![]() This is an exciting area of finance as it has the potential to greatly improve business decision making. In future posts I plan on reconstructing several more examples including presenting a real estate case study. For a more extensive list see Nadarajah, Margot, & Secomandi (2017). Within the real options framework it has been applied to valuing pharmaceutical R&D, gas storage facilities, and renewable energy just to name a few. ![]() However the algorithm is very general and highly adaptable to many real options situations. This algorithm was introduced in an influential paper for the valuation of American style options, Longstaff & Schartz (2001). So I thought I would post a basic real options model that is solved using an approximate dynamic programming technique called Least Squares Monte Carlo simulation (LSM). Additionally advances in both algorithms and computing power allow the use of simulation to solve real options problems without the use of any sophisticated mathematics. With the rise of data science, and businesses becoming more comfortable with quantitative approaches to decision making, I suspect that real options analysis will become a standard technique in the financial analyst’s arsenal. They dedicated their book to “The Future” as they foretell of a time when real options analysis is the dominant paradigm for investment valuation and capital budgeting. The classic academic text on real options is Dixit & Pindyck (1994). Many finance academics have long touted the superiority of the real options valuation approach for capital investment analysis over the traditional discounted cash flow method.
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