Competing Investment Options - Assignment Example

rtfоliо Маnаgеmеnt Name Institution Lecturer Q1: Introduction Business organizations as well as companies have the tendency of choosing investments with the desire to optimize some goals and as always are limited by constraints. Competing investment optionscontrol how particular assets are divided amongst these investment alternatives. This is done with the hope of effectively minimizing risk in order to achieve a desired level of return. This can either take the form of investment return or the overall return. In the event that the allocation satisfies the objectivesset within the limits of prevailing constraints, then it can be assumed to be efficient. Thisdistribution is believed to be anaffiliate of the efficient set at a point on an efficient frontier. It is efficient given the fact that it is seen to control off-frontier, inner points in the risk-return space. (Hubbard, 2007) Discussion Efficient Frontier Analysis case although, is very fascinating, it is irrationalthis is purely so due to the basis for Efficient Frontier Analysis as well as the projectedpassage of action that it brings about has no connection to the variables that dictate investing outcomes. Efficient Frontier Analysis 1a) It is worth noting that Efficient Frontier analysis forms part of the line on a risk-reward graph.This line consists with all proficient investment sets; portfolios that offer the ultimateanticipated return on investment forone particular level of risk or, for any predictable return, these are those investments that have the least volatility. (Chandra, 2007) All portfolios on the vertical dabbed line above have a similar hazard. Of the considerable number of portfolios for a particular level of hazard, for example, the portfolio spoke to by the blue square at the catch of the vertical and even specked blue lines, offers the best expected return for that level of venture hazard; subsequently, all portfolios for that level of speculation hazard that are beneath the blue square should be changed to the most effective portfolio; the blue square, the Optimum Portfolio. (Chandra 2003) The portfolio focuses on the outline are dictated by three elements while regularly utilizing verifiable data: In this particular case, we treat “The efficient frontier” as the set of optimal portfolios aimed at delivering the highest anticipated return for a specific risk level or the most minimal risk for a particular level of anticipated return. In many occasions it is noted that Portfolios that lie way below the “efficient frontier” tend to be sub-optimal, this is so because under normal circumstances, they tend not to provide us with enough return for a particular set level of risk. On the other hand, it is noted that Portfolios that tend to somehow cluster to the right of the “efficient frontier” tend to show sub-optimal characteristics, this is due to the fact that they have elevated level of risk for a given rate of return. One assumption with regard to investment holds that a higher degree of risk simply implies that there is a higher potential return. On the other hand, it is noted that a majority of investors who tend to take on a lower degree of risk tend to have an equally low return. With regard to Markowitz's theory, it is explained that there exist an optimal portfolio which normally can be set with an ideal equilibrium between projected risk and anticipated return. Expected Returns: This refers to the expected or average rate of return of an investment Volatility: This refers to the degree of random variability. One key aspect that is worth noting is that an investment that fluctuates widely over time has high volatility. Stable prices indicate that the particular investment in question has low volatility. Correlation: Correlation deals with the extent to which two investments tend to track one another. Stock prices as well as bonds have a likely tendency of going in the opposite direction. This therefore implies that they are negatively correlated (Bouchad, 2003) On the other hand auto parts stocks and auto stocks go up and down collectively. This therefore implies that they are positively correlated. It is thus observed that to reduce risk and the possibility for return of a portfolio then it is important to invest in negatively correlated investments; both stocks and bonds. On the other hand, it is possible to increase risk as well as the possibility of investment return by investing in only those analyses that show positively correlated investments; auto parts and autos. However, past execution is not a pointer of future venture comes about,' which, as any individual who has spent more than a nanosecond in the budgetary markets would know, ought to know to be totally valid. (Sonnet, 2004) The business organization will be tasked with the responsibility of taking a look at its P/E ratio to decipherwhether or not it is sailing high or low. In the midst of vulnerabilityFree- Stateof affairs in which this is likely to appreciate and contribute more. Keeping the hazard free security at a level of approximately 20% seems as one of the most ideal path in keeping ones organization out of trouble. For a given scenario of an expansive top stock as organization A, the investor will be tasked with the need to state in advance the prevailing standard deviation as well as the market ruin which in most instances are normally unquestionably apparent are such elements of connection coefficient (Sonnete, 2004). Under these circumstances the Correlation Coefficient normally oscillates between -1(negative one) and +1 (positive one). It is not a momentum oscillator nevertheless it swings from periods of normally positive correlation to period’s negative correlation. Under these circumstances, the +1 is considered a perfect positive correlation, which is normally rare. Anything between 0 and +1 brings the information that two or more securities swinging in a similar direction. Normally the degree of positive correlation will keep on alternating over time. With the intention of truly expanding from stocks, it is often compulsory to look beyond the stock market. It is apparently clear for one to observe the Correlation Coefficients dint below zero occur frequently in this particular illustration. Under this example it is observable that the 20+ year Bond ETF (TLT) exemplifies bonds, these bonds are negatively correlated with stocks on many occasions, a move between periods of positive and negative correlation. On the whole, it has been more positively correlated than negative the last 3 years. Looking at B it is apparently clear that it appears split between periods of positive and negative correlation. The value 0.30 correlation despite being positively correlated will have to be undergoing some adjustment if it is to boost theoverall reliability of stocks to a strong correlation. So far up to this with these explanation it can be seen that Investing more on the risk free securities is likely to boost the investment potential of A and B in the long run. In summary therefore, we can find a universal relationship that exists between the mean correlation amongst the DJIA components which in most cases will be considered as a stock market portfolio and the stabilized returns of this portfolio. This suggests that a broadening break down normally occurs whenever stable correlations are most preferred for purposes of portfolio safety. The suggestion therefore is to anticipate underlying correlation hazards the possibility exists to hedge index derivatives. The results in this case could also shed light on why correlation risks in mortgage bundles were underestimated at the beginning of the recent financial crisis. 1b) The logic of increasing beta by borrowing more money is wrong. This is because there is a high likelihood of the following forces starting to work against debt since the debt ratio will increase. One is that the rate of borrowing will increase as one continues to borrow more as explained by Bouchaud et al (2003). This will mean the company will have a higher default risk which is not a good thing for investors. The second thing that happens on borrowing more money to increase the value of beta is that the company will be affected by a high debt level. The high debt level will be accompanied by high interest rates. The interest expenditures may exceed the operating income thus lowering the expected returns to the shareholders. Corporate treasures should understand that debt is more of a “raw material” and less a source of capital and thus should avoid that logic of borrowing more money in order to increase the beta (Bouchaud et al 2003). Q3: The intervention required to return stock to a beta of 1.10 targets is to eradicate all redundant stocks, with low moving averages. (1700 OP and 500 LLP) Graham and Harvey (2002) find that 73.5 percent of respondents to their survey show that the company of the survey respondent uses the capital asset pricing model (CAPM) to control the constituent cost of common stock equity capital. This paper focuses on the use the CAPM to compute the required rate of return for Company. The required rate of return for Company is the least rate of return demanded by stockholders of Company stock. The model used in this paper is based on the CAPM derived from the work of Sharpe (1964). RKO = Rf + BetaKO (Rm – RF) RKO = the required rate of return for Company Stock Rf = the risk free rate of return BetaKO = the beta coefficient for Company Rm = the rate of return on the stock market (Rm – RF) = the market risk premium The adjusted stock price for Company and the S&P 500 Index are employed in the computation of a five-year, monthly series of returns. The typical line is the regression line from the regression in which the monthly returns for the S&P 500 Index are the independent variables and the monthly returns for A are the dependent variables. The regression is done with the help of the Data Analysis Tool Pak in Excel and the Chart function. SBBI 2007 data to compute the required rate of return using the market model. And computed a required rate of return for company equal to 10.77%  3) C Formula of estimating the value Stock Beta Value ($) Estimated Value 1000 JUI 1.07 11750 12589.28571 500LLO 0.92 9500 10178.57143 2300 KI 1.1 12000 12857.14286 1200NMB 1.22 17875 19151.78571 500 ERW 1.1 8875 9508.928571 1700 OP 0.88 10625 11383.92857 900 XXC 1 12455 13344.64286 1200 PPM 1.03 13800 14785.71429 2500 PPU 1.22 9500 10178.57143 1500WQE 1.14 11000 11785.71429 total 117380 125764.2857 REFERENCES O'Hara, M. Market Microstructure Theory (Blackwell, Cambridge, Massachusetts, 1995). Bouchaud, J. P. & Potters, M. Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management (Cambridge University press, 2003). Sornette, D. Why stock markets crash: critical events in complex financial systems (Princeton University Press, 2004). Volt, J. The statistical mechanics of financial markets (Springer Verlag, 2005). Sinha, S., Chatterjee, A., Chakraborti, A. &Chakrabarti, B. K. Econophysics: an introduction (Wiley-VCH, 2010). Preis, T. Ökonophysik: Die Physik des Finanzmarktes (GablerVerlag, 2011). Abergel, F., Chakrabarti, B. K., Chakrabarti, A. &Mitra, M. Econophysics of Order-driven Markets (Springer Verlag, 2011). Takayasu, H. ed., Practical Fruits of Econophysics (Springer, Berlin., 2006). Gabaix, X., Gopikrishnan, P., Plerou, V. & Stanley, H. E.A theory of power-law distributions in financial markets.Nature 423, 267-270 (2003). Feng, L., Li, B., Podobnik, B., Preis, T. & Stanley, H. E. Linking agent-based models and stochastic models of financial markets. Proc. Natl. Acad. Sci. U.S.A 109, 8388 (2012). Read More