use of a disaggregated approach. Xu and Malkiel (2003) applied a decomposition method and a disaggregated approach method to decompose volatility of stock returns into systematic volatility and idiosyncratic volatility and found that corporate private information could be reflected to its stock price faster when the institutional investors held a higher percentage of that company’s stock. Hatemi-J, A. and M. Irandoust

The success of the model is attributed to Yale’s ability to combine both quantitative analysis (mean-variance analysis) with market judgments to structure its portfolio. In addition, Yale also uses statistical analysis to actively test their models with factors affecting the market, therefore understanding the sensitivity of their portfolio in response to various market changes. Yale also follows and forecasts the cash flow of private equity and real assets in its portfolio to decide the need for hedging.

As indicated by the case study S&P 500 index was use as a measure of the total return for the stock market. Our standard deviation of the total return was used as a one measure of the risk of an individual stock. Also betas for individual stocks are determined by simple linear regression. The variables were: total return for the stock as the dependent variable and independent variable is the total return for the stock. Since the descriptive statistics were a lot, only the necessary data was selected (below table.)

Even though there are flaws in the CAPM for empirical study, the approach of the linearity of expected return and risk is readily relevant. As Fama & French (2004:20) stated “… Markowitz’s portfolio model … is nevertheless a theoretical tour de force.” It could be seen that the study of this paper may possibly justify Fama & French’s study that stated the CAPM is insufficient in interpreting the expected return with respect to risk. This is due to the failure of considering the other market factors that would affect the stock price.

Despite this, however, some have since suggested that their model is pure economics, and is only valid in a theoretical world that doesn’t reflect some of the frictions that actual financial markets do.

If the assets exist to help meet a liability, the liability should be considered in the process; 3. Basing one’s decision solely on an asset allocation’s mean and variance is insufficient to base one’s decisions, in a world in which asset class returns are not normally distributed; and, 4. Most investors have multi-period objectives and the mean-variance framework is a single period model. These potential shortcomings are the likely reasons that practitioners have not fully embraced meanvariance optimization. For a number of practitioners, mean-variance optimization creates the illusion of quantitative sophistication; yet, in practice, asset allocations are developed using judgmental, ad hoc approaches. Recent advances significantly improve the quality of typical mean-variance optimizationbased asset allocations that should allow a far wider audience to realize the benefits of the Markowitz paradigm, or at least the intent of the paradigm. In this article, we focus on the first issue: the lack of diversification that can result from traditional meanvariance optimization. We begin with two examples in which traditional mean-variance optimization