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Improved Decision Making Under Uncertainty: Incorporating a Monte Carlo Simulation into a Discounted Cash Flow Valuation for Equities
Discounted cash flow valuation (DCF) models are a common approach to valuing equities and traditionally they aim to provide the most likely outcome as a single point estimate. However, this approach ignores the uncertainty related to the drivers of value for a security. In order to gain a more accurate representation of the intrinsic value of a security, the uncertainty related to the inputs of the model should be included in the valuation. Monte Carlo simulation is a method used in modeling systems that are affected by randomness. Through random sampling, multiple scenarios can be generated and the nature of the randomness can be assessed. Monte Carlo simulation is often used in solving problems for which there is no analytical solution. The idea behind the method is to iterate a process thousands of times with random input variables to attain a probability distribution of all possible outcomes instead of a single point estimate. In the context of valuation, this method can be used to sample future cash flows, which can then be discounted, resulting in a distribution of possible values for the security. This study aims introduce a method for incorporating a Monte Carlo simulation aspect into a DCF valuation model and to then examine the benefits of such an approach. The theoretical framework presents the basic theories of discounted cash flow valuation and an introduction to the Monte Carlo simulation method. In the empirical section of this study I describe the model used and present the valuations I performed on several companies. The findings of this study were that by putting the valuation in context with the uncertainty related to the asset, the investment decision could be improved. By viewing the upside the valuation model implies in relation to the amount of uncertainty related to the models inputs, provides a new aspect to the decision making. The investor can view the results not only through the expected return but also through the level of uncertainty.
Improved Decision Making Under Uncertainty: Incorporating a Monte Carlo Simulation into a Discounted Cash Flow Valuation for Equities
Discounted cash flow valuation (DCF) models are a common approach to valuing equities and traditionally they aim to provide the most likely outcome as a single point estimate. However, this approach ignores the uncertainty related to the drivers of value for a security. In order to gain a more accurate representation of the intrinsic value of a security, the uncertainty related to the inputs of the model should be included in the valuation. Monte Carlo simulation is a method used in modeling systems that are affected by randomness. Through random sampling, multiple scenarios can be generated and the nature of the randomness can be assessed. Monte Carlo simulation is often used in solving problems for which there is no analytical solution. The idea behind the method is to iterate a process thousands of times with random input variables to attain a probability distribution of all possible outcomes instead of a single point estimate. In the context of valuation, this method can be used to sample future cash flows, which can then be discounted, resulting in a distribution of possible values for the security. This study aims introduce a method for incorporating a Monte Carlo simulation aspect into a DCF valuation model and to then examine the benefits of such an approach. The theoretical framework presents the basic theories of discounted cash flow valuation and an introduction to the Monte Carlo simulation method. In the empirical section of this study I describe the model used and present the valuations I performed on several companies. The findings of this study were that by putting the valuation in context with the uncertainty related to the asset, the investment decision could be improved. By viewing the upside the valuation model implies in relation to the amount of uncertainty related to the models inputs, provides a new aspect to the decision making. The investor can view the results not only through the expected return but also through the level of uncertainty.
Improved Decision Making Under Uncertainty: Incorporating a Monte Carlo Simulation into a Discounted Cash Flow Valuation for Equities
Nurminen, Jack (author) / Haaga-Helia ammattikorkeakoulu
2016-01-01
10024/442
Theses
Electronic Resource
English
DDC:
690
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