AU2016281207A1 - Systems and methods for valuing assets - Google Patents

Abstract

Systems and computer implemented methods comprise a financial valuation model. A processor to: identify key revenue and expense parameters of the financial valuation model; select from the key revenue and expense parameters a central parameter upon which to base a financial valuation of an asset; read from one or more of the storage devices, a historical dataset for each of the key revenue and expense parameters; statistically analyse the historical dataset for each of the key revenue and expense parameters to determine a correlation between the central parameter and each of the other key revenue and expense parameters; create or modify the financial valuation model based on the correlation between the central parameter and each of the other key revenue and expense parameters; and execute the created or modified financial valuation model including the central parameter and each of the other key revenue and expense parameters to generate the financial valuation of the asset.

Description

FIELD OF THE INVENTION [1] The present invention relates to systems and computer implemented methods for valuing assets, such as, but not limited to mining assets. In particular, the present invention relates to systems and computer implemented methods for creating and/or modifying financial models used for valuing assets.

BACKGROUND TO THE INVENTION [2] In finance, asset valuation is the determination of a value of an asset. For example, the value of an asset may be needed to sell, tax, insure, invest in and/or borrow against the asset. The value of an asset is typically estimated based on parameters that affect the value of the asset. For example, when valuing an entity, such parameters may relate to revenues and expenses of the entity.

[3] The value of an asset is often estimated via a computer using a financial valuation model, which can include parameters that affect the value of the asset. For example, in mining and many other fields, the value of assets can be based on the revenue received for a product sold, the expense of components and/or the expense of production.

[4] Computer implemented financial valuation models can more accurately value entities where revenues and expenses can be controlled or accurately predicted by those managing the entity. However, current computer implemented financial valuation models struggle to accurately value longer life entities where both the revenue and expenses are driven by market forces. Errors produced by such computer implemented financial valuation models when attempting to determine an ultimate value for an asset can lead to suboptimal strategic decisions being made.

[5] For example, the expense of production in both a factory producing copper widgets and a copper mine are likely to be heavily impacted by systemic market forces. However, a factory producing copper widgets can largely set the sales price of widgets based on past sales and therefore can control its revenue. In contrast, a copper mine which will be able to sell copper at a price in line with a global spot price has less direct control over its revenue.

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OBJECT OF THE INVENTION [6] It is a preferred object of the invention to provide an improved system and/or computer implemented method for valuing assets that addresses or at least ameliorates one or more of the aforementioned problems of the prior art and/or provides a useful commercial alternative.

SUMMARY OF THE INVENTION [7] The present invention relates to systems and computer implemented methods for valuing assets, such as, but not limited to mining assets. In particular, the present invention relates to systems and computer implemented methods for creating and/or modifying financial models used for valuing assets.

[8] In one form, although not necessarily the broadest form, the invention resides in a computer implemented method comprising:

identifying, at a processor, key revenue and expense parameters of a financial valuation model;

selecting, via the processor from the key revenue and expense parameters, a central parameter upon which to base a financial valuation of an asset;

reading, via the processor from one or more of the storage devices in communication with the processor, a historical dataset for each of the key revenue and expense parameters;

statistically analysing, via the processor, the historical dataset for each of the key revenue and expense parameters to determine a correlation between the central parameter and each of the other key revenue and expense parameters;

creating or modifying, via the processor, the financial valuation model based on the correlation between the central parameter and each of the other key revenue and expense parameters; and executing, via the processor, the created or modified financial valuation model including the central parameter and each of the other key revenue and expense parameters to generate the financial valuation of the asset.

[9] Suitably, selecting the central parameter upon which to base a financial valuation comprises:

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PCT/AU2016/050521 statistically analysing, via the processor, the historical data for each key revenue and expense parameter to determine the central parameter based on which parameter will deliver a most accurate valuation outcome.

[10] Suitably, selecting the central parameter upon which to base a financial valuation comprises:

determining, via the processor, the central parameter based on an analysis of the key revenue and expense parameters to which the valuation will be most sensitive.

[11] Suitably, selecting the central parameter upon which to base a financial valuation comprises:

receiving, via an input in communication with the processor, a selection of the central parameter from the key revenue and expense parameters.

[12] In some embodiments, the method further comprises generating an equation for each of the other key revenue and expense parameters based on the correlation between the central parameter and the subject parameter, each equation derived from historical datasets and each equation to predict the other key revenue and expense parameters based on a prediction of the central parameter.

[13] In some embodiments, the method further comprises generating, via the processor, a predictive future dataset for the central parameter.

[14] In some embodiments, the method further comprises generating, via the processor, a predictive future dataset for each of the other key revenue and expense parameters based on the equation for the respective other key revenue or expense parameter and the predictive future dataset for the central parameter.

[15] In some embodiments, the method further comprises reviewing, via the processor, an accuracy and/or reasonableness of results of the created or modified financial valuation model.

[16] Suitably, the accuracy is determined based at least in part on a strength of the determined correlations between the central parameter and each of the other key revenue and expense parameters.

[17] In some embodiments, the method further comprises selecting, via the processor, a different central parameter from the key revenue or expense parameters

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PCT/AU2016/050521 if results of the created or modified financial valuation model are determined to be inaccurate and/or unreasonable.

[18] Suitably, the key revenue and expense parameters include one or more of the following:

one or more significant revenue parameters; and all significant cost line items.

[19] Suitably, the historical datasets include one or more of the following:

one or more financial datasets and/or indices for each of a plurality of countries;

one or more global datasets and/or indices; and one or more cost indices.

[20] In some embodiments, the method comprises identifying, via the processor, one or more groups of revenue and/or expense parameters including the key revenue and expense parameters. The one or more groups of revenue and/or expense parameters can be selected from the following: central parameters; global parameters; local parameters; unique parameters.

[21] Suitably, a correlation between the central parameter and each of the one or more groups of parameters is determined.

[22] In another form, although not necessarily the broadest form, the invention resides in a system comprising:

a financial valuation model;

a processor in communication with the financial valuation model;

one or more storage devices in communication with the processor, one or more of the storage devices storing computer readable code components executable by the processor to:

identify key revenue and expense parameters of the financial valuation model;

select from the key revenue and expense parameters, a central parameter upon which to base a financial valuation;

read from one or more of the storage devices, a historical dataset for each of the key revenue and expense parameters;

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PCT/AU2016/050521 statistically analyse the historical dataset for each of the key revenue and expense parameters to determine a correlation between the central parameter and each of the other key revenue and expense parameters;

create or modify the financial valuation model based on the correlation between the central parameter and each of the other key revenue and expense parameters; and execute the created or modified financial valuation model including the central parameter and each of the other key revenue and expense parameters to generate the financial valuation of the asset.

Preferably the one or more of the storage devices store computer readable code components executable by the processor to perform one or more of the aforementioned method steps.

In a further form, although not necessarily the broadest form, the invention resides in a non-transitory computer readable medium having stored thereon computer readable code components executable by a processor to perform one or more or the aforementioned method steps.

[23] Further forms and/or features of the present invention will become apparent from the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS [24] In order that the invention may be readily understood and put into practical effect, reference will now be made to preferred embodiments of the present invention with reference to the accompanying drawings, wherein like reference numbers refer to identical elements. The drawings are provided by way of example only, wherein:

[25] FIG. 1 is a diagram of a system according to one embodiment of the invention;

[26] FIG. 2 is a general flow diagram of a method according to one embodiment of the invention;

[27] FIG. 3 is 3 general flow diagram further illustrating the method according to embodiments of the invention;

[28] FIG. 4 is a general flow diagram further illustrating the method according to embodiments of the invention;

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PCT/AU2016/050521 [29] FIG. 5 is 3 general flow diagram further illustrating the method according to embodiments of the invention;

[30] FIG. 6 shows an example graph of a historical dataset for a key expense parameter and an example graph of a historical dataset for a key revenue parameter;

[31] FIG. 7 illustrates a graphical user interface (GUI) showing a determination of a correlation between the key expense parameter and the key revenue parameter (central parameter) of FIG. 6;

[32] FIG. 8 illustrates a GUI showing a residual analysis for a linear regression line of FIG. 7;

[33] FIG. 9 illustrates a GUI showing a summary or report of the statistical analysis of FIGS. 7 and 8;

[34] FIG. 10 illustrates a GUI showing a report from a review of an accuracy and/or reasonableness of results of the statistical analysis of FIGS. 7 and 8;

[35] FIG. 11 illustrates a GUI showing predicted expense parameter values based on a value of the central parameter; and [36] FIG. 12 is a diagram of a financial valuation model according to one embodiment of the invention.

[37] Skilled addressees will appreciate that elements in the drawings are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the relative dimensions of some of the elements in the drawings may be distorted to help improve understanding of embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION [38] The present invention relates to systems and computer implemented methods for valuing assets, such as, but not limited to mining assets. In particular, the present invention relates to systems and computer implemented methods for creating and/or modifying financial models used for valuing assets.

[39] FIG. 1 is a diagram of a system 100 according to one embodiment of the invention. The system 100 comprises a financial valuation model 110, a processor 120 and one or more storage devices 130 in communication with the processor 120.

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PCT/AU2016/050521 [40] One or more of the storage device(s) 130 store on a computer readable medium 140 computer readable code components 150 executable by the processor 120 to perform aspects of the invention. In preferred embodiments, the computer readable code components include computer readable code components executable by the processor to interact with the financial valuation model 110, for example, to create, read, modify and execute aspects of the financial valuation model 110. In some embodiments of the invention, one or more of the storage devices store the financial valuation model 110. The system also comprises one or more output devices, such as one or more displays 160 in communication with the processor 120. The one or more displays 160 can be in the form of a touch screen such that the display 160 also functions as an input device. The one or more displays 160 display one or more graphical user interfaces (GUIs) as described herein to output the financial valuations of the assets generated in accordance with the present invention.

[41] In some embodiments, one or more of the storage device(s) 130 store historical datasets relating to parameters of the financial valuation model 110, for example, in one or more databases. In some embodiments, the historical datasets are used to determine correlations between the parameters of the financial valuation model 110.

[42] FIG. 2 is a flow diagram of a computer implemented method 200 according to one embodiment of the invention. For example, the method 200 can be performed by the processor 120 by executing at least some of the computer readable code components 150 stored on the one or more of the storage device(s) 130. The method 200 comprises the following steps.

[43] At step 210, the method 200 comprises identifying, at a processor, such as processor 120, key revenue and expense parameters of a financial valuation model. In some embodiments, the key revenue and expense parameters include one or more significant revenue parameters and/or all significant cost line items.

[44] For example, in a financial valuation model for a mining asset, the one or more significant revenue parameters might include one or more commodity prices and the cost line items might include a cost of one or more of the following: diesel, explosives, electricity, labour, chemicals and steel based products.

[45] At step 220, the method 200 comprises selecting, via the processor 120 from the key revenue and expense parameters, a central parameter upon which to base a

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PCT/AU2016/050521 financial valuation. In some embodiments, selecting a central parameter upon which to base a financial valuation comprises statistically analysing, via the processor 120, the historical data for each key revenue and expense parameter to determine the central parameter based on which parameter will deliver a most accurate valuation outcome. In some embodiments, selecting a central parameter upon which to base a financial valuation comprises determining, via the processor 120, the central parameter based on an analysis of the key revenue and expense parameters to which the valuation will be most sensitive. In some embodiments, selecting a central parameter upon which to base a financial valuation comprises receiving, via an input in communication with the processor 120, a selection of the central parameter from the key revenue and expense parameters.

[46] For example, for a copper mine, the majority of the key cost drivers would be correlated to the copper price and the copper price would typically be a stated corporate assumption specific for that company or project. Therefore, the copper price could be selected as the central parameter.

[47] At step 230, the method 200 comprises reading, via the processor 120 from one or more of the storage devices in communication with the processor, a historical dataset for each of the key revenue and expense parameters. For example, the historical datasets include one or more of the following: one or more financial datasets and/or indices for each of a plurality or a majority of countries; one or more global datasets and/or indices; and one or more cost indices. The historical datasets analysed may also include a gross domestic product (GDP) or a gross national income (GNI).

[48] At step 240, the method 200 comprises statistically analysing, via the processor 120, the historical dataset for each of the key revenue and expense parameters to determine a correlation between the central parameter and each of the other key revenue and expense parameters. In some embodiments, a plurality of central parameters is selected at step 220 and a correlation is determined between each central parameter and each of the other key revenue and expense parameters.

[49] In some embodiments, the other key revenue and expense parameters are grouped, for example, into logical groupings, and a correlation between the central parameter(s) and each group of parameters is determined. In some embodiments, the one or more groups of revenue and/or expense parameters can be selected via

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PCT/AU2016/050521 an input of a computing device comprising the processor 120. Alternatively, the one or more groups of revenue and/or expense parameters can be grouped via the processor 120 based on a correlation between the parameters.

[50] The key revenue and expense parameters can be grouped, for example, into the following groupings:

- central parameters being parameters to which other parameters would be correlated;

- global parameters being parameters driven by systemic market forces and therefore correlated to the central parameter(s);

- local parameters being parameters driven largely by localised economic movements, which would need to be modelled on a regional basis; and

- unique parameters being parameters that are anomalous and therefore would need to be assessed on a case by case basis.

[51] Some parameters or parameter groupings may be more obvious to pair than others. In one example, the copper price could be paired with either the London Metal Exchange (LME) copper spot price or the United States (US) copper spot price, both of which are very similar. In contrast, if a parameter grouping was developed comprised of consumables including steel and chemicals, for example, the combined operating costs for a grinding circuit and a flotation circuit, then this may require a greater level of analysis, and possibly the development of a composite historical dataset specifically for the subject parameter grouping.

[52] Where one or more parameters or parameter groupings are not accurately identifiable within the database, an assessment of the degree of correlation between the one or more parameters or parameter groupings and a potential paired dataset can be conducted, for example, as detailed in method 400 shown in FIG. 4.

[53] At step 250, the method 200 comprises creating or modifying, via the processor, the financial valuation model based on the correlation between the central parameter and each of the other key revenue and expense parameters. In preferred embodiments, the financial valuation model will incorporate all key revenue and expense parameters including, for example, the central parameter(s), the global parameter(s) correlated to the central parameters, the local parameter(s) and the unique parameter(s).

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PCT/AU2016/050521 [54] At step 260, the method 200 comprises executing, via the processor 120, the created or modified financial valuation model including the central parameter and each of the other key revenue and expense parameters to generate the financial valuation of the asset.

[55] FIG. 3 is a flow diagram of a method 300 according to one embodiment of the invention. The method 300 can be performed in conjunction with the method 200. The method 300 comprises the following steps.

[56] At step 310, the method 300 comprises generating an equation for each of the other key revenue and expense parameters based on the correlation between the central parameter and the subject parameter, where each equation is derived from historical datasets. Each equation is then used to predict the other key revenue or expense parameter based on a prediction of the central parameter.

[57] At step 320, the method 300 comprises generating, via the processor 120, a predictive future dataset for the central parameter. For example, many large mining companies develop and control tightly held predictive models for central commodity datasets which could be used as the predictive future dataset for the central parameter. Alternatively, for example, the predictive future dataset could be generated, via the processor, based on a stochastic mean-reverting model using n scenarios simulated via a Monte Carlo method. However, a skilled addressee will appreciate the predictive future dataset may be predicted, via the processor, using another technique and/or be based on a non-reverting model.

[58] At step 330, the method 300 comprises generating, via the processor 120, a predictive future dataset for each of the other key revenue and expense parameters based on the equation for the respective key revenue or expense parameter and the predictive future dataset for the central parameter.

[59] FIG. 4 is a flow diagram of a method 400 according to one embodiment of the invention. The method 400 can be performed in conjunction with the method 200 and in some embodiments, with the method 300. The method 400 comprises the following steps.

[60] At step 410, the method 400 comprises determining, via the processor, a correlation between one or more parameters or parameter groupings and a potential paired dataset.

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PCT/AU2016/050521 [61] At step 420, the method 400 comprises grouping, via the processor 120, the one or more parameters or parameter groupings with the potential paired dataset, based on the correlation of the one or more parameters or parameter groupings with the potential paired dataset. For example, the one or more parameters or parameter groupings are grouped with the potential paired dataset if the correlation is above a certain threshold, and are not grouped if the correlation is below the certain threshold.

[62] FIG. 5 is a flow diagram of a method 500 according to one embodiment of the invention. The method 500 can be performed in conjunction with the method 200 and in some embodiments, with the method 300 and/or the method 400. The method 500 comprises the following steps.

[63] At step 510, the method 500 comprises reviewing, via the processor 120, an accuracy and/or reasonableness of results of the created or modified financial valuation model. In some embodiments, the accuracy is determined based at least in part on a strength of the determined correlations between the central parameter and each of the other key revenue and expense parameters.

[64] At step 520, the method 500 comprises selecting, via the processor 120, a different central parameter from the key revenue or expense parameters if results of the created or modified financial valuation model are determined to be inaccurate and/or unreasonable. For example, the central parameter can be selected from the key revenue or expense parameters as previously described. In some embodiments, one or more steps of methods 200, 300 and/or 400 are repeated for the different central parameter.

[65] FIGS. 6-11 illustrate an example statistical analysis to determine a correlation between the central parameter and another key parameter or parameter grouping. A highly positive correlation indicates that the central parameter when used as a predictive measure for the other key parameter can explain a significant proportion of the variation in the other key parameter.

[66] FIG. 6 shows an example graph 610 of a historical dataset for a key expense parameter and an example graph 620 of a historical dataset for a key revenue parameter for a copper mine. The key expense parameter is a Surface Mining Operational Expenses (OPEX) Index. The Surface Mining OPEX Index accounts, for

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PCT/AU2016/050521 example, for the cost of fuel, explosives, electricity, labour, chemicals and machinery used to mine the copper. The key revenue parameter is a Nominal Copper Price.

[67] FIG. 7 illustrates a graphical user interface (GUI) 700 showing a determination of a correlation between the Surface Mining OPEX Index and the Nominal Copper Price. The GUI 700 comprises a graph 710 of the Surface Mining OPEX Index vs the Nominal Copper Price. The Nominal Copper Price is selected to be the central parameter and is assigned to the horizontal axis 712 of the graph 710. The Surface Mining OPEX Index, being the other key parameter, is assigned to the vertical axis 714 of the graph 710.

[68] A correlation is determined via a linear regression line 716 that is fitted to the graph 710. An equation 718 for the linear regression line 716 is:

Y = 47.69 + 0.006052 X where Y is the Nominal Copper Price and X is the Surface Mining OPEX Index.

[θθ] The GUI 710 shows a statistical significance 720 of the correlation. The statistical significance 720 is represented by a p-value, which is used as an indication as to whether the relationship between the Surface Mining OPEX Index and the Nominal Copper Price would be considered to be statistically significant. The resulting p-value is < 0.001. Typically an alpha level of 0.05 is selected, and when the p-value is less than the alpha level (0.05 in this case) the result is considered to mean that the relationship is statistically significant. Therefore, the relationship between the Surface Mining OPEX Index and the Nominal Copper Price would be said to be statistically significant at the 95% confidence level.

[70] The GUI 710 shows a coefficient of determination 730. The coefficient of determination 730 represents the percentage of variation that can be explained by the equation 718. The coefficient of determination (R-squared) 730 is 82.33%.

[71] The GUI 710 shows a correlation coefficient 740. The correlation coefficient 740 is a measure of correlation between the Surface Mining OPEX Index and the Nominal Copper Price. The correlation coefficient 740 is 0.91, which is a strong correlation.

[72] FIG. 8 illustrates a GUI 800 showing a residual analysis for the linear regression line 716. The GUI 800 shows a first residual graph 810 and a second residual graph 820. The first residual graph 810 is a plot of the residuals against the

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PCT/AU2016/050521 fitted values for the Surface Mining OPEX Index. A guide 815 shows patterns to look for in the first residual graph 810. If these patterns are identified in the first residual graph 810, then a different regression line can be fitted to the graph 710 and the equation 718 can be updated accordingly. An outlier 812 is identified and highlighted on each residual graph 810, 820.

[73] FIG. 9 illustrates a GUI 900 showing a summary or report of the statistical analysis. The report includes the equation 718, the graph 716 with the outlier 812 highlighted, a summary of results for a linear model 910 and a summary of results for a quadratic model 920.

[74] The strength of the correlation between the central parameter and each of the other key revenue and expense parameters can have a significant effect on the accuracy of the financial valuation model. If the results of the statistical analysis are determined to be inaccurate and/or unreasonable, the statistical analysis can be performed with different datasets being selected and/or a different central parameter being selected.

[75] FIG. 10 illustrates a GUI 1000 showing a report from a review of an accuracy and/or reasonableness of results of the statistical analysis. The GUI 1000 shows a report on the amount of data 1010, a report on unusual data 1020, a report on normality 1030 and a report on model fit 1040.

[76] The report on the amount of data 1010 shows a determination of whether enough historical data was used to provide a precise estimate of the correlation. For example, the report on the amount of data 1010 in FIG. 10 notes that only 26 data points were used, but more than 40 data points are recommended to provide a precise estimate of the correlation.

[77] The report on unusual data 1020 identifies unusual data such as the outlier 812. For example, the report on unusual data 920 in FIG. 10 suggests that because the outlier 812 has a strong influence on the equation, a cause of the outlier 812 be identified and/or that the statistical analysis be repeated without the outlier 812.

[78] The report on normality 1030 identifies whether the statistical significance 720 can be considered to be accurate. For example, the report on normality 1030 in FIG. 10 suggests that to consider the statistical significance 720 to be accurate, at

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PCT/AU2016/050521 least 15 data points should be used and the residuals should be normally distributed. The graph 710 and the linear regression line 716 meet this requirement.

[79] The report on model fit 1040 recommends checking specific aspects of the model and data to ensure that they meet the goals of the financial evaluation. For example, the report on model fit 1040 in FIG. 10 recommends checking that the model adequately covers the relevant range of Nominal Copper Prices, properly fits any curvature in the historical data and fits well in areas of special interest.

[80] FIG. 11 illustrates a GUI 1100 showing a prediction report for Surface Mining OPEX Index values 1120 based on a value of the Nominal Copper Price 1110. The GUI 1100 also shows a 95% confidence interval 1130. The 95% confidence interval is shown as an upper limit 1150 and a lower limit 1160 on a graph 1140 of the Surface Mining OPEX Index values vs the Nominal Copper Price.

[81] A statistical analysis can be performed via the processor for each of the other key revenue and expense parameters, and an equation is determined for each of the other key revenue and expense parameters.

[82] The equations can then be used to generate the predictive future dataset for each of the other key revenue and expense parameters based on the equation for the respective key revenue or expense parameters and a predictive future dataset for the central parameter. In some embodiments, this forms a profile of annual predicted values for each of the key revenue or expense parameters. Each profile can then be assigned, for example, to the key revenue and/or expense parameters, such as the initial component line items, in the financial valuation model.

[83] FIG. 12 is a diagram of a financial valuation model 110 according to one embodiment of the invention. The financial valuation model 110 comprises one or more central parameters 112, zero or more global revenue parameters 114, one or more global expense parameters 115, zero or more local parameters 116 and zero or more unique parameters 117.

[84] The global revenue parameters 114 and the global expense parameters 115 include one or more profiles correlated to the future dataset(s) of the one or more central parameters 112.

[85] When executing the financial valuation model 110, the current value for each key revenue and/or expense parameter can be designated as a starting value.

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Future values of the asset can then be predicted based on the assigned profiles and the starting values.

[86] The results of the financial valuation model 110 would then be reviewed for accuracy and reasonableness and one or more steps of any of the preceding methods are repeated where parameters are inaccurate or unreasonable.

[87] In some embodiments, an internal cashflow probability is calculated, for example, to quantify the measurable internal risk relating to future price variabilities within the cashflow, and then report the outcome as a probability, for example, for one or more periods. A value at risk (VaR) style metric can then be calculated based on the calculated probabilities. In some embodiments, the probabilities are calculated based on the equations for the key revenue and expense parameters.

[88] In some embodiments, the uncertainty relating to potential variability in the central parameter is excluded from the calculation. As such, the calculation then focusses on the resultant systemic risks relating to the potential variability in the remaining parameters. In some embodiments, the uncertainty pertaining to the central parameter can be discretely quantified via a stochastic analysis, exclusive of the majority of the risk resulting from movements in costs.

[89] In preferred embodiments, the internal cashflow probability is calculated based on the calculated individual period probabilities, weighted by a discount factor calculated from an associated discount rate. In some embodiments, the R-Squared value derived from the equations is also included in the final probability calculation.

[90] In some embodiments, separate calculations are conducted for each individual section of the cashflow statement. For example the internal cashflow probability could be calculated for the operating costs of one department, or for the entire operation/project.

[91] In some embodiments, the internal cashflow probability is calculated using the following formulae. The formulae are based on a regression analysis using a linear equation, such as, the equations for the key revenue and expense parameters. The skilled addressee will appreciate that regression analysis equations of an alternative structure will require modifications to the equations and the approaches outlined.

[92] Regression analysis equation: y = a + bx

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PCT/AU2016/050521 [93] Here y = the correlated parameter, x = the central parameter, b = the slope constant derived from the regression analysis, and a = a constant derived from the regression analysis. For example, a and b are derived via a least squares methodology, for example, as discussed herein.

[94] Prediction interval for y_new: y_new = (a + bx_new) ± t_c[(SEy)² + (SEy_new)²]⁰·⁵ [95] Here y_new = new predicted value for the correlated parameter, x_new = new model input variable for the central parameter, t_c - the relevant value for the Student’s t-distribution, and SE = the relevant standard error of the associated coefficient.

[96] Discount factor equation: DF = 1 / (1 + r)ⁿ [97] Here DF = discount factor, r = rate and n = number of periods.

[98] In some embodiments, the calculation of the internal cashflow probability is based on one or more predictive future datasets derived using the regression analysis equations, or on one or more predictive future datasets from an alternative source, such as, a database.

[99] In some embodiments, the equation for the prediction interval is rearranged and solved for t_c to provide a prediction interval for the predicted correlated value (y_new ) as a function of the central parameter (x_new). In some embodiments, the prediction interval is calculated by iteratively increasing the confidence level until the range of the prediction interval contains the predicted correlated value. A similar result for the prediction interval is delivered via either approach.

[100] Once the prediction interval has been calculated for each period in a time series, the outcome is adjusted to be a probability. In some embodiments the probability will then be adjusted for the R-squared value of the regression model. In some embodiments, such adjustments would be applied via at least the following:

[101] For all values in any predicted correlated time series, a probability adjustment of P = (1 - PI) is applied, where P is the probability, and PI is the calculated prediction interval.

[102] Where the predicted correlated data series was derived using the regression analysis equation, the R-squared value is subtracted from the calculated probability. Where the predicted correlated data series was not derived using the regression

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PCT/AU2016/050521 analysis equation, the R-squared value is be added to the calculated probability, and the probability is limited to a maximum probability of 1.

[103] Once the adjusted probability has been calculated for each period in the time-series, the adjusted probability is then weight averaged by the discount factor series to provide a final internal cashflow probability.

[104] The present invention therefore provides a basis and a methodology to improve financial valuation models for the valuation of longer life entities in which the financial outcomes are primarily governed by systemic market forces on both the expense (cost) and revenue (price) sides of the cashflow. Embodiments of the present invention quantifiably increase the statistical probability of the proximity of the calculated valuation outcome to the asset value, and thereby reduce the overall level of uncertainty of the valuation.

[105] For example, using the present invention an entity being valued would firstly have one or more central parameters selected, to which the other parameters could be assessed for correlation. The other parameters can be either grouped into logical groupings, or modelled individually, with each being assessed for correlation to the central parameter(s). A database of relevant commodity prices, economic growth indexes, cost profiles, cost indexes, etc. can be used to determine the correlations via statistical models such as regression models. The equations generated via these statistical models can then be used to model the parameters moving through time and be entered into the financial valuation model to produce an improved valuation. This improved valuation would then include a probability based on the strength of the correlations between the component parameters of the financial valuation model.

[106] In this specification, the terms “comprises”, “comprising” or similar terms are intended to mean a non-exclusive inclusion, such that an apparatus that comprises a list of elements does not include those elements solely, but may well include other elements not listed.

[107] The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any form of suggestion that the prior art forms part of the common general knowledge.

[108] Throughout the specification the aim has been to describe the invention without limiting the invention to any one embodiment or specific collection of features.

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Persons skilled in the relevant art may realize variations from the specific embodiments that will nonetheless fall within the scope of the invention.

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Claims (17)

1. A computer implemented method comprising:

identifying, at a processor, key revenue and expense parameters of a financial valuation model;

selecting, via the processor from the key revenue and expense parameters, a central parameter upon which to base a financial valuation of an asset;

reading, via the processor from one or more storage devices in communication with the processor, a historical dataset for each of the key revenue and expense parameters;

statistically analysing, via the processor, the historical dataset for each of the key revenue and expense parameters to determine a correlation between the central parameter and each of the other key revenue and expense parameters;

creating or modifying, via the processor, the financial valuation model based on the correlation between the central parameter and each of the other key revenue and expense parameters; and executing, via the processor, the created or modified financial valuation model including the central parameter and each of the other key revenue and expense parameters to generate the financial valuation of the asset.

2. The method of claim 1, wherein selecting the central parameter upon which to base the financial valuation comprises statistically analysing, via the processor, the historical data for each key revenue and expense parameter to determine the central parameter based on which parameter will deliver a most accurate valuation outcome.

3. The method of claim 1, wherein selecting the central parameter upon which to base a financial valuation comprises determining, via the processor, the central parameter based on an analysis of the key revenue and expense parameters to which the valuation will be most sensitive.

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4. The method of claim 1, wherein selecting the central parameter upon which to base a financial valuation comprises receiving, via an input in communication with the processor, a selection of the central parameter from the key revenue and expense parameters.

5. The method of any preceding claim, further comprising generating an equation for each of the other key revenue and expense parameters based on the correlation between the central parameter and the subject parameter, each equation derived from historical datasets and each equation to predict the other key revenue and expense parameters based on a prediction of the central parameter.

6. The method of claim 4, further comprising generating, via the processor, a predictive future dataset for the central parameter.

7. The method of claim 5, further comprising generating, via the processor, a predictive future dataset for each of the other key revenue and expense parameters based on the equation for the respective other key revenue or expense parameter and the predictive future dataset for the central parameter.

8. The method of any preceding claim, further comprising reviewing, via the processor, an accuracy and/or reasonableness of results of the created or modified financial valuation model.

9. The method of claim 7, wherein the accuracy is determined based at least in part on a strength of the determined correlations between the central parameter and each of the other key revenue and expense parameters.

10. The method of claim 7 or 8, further comprising selecting, via the processor, a different central parameter from the key revenue or expense parameters if

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11. The method of any preceding claim, wherein the key revenue and expense parameters include one or more of the following:

one or more significant revenue parameters; and all significant cost line items.

12. The method of any preceding claim, wherein the historical datasets include one or more of the following:

one or more financial datasets and/or indices for each of a plurality of countries;

one or more global datasets and/or indices; and one or more cost indices.

13. The method of any preceding claim further comprising:

identifying, via the processor, one or more groups of revenue and/or expense parameters including the key revenue and expense parameters; and determining, via the processor, a correlation between the central parameter and each of the one or more groups of parameters.

14. The method of claim 13, wherein the one or more groups of revenue and/or expense parameters are selected from the following: central parameters; global parameters; local parameters; unique parameters.

15. A system comprising:

a financial valuation model;

a processor in communication with the financial valuation model; and one or more storage devices in communication with the processor, one or more of the storage devices storing computer readable code components executable by the processor to:

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PCT/AU2016/050521 identify key revenue and expense parameters of the financial valuation model;

select from the key revenue and expense parameters, a central parameter upon which to base a financial valuation of an asset;

read from one or more of the storage devices, a historical dataset for each of the key revenue and expense parameters;

statistically analyse the historical dataset for each of the key revenue and expense parameters to determine a correlation between the central parameter and each of the other key revenue and expense parameters;

create or modify the financial valuation model based on the correlation between the central parameter and each of the other key revenue and expense parameters; and execute the created or modified financial valuation model including the central parameter and each of the other key revenue and expense parameters to generate the financial valuation of the asset.

16. The system of claim 15, wherein the one or more of the storage devices store computer readable code components executable by the processor to perform the method of any one of claims 2-14.

17. A non-transitory computer readable medium having stored thereon computer readable code components executable by a processor to perform the method of any one of claims 1 -14.

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FIG. 12