Project Management Journal

second scenario, the firm decides to invest in two industries that have the most negative correlation. As shown in the correlation matrix (equation 18), copper and GBP/USD have the most negative correlation ( 20.605). Also, copper and aluminum have a high positive correlation (0.869).

To simplify, we disregarded the fact that different projects have different returns and supposed instead that three projects have similar characteristics from a project return viewpoint. That is, based on reverse engineering, we consider the return of three projects as equal to 200,000 monetary units. According to the last selling price of products derived from the market, the production volume of project 1 is 25. 27 tons of copper, the production volume of project 2 is 98.06 tons of aluminum, and the return of project 3 is 123,708 currencies GBP (a project invested abroad that its return is in currency). Therefore, project return is calculated by the amount of currency multiplied by the exchange rate to convert the unit to dollars. The following correlation matrix helps make sense of the relationship between prices. It should be noted that correlation among factors is achieved by their historical price.

this, we investigate the point in a second case example (B).

Case Example B

In order to better show the effects of correlation, we modeled two different scenarios for investing. In each one, the portfolio is composed of two projects and portfolio return is the simple sum of the two projects. By modeling these scenarios, we are able to highlight the importance and impact of different diversification strategies on the portfolio. In the first scenario, the firm decides to invest in two industries that have a high positive correlation (the biggest number in the matrix). In the

705397 ( 7.053 3 105). Other important parameters such as standard deviation and mean are also shown. This means that, given a confidence level, the firm’s cash flow in only 5% of the situations is less than 705397 and the firm’s cash flow (with a 95% certainty) will not be less than 705397.

Multiple simulated examples were run to determine which factors influence the selection or rejection of projects in a period. After analyzing the results and selecting the projects in each period, we get another important result: In the same situation, the model selects projects from the industries that have the least correlation to reduce risk (variance). To illustrate