Patent Publication Number: US-2005119923-A1

Title: Value movement forecasting system and method

Description:
BACKGROUND  
      1. Field of the Invention  
      The present invention relates generally to systems and methods of financial analysis and more particularly to systems and methods of forecasting value movements.  
      2. Background Art  
      In the financial services industry, an analysis of economic data is often performed to forecast value movements of various financial measures. These financial measures range from broad stock market indexes to equity share prices to publicly available socioeconomic statistics. Once the value movements of the financial measures are forecast, a financial decision can be made based on the forecasted value movements. The success of the financial decision often depends on the accuracy of the forecasted value movements.  
      Various methods and techniques have been employed to forecast the value movement of a financial measure. In one approach, a mathematical model is evaluated on numerical economic data. The numerical economic data typically includes economic statistics or well-defined and easily ascertainable economic values, such as an unemployment rate or an inflation rate.  
      Known systems and methods for forecasting value movements of financial measures have provided limited predictive accuracy. Although some of these systems and methods have accurately forecast value movements of financial measures in some instances, these systems and methods have not accurately forecast value movements of financial measures in other instances. Accordingly, there exists a need for a system and method that can accurately and reliably forecast a value movement of a financial measure.  
     SUMMARY OF THE INVENTION  
      The present invention addresses the need for a system and method that can accurately and reliably forecast a value movement of a financial measure.  
      In a method in accordance with the present invention, a first set of publications is collected based on a financial measure. Characteristic variables are identified by performing linguistic analysis on the first set of publications. One or more values (i.e., first values) are computed for each characteristic variable based on the first set of publications. A forecasting function is created based on the characteristic variables and the first values.  
      A second set of publications is collected based on the financial measure and a value (i.e., second value) is computed for each characteristic variable by performing linguistic analysis on the second set of publications. A value movement forecast for the financial measure is then computed based on the forecasting function and the second values.  
      A system in accordance with the present invention includes a publication collection engine that collects a first set of publications based on a financial measure. A forecasting function generator identifies characteristic variables and computes one or more values (i.e., first values) for each characteristic variable by performing linguistic analysis on the first set of publications. Additionally, the forecasting function generator creates a forecasting function for the financial measure based on the characteristic variables and the first values.  
      The publication collection engine collects a second set of publications based on the financial measure. A value movement forecast generator computes a value (i.e., second value) for each characteristic variable by performing linguistic analysis on the second set of publications. The value movement forecast generator then computes a value movement forecast for the financial measure based on the forecasting function and the second values.  
      A computing system in accordance with the present invention includes a computing processor, a memory device, an input-output device, a publication collection engine and a forecasting function generator. The computing processor executes the publication collection engine to collect a first set of publications based on a financial measure and to read the first set of publications from the input-output device into the memory device. The computing processor executes the forecasting function generator to identify characteristic variables and compute one or more values (i.e., first values) for each characteristic variable by performing linguistic analysis on the first set of publications, and to create a forecasting function based on the characteristic variables and the first values.  
      The computing system can also include a value movement forecast generator. The computing processor executes the publication collection engine to collect a second set of publications based on the financial measure and to read the second set of publications from the input-output device into the memory device. The computing processor executes the value movement forecast generator to determine a value (i.e., second value) for each characteristic variable by performing linguistic analysis on the second set of publications, and to compute a value movement forecast for the financial measure based on the forecasting function and the second values.  
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       FIG. 1  is a block diagram of a prior art computing system;  
       FIG. 2  is a block diagram of a value movement forecasting system, in accordance with the present invention;  
       FIG. 3  is a block diagram of the publication collection portion of the value movement forecasting system shown in  FIG. 2 , in accordance with one embodiment of the present invention;  
       FIG. 4  is a block diagram of the forecasting function generating portion of the value movement forecasting system shown in  FIG. 2 , in accordance with one embodiment of the present invention;  
       FIG. 5  is a block diagram of the value movement forecasting portion of the value movement forecasting system shown in  FIG. 2 , in accordance with one embodiment of the present invention;  
       FIG. 6  is a flow chart of a method for creating a forecasting function and computing a value movement forecast, in accordance with the present invention;  
       FIG. 7  is a flow chart of a portion of the method shown in  FIG. 6  for identifying characteristic variables, in accordance with one embodiment of the present invention; and  
       FIG. 8  is a flow chart of a portion of the method shown in  FIG. 6  for identifying characteristic variables, in accordance with another embodiment of the present invention.  
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
      In accordance with the present invention, a set of publications related to a financial measure and published during a characteristic period in which the financial measure exhibits a value movement is analyzed to identify linguistic characteristics and their values in the set of publications. A mathematical model is created based on the linguistic characteristics to compute a forecast for the value movement based on values of the linguistic characteristics. Another set of publications related to the financial measure and published during a forecast period is analyzed to compute values for the linguistic characteristics in this set of publications. The mathematical model is then evaluated on these values to compute a value movement forecast for the financial measure relative to the forecast period.  
      The first set of publications related to the financial measure and published during the characteristic period is collected from publicly or privately available publications. The linguistic characteristics identified in the first set of publications include word expression pairs, each including a keyword expression and a significant word expression. The keyword expressions are words or combinations of words related to the financial measure that are obtained from a source (e.g., an expert in the relevant art). The significant word expressions are words or combinations of words identified from the first set of publications by performing linguistic analysis on the first set of publications. A predetermined characteristic of each word expression pair (e.g., frequency of appearance of the word expression pair in the first set of publications) is identified as a characteristic variable and one or more values (i.e., first values) are computed for the characteristic variable. A forecasting function is created for the financial measure based on the characteristic variables and the first values. The second set of publications related to the financial measure and published during the forecast period is collected from publicly or privately available publications. Linguistic analysis is performed on the second set of publications to identify word expression pairs and compute a value (i.e., second value) for each characteristic variable based on the predetermined characteristic (e.g., frequency of appearance of the word expression pair in the second set of publications). The forecasting function is then evaluated on the second values to compute a value movement forecast for the financial measure relative to the forecast period (i.e., a predicted value movement of the financial measure that is to occur after the forecast period).  
      Referring to  FIG. 1 , a general purpose computing system  100  known in the art is shown. The computing system  100  includes a processor  105 , a memory device  110  and an input-output device  115  that are each coupled to a computer bus  120  and communicate with each other through the computer bus  120 . The processor  105  communicates with the memory device  110  to retrieve data from the memory device  110  and to store data into the memory device  110 . Additionally, the processor  105  and the memory device  110  communicate with the input-output device  115  to obtain data from and provide data to the input-output device  115 .  
      Referring now to  FIG. 2 , a value movement forecasting system  200  in accordance with the present invention is shown. The value movement forecasting system  200  includes a publication collection portion  205 , a forecasting function generating portion  210  and a value movement forecasting portion  215 . The publication collection portion  205  accesses publications  220  to select and retrieve publications related to a financial measure. The publication collection portion  205  stores the selected publications into a publication database  225 , as is explained more fully herein. The forecasting function generating portion  210  generates a forecasting function  230  for the financial measure based on selected publications in the publication database  225 , as is explained more fully herein. The value movement forecasting portion  215  computes a value movement forecast  235  for the financial measure based on selected publications in the publication database  225  and the forecasting function  230 , as is explained more fully herein.  
      The publication collection portion  205  of the value movement forecasting system  200  includes publications  220 , a publication collection engine  240  and the publication database  225 . The publications  220  can be any information that is in an electronic format, convertible into an electronic format, or readable with an electronic device. Further, the publications  220  can be publicly available information or private information. For example, a publication  220  can be a news article accessible via the Internet or the close caption text in a television broadcast. As another example, a publication  220  can be a text file in a personal computing system or a computer record in a computer database.  
      The publication collection engine  240  accesses the publications  220  to select and retrieve publications related to the financial measure. For example, the publication engine  240  can query a computer database to retrieve publications (i.e., records) related to the financial measure. As another example, the publication engine  240  can execute a search based on the financial measure by using a search engine to identify websites on the Internet and retrieve publications contained in the web pages of the websites (i.e., scrape the website to identify and extract information from the web pages in the websites). The publication collection engine  240  stores the selected publications into the publication database  225 , as is explained more fully herein. Additionally, the publication collection engine  240  can filter the selected publications before storing the selected publications into the publication database  225 , as is explained more fully herein.  
      The forecasting function generating portion  210  of the value movement forecasting system  200  includes the publication database  225 , a forecasting function generator  245 , and the forecasting function  230 . The forecasting function generator  245  selects and accesses a first set of publications in the publication database  225  and generates the forecasting function  230  based on the first set of publications in the publication database  225 , as is explained more fully herein.  
      The value movement forecasting portion  215  of the value movement forecasting system  200  includes the publication database  225 , the forecasting function  230  and a value movement forecast generator  250 . The value movement forecast generator  250  selects and accesses a second set of publications in the publication database  225  and generates a value movement forecast  235  based on the second set of publications in the publication database  225  and the forecasting function  230 , as is explained more fully herein. Additionally, the value movement forecast generator  250  can select and access other sets of publications in the publication database  225 , generate a value movement forecast  235  for each of these sets of publications, and compare each of these value movement forecasts  235  to value movement characteristics exhibited by the financial measure to evaluate the effectiveness of the forecasting function  230 , as is described more fully herein. For example, the value movement forecast generator  250  can evaluate the accuracy, consistency and reliability of the forecasting function  230  on different sets of publications in the publication database  225  to determine whether the forecasting function  230  meets a given set of specifications (e.g., production standards).  
      Referring now to  FIG. 3 , one embodiment of the publication collection portion  205  of the value movement forecasting system  200  is shown. In this embodiment, the publication collection engine  240  includes a crawler  300 , which includes a buffer  305 . The crawler  300  selects and retrieves publications related to the financial measure from the publications  220  and stores the selected publications in the buffer  305  of the crawler  300 . For example, the crawler  300  can perform a search on the Internet to retrieve the selected publications related to the financial measure. The crawler  300  transfers the selected publications in the buffer  305  of the crawler  300  to the publication database  225 . In one embodiment, the crawler  300  periodically transfers the selected publications in the buffer  305  of the crawler  300  to the publication database  225 .  
      In another embodiment, multiple crawlers  300  perform a search on the Internet to retrieve the selected publications related to the financial measure from the publications  220 . In this embodiment, the crawlers  300  can execute the same searching algorithm or different searching algorithms to select and retrieve publications from the publications  220  based on a search key related to the financial measure. For example, each crawler  300  can execute the same search engine (e.g., the search function of MSN.com) with a search key (e.g., the text “Semiconduct*”) that is related to a financial measure (e.g., the Philadelphia Semiconductor Index). The search key can be a word, word fragment, phrase or combination of words that is related to the financial measure. Further, each word or word fragment in the search key can include a wildcard character (e.g., “*”) to include expansions of the word or word fragment in the search (i.e., word stemming). As another example, each crawler  300  can execute a different search engine with the search key that is related to the financial measure. Multiple crawlers  300  can improve the reliability of the publication collection engine  240  but may result in duplicate selected publications in the publication database  225 .  
      In another embodiment, the publication collection engine  240  includes a quick matching filter  310  and a pattern matching filter  315 . In this embodiment, the crawlers  300  transfer the selected publications in the buffers  305  of the crawlers  300  to the quick matching filter  310 . The quick matching filter  310  receives the selected publications from the crawlers  300 , filters out duplicate publications, and transfers the remaining publications to the pattern matching filter  315 . Duplicate publications are identical publications selected and retrieved by the crawlers  300  from the publications  220 .  
      In this embodiment, the crawlers  300  compute a hash function (e.g., checksum) for each selected publication retrieved from the publications  220 . The crawlers  300  transfer the hash functions together with the corresponding selected publications to the quick matching filter  315 . The quick matching filter  315  receives the selected publications and hash functions from the crawlers  300  and filters out duplicate publications based on the hash functions (e.g., removes publications that have the same hash function).  
      Further, in this embodiment, the pattern matching filter  315  receives the filtered publications from the quick matching filter  310 , filters out similar publications, and transfers the remaining publications to the publication database  225 . A publication is considered similar to another publication if the text in these publications is not identical but the information conveyed by these publications is essentially the same (e.g., the publications include the same syndicated content). The filtering methods and techniques used in the quick matching filter  310  and the pattern matching filter  315  are known in art. A discussion of some of these methods and techniques can be found in “On the resemblance and containment of documents” by Andrei Z. Broder, In Compression and Complexity of Sequences (SEQUENCES &#39;97), pp. 21-29 (IEEE Computer Society, 1998), which is incorporated herein by reference in its entirety.  
      It is to be understood that the quick matching filter  310  and the pattern matching filter  315  are optional in the publication collection engine  240 . It is to be further understood that the publication collection engine  240  can include the quick matching filter  310  or the pattern matching filter  315 , or both.  
      Referring now to  FIG. 4 , one embodiment of the forecasting function generating portion  210  is shown. In this embodiment, the forecasting function generator  245  includes a linguistic analyzer  400  and a modeling engine  405 . The linguistic analyzer  400  selects a first set of publications in the publication database  225  based on a characteristic period, identifies characteristic variables for the first set of publications, and computes one or more first values for each characteristic variable, as is explained more fully herein. The modeling engine  405  creates the forecasting function  230  based on the characteristic variables and the first values, as is explained more fully herein.  
      The linguistic analyzer  400  identifies publications from the publication database  225  that are associated with a characteristic period to select the first set of publications. In one embodiment, the linguistic analyzer  400  identifies publications in the publication database  225  that have been published within the characteristic period to select the first set of publications. The characteristic period is a period during which the financial measure has exhibited value movement characteristics that are to be forecast by the value movement forecasting portion  215  of the value movement forecasting system  200 . For example, a value movement characteristic can be a rising value, falling value, or flat value of a financial measure, such as a commodity value or equity share price. In one embodiment, the characteristic period is subdivided into a number of time slices that is equal to the number of first values of each characteristic variable, as is explained more fully herein. For example, the characteristic period can be a period of months or years and each time slice can be a period of hours, days, weeks or months.  
      It is to be understood that the characteristic period need not be a single period in the present invention, and the characteristic period can be a collection of periods in which the financial measure exhibits the value movement characteristics that are to be forecast. For example, the characteristic period can be a collection of isolated and noncontiguous periods in which the financial measure exhibits at least one of the value movement characteristics that is to be forecast.  
      The linguistic analyzer  400  performs linguistic analysis on the first set of publications in the publication database  225  to identify characteristic variables for the first set of publications in the publication database  225 , as is explained more fully herein. Additionally, the linguistic analyzer  400  performs linguistic analysis to compute one or more first values for each characteristic variable from the first set of publications in the publication database  225 , as is explained more fully herein. The linguistic analysis performed by the linguistic analyzer  400  is based on linguistic analysis methods and techniques known in the art. A discussion of some of these methods and techniques can be found in “Statistical Language Learning” by Eugene Charniak (The MIT Press, 1994) and “Untangling Text Data Mining” by Marti A. Hearst (Proceedings of ACL&#39;99: the 37 th  Annual Meeting of the Association for Computational Linguistics, University of Maryland, Jun. 20-26, 1999), which are incorporated herein by reference in their entireties.  
      The linguistic analyzer  400  identifies characteristic variables based on word expression pairs in the first set of publications in the publication database  225 , as is explained more fully herein. Each word expression pair includes a keyword expression and a significant word expression. The keyword expressions can be words, phrases or combinations of words related to the financial measure that are selected by an expert in the relevant field. For example, the financial measure can be the Philadelphia Stock Market Index (SOXX) and the keyword expressions can include “earnings”, “revenue” and “sales”. The significant word expressions can be words, phrases or combinations of words derived from selected publications in the first set of publications that summarize the content of the selected publications. In this example, the significant word expressions can be “income”, “cash flow” and “net sales”. Further, in this example, a word expression pair can be the keyword expression “earnings” paired with the significant word expression “cash flow”.  
      The linguistic analyzer  400  computes one or more first values for each characteristic variable based on the predetermined characteristic. The first value of a characteristic variable is computed based on the publications  220  published or collected within the characteristic period. In one embodiment, the predetermined characteristic is the frequency of appearance of both the significant word expression and the keyword expression of a word expression pair identified in the first set of publications in the publication database  225 . In another embodiment, the characteristic period is subdivided into time slices and a first value is computed for each time slice for each characteristic variable.  
      The modeling engine  405  creates the forecasting function  230  based on the characteristic variables and the first values. The forecasting function  230  has one or more of the characteristic variables as inputs and the value movement forecast  235  as an output. The forecasting function  230  can be a mathematical function, a mathematical model, a computing function, or a computing program. In one embodiment, the forecasting function  230  is a mathematical model developed based on statistical learning techniques known in the relevant art. In another embodiment, the forecasting function  230  is a statistical function. In this embodiment, the number of first values of each characteristic variable is a statistically significant number and the characteristic period is subdivided into a number of time slices that is equal to the number of first values. Further, in this embodiment, each first value of a characteristic variable corresponds to a time slice and is computed based on the publications in the first set of publications in the publication database  225  that have been published or collected within the time slice.  
      It is to be understood that the methods and techniques for creating the forecasting function  230  are not limited to the examples presented herein, and that the forecasting function generator  245  can employ any methods or techniques known in the art to create the forecasting function  230 . Some of the methods and techniques known in the relevant art for developing the forecasting function  230  are described in “Machine Learning” by Tom M. Mitchell (McGraw-Hill, 1997), which is incorporated herein by reference in its entirety.  
      Referring now to  FIG. 5 , one embodiment of the value movement forecasting portion  215  is shown. In this embodiment, the value movement forecast generator  250  includes a linguistic analyzer  500  and a computing engine  505 . The linguistic analyzer  500  selects a second set of publications in the publication database  225  based on a forecast period and computes a second value for each of the characteristic variables in the forecasting function  230  based on the second set of publications, as is explained more fully herein. The computing engine  505  evaluates the forecasting function  230  with the second values and generates the value movement forecast  235 , as is explained more fully herein.  
      The linguistic analyzer  500  identifies publications from the publication database  225  that are associated with a forecast period to select the second set of publications. In one embodiment, the linguistic analyzer  500  identifies publications in the publication database  225  that have been published within the forecast period to select the second set of publications. The forecast period can be any period within, outside, or overlapping with the characteristic period. In one embodiment, the forecast period is outside of the characteristic period and is later than the characteristic period. In another embodiment, the length of the forecast period is the same as the length of a time slice in the characteristic period.  
      The linguistic analyzer  500  performs linguistic analysis on the second set of publications in the publication database  225  to compute a second value for each of the characteristic variables, as is described more fully herein. In one embodiment, the linguistic analyzer  500  identifies word expression pairs from the characteristic variables in the forecasting function  230 . In this embodiment, the linguistic analyzer  500  performs linguistic analysis on second set of publications to compute the second value for each characteristic variable based on a predetermined characteristic of the word expression pairs (e.g., frequency of appearance of a word expression pair in the second set of publications). It is to be understood that the linguistic analyzer  400  of  FIG. 4  and the linguistic analyzer  500  of  FIG. 5  can be the same linguistic analyzer in the present invention.  
      The computing engine  505  evaluates the forecasting function  230  with the second values to compute the value movement forecast  235 . In one embodiment, the computing engine  505  is a computing process that executes computer program code for evaluating the forecasting function  230  to compute the value movement forecast  235 . The value movement forecast  235  is a predicted value movement for the financial measure relative to the forecast period (i.e., a predicted value movement of the financial measure that is to occur after the forecast period).  
      Referring now to  FIG. 6 , a flow chart of a method for computing the value movement forecast  235  is shown. In step  600 , a first set of publications in the publication database  225  is collected based on a characteristic period. In this process, the publication collection engine  240  accesses publications  220  to select and retrieve publications related to a financial measure. The publication collection engine  240  stores the selected publications into the publication database  225 , as is explained more fully herein. Further, the forecasting function generator  245  identifies publications in the publication database  225  as the first set of publications, based on a characteristic period, as is explained more fully herein.  
      In step  605 , characteristic variables are identified for the first set of publications in the publication database  225 . In this process, the forecasting function generator  245  performs linguistic analysis on the first set of publications to identify the characteristic variables for the first set of publications, as is explained more fully herein. In one embodiment, a linguistic analyzer  400  of the forecasting function generator  245  selects the first set of publications from the publication database  225  based on the characteristic period and performs linguistic analysis on the first set of publications to identify the characteristic variables.  
      In step  610 , one or more first values are computed for each characteristic variable. In this process, the forecasting function generator  245  computes the first values for each characteristic variable based on the first set of publications and the predetermined characteristic (e.g., frequency of appearance of the word expression pair of the characteristic variable in the first set of publications in the publication database  225 ), as is explained more fully herein. In one embodiment, the linguistic analyzer  400  of the forecasting function generator  245  computes the first values for the characteristic variables, as is explained more fully herein.  
      In step  615 , the forecasting function  230  is created. In this process, the forecasting function generator  245  creates the forecasting function  230  based on the characteristic variables and the first values, as is explained more fully herein. In one embodiment, the modeling engine  405  of the forecasting function generator  245  creates the forecasting function  230 , as is explained more fully herein.  
      In step  620 , a second set of publications in the publication database  225  is collected based on a forecast period. In this process, the publication collection engine  240  accesses publications  220  to select and retrieve publications related to the financial measure. The publication collection engine  240  stores the selected publications into the publication database  225 , as is explained more fully herein. Further, the value movement forecast generator  250  identifies publications in the publication database  225  as the second set of publications, based on the forecast period, as is explained more fully herein.  
      In step  625 , a second value is computed for each characteristic variable. In this process, the linguistic analyzer  500  of the value movement forecast generator  250  computes the second value for each characteristic variable based on the second set of publications and the predetermined characteristic (e.g., frequency of appearance of the word expression pair of the characteristic variable in the second set of publications in the publications database  225 ), as is explained more fully herein.  
      In step  630 , the value movement forecast  235  is computed. In this process, the computing engine  505  of the value movement forecast generator  250  evaluates the forecasting function  230  on the second values to compute the value movement forecast  235 , as is explained more fully herein.  
      Also in step  630 , the value movement forecast generator  250  can evaluate the effectiveness of the forecasting function  230 . In this process, the value movement forecast generator  250  accesses different sets of publications in the publication database  225  based on different periods during which the financial measure exhibits known value movement characteristics. The value forecast generator  250  generates a value movement forecast  235  for each set of publications and compares the value movement forecasts  235  to the corresponding value movement characteristics of the financial measure to evaluate the effectiveness of the forecasting function  230 . For example, the value forecast generator  250  can evaluate the accuracy, consistency and reliability of the forecasting function  230  to determine whether the forecasting function  230  meets a given set of specifications (e.g., production standards) for commercial use of the forecasting function  230 .  
      Referring now to  FIG. 7 , a flow chart of one embodiment of the portion of the method shown in  FIG. 6  for identifying characteristic variables (step  605 ) is shown. In step  700 , keyword expressions related to the financial measure are obtained. In one embodiment, an expert selects the keyword expressions based on knowledge of the relevant field.  
      In step  705 , publications are identified from the first set of publications based on the keyword expressions. In this process, the linguistic analyzer  400  of the forecasting function generator  245  performs linguistic analysis on the first set of publications in the publication database  225  to identify for each keyword expression those publications that contain the keyword expression.  
      In step  710 , one or more significant word expressions are identified within the identified publications for each keyword expression, and a predetermined characteristic of each significant word expression is identified as a characteristic variable. In this process, the linguistic analyzer  400  performs linguistic analysis on the publications identified for each keyword expression to identify one or more significant word expressions (i.e., one or more word expression pairs). Additionally, the linguistic analyzer  400  identifies a predetermined characteristic of each significant word expression (e.g., frequency of appearance of the significant word expression within the identified set of publications) as a characteristic variable, as is explained more fully herein.  
      Referring now to  FIG. 8 , a flow chart of another embodiment of the portion of the method shown in  FIG. 6  for identifying characteristic variables (step  605 ) is shown. In step  800 , significant word expressions are identified for the first set of publications in the publication database  225 . In this process, the linguistic analyzer  400  of the forecasting function generator  245  performs linguistic analysis on the first set of publications in the publication database  225  to identify the significant words from the first set of publications.  
      In step  805 , keyword expressions are obtained based on the financial measure. In one embodiment, an expert selects the keyword expressions based on knowledge of the relevant field.  
      In step  810 , combinations of significant word expressions and keyword expressions are identified within the first set of publications, and a predetermined characteristic of each combination is identified as a characteristic variable. In this process, the linguistic analyzer  400  performs linguistic analysis on the first set of publications in the publication database  225  to identify combinations of significant word expressions and keyword expressions (i.e., word expression pairs). Additionally, the linguistic analyzer identifies a predetermined characteristic of each word expression pair (e.g., frequency of appearance of the word expression pair in the first set of publications) as a characteristic variable, as is explained more fully herein.  
      Although embodiments of the present invention have been explained herein with reference to a value movement forecast for a financial measure, it is to be appreciated that the present invention can be practiced to compute a value movement forecast of a non-financial measure. It is to be further appreciated that the present invention can be practiced to compute a value movement forecast of any measure that exhibits a value movement characteristic.  
      The embodiments discussed herein are illustrative of the present invention. As these embodiments of the present invention are described with reference to illustrations, various modifications or adaptations of the methods and/or specific structures described may become apparent to those skilled in the art. All such modifications, adaptations, or variations that rely upon the teachings of the present invention, and through which these teachings have advanced the art, are considered to be within the spirit and scope of the present invention. Hence, these descriptions and drawings should not be considered in a limiting sense, as it is understood that the present invention is in no way limited to only the embodiments illustrated.