Analysis of Value Stocks - Example

Name: Lecturer: Course name: Course code: Date A. time series graphs of the five portfolio log price indexes over the period 1946 to 2013. Similarities of the above graph of the five portfolio samples From the above graph analysis, it can b e depicted that the trend in graph is similar in that the trend start with declining trend ,this is due poor economic condition caused by inflation in the early 1940s the economic recession made the stock exchange to experience a declining trend stock market securities and thus the industrial security market declined as well, The decline on this log price made other business with small capital base to wound up due to ;liquidity problem combined with poor market (Andrew W. Lo, 2011). Only the string companies with strong capital base survived the recession. From the above five portfolios, it is eminent that they are one of the key sector in the economy and thus the effect of inflation sli8ghly affect their trend in stock prices. The graph consequently increases as time goes by, It depicts an ideal economic progression from the emergence of the inflation and in the latest years since, after economic recession, the business started to revive to their normal business operation, reported net profit to the business stat to increase and consequently investors invested in the company’s security and as a return, the stock market depicts an incline trend in the value of stock market trading prices. In this regard, the overall stock prices in the market lead to increase in the value log prices industrial average and consequently, the value of the above key portfolios depicts an increasing trend., Differences between the industries Though the trend in prices depicts a similar flow, the difference is with the value of the log price in the security market. Different company in deferent industries depicts a different value l of the log process (Beyond, 2004). This is due to the fact that different companies have different level of capital base and thus the business will portray a high level of reported net profit to the company since, the company liquidity position as well as the profitability ratio is high hence the investors will attracted into the business. In this regards, the economic recession will affect the trend in the level of the log prices but at a much higher level as compared to the trend in log prices of those companies with low capital base and low profitability ratio. In ascertaining the trend in log prices of the above five portfolios, the log indexes is used instead of the original indexes since, the log prices depict the intrinsic value of the stock and that the company’s business situation is clearly depicted. In this regard, it is clear to comprehend the performance of the company since the company’s log index provides the current business performance as far as economic inflation is concern . Question B Monthly log return The skeweness of the above returns The above graph is skewed to the right implying that it is negatively skewed. In this regard, it means that the skewered of – 1.53 depict an even distribution of stock prices which is quite higher than normal distribution and consequently it is unfavorable trend in stock prices and thus the portfolio securities are not dependable (CFA, 2010). A potential investor should therefore ensures that he hold an asset of portfolio in order to diversify the risk of investment as portrayed by the above trend in negative skeweness, in this regard, the portfolio security prices are aggressive and thus they are with high volatility as well as they provide high return to an investors. Care should be taken in making an investment decision of whether to invest on such security or not. An ideal investment approach is to hold portfolio of asset since, this will diversify the investment risk as well as realize high returns from those aggressive securities with high volatility Kurtosis analysis of the return The above graph depict a sharp kurtosis implying that the portfolio will generate high return from investment .In the S&p 500,the monthly returns depicts a kurtosis of 30.8 which is far much higher than the Gaussian distribution. In this regard, it depicts that the overall portfolio performance depict a high returns and thus a potential investor should consider investing in this portfolio asset in order to maximize profit and minimize risk of single investment, care should be taken on investing such securities since a sharp kurtosis depict a very aggressive security prices and consequently, they are associated with volatility (Keith Cuthbertson, 2001). Since the future trend of this aggressive security is unknown, an ideal investment strategy is to hold a portfolio of asset in order to diversify the risk and maximize on the high returns from investment. Jarque-Bera test: The value of the Jarque-Bera test analysis for the log return of the of the above average portfolios is 816.3.This value is quite higher than the critical value of 9.21 which is a 99% confidence interval at 2 degree of freedom . In this test analysis of stock returns, where the null hypothesis is, S = K = 0, Then it implies that the distribution of the Jarque-Bera statistic is X^2 with two degrees of freedom, this degrees of freedom is depicted from the fact that skeweness and kurtosis is employed in scheming the Jarque-Bera statistic (Keith Cuthbertson, 2005). This result therefore makes us to reject the null hypothesis test and accept the alternative hypothesis. The Jarque-Bera test analysis confirms that the theoretical dispersion of the monthly log returns of portfolio security indexes is far from the normal distribution. This is in harmony with the negative skeweness depicted as well which the deviation is far from the mean. From the above F-test data analysis, the equation of the function is y= -0.3808+1.043x+1.399x2-0.2918x3 The X1 co-efficient is interpreted in the normal approach for an investment beta. The equation can thus be interpreted that, for a single unit of unit increase of excess return on the security marker return having hold constant the x2, x3 values, the increase in surplus returns on the portfolio returns is anticipated to be 1.044 implying that the securities are very aggressive in the stock market with high risk as compared to the stock market risk with excess returns. The X2 co-efficient, the proceeds of the portfolio depict the increase in the level of sales by a million dollars with x1, x3 being fixed variable (Knight, 2004). The surplus returns of the securities are anticipated to decline on average by 1.4%.The x3 co-efficient depict the debts and thus, the unit increase while holding excess return fixed, is anticipated to decline on average by 0.2918 .it can as well be depicted that the values of the F-test is 1770 and thus the null hypothesis can be rejected and accept the alternative hypothesis since, at least one of the one of Bsmb and B Hml is greater than zero (>0).The F-test analysis can as well as be concluded as follows Reject Null hypothesis since it is>0 Question c. Estimate the CAPM and the Fama-French three-factor model for each industry Capm=E (RI) =RF=B (E (Rm)-RF) Where ER is the expected return, Rm is the market rerun B is the beta of the security RF is the risk free rate of return CAPM output for the portfolio returns Dependent Variable: Y1 Method: Least Squares Date: 09/06/14 Time: 20:55 Sample: 1946M01 2013M12 Included observations: 816 Variable Coefficient Std. Error t-Statistic Prob.   C -0.076500 0.109795 -0.696753 0.4862 MKT_RF 1.073128 0.025258 42.48635 0.0000 R-squared 0.689205     Mean dependent var 0.579055 Adjusted R-squared 0.688823     S.D. dependent var 5.566631 S.E. of regression 3.105246     Akaike info criterion 5.106511 Sum squared resid 7849.038     Schwarz criterion 5.118041 Log likelihood -2081.456     Hannan-Quinn criter. 5.110936 F-statistic 1805.090     Durbin-Watson stat 1.640217 Prob(F-statistic) 0.000000 Dependent Variable: Y, Method: Least Squares Date: 09/09/14 Time: 13:29 Sample (adjusted): 1946M01 2013M12 Included observations: 816 after adjustments Variable Coefficient Std. Error t-Statistic Prob.   C 22952.21 1260.395 18.21034 0.0000 X 302.4975 289.9521 1.043267 0.2971 R-squared 0.001335     Mean dependent var 23137.00 Adjusted R-squared 0.000108     S.D. dependent var 35648.70 S.E. of regression 35646.77     Akaike info criterion 23.80315 Sum squared resid 1.03E+12     Schwarz criterion 23.81468 Log likelihood -9709.686     Hannan-Quinn criter. 23.80758 F-statistic 1.088406     Durbin-Watson stat 0.004662 Prob(F-statistic) 0.297134 Dependent Variable: Y3 Method: Least Squares Date: 09/06/14 Time: 20:55 Sample: 1946M01 2013M12 Included observations: 816 Variable Coefficient Std. Error t-Statistic Prob.   C -0.165446 0.154757 -1.069072 0.2854 MKT_RF 1.436087 0.035602 40.33765 0.0000 R-squared 0.666547     Mean dependent var 0.711834 Adjusted R-squared 0.666138     S.D. dependent var 7.574959 S.E. of regression 4.376873     Akaike info criterion 5.792994 Sum squared resid 15593.81     Schwarz criterion 5.804525 Log likelihood -2361.542     Hannan-Quinn criter. 5.797419 F-statistic 1627.126     Durbin-Watson stat 1.794014 Prob(F-statistic) 0.000000 Dependent Variable: Y4 Method: Least Squares Date: 09/06/14 Time: 20:56 Sample: 1946M01 2013M12 Included observations: 816 Variable Coefficient Std. Error t-Statistic Prob.   C 0.051916 0.091237 0.569021 0.5695 MKT_RF 1.057061 0.020989 50.36254 0.0000 R-squared 0.757043     Mean dependent var 0.697656 Adjusted R-squared 0.756744     S.D. dependent var 5.231843 S.E. of regression 2.580394     Akaike info criterion 4.736210 Sum squared resid 5419.966     Schwarz criterion 4.747740 Log likelihood -1930.373     Hannan-Quinn criter. 4.740635 F-statistic 2536.386     Durbin-Watson stat 1.708045 Prob(F-statistic) 0.000000 Dependent Variable: Y5 Method: Least Squares Date: 09/06/14 Time: 20:56 Sample: 1946M01 2013M12 Included observations: 816 Variable Coefficient Std. Error t-Statistic Prob.   C 0.040571 0.102050 0.397560 0.6911 MKT_RF 1.026827 0.023477 43.73836 0.0000 R-squared 0.701508     Mean dependent var 0.667842 Adjusted R-squared 0.701142     S.D. dependent var 5.279537 S.E. of regression 2.886214     Akaike info criterion 4.960216 Sum squared resid 6780.809     Schwarz criterion 4.971747 Log likelihood -2021.768     Hannan-Quinn criter. 4.964642 F-statistic 1913.044     Durbin-Watson stat 1.685252 Prob(F-statistic) 0.000000 Question D. Testing the Hypothesis The t-statistical testing depicts a t-distribution of {100-4=96} under the null hypothesis testing. At 95% confidence level, We reject the null hypothesis since, the p-values is Read More