Methods and systems for using multiple data sets to analyze performance metrics of targeted companies
New and improved methods and systems for modeling the performance of selected company metrics. Multiple, nontraditional sets of objective data along with mathematical analytical techniques are used to provide transparency and visibility into company performance relating to the particular metrics. Company inflection points and changes in strategy may be identified. The performance of a company and/or the performance of a selected industry or industry sector may be analyzed.
1. A method for preparing a model to analyze a performance metric of a selected company, comprising:
identifying, by at least one computer, a first data source having a first set of non-fundamental data pertinent to the performance metric of the selected company;
receiving, by said at least one computer, the first set of data;
identifying, by said at least one computer, an additional data source, the additional data source having an additional set of non-fundamental data different from the first set of data and pertinent to the performance metric of the selected company;
receiving, by said at least one computer, the additional set of data;
combining, by said at least one computer, the first set of data and the additional set of data in a combined data set;
selecting, by said at least one computer, an analytical process to apply to the combined data set;
applying, by said at least one computer, the selected analytical process to the combined data set to develop a model of the performance metric; and
validating, by said at least one computer, the model of the performance metric against the actual performance of the selected company.
2. A method operable on a computer for preparing a model to analyze a performance metric of a selected company, comprising:
a) identifying, by at least one computer, a first data source having a first set of data pertinent to the performance metric of the selected company;
b) receiving, by said at least one computer, into the computer the first set of data;
c) validating, by said at least one computer, the first set of data;
d) identifying, by said at least one computer, an additional data source, the additional data source having an additional set of data different from the first set of data and pertinent to the performance metric of the selected company;
e) receiving, by said at least one computer, into the computer the additional set of data;
f) validating, by said at least one computer, the additional set of data;
g) combining, by said at least one computer, the first set of data and the additional set of data in a combined data set;
h) selecting, by said at least one computer, an analytical process to apply to the combined data set;
i) applying, by said at least one computer, the selected analytical process to the combined data set to develop a model of the performance metric; and
j) validating, by said at least one computer, the model of the performance metric against the actual performance of the selected company.
3. The method of claim 2 and further comprising the step of, for at least a selected one of the first data set and the additional data set, prior to step g), performing the steps of:
selecting, by said at least one computer, an analytical process to apply to the selected data set;
applying, by said at least one computer, the selected analytical process to the selected data set to develop a model of the performance metric; and
validating, by said at least one computer, the model of the performance metric against the actual performance of the selected company.
4. The method of claim 3 wherein, if the step of validating the model of the performance metric against the actual performance of the selected company fails then discarding the selected data set and performing steps d), e), f), g), h), i) and j) for an additional data set.
5. The method of claim 2 and further including the step of repeating steps d), e), f), g), h), i) and j), by said at least one computer, for at least one additional data set.
6. The method of claim 5 wherein at least one of the first data set and additional data sets are licensed from a third-party.
7. The method of claim 5 wherein at least one of the first data set and additional data sets are proprietary to an operator of the computer.
8. The method of claim 5 wherein the at least one proprietary data set is collected from publicly available data by an operator of the computer.
9. The method of claim 5 wherein each of the first data set and the additional data set comprise non-fundamental company data.
10. The method of claim 2 wherein the steps c) and f) of validating each of the first set of data and the additional set of data includes the steps of: cleaning the data to remove extraneous data; and evaluating the validity of the cleaned data.
11. The method of claim 2 wherein the selected analytical process is selected from the group comprising a regression analysis, a neural network analysis and a spectral analysis.
12. The method of claim 2 wherein at least one of the first data set and the additional data set comprises data output from a model of the performance metric.
13. The method of claim 2 wherein at least one of the first data set and the additional data set comprise data collected from a publicly accessible Internet web site.