Estimating shareholder value potential using Data Envelopment Analysis

Managers often claim that they want to “maximize shareholder value.” Students are taught in business schools that the ultimate goal of a corporation is to “maximize shareholder value.” However, it is unclear how one would measure the maximization of shareholder value. This research proposes a model to measure the maximization of shareholder value using the linear programming technique called Data Envelopment Analysis (DEA).; The concept of “maximizing shareholder value” and the linear programming technique of DEA have never been combined. The financial variable that is used to measure shareholder value is economic profit. Economic profit is defined as the spread between the return on invested capital and the cost of capital, times the amount of invested capital. In the proposed model, DEA is used with maximization of economic profit (the output variable) as the objective. The value drivers of economic profit are the financial variables of return on invested capital (ROIC) and net operating profit less adjusted taxes (NOPLAT). NOPLAT and ROIC are used as the input variables in the proposed DEA model. The sample industry chosen for analysis is the electric utility industry.; The results of the DEA model give an estimate of how much potential improvement there is in a company's shareholder value. This amount of potential improvement is called Shareholder Value Potential (SVP). The research question that is examined is: Can SVP be estimated using DEA? Given the methodology used to calculate ROIC, NOPLAT and economic profit and the DEA model assumptions of variable returns-to-scale and output maximization, this research shows that SVP can be estimated using DEA.; Estimating SVP would allow managers and financial analysts to set targets for maximization of shareholder value.