Asset Allocation Methods - Term Paper Example

Name: Student number: Name of Lecturer: Subject: TABLE OF CONTENTS TABLE OF CONTENTS 2 Executive Summary 3 Asset Allocation Methods 3 1.0 Historical data. 3 2.0 Cons and pross of trend forecasting and analysis method 3 3.0 Assets allocation methods decisions used 4 4.0 Mean variation optimization. 5 5.0 Expected Arithmetic Mean Return 5 6.0 Risk standard deviation 5 7.0 Value at Risk/Conditional value at risk 6 8.0 Investment objectives 7 9.0 Part II Portfolio Management 7 10. Justification of the method 8 11.0 Conclusion 8 References 9 Appendices 10 Executive Summary Asset allocation methods and porfolio managementare some of the widely discussed concept in the filed of asset and stock market. This paper gives indepth study of asset allocation in risk management using historical data and employee trend data analysis to come up with its findings.arithmetic mean, standard deviation Value at Risk and other risk evaluation strategies are used in the data analysis. Excel part one gives the analysis of the various asset allocation calculation. Advantages and disadvantages are given and the expalanation are supported with the statistical data in the appendices excel part1. In the second part of portfolio management of stock prices, the discussion is based on the mementum nature, that is downwarend and upward movement trends in the stock prices. Appendix 2 that is excel part 2 gives the insigh calculation of the portfolios using weighted mean method. The paper ends with giving details conclusion and recommendations to be used in making investment decisions. Asset Allocation Methods 1.0 Historical data. From the asset historical data given, the historical average return is calculated with reference to past historical data. Excel part 1 gives the information of the retutn calculation. The average is calculated by adding the total monthly returns and dividing by twelve month to get the average return (Markowitz, 2005). Then the retun of portfolio is arrived by dividing the first year average price then subracting one to come up with the returns. 2.0 Cons and pross of trend forecasting and analysis method In most cases, business owners and managers do use trends analysis in carrying out verifieble future outcomes of the business. Trend analysis can be used to act or to give warnings on the future impending problems in business and this early warnings and indicators will be used by the management to change in the tactics used in the management. This is only possible where accurate historical data are availble (Kaplan and James, 2004). In our case, there are very accurate historical data which has been used in calculating the retuns on the assets of different classes. In excel part one, different assets classess are calculated based on the historical data given. The trend anlysis is employed and it useful in that; i. Trends analysis is one of the most precise tools for giving analysis on the market trends, sales forecasting, invetntory level and intrest rates. ii. Trend analysis is limitless with the availability of data. It can also be subjected to through scrutiuny and this can help in enhancing its reliability. 3.0 Assets allocation methods decisions used Asset allocation is the process through which assets and investments are being divided among different kinds of asset groups. They include asstes like; i. Real estates ii. Cash iii. Bonds iv. Stock and many others. This is done with the soul aim of achieving a more feasible categories and combination of rewards and risk which can meet an investor needs and goals during specific time. The portfolio process consists of three major steps and processes. First is the specification by the investor on the basic assumptions of asset classes and models. For each and every asset class, co-movements, return and risk are analyzed among different asset classes. In the second method, portfolio optimization are algorithm to calculate the percentages of allocations to different asset classes, this is called asset mixed. The third approach is where asset mix returns and forecasting wealth are projected on the various asset investment classes. Probabilities are also used to project the potential outcome. 4.0 Mean variation optimization. This is one of the standard means of creating the most efficient asset allocation strategies. Though this methods has been used for several years, it is faced by several limitations which limits its effective as asset allocation method, Mean variation method do not take into account the fat-tailed classes of asset return distribution which cannot match the real world class historical data. The mean variation used in the data give the information that the risk is nor wide spread among different asset classes but is more concentrated in one class of assets. This gives that some assets are more risky than others so a more precise decision tool should be employed in making the decision on the type of asset to invest in. 5.0 Expected Arithmetic Mean Return This is the measure of asset reward using conventional mean return of variance optimization. This is where a single period model where expected arithmetic means of return is being forecasted over the return of the next period of different classes of asset investments. Theoretically, this can be expressed as the mean probability of all the average possible returns of the asset class. From excel part1, the value of arithmetic mean of 4.0% and 5.4% shows that the asset reward is less risky and the market condition is favorable. 6.0 Risk standard deviation Standard deviation normally is the measure of the degree of dispersion of the mean and it normally represent the volatility of the asset investment. It represent the variation in the cycles of the of asset performance (Levy et al, 2007) From the data which is given, the volatility of the investment is high with standard deviation of 2.07, 1.0 and others shows high market volatility and this is a clear indicators to the investors on unfavorable market condition on different assets. 7.0 Value at Risk/Conditional value at risk VaR is the measure of downside risk and it is one of the most appropriate methods for measuring downside risk for investors who are more concerned with this type of risk. It is important when an investor is investing in fat tailed returns or when one is investing in symmetrical assets (Fishburn, 1997). Many people refer to it by different names which includes; i. Mean short fall ii. Expected short fall iii. Expected short tail loss iv. Tail VaR VaR is the highest loss or loss which is maximum and it does not exceed a certain limit of a given probability which is spread over a given period of time. Like in our case, with probability of 0.99, VaR portfolio is 33.25% which simply implies that in the next one year there is 0.99 chances of losing 33.25% of total portfolio value which is not a good indicator as the chances of losing portfolio value is high. Though CVaR and VaR are more less the same, the value of figures received during their calculation depend largely on the distribution of different assets. 8.0 Investment objectives The portfolio analysis as met the objectives of the investment objectives in several ways which includes; It has able to give indebt future potential risks which are available in the asset allocation process The use of both VaR and standard deviation as help in giving a clear indication of comparing the performance of asset portfolio over along period of time Advantages of the analysis method i. It is simple compared to other methods of asset allocation ii. It is compare the past, present and future risk in the asset portfolio iii. It has used both the present data and future data iv. The methods are easy to understand. 9.0 Part II Portfolio Management The paper uses the data from the year1999 to 2009. The portfolio stock performance depends on several factors which influence their performance. One of such factors includes fund managers professional level. The fund management which is headed by certified financial analyst in most cases outperforms those who are headed by risk managers. The active collar strategy is where tow sets of market signals are used to adjust the money level and the calls and also to put the number of written calls on each and every trading date. The first indicator is the momentum movement indicator also known as simple moving average method. If the short term moving average is above the long term moving average then the indexes are said to be in an upward trends and if the short term moving average are below the long term average then index are said to be downward momentum trends. In the study we have used a three month as short term moving average and one year moving as along term moving average. Incase of an upward trend in the market then it shows that the collar is widening with both the call and put share indices (Harlow, 1991). The volatility is another indicator which has been used. Using the share prices, weighted average of the market is calculated and higher value indicated high volatility while low value of weighted average indicates low volatility (Hill et al, 1999). In our study, the volatility of stock price indices are low as the below 0.5 in most cases. The standard deviation helps in measuring the degree of disparity in the market volatility. 10. Justification of the method This approach is widely used among many financial analyst because it gives both the degree and time frame of market volatility which can not be found in other methods hence an investor will be in a position to make an up right decision on the best investment option. Secondly the method is simple and easy to understand even with people who do not have background of portfolio management hence most recommeded to used in most of market analysis. Since it uses historical data, the information is always accurate in predicting the future hence is better than other methods. 11.0 Conclusion Both the asset allocation methods and portfollio management concept needs to be simplified so that investors can easily use them in investment decisions. References Fishburn, Peter C (1997) “Mean-Risk Analysis with Risk Associated with Below-Target Returns” American Economic Review, March Harlow, W. Van (1991). “Asset Allocation in a Downside-Risk Framework.” Financial Analysts Journal, September/October Hill, I.D., R. Hill, and R. L. Holder (1999) "Fitting Johnson Curves by Moments," Applied Statistics, Kaplan, Paul D (2012) Frontiers of Modern Asset Allocation, Hoboken, NJ: John Wiley & Sons, Inc. Kaplan, Paul D. and James A (2004) Knowles. “Kappa: A Generalized Downside Risk- Adjusted Performance Measure.” Journal of Performance Measurement, Spring Krokhmal, Pavlo, Jonas Palmquist, and Stanislav Uryasev (2002) “Portfolio Optimization with Conditional Value-at-Risk Objective and Constraints.” Journal of Risk, . Levy, Haim and Harry M. Markowitz. (2007) “Approximating Expected Utility by a Function Of Mean and Variance.” American Economic Review, Markowitz, Harry M (2005) Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons, Appendices Read More