Strategic Asset Allocation of Pension Funds; An Application of Markowitz Portfolio Theory

Abstract

The management of pension funds is a very sensitive and important aspect that has a bearing on the quality of life given by retirees when they come to the end of their useful work life. Therefore their risk management is very crucial. The financial products n which the pension assets are invested in have different levels of risk that investment managers are loo of. Their aim is therefore to maximize the returns for their members while taking the minimum possible risk with their resources. Markowitz provided a solution to this problem through the mean-variance model, which has been critically analyzed and used to various investment portfolios. This study was therefore aimed at finding an optimal way of allocating the pension funds keeping in mind their risk characteristics. The results from the analysis done showed tremendous improvement in terms of efficiency in the allocation of the pension assets to various investment opportunities. The optimal restricted portfolio gave us a return of 9.47% while the unrestricted one gave us 13.45%. this came with a standard deviation of 10.45% and 13.74% respectively. Therefore, the investor can be able to invest 62% and 60% of the restricted and unrestricted portfolios respectively in the risky portfolio and 38% and 40% respectively in the risk free asset. From the data used for this study, this will give the best returns. This thesis also explores the improvements that can be made to index funds by removing the link between pricing errors and portfolio weights and compared their performance with that of actively managed funds. Index funds today tend to overweight over valued companies, leading to serious performance lags especially during pricing bubbles. The Markowitz optimization formulation is used in combination with the fundamental metrics and the weightings from the solutions to the mean variance optimizations were used to calculate the expected returns. The Sharpe ratio for the S&P 500 was 0.07 and that of the Mean Variance Optimization was 0.57. The results clearly demonstrate that the MVO outperforms the S&P 500. By using fundamental metrics such as P/E ratio, sales, book value and dividends to evaluate the size of a company rather than the traditional market capitalization, significant improvements can be made to the value of a portfolio.