Patent Application
20150019300
The present disclosure relates to the field of data processing, in particular, to apparatuses, methods and storage medium associated with determining a valuation of one or more companies, based on economic performance metric values.
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Traditional valuation of companies often involve employment of subject factors such as strategic value, market momentum, market sentiment, synergistic potentials, and so forth. As a result, traditional valuation has been inconsistent and unreliable.
Embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. To facilitate this description, like reference numerals designate like structural elements. Embodiments are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings.
Apparatuses, methods and storage medium associated with determining valuation(s) for one or more companies, based on economic performance metric values, are disclosed herein. In embodiments, a method for determining valuation of a company may include ingesting, by a computing device, a plurality of valuations and a plurality of objectively measurable economic performance metric values of a plurality of other companies, and filtering out outlying ones of the valuations or the economic performance metric values of the other companies. The method may further include computing value driver model parameters and risk ratio model parameters of a valuation model; and outputting the model parameters of the valuation model to a modeler to user to compute an economic performance metric values based valuation of a company.
In embodiments, an apparatus, e.g., a smartphone or a computing tablet, may include one or more processors, and storage medium having an analyzer and/or a modeler configured to cause the apparatus, in response to operation by the one or more processors, to perform various aspects of the above described methods and their variants.
In embodiments, at least one storage medium may include instructions configured to cause an apparatus, in response to execution by the apparatus, to perform various aspects of the above described methods and their variants.
In the detailed description to follow, reference is made to the accompanying drawings which form a part hereof wherein like numerals designate like parts throughout, and in which is shown by way of illustration embodiments that may be practiced. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. Therefore, the following detailed description is not to be taken in a limiting sense, and the scope of embodiments is defined by the appended claims and their equivalents.
Various operations may be described as multiple discrete actions or operations in turn, in a manner that is most helpful in understanding the claimed subject matter. However, the order of description should not be construed as to imply that these operations are necessarily order dependent. In particular, these operations may not be performed in the order of presentation. Operations described may be performed in a different order than the described embodiment. Various additional operations may be performed and/or described operations may be omitted in additional embodiments.
For the purposes of the present disclosure, the phrase “A and/or B” means (A), (B), or (A and B). For the purposes of the present disclosure, the phrase “A, B, and/or C” means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B and C).
The description may use the phrases “in an embodiment,” or “in embodiments,” which may each refer to one or more of the same or different embodiments. Furthermore, the terms “comprising,” “including,” “having,” and the like, as used with respect to embodiments of the present disclosure, are synonymous.
As used hereinafter, including the claims, the term “module” may refer to, be part of, or include an Application Specific Integrated Circuit (“ASIC”), an electronic circuit, a processor (shared, dedicated, or group) and/or memory (shared, dedicated, or group) that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. The term “closed captions” is to include traditional closed captions and/or subtitles.
Referring now
Data processor 112, in embodiments, may be configured to ingest valuations and economic performance metric values of companies, in different formats and store them in a common format for analyzer 116. Economic performance metric values may include but not limited to revenues, sales, earnings before interest, tax, deduction and amortization (EBITDA), earnings before interest and tax (EBIT), gross profits, net profits, net incomes, operating income before interest, tax, deduction and amortization (OBITDA), operating income before interest, deduction and amortization (OBIDA), operating margin, cash, inventory, accounts receivable and other assets, accounts payable, short and long-term debt and other non-debt liabilities, company age, book values and other relevant financial information. In embodiments, economic performance metric values of hundreds or thousands of companies of different industries and/or sectors are ingested.
Analyzer 116, in embodiments, may be configured to analyze the data as further described in
In embodiments, the pre-processors 112 and the analyzer 116 may be disposed on a first computing device, whereas the modeler 118 may be operated by a second computing device, using the model parameters, and economic performance metric values of the company, to compute the economic performance metric based valuation of the company.
During removal of outliers (202), filtering out outlying ones of the valuations or the economic performance metric values of the other companies may include identifying and removing outlying ones of the valuations or the economic performance metric values of the other companies, by respectively comparing the valuations or the economic performance metric values of the other companies to both empirical and modeled distributions of the valuations or the economic performance metric values of the other companies.
Filtering out outlying ones of the valuations or the economic performance metric values of the other companies may also include identifying and removing outlying ones of the valuations or the economic performance metric values of the other companies, by applying a combination of Peirce's criterion, and a modification Dixon's Q test and scatter entropy (a measure of dispersion created by the inventor and not yet published) to the valuations or the economic performance metric values of the other companies.
From removing outliers operation (202), analysis process 200 may proceed to Phase I calculate value driver model parameters (203), where value driver model parameters of the valuation models may be calculated. Examples of value driver model parameters may include filtering parameters, outlier exclusion parameters, regression coefficients, industry specific weightings, similarity measures, dispersion measures, and tuning parameters. Value driver model parameters calculation (203) will be now described with references to
Referring now to
Thereafter, value driver model parameters calculation (203) may then proceed to compute model parameters for the valuation models of the various industries or sections (212). In embodiments, the valuation model may include an implicitly defined function configured to yield the valuation of the company based on a self-reference relationship, e.g., between the function of the value and its relationship to the plurality of economic performance metric values of the company. The model parameters may comprise parameters of the implicitly defined function. The implicit function theorem allows the derivative of the resulting function to be calculated. In turn, optimizers based on e.g., the Newton-Raephson or other methods can be applied to solve for the initial valuation estimate.
From model parameter computation for various industries/sectors (212), value driver model parameters calculation (203) may proceed to calculate weighting (213) to tune each parameter, industry and sector. In some embodiments, scatter entropy and techniques from the field of robust statistics may be used calculate weightings.
Referring back to
Referring now to
From pre-estimate the economic performance metric values based valuations and compute residuals (221), Phase II (204) may proceed to Principal Component Analysis (222), where Factor Analysis may be performed on key ratios and risk factors for purposes of dimension reduction to reduce computing time, to orthogonalize data to reduce sensitivity to noise and to improve the explanatory power of the model.
From Principal Component Analysis (222), Phase II (204) may proceed to Regress Residuals (223), where residuals are regressed against the factors and/or principal components using methods from Robust Statistics. The preliminary model estimate may then be further refined based on risk factor loadings.
From Regress Residuals (223), Phase II (204) may proceed to Optimize Tuning Parameters (224), where the parameters may be optimized by adjusting them and re-estimated until an exit criterion is reached. An example of an exit criterion may be incremental fit improvement falling below a threshold at the end of a Phase. The model parameters may then be stored, and the process may be repeated for the remainder of industries and sectors.
Referring now to
Process 300 may start with the computer system receiving company metrics (302), industry and sector information and/or other relevant information. These metrics may be preprocessed, transformed and centered to prepare them for further processing.
From 302, process 300 may proceed to having the system pre-estimates an initial valuation (304) from Value Drivers using Phase I parameters and tuning parameters calculated and stored, as earlier described. In some embodiments, preliminary stages using these parameters may be preprocessed and stored to improve system response times.
In embodiments, the calculation may include solving an implicitly defined function with a 2-stage optimizer based on the Newton-Raephson or other methods, with guaranteed convergence.
From 302, process 300 may proceed to have the system apply risk and ratio adjustments (305) to the valuation using factor loadings computed and stored by Phase II, as earlier described, to calculate the final valuation estimate.
From 305, for some embodiments, process 300 may proceed to have comparable and supporting data (306) gathered and computed by the system to provide supporting documentation for the valuation report. Estimation, interpolation and smoothing techniques from the field Robust Statistics may be used to perform the calculations.
With or without performing 306, process 300 may proceed to have the system compile an Economic Performance Metric Based Valuation Report (308), which provide the economic performance metric based valuation. In embodiments, the report may include input parameters, supporting data, charts and graphs calculated, along with other descriptions and information.
Referring now to
Each of these elements may perform its conventional functions known in the art. In particular, system memory 404 and mass storage devices 406 may be employed to store a working copy and a permanent copy of the programming instructions implementing the operations associated with Analyzer 116 of
The permanent copy of the programming instructions may be placed into permanent storage devices 406 in the factory, or in the field, through, for example, a distribution medium (not shown), such as a compact disc (CD), or through communication interface 410 (from a distribution server (not shown)). That is, one or more distribution media having an implementation of the agent program may be employed to distribute the agent and program various computing devices.
The number, capability and/or capacity of these elements 410-412 may vary, depending on the intended use of example computer 400, e.g., whether example computer 400 is a stationary computing device like a set-top box or a desktop computer, or a mobile computing device, like a smartphone, tablet, netbook, or laptop. The constitutions of these elements 410-412 are otherwise known, and accordingly will not be further described.
Although certain embodiments have been illustrated and described herein for purposes of description, a wide variety of alternate and/or equivalent embodiments or implementations calculated to achieve the same purposes may be substituted for the embodiments shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the embodiments discussed herein. Therefore, it is manifestly intended that embodiments described herein be limited only by the claims.
Where the disclosure recites “a” or “a first” element or the equivalent thereof, such disclosure includes one or more such elements, neither requiring nor excluding two or more such elements. Further, ordinal indicators (e.g., first, second or third) for identified elements are used to distinguish between the elements, and do not indicate or imply a required or limited number of such elements, nor do they indicate a particular position or order of such elements unless otherwise specifically stated.
