Patent Application
20170286981
The invention relates in general to data analysis and, specifically, to a system and method for analyzing geopolitical attributes over time via a digital computer.
As companies grow and become successful, expansion into different markets is frequently considered. A decision to expand is generally well thought out and researched extensively due to the high costs and risk associated with expansion. The research can be focused on factors, such as location, taxes, labor laws, consuming public, and risk, for each market in consideration. Results of the research are then used to make a determination for or against expansion.
For example, once the appropriate research has been conducted, the results are provided to one or more decision makers to consider whether expansion is beneficial. If yes, plans to expand are usually generated and executed. Thus, a company looking to expand into an emerging market generally makes the decision to expand based on a point-in-time consideration of market risk based on the research results without considering any further changes in the market over time.
However, expanding to a new market is time-consuming and can take months or years before business in the expansion market is fully operational. From the time the research results are provided for a particular market to the time the expansion is completed, risk of expansion within the market can change dramatically. Determining risk over a single period of time without further analyzing risk level changes can lead companies to relying on the risk analysis over a particular period of time when, in contrast, the level of risk can be constantly changing. If the risk level changes drastically enough, expansion may no longer be beneficial for the company or performance of an already established expansion market may decrease.
Currently, risk can be calculated using the Zurich International risk model, which measures risk over a single predefined period of time. A decision for expansion is then made based on a measure of risk for the corresponding period of time. Thus, the model fails to provide continuous updates to the risk measure to ensure a decision to expand remains beneficial or to utilize the calculated risk to assist established companies in adjusting work performance based on the calculated risk for a particular market.
Thus, there remains a need for continuously and accurately evaluating risk in different markets, as well as maintaining and improving market performance based on the risk evaluations, to ensure that expected returns justify the risk. Preferably, the risk evaluation provides a measure of how the risk changes over a periods of time for a market.
An embodiment provides a computer-implemented system and method for analyzing geopolitical attributes over time via a digital computer is provided. Markets of interest are identified. Qualitative data regarding one or more attributes is received for each of the markets of interest at predefined time periods. A score is determined for each market based on the qualitative data for that market. The score for each market is then classified as having a location desirability value. The scores and the location desirability values are displayed for each of the markets at each of the predetermined times.
Still other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein are described embodiments by way of illustrating the best mode contemplated for carrying out the invention. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and the scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.
Currently, companies looking to expand into new market places, as medium- to long-term investments, spend large amounts of time and money for researching the viability of expansion and the actual expansion. The decision to expand is usually based on a point-in-time consideration of the market risk for expansion without considering any further changes in the market risk over time. However, at some point during the time period of approving the expansion and completing the expansion, the risk level may change drastically such that expansion is no longer feasible. Consistent determination of market risk over time allows companies to make accurately informed decisions about expansion and location desirability. Once an expansion has been completed, the market risk can be used to predict and improve performance in the expansion market.
Dynamic analysis of market risk allow business managers to acquire a deep understanding of a market's operating condition over time, which can be used to make informed decisions regarding medium and long-term investments.
The market data 26 can include commercial risk information and geopolitical risk information, which is grouped into risk categories, including political, judicial and regulatory, economic, financial, social, environmental, and oil and gas services. Each geopolitical risk category includes discrete risk factors or attributes, including political stability, rule of law, and government control. Hereinafter, the terms “attributes” and “factors” are used interchangeably with the same intended meaning, unless otherwise indicated. Commercial and technical risk information is uniquely specific to an industry and include risk factors, such as labor, education, industrial relations, the competitive environment, currency stability, import-export regulations and infrastructure readiness. Other examples of risk categories and factors are possible. The market data can be collected from different resources, such as individuals, reports, and data requests, and can include qualitative or quantitative data, which is used to determine an amount of risk for a market.
The data server 24 provides the market data 26 to the risk server 14 for storing in the database 20. The scorer 17 utilizes the market data to calculate risk scores, including risk factor scores for each risk factor, category risk scores for each geopolitical risk category and commercial risk category, and market risk scores for each market identified by the user. The risk scores can be stored in the database for future use. If the market data includes qualitative data, quantitative values are assigned for use in calculating the risk scores, as further described below with reference to
The computing devices and servers can each include a central processing unit, random access memory (RAM), input/output ports, non-volatile secondary storage, such as a hard drive or CD ROM drive, network interfaces, peripheral devices, including user interfacing means, such as a keyboard and display, and one or more modules for carrying out the embodiments disclosed therein. Program code, including software programs, and data are loaded into the RAM for execution and processing by the CPU and results are generated for display, output, transmittal, or storage.
The modules can be implemented as a computer program or procedure written as source code in a conventional programming language and is presented for execution by the central processing unit as object or byte code. Alternatively, the modules could also be implemented in hardware, either as integrated circuitry or burned into read-only memory components, and each of the computing devices and server can act as a specialized computer. For instance, when the modules are implemented as hardware, that particular hardware is specialized to perform message prioritization and other computers cannot be used. Additionally, when the modules are burned into read-only memory components, the computing device or server storing the read-only memory becomes specialized to perform the message prioritization that other computers cannot. Other types of specialized computers are possible. Further, the management system can be limited to specific clients and specific hardware on which the management system is implemented, as well as limited by a subscription service to only subscribing clients. The various implementations of the source code and object and byte codes can be held on a computer-readable storage medium, such as a floppy disk, hard drive, digital video disk (DVD), random access memory (RAM), read-only memory (ROM) and similar storage mediums. Other types of modules and module functions are possible, as well as other physical hardware components.
Dynamically analyzing risk in multiple markets can provide users with the information necessary to make well-informed decisions regarding new business ventures, such as market expansion, as well as maintaining business in existing markets.
A quantitative risk score is calculated (block 33) for each of the markets based on the qualitative answer scores and can be used to adjust outcomes at a project or portfolio level. In one example, a risk score for each market is calculated by converting the qualitative answers to quantitative values and summing those values.
As described above, the binary qualitative answers to the questionnaire can be associated with predetermined values. For example, with respect to the question, “has there been a change in political party?,” a yes response may receive a value of 0.08 while a no response can receive a value of 0.01, such that a higher value signifies more risk. Thus, to convert the qualitative responses to quantitative data, the predetermined value associated with the response is assigned (block 41) to each question. In a further example, weights are assigned to one or more of the questions based on an importance of that question to the quantitative market risk score and used to adjust the quantitative score assigned. Specifically, the weight is multiplied by the predetermined quantitative value for the answer received to obtain an adjusted quantitative score. The weights can change over time, as well as on a client-to-client basis or an industry basis. The weights can be determined automatically or by the user. However, other means for determining the quantitative answer scores are possible, including utilizing a constant. In this example, the constant can be multiplied by the weight and assigned quantitative score.
A risk factor score can then be calculated for each risk factor for the geopolitical risk categories and the commercial risk by summing (block 42) the predetermined values for those questions belonging to that risk factor. For instance, returning to the above example, the question “has there been a change in political party?” falls under the risk factor for governance structure within the political risk category. The question “has there been a change in leadership” also falls under the governance structure risk factor. However, the question “has there been a change in national security?” falls under a different risk factor for national security, which is also included in the political risk category. Thus, the predetermined response values for the questions regarding political party and leadership are summed to determine a risk score for the governance structure risk factor.
Next, a category risk score can be calculated for each geopolitical risk category and for commercial risk by summing (block 43) the risk factor scores associated with each risk category. Then, the category risk scores for each market can be summed (block 44) to determine the market risk score for each market in the user's portfolio. However, if commercial risk does not include multiple categories, the risk factor scores can be summed and used as the market risk score. In a further embodiment, a weight for one or more of the categories can be used, such that the risk score for a particular category is multiplied by a weight and then summed with the other category risk scores to determine the market risk score. In yet a further embodiment, a constant can be used in addition or in lieu of the weights. Other methods for calculating the risk factor scores, category risk scores, and market risk scores are possible, such as by determining a mean or median of the scores. The risk factor scores, category risk scores, and market risk scores can be calculated periodically over a period of time to provide a context for business decisions made in the past and to allow companies to improve business performance. In one example, the risk scores are calculated every quarter to track trends in each market.
Returning to the above discussion with respect to
The risk levels can be displayed (block 35) for each market of interest along with the current market risk scores and optionally, past market risk scores. In one embodiment, a visual representation of the risk level can include a speedometer view that maps the risk for each market based on three levels of risk: low, medium, and high, as further described below with reference to
The market risk analysis can be offered based on a subscription and subscribers can receive updated risk scores at predetermined times, such as annually, quarterly, or monthly, as well as at other times, such as when requested. The risk scores can be stored and used by a company to determine or review why decisions were made and the conditions that existed at the time the decision was made. Further, once one or more of the markets has been selected for expansion, actual performance data in those markets can be recorded and provided with the current risk scores.
The risk factor scores and category risk scores can be recorded and stored as a risk scorecard to provide a historical record of the risk analysis for each market.
As shown on the risk scorecard, the risk factors are each associated with at least one score 54 calculated at a predetermined time, such as every quarter. The risk factors can include, for example, government structure, political stability, corruption, and national security for the political category, while the risk factors for the social risk category can include labor unions, employee wellness, educational restructure, and public security. Other risk categories and risk factors are possible.
A category risk score or weight 53 can be determined for each risk category. Each category risk score can be based on risk factor scores 54 for each of the risk factors in that category and can be calculated as a summation of the risk factor scores for the risk factors. Alternatively, the category risk score can be calculated as an average of the risk factor scores for the associated risk factors of that category or as a summation using weights associated with one or more of the risk factors. The category risk weights can be updated annually based on the quarterly risk scores for the risk factors associated with that category for the previous year. Other times for calculating the risk factor scores and updating the category risk scores are possible.
In a further embodiment, the commercial risk factor scores and commercial market risk scores can be included on the same card as the geopolitical risk scores or on a different scorecard.
Once determined, the market risk scores for geopolitical risk and commercial risk can be displayed.
In addition to displays of current risk scores, historical risk scores can also be displayed to provide an overall view of a risk climate for one or more markets.
In the current example, the graph 70 represents a portfolio of markets for an oil and gas industry services corporation and displays market risk scores for four different markets over three quarters, during which the market risk scores were calculated at the end of each quarter. Each of the different markets can be identified by an assigned color or pattern of the nodes, which can be provided in a legend 73 of the markets. For two of the countries, Congo and Libya, the risk profiles changed rapidly, which is evidenced by the increase in market risk scores for both geopolitical risk and commercial risk over the time period of three quarters. Reasons for the rapid change, especially the increase in risk, can be due to growing political instability and currency devaluation, as well as many other factors. While, during the same time, the other two countries of Nigeria and Angola, demonstrated more stability on geopolitical, as well as industrial risk frontiers, as shown by the clustering of nodes for the market risk scores in the low risk range.
Further, mapping of the historical market risk scores with respect to values for a historical return on assets in established markets can help users to evaluate their product lines across markets.
The return data and risk data for plotting in the risk-return profile can be collected over a period of time. In one embodiment, a time period of eight quarters is used to collect the data; however, other periods of time are possible. To generate a risk-return profile, a standard deviation of risk, such as the market risk scores, for each market is determined and plotted against an average return over the eight quarters as a historical market value. Further, a historical average of the plotted values for all the markets is determined and plotted within the risk-return profile. Alternatively, the risk-return performance can be plotted using a weighted portfolio of markets for the product line, as well as simulated weights. The simulated weights can be determined using Monte Carlo simulations, as well as other methods for generating simulations.
The risk-return profiles allow a user to evaluate a particular product line's local performance optimization, as well as the optimization of the product line across the portfolio of markets. Based on the optimization evaluation, a user can adjust a level of activity in various markets to increase optimization of the portfolio. The risk-return profile 80 of
The risk-return profile 85 of
Ideally, the individual historical market values should be close to the historical average. One or more of the markets can be shifted vertically or horizontally to improve performance and move closer to the historical average.
Upon implementation of the performance improvement initiatives, the risk-return values for each mark are determined at predetermined time periods and if the initiatives are successful, shifting of the risk-return values for one or more markets will appear. The shifted risk-return profile 92 shows that the newly calculated risk-return values for Congo 96 and Angola 97 are now closer to the historical average 95 based on implementation of the performance improvement initiatives.
In a further embodiment, a global shifting of all the markets in the portfolio can occur to increase return values.
The risk-return profiles can also be used to help identify a need to improve a user's performance through portfolio management. Portfolio management includes improving a weight of activity performed in specific markets to maximize the market portfolio's risk-return metric.
While the invention has been particularly shown and described as referenced to the embodiments thereof, those skilled in the art will understand that the foregoing and other changes in form and detail may be made therein without departing from the spirit and scope of the invention.
