‘The problems of Monte Carlo Simulation’ by David Nawrocki
This article describes the problems associated with using the Monte Carlo Simulation Model as a tool for determining future investment outcomes for investors. The tool is widely used by Financial Advisors as a means of showing investors future returns on investments. The article discusses why the use of Monte Carlo Simulation in financial planning is difficult and can lead to incorrect decisions which can have a detrimental impact on investors’ expectations of expected returns. The article tells us that Monte Carlo Simulation uses assumptions based on normal distributions and correlation coefficients of zero, neither of which are real in the financial markets.
The article discusses why Monte Carlo Simulation should only be used when there is no data available or it is too expensive to implement and why other methods may provide the same or better answers without being assumptive. The author uses evidence from previous authors highlighting the problems with Monte Carlo Simulation and the use of alternatives as a more accurate way of forecasting future returns for an investor. There are four alternatives shown and discussed, however, the article explores the use of exploratory simulation which states can provide more accurate answers, without assumptions and is easier to use. The article highlights that the problem of risk versus uncertainty and how this confuses Financial Planners and thus their clients, by using normal distribution and zero correlation in assumption sets for the Monte Carlo Simulation without taking into account economic conditions which can lead to incorrect outcomes and decisions for the client. The article discusses why many authors do not recommend the use of Monte Carlo simulation and the different problems that are basic in the assumption set of the model. The article also looks at using correlation coefficients to measure interrationships between variables,...
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