AN APPLICATION OF THE REAL OPTIONS METHOD CONCERNING LEARNING OPTIONS TO EVALUATE THE VALUE OF BIOTECHNOLOGY PROJECTS - Jalaludin, Paiz
The traditional Discounted Cash Flows (DCF) method, which is used often to evaluate the value of a project, still ignores the flexibility and uncertainty so that the estimation results with this method lead to many mistakes in decision making. This matter made the real-world activists try to reduce stock valuation techniques in the financial sector to be applied in real assets, which came to be known as real options. Real options, in its practice, use the resemble approach as stock options, which includes using the binomial model, because the real options are derived from the valuation method of the value of finance options. Among the binomial models used to calculate project values are the simple binomial model of Benninga version and the modified binomial model of Guthrie version which is well known as learning options method. Benninga’s binomial model is sufficiently perfect and flexible in estimating the project values, while the learning options method uses more complex parameters. The using of more complex parameters is because it considers the possibility of right for the manager to study the characteristics of the project. Thereby, the manager will know the right time to start investing. In this research, the writer made a few modifications to both methods by including several types of real options and then the application is simulated in a project. The learning options method, in particular, is simulated in biotechnology projects, because the type of the project has a relatively high level of uncertainty, so managers need the features that are in the method. Then a comparison is made between the calculation results of the DCF method with the real options method, and between a project that contains many options and a project that contains fewer options. The result of this study states that the calculation with the real options method is more optimal than the DCF method calculation and the project that has many more options is more optimal compared to the project that contains fewer options. Keywords: real options, learning options