Monday, August 26, 2019

Portfolio management Statistics Project Example | Topics and Well Written Essays - 2250 words

Portfolio management - Statistics Project Example In this project, the prices of the stocks provide the weights of the portfolios for all the stocks provided. The monthly returns for the stocks in the investment pool are calculated with the formula in equation 1 below: Where xp is the monthly expected return, pi is the weight of the portfolio and n is the average number of assets. The values of calculation of the expected monthly returns are presented in the table 1 presented below: The returns computed for the years of this study show the expected return increasing from left to right for all the stocks except IBM that drops at the end of the period within the time series. The stock returns values experience wide variance due to the fluctuation in portfolio weights across the period. The process of refining the investment involved ignoring the portfolio with low weights and retaining the high weight portfolio. The selection aimed at picking 3 stocks with the best returns to represent the high efficiency required in the pool decision. The high efficiency stocks were found to be IBM and MMM. The decision was made on the values based on the original currency returns. The time series for the refined investment pool carries the following stock: The major reason for reducing the number of stocks in the refined investment is that many assets have caused a wide variation of the portfolio weights and return on investment (Tobin 1958, p. 65). The analysis sets up individual each of the assets independently to as to classify them as either risky assets or risk free assets using the correlation projections. The refinement judges the investment by their return, hence; it operates with the few selected manageable stocks to reduce the portfolio size by ignoring the low return stocks. The tangent portfolio was constructed using the Matlab program. The program uses the data entries from the covariance matrix with the new weights of portfolios. The mean return values and the optimal portfolio

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