Method for performing monte carlo risk analysis of business scenarios

• US 20040073505A1
• Filed: 10/09/2002
• Published: 04/15/2004
• Est. Priority Date: 10/09/2002
• Status: Abandoned Application

• Current Assignee: James Foley Wright
• Original Assignee: James Foley Wright
• Inventors: Wright, James Foley
• Application Number:
  US10/268,393
• Publication Number:
  US 20040073505A1
• Time in Patent Office: Days
• US Class Current: 705/36
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

The present invention uses Monte Carlo simulation techniques to evaluate the risk of business scenarios. A method of angular approximations (Gaussangular distributions™) is used to simulate symmetrical and unsymmetrical bell-shaped, triangular, and mesa-type distributions that fit data required by the metrics in the Monte Carlo calculation. The mathematical functionality of these Gaussangular distributions is comprised of their extremes, the most likely value, and a variable analogous to its standard deviation.

Claims

1. A stochastic process for simulating on a computer or computer system the behavior and consequences of a scenario, the process comprising:

a) using a metric, either static or dynamic, that realistically simulates the scenario being modeled;

b) using distribution functions, either symmetrical or unsymmetrical, that best describe the available data for each of the input variables of the metric used to simulate the scenario;

c) performing enumerable iterations, wherein a new numeric solution to the metric is calculated in each iteration by selecting new values for each input variable within its distribution by using a new pseudo-random number and the probability distribution function for that input variable;

d) placing each of the enumerable solutions to the metric from each iteration into a discrete frequency distribution;

e) converting the discrete frequency distribution into a discrete probability distribution; and

f) using the discrete probability distribution for the metric to analyze the scenario predicted by the metric by calculating parameters comprising the mean value of the metric, the most likely value of the metric, the probability the metric will have at least a certain value, the probability the metric will be more than at least a certain value, and the probability that the metric will lie between certain bounds.

2. The process described in claim 1, wherein said scenarios are business investments the possible metrics for each year is determined by the user but can comprise such evaluations as discounted cash flows, profitability index, pre-tax profit, and after-tax cash flows.

3. The process described in claim 1, wherein said scenarios are the future behavior of an existing business the possible metrics for each year is determined by the user but can comprise such evaluations as discounted cash flows, profitability index, pre-tax profit, and after-tax cash flows.

4. The process described in claim 1, wherein the said stochastic process used may be also known as the Monte Carlo simulation method which uses a distribution function to represent each input variable in the metric and the end result of the calculational process is a discrete distribution representing the metric.

5. A process for creating on a computer or computer system an angular approximation to a continuous PDF (probability density function), p(x), the process comprising:

a) using the minimum value of x, x_min, and the maximum value of x, x_max to define the boundaries of the PDF where the p(x)=0;

b) using the most likely value of x, x_likely, to define the point where p(x) is at a maximum;

c) using break points to be those points where any two straight-line segments intersect at an angle not equal to zero degrees (0°

) including at x_likely;

d) using a series of straight-line segments that run consecutively from x_min to the first break point, then continuing from break point to break point, and ending from the last break point to x_max;

e) associating the inverse of the area between one break point near x_min and one break point near x_max to represent the effective standard deviation which is proportional to the square of the second central moment of the Gaussangular distribution;

f) whereas the angular approximation may be either symmetrical or unsymmetrical with respect to the distances |x_max−

x_likely| and |x_likely−

x_min|;

g) whereas the angular approximation may be either symmetrical or unsymmetrical with respect to the lengths of the line segments in the approximation; and

h) whereas the approximation to the continuous probability density function is a mathematical function comprising the variables x_min, x_likely, x_max, and the break points;

6. The process described in claim 5, wherein said approximation can represent symmetrical or unsymmetrical triangular or mesa-type distributions.

7. The process described in claim 5, wherein said approximation can represent Gaussian or normal distributions or unsymmetrical bell-shaped distributions.