Investment Objective and Policy - Literature review Example

Portfolio management By Investment objective and policy The principal objective of the prevailing fund is to provide a high-income current by primarily investing in fixed income securities. Since it is a secondary objective, the Fund enhances capital appreciation with limitation to its principal investment role. The objectives and the limitations of the investment fund set standards in investment restrictions are of fundamental policies that can never be changed without the authorization from the holders of majority shares (Brentani, 2003, pp. 124-189). Fundamental objective for the underlying company is to maximize capital growth through investing in different portfolio with private equity funds and with all being private companies. The policy the company employs is of unquoted investments, in a more general way, the company subscribe for investments in primary investment commonly referred as new private equity funds and buying secondary investments and with time the company capitalize further on its activities and ends up acquiring direct holding in within the unquoted companies. Fund investment may be made directly or indirectly via one or more holdings. Over-commitment, this is a situation where the company is committing in excess of the available uninvested assets to invest in private equity with a view that such commitments will be met from uncertain future cash flows and through borrowing and capital raisings where applicable (Rajagopal, 2012, pp. 178-234). The company may still borrow to make more investment and may again use its borrowing power to for the management of its cash flows more flexible hence facilitating the company to invest as and when suitable opportunities arise (Brentani, 2003, pp. 124-189). Rationale behind portfolio construction The construction techniques for a portfolio based on a predicted risk with less or no returns are very common in the recent past (Levine, 2005, pp.256-278). The policy adopted by the company is for a global approach where the strategy is unidirectional; to mitigate investment risks via diversification of its portfolios by investment stage, geography, and sector. The Company invests in primary investment quoted above. Moreover, over certain period of time the company may hold quoted investments as a result of those investments being distributed to the very company. Moreover, investments funds as a result of an investment in an unquoted company being quoted which the company would not otherwise invested on yet it deserves the right to invest in quoted securities once it is in the interest of the company (Brentani, 2003, pp. 124-189). Meanwhile the company still has a broader way of investing such as in any financial instrument, being inclusive of interests in partnerships both limited or other collective investment schemes as well as securities. Within the underlying situation of the Monte Carlo simulation is used to generate years of daily data. Overall, a Monte Carlo simulation adheres to all kind of analysis through building samples of possible outcomes via substituting a wide range of values popularly referred as a probability distribution as long as a factor has any kind of inherent uncertainty. Results are then calculated over time, and each time applies a different set of random values obtained from the probability function (Levine, 2005, pp.256-278). However, it is possible to simulate years of data that does have similar risk features as the underlying asset of classes. In the example, we used a multivariate normal distribution with its own parameters so as to describe the market riskiness’ of asset returns. Importantly, the input parameters are just reference points because the multivariate normal distribution randomly generates values in the normal standard distribution. For simulation, the results are always seen as an extra source of information as opposed to other explains the framework. Due to this, the entire results are treated with care and should be seen as a quantitative finance. Research papers reveal that, asset classes have and are expected to have sharp ratios of about 0.2-0.3 because, in normal conditions, there needs to be some extra return to compensate investors for excess risks so that the ratio should be positive. Nevertheless, the very ratio cannot be an extremely large positive value since that would make the investments attract a substantial amounts of capital that would bid up their prices and lower their expected return (Rajagopal, 2012, pp. 178-234). Therefore, we have put to use the average return estimation for every class producing a sharp ration of 0.2-0.3 while the risk-free rate is 1.5 percent. Low correlation asset classes and volatility relative to other asset classes receive higher weighting within the portfolio. In the end, weighting lead to the effect that all underlying asset classes have the lowest volatility weighted average correlation coefficient to one another (Brentani, 2003, pp. 124-189). This is the simple version of construction technique since the advanced ones also involves parts from the inverse volatility concept that undergoes evaluation separately Every asset is weighted in inverse proportion to its volatility and then rescaled to sum up to 1. It follows that lower weights are given to high volatility assets and higher weights too low volatility securities. The existing concept frequently is being incorporated with the corresponding risk parity approach therefore, it misleads people because the two are quite similar (Levine, 2005, pp.256-278). All the same, since the overall portfolio volatility is not a summation function of the underlying volatilities, it follows that each asset class is not contributing exactly the same amount of risk to the overall portfolio. Portfolio characteristics and components Taking n assets and corresponding s variable states of the universe possessing R an {n*s} matrix within which the underlying components within row i and corresponding column j depicting the return of asset i within the country of universe. For instance, taking n=3 and corresponding s=4: Good Fair Poor Bad Asset 1 5 5 5 5 Asset 2 10 8 6 -5 Asset 3 25 12 2 -20 Let x be an {n*1} vector of asset holdings in a portfolio. For example: x Asset1 0.20 Asset2 0.30 Asset3 0.50 The portfolios for each of the states will be calculated easily using the subsequent stages. The {1*s} vector of portfolio returns in the states (rp) will be: rp = x*R Here: Red Green Yellow Blue rp 16.50 9.40 3.80 -10.50 Now, let p be an {s*1} vector of the probabilities of the various states of the world. In this case: p Good 0.40 Fair 0.30 Poor 0.20 Bad 0.10 The expected return of the portfolio will be: ep = rp*p In this case: ep = 9.13 It is useful to write the expression for expected return in terms of its fundamental components: ep = x*R*p The products of the above three terms can be computed in either of two ways. Above, we computed x*R, then multiplied the result by p or we could have multiplied x by the result obtained by multiplying R time’s p: ep = x*(R*p) The parenthesized expression is an {n*1} vector in which each element is the expected return (or value) of one of the n securities. Let e be this vector: e = R*p Here: e Asset1 5.00 Asset2 7.10 Asset3 12.00 Using these results we may write: ep = x*e From the above, the expected return on a portfolio, which is similar to the underlying the product of the vector of its asset holdings and corresponding the vector of the asset expected returns, this is the case whether the returns are discrete or continuous The units used for the values in vectors x and e will depend on the application. In some cases, physical units may be appropriate for x. Moreover, the prevailing values and the corresponding proportions of the total value. The units selected are utilized in finding the terminal period of the value of the prevailing portfolio. It is also used in finding the final period values per unit of exposure is placed within vector e and the number of units of each asset held placed in vector x. in case the expected outcome were the only value appropriate characteristic of a portfolio, making decisions on investments would have been easy. Still risk is a relevant factor, and its determination mounts a substantial challenge. References Brentani, C. (2003). Portfolio Management in Practice. Burlington, Elsevier. http://www.123library.org/book_details/?id=33945. Fabozzi, F. J. (1998). Active equity portfolio management. New Hope, Pa, Frank J. Fabozzi Associates. Levine, H. A. (2005). Project portfolio management a practical guide to selecting projects, managing portfolios, and maximizing benefits. San Francisco, Jossey-Bass. http://site.ebrary.com/id/10301234. Smithson, C. (2003). Credit portfolio management. Hoboken, N.J., John Wiley. http://public.eblib.com/choice/publicfullrecord.aspx?p=469922.. Rajagopal, S. (2012). Portfolio management how to innovate and invest in successful projects. Basingstoke, Palgrave Macmillan. Perry, M. P. (2011). Business driven project portfolio management: conquering the top 10 risks that threaten success. Ft. Lauderdale, Fla, J. Ross Pub. Rad, P. F., & Levin, G. (2006). Project portfolio management tools and techniques. New York, NY, IIL Pub. Ranganatham, M., & Madhumathi, R. (2006). Investment analysis and portfolio management. Delhi, India, Pearson Education/Dorling Kindersley (India). http://proquest.safaribooksonline.com/?fpi=9788177582291. Epmc, Inc. (2013). Project portfolio management a view from the management trenches. Hoboken, N.J., Wiley. http://rbdigital.oneclickdigital.com. Cuthbertson, K., & Nitzsche, D. (2005). Quantitative Financial Economics Stocks, Bonds and Foreign Exchange. Chichester, John Wiley & Sons. http://www.123library.org/book_details/?id=6376. . Read More