Abstract:
A computer-implemented simulation calculates financial consequences and likelihoods for a large number of potential real life events based on demographic data and based on an individual&#39;s responses to a questionnaire, in order to predict which types of financial products might best serve the individual&#39;s long term needs based on market trends extrapolated from historical market data. The computer-implemented simulation model provides an objective, non-speculative basis for recommending financial products that are most likely to meet the individual&#39;s present and future financial needs. The model assumes that any number of different products can satisfy customer needs for matters such as wealth generation, wealth and social status preservation, wealth transfer, or charitable giving. The model also factors whether existing and anticipated funding sources are to be dispensed in the form of a sinking or sustainable fund. Product affordability is also taken into consideration.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates to computer-implemented investment systems and methods and, more particularly, to systems and methods for predictive estimation of optimal wealth management portfolios. 
       BACKGROUND OF THE INVENTION 
       [0002]    For simplicity, customers typically seek to allocate their financial assets into a single investment vehicle that can assure their present and long term financial needs. Unfortunately, current regulatory constraints effectively prevent the development of a single product that can consistently match customer needs and aspirations. For example, needs based selling regulations constrain producers from speculative recommendations of products that do not serve an existing or impending financial goal. The vague and subjective standard for “impending” causes avoidance of any recommendations that might be seen as speculative. Additionally, no single investment vehicle is presently available that can be expected to provide a predictably varying risk/return profile over the long term. As a result, financial planners are not able to recommend any single investment vehicle that will meet their customers&#39; long term needs. Instead, financial planners must ladder and otherwise transition different investment vehicles based on their customers&#39; own best estimates of future needs and risk tolerance. 
         [0003]    Accordingly, it is desirable to provide an improved system and method for objectively estimating an individual&#39;s near term and long term financial needs, and determining which financial products are most likely to meet the individual&#39;s present and future financial needs. 
       BRIEF SUMMARY OF THE INVENTION 
       [0004]    According to an embodiment of the present invention, a computer-implemented simulation model calculates financial consequences and likelihoods for a large number of potential real life events based on demographic data and based on an individual&#39;s responses to a questionnaire, in order to predict which types of financial products might best serve the individual&#39;s long term needs based on market trends extrapolated from historical market data. This needs based modeling assumes that any number of different products can satisfy customer needs for matters such as wealth generation, wealth and social status preservation (e.g., retirement funding), wealth transfer (e.g., death benefits), or charitable giving. The model would also factor whether existing and anticipated funding sources (e.g., income earned, asset appreciation, inheritance, etc.) are to be dispensed in the form of a sinking or sustainable fund. Product affordability is also taken into consideration. Accordingly, the computer-implemented simulation model provides an objective, non-speculative basis for recommending financial products that are most likely to meet the individual&#39;s present and future financial needs. 
         [0005]    According to an embodiment of the present invention, a computer system for life span solution-based modeling of recommended instructions for allocating investment inputs includes a data storage structure and a computer processor configured to read from the data storage structure a plurality of individual responses to a standardized questionnaire, a set of demographic data, a plurality of market priors, and characteristics of a plurality of investment vehicles, correlate at least part of the set of demographic data to the individual responses, calculate a plurality of sequences of possible life events based on the individual responses and the demographic data, and a plurality of sequences of possible market trends based on the market priors, further calculate a time-varying individual need based on the plurality of possible sequences of life events and the plurality of sequences of possible market trends, and establish an optimal sequence of instructions for allocating investment inputs based on the time-varying individual need, the characteristics of the plurality of investment vehicles, and the plurality of possible market trends, wherein the investment vehicles include fixed annuities, tax-deferred investments, mutual funds, direct equity investments, secured lending instruments, variable annuities, variable universal life insurance, term life insurance, property and casualty insurance, and umbrella liability insurance. 
         [0006]    One aspect of the present invention is that the computer system for life span solution-based modeling of recommended instructions for allocating investment inputs also would accept various data input parameters and business rules that would be used to develop decision trees. By changing the values of the input parameters, the financial planner can do “what-if” studies to see what happens when the inputs change on a current and long-term basis. 
         [0007]    Another aspect of the present invention is that the results generated by the computer system for life span solution-based modeling of recommended instructions for allocating investment inputs can be represented and displayed as probability distributions (or histograms) or pie charts. These depictions also can include descriptions of assumptions used to develop the simulation, reliability predictions, margins of error, and degrees of confidence regarding the simulation results. 
         [0008]    A further aspect of the present invention is that the computer system for life span solution-based modeling of recommended instructions for allocating investment inputs would utilize a Monte Carlo simulation method for iteratively evaluating random data inputs such as assumed inflation rates, earning potential, market movements, portfolio returns, and the desired method of funding expected personal and family needs. A Monte Carlo simulation method is preferred because such a model is complex, nonlinear, and combines a large number of uncertain parameters. 
         [0009]    A further aspect of the present invention is that the computer system for life span solution-based modeling of recommended instructions for allocating investment inputs can automatically repeat the calculation of financial consequences and likelihoods for a large number of potential real life events based on demographic data and based on the individual&#39;s previous or updated responses to the questionnaire, at predefined time intervals, in order to update the system&#39;s predictions as to which types of financial products might best serve the individual&#39;s long term needs based on market trends extrapolated from historical market data. 
         [0010]    Yet another aspect of the present invention is that, based on periodic automatic updates of the calculated financial consequences and likelihoods, the computer system can automatically reallocate investment assets among various investment vehicles at the predefined time intervals, so as to better meet the individual&#39;s present and future financial needs. 
         [0011]    These and other objects, features, and advantages of the present invention will become apparent in light of the detailed description of the best mode embodiment thereof, as illustrated in the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]      FIG. 1  is a schematic diagram of a system for life span solution-based modeling of recommended investment input allocations, according to an embodiment of the present invention; 
           [0013]      FIG. 2  is a schematic diagram of the system of  FIG. 1 , implemented using a standalone server computer; 
           [0014]      FIG. 3  is a schematic diagram of the system of  FIG. 1 , implemented using a distributed network architecture; 
           [0015]      FIG. 4  is a flowchart illustrating a process for life span solution-based modeling of recommended investment input allocations, according to an embodiment of the present invention; 
           [0016]      FIG. 5  is a bar chart illustration of an optimal sequence of investment allocations, according to an embodiment of the present invention; 
           [0017]      FIG. 6  is a table of criteria for adjusting recommended investment allocations, according to an embodiment of the present invention; 
           [0018]      FIG. 7  is a pie chart illustration of an optimal sequence of investment input allocations, labeled by investment purpose, according to an embodiment of the present invention; and 
           [0019]      FIG. 8  is a pie chart illustration of an optimal sequence of investment input allocations, labeled by investment vehicle, according to an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
       [0020]    Referring to  FIG. 1 , a process  10  for periodic rebalancing of investment input allocations among investment vehicles is implemented in a network computer system  12 , using a life-span-solution-based modeling approach. The network computer system  12  may be configured in many different ways. For example, the system  12  may include a conventional standalone server computer  14 , as shown in  FIG. 2 . Alternatively, the system  12  can be configured in a distributed architecture  26 , as shown in  FIG. 3 . 
         [0021]    Referring to  FIG. 2 , the conventional standalone server computer  14  includes at least one controller, processor, or central processing unit (CPU)  16 , at least one communication port  18 , and at least one data storage structure  20 . The processor  16  may include one or more conventional microprocessors and one or more supplementary co-processors such as math co-processors. The communication port  18  may include multiple communication channels for simultaneous communication with, for example, other processors, servers or client terminals  22 , or a display unit  24 , any of which may be configured to provide various user interfaces  25 , such as an investment platform interface for purchasing of investment vehicles. Devices in communication with each other need not be continually transmitting to each other. On the contrary, such devices need only transmit to each other as necessary, may actually refrain from exchanging data most of the time, and may require several steps to be performed to establish a communication link between the devices. For example, the communication port  18  may include wire modems, wireless radio, infrared, visible laser, or UV laser transceivers, or audio transceivers. The communication port  18  and the at least one data storage structure  20  are in communication with the processor  16  to facilitate the operation of the network server  14 . The data storage structure  20  may comprise an appropriate combination of magnetic, optical and/or semiconductor or flash memory, and may include, for example, RAM, ROM, an optical disc such as a compact disc and/or a hard disk or drive. The processor  16  and the data storage structure  20  each may be, for example, located entirely within a single computer or other computing device; or connected to each other by a communication medium, such as a USB port, serial port cable, a coaxial cable, an Ethernet type cable, a telephone line, a radio frequency transceiver or other similar wireless or wireline medium. 
         [0022]    Each user device or computer or client terminal  22  may include any one or a combination of a keyboard, a computer display, a touch screen, LCD, voice recognition software, an optical or magnetic read head, or other input/output devices required to implement the above functionality. 
         [0023]    Each display unit  24  may include any one or a combination of a computer display, a printer, a CD/DVD burner, a magnetic tape drive, a magnetic disk drive, an LCD array, a voice speaker, a network connection, or similar output device. 
         [0024]    Referring to  FIG. 3 , the distributed network architecture  26  includes several distributed servers  28  and at least one data storage device  30 . Each distributed server  28  includes a processor  16  and a data storage structure  20 . The distributed servers  28  and the data storage device  30  are in communication with a communications hub or port  32  that serves as a primary communication link with other servers, client or user terminals  22  and other related devices including one or more display units  24 . The communications hub or port  32  may have minimal processing capability itself, serving primarily as a communications router, or may also act as another distributed server  28 . A variety of communications protocols may be part of the system, including but not limited to: Ethernet, SAP, SAS.™., ATP, Bluetooth, and TCP/IP. 
         [0025]    At least one of the data storage structures  20  or the data storage device  30  is encoded with (i) a program and/or algorithm(s)  34  (e.g., computer program code and/or a computer program product) adapted to configure one or more of the processors  16  to perform the computerized process  10  for life span solution-based modeling of recommended investment input allocations, as described in detail hereinafter; and/or (ii) at least one database  36  configured to store information required, manipulated, or produced by one or more of the processors  16  according to the computerized process  10  described by the program  34 . 
         [0026]    The computer program  34  for configuring the processor  16  to implement the process  10  (and other functions described herein) can be developed by a person of ordinary skill in the art, and is not described in detail herein. Suitable computer program code may also be provided for performing numerous other functions such as generating notifications at selected time intervals. For example, in addition to instructions for configuring the processor  16  to perform the process  10 , the program  34  also may include program elements such as an operating system, a database management system and “device drivers” that allow the processor to interface with computer peripheral devices (e.g., a video display, a keyboard, a computer mouse). The instructions of the program  34  may be read by the processor  16  from the data storage structure  20 . The program  34  may be stored, for example, in a compressed, an uncompiled and/or an encrypted format, and may include computer program code. While execution of sequences of instructions in the program  34  will cause the processor  16  to perform the steps of the computerized process  10  as described below, hard-wired circuitry may be used in place of, or in combination with, software instructions for implementation of the computerized process  10 . Thus, embodiments of the present invention are not limited to any specific combination of hardware and software. 
         [0027]    Alternatively, as shown in  FIG. 3 , the program  34  may be embodied in another computer-readable medium  38  that provides or participates in providing instructions to the processor  16  (or any other processor of a computing device described herein) for execution. The computer-readable medium  38  may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes the main memory. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to the processor. Transmission media may carry acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media  38  include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM or EEPROM (electronically erasable programmable read-only memory), a FLASH-EEPROM, any other memory chip or cartridge, a carrier wave encoded with data by amplitude, phase, and/or frequency modulation, or any other medium from which a computer can read. 
         [0028]    Various forms of the computer-readable medium  38  may be involved in configuring the processor  16  (or any other processor of a device described herein) to perform the computerized process  10 . For example, as shown in  FIG. 3 , the program  34  may initially be borne on a magnetic disk of a remote computer  40 . The remote computer  40  can load the instructions into its dynamic memory and send the instructions over a telephone line  42  using a first modem  44 . A second modem  46  local to a computing device (e.g., the server  14 ) can receive the data on the telephone line  42  and use an infrared transmitter  48  to convert the data to a wireless signal  50 . An infrared detector  52  can receive the data carried in the wireless signal  50  and transfer the data through the communication port  18  to the processor  16 . In addition, instructions may be received via the communication port  18  as electrical, electromagnetic or optical signals, conveyed either on optical or electromagnetic cables or as wireless carrier waves that carry data streams representing various types of information. 
         [0029]    The database  36  may include multiple records  54 , each record including fields specific to the present invention such as but not limited to individual responses  56 , market priors  58 , demographic data  60 , possible life events  62 , market trends  64 , expected expense ranges  66 , individual needs  68 , profiles of investment vehicles  70 , return-on-investment ranges  72 , individual need allocations  74 , instructions for allocating investment inputs  76 , and associated probabilities and time sequencing data. The investment vehicles  70  can include fixed annuities, tax-deferred investments, mutual funds, direct equity investments, secured lending instruments, variable annuities and other sorts of “longevity insurance”, variable universal life insurance, term life insurance, educational savings plans, property and casualty insurance, and umbrella liability insurance. 
         [0030]    In operation, as shown in  FIGS. 1 and 4 , the computerized process  10  for life span solution-based modeling of recommended instructions for allocating investment inputs includes a plurality of steps implemented by the system  12 . At a step  80  of the computerized process  10 , as shown in  FIG. 4 , the system  12  receives a plurality of individual responses  56  to a standardized questionnaire and provides the plurality of individual responses  56  to the processor  16 . The plurality of individual responses  56  can be obtained directly by interaction of an individual with the system  12 , or can be previously entered into the data storage structure  20  by a financial advisor  82 , as shown in  FIG. 1 . The financial advisor  82  may be an individual, a corporation, or any other legal entity. Administration of the standardized questionnaire can optionally be included in the computerized process  10 . Preferably, whatever standardized questionnaire is used provides an array of predetermined numeric or multiple-choice responses such that the individual responses  56  can easily be data coded for correlation with demographic data  60  encoded into the data storage structure  20 . 
         [0031]    At a step  84 , the processor  16  uses the individual responses  56  to look up, correlate, or otherwise identify relevant demographic data  60 , previously developed by data miners  86 . Preferably, the data storage structure  20  is encoded with a large set of demographic data  60  tagged according to the degree of correlation of each data element with each of the predetermined responses. 
         [0032]    At a step  88 , the processor  16  calculates a plurality  90  of possible sequences of life events  62  based on the individual responses  56  and the demographic data  60 , as shown by the boxed labels and arrows in  FIG. 4 . Preferably, Monte Carlo analysis is used to generate the plurality  90  of possible sequences of life events  62 . A Monte Carlo simulation method is preferred because such a model is complex, nonlinear, and combines a large number of uncertain parameters. Monte Carlo analysis can include random assessment of input variables such as earning potential, timing of children&#39;s education, likelihood of major purchases, and other individual and family life events bounded by the demographic data  60 . The individual responses  56  also can be used to “force” the Monte Carlo analysis of possible life events  62 . 
         [0033]    At a step  92 , the processor  16  determines a plurality  94  of possible sequences of market trends  64  based on the market priors  58  previously compiled by the data miners  86 . Market priors  58  can be evaluated using any variety of known statistical trading forecast algorithms in order to develop a plurality of sequences of possible market trends  64 . For example, a Monte Carlo simulation method can be used for iteratively evaluating bounded random variations on market priors  58  such as assumed inflation rates, market movements, and portfolio returns. In some embodiments, the individual responses  56  can be used to force the Monte Carlo analysis so that the simulation of possible market trends  64  is biased toward an individual&#39;s expectations. Alternative trading forecast algorithms, which can be utilized for determining sequences of possible market trends  64 , include artificial neural networks, exponential moving averages, predictive vector quantizations, Lempel-Ziv algorithms, distortion controlled algorithms, kernel regression, and multispectral algorithms. Preferably, multiple sequences of possible market trends  64  are determined for each possible life event  62 . 
         [0034]    At a step  96 , the processor  16  estimates a financial consequence (expressed as an expected expense range  66 ) for each possible life event  62  based on the possible market trends  64 . The expected expense ranges  66  can be expressed as present-value or future-value monetary amounts. For example, the expected expense range  66  of a possible health-care-related life event  62  may be driven upward by some possible market trends  64 , but may also be held downward by other possible market trends  64 . The high and low ends of the expected expense range  66  can be determined within a given confidence interval based on statistical analysis of the possible market trends  64 . Similarly, the cost of raising and educating a child may be driven upward or held downward by yet other possible market trends  64  as well as by demographic data  60  or health-related individual responses  56 . 
         [0035]    Preferably, each expected expense range  66  is expressed as a range of future-value monetary amounts at a future time concurrent with the underlying possible life event  62 , so that the plurality of expected expense ranges  66  can be expressed as a sequence of future-value monetary amount ranges. Additionally, the expected expense ranges  66  can be discounted by estimated likelihoods  98  of the corresponding possible life events  62 . 
         [0036]    At a step  100 , the processor  16  calculates a time-varying individual need  68  based on the expected expense ranges  66 . For example, the time-varying individual need  68  can be calculated by summing the likelihood-discounted future-value monetary amount ranges corresponding to the expected expense ranges  66  for all of the possible life events  62 . 
         [0037]    At a step  102 , the processor  16  calculates probable return-on-investment ranges  72  for each of the plurality of investment vehicles  70  based on the possible market trends  64 . Preferably, the return-on-investment range for each investment vehicle is determined in a granular, time-varying fashion, so that overall portfolio return-on-investment can be optimized by prospective rebalancing of portfolio value among the plurality of investment vehicles. Preferably, each return-on-investment range is determined by statistical analysis of a large set of relevant possible market trends  64 . Monte Carlo analysis of the market priors  58  is the preferred mode for generating and analyzing large sets of possible market trends  64 . 
         [0038]    At a step  104 , the processor  16  calculates an optimal sequence  106  of individual need allocations  74  among a plurality of investment vehicles  70  based on the possible market trends  64 , the possible life events  62 , and the return-on-investment ranges  72 . Analysis also can be performed to identify the timing of a peak value of individual need  68 , where a portfolio based on the optimal sequence  106  of individual need allocations  74  can shift over from a sustainable model to a sinking fund model. 
         [0039]    At a step  108 , the processor  16  displays the optimal sequence  106  of individual need allocations  74  among investment vehicles using the display unit  24 . For example, each individual need allocation  74  in the sequence  106  can be illustrated by a bar graph as shown in  FIG. 5 , where each segment of a bar indicates a portion of the individual need  68  that could be met by one of the recommended investment vehicles  70 . The individual need allocations  74  also could be displayed as percentage tables or as absolute future-value monetary amounts. The individual responses  56  can include responses indicating preferred methods for funding various expected personal and family needs, and the optimal sequence  106  of individual need allocations  74  can be adjusted based on the individual responses  56 . 
         [0040]    At a step  110 , the processor  16  calculates an optimal sequence  112  of instructions for allocating investment inputs  76 , based on the individual responses  56 , the demographic data  60 , the return-on-investment ranges  72 , and the optimal sequence  106  of individual need allocations  74 . For example, the optimal sequence  112  of instructions for allocating investment inputs  76  can be selected using various criteria such as a decision tree or a table of factors  600 , as shown in  FIG. 6 . The individual responses  56  can include refusals of, or limitations on, investment inputs to specific investment vehicles  70 . The optimal sequence  112  of instructions for allocating investment inputs  76  can be rebalanced according to the investment-vehicle-specific refusals or limitations. 
         [0041]    At a step  114 , the processor  16  displays the optimal sequence  112  of instructions for allocating investment inputs  76  using the display unit  24 . The optimal sequence  112  of instructions for allocating investment inputs  76  can be displayed using a purpose-labeled pie chart report, as shown in  FIG. 7 . Alternatively, the optimal sequence  112  of instructions for allocating investment inputs  76  can be displayed using a pie chart report labeled by investment vehicle  70 , as shown in  FIG. 8 . The instructions for allocating investment inputs  76  also could be displayed as percentages or as absolute future-value monetary amounts. 
         [0042]    The optimal sequence  112  of instructions for allocating investment inputs  76  and the optimal sequence  106  of individual need allocations  74  can be provided to the financial advisor  82  for use in administering an individual&#39;s portfolio. Alternatively, the optimal sequence  112  of instructions for allocating investment inputs  76  and the optimal sequence  106  of individual need allocations  74  can be encoded into the data structure  20  for automatically rebalancing investment assets and investment inputs. 
         [0043]    As a specific example of how the process  10  can be used, a thirty (30) year old individual may provide individual responses  56  in the present day that closely correlate to a particular set of demographic data  60 . Among other possible life events, the correlated demographic data  60  may show an eighty percent (80%) chance that the individual will need long-term inpatient medical care if the individual survives to age eighty (80). The demographic data may also show a five percent (5%) chance that the individual will live ten (10) or more years beyond entry into long-term inpatient care at age eighty. Based on a statistical analysis of possible market trends  64 , ten years of long-term inpatient medical care fifty years in the future might have an expected expense range  66  of about $6M-$20M. Based on the possible market trends  64 , and based on individual responses indicating expected available investment inputs of about $3M over the next fifty years, a narrow selection of high-risk, high-return investment vehicles  70  might be expected to achieve return-on-investment ranges  72  sufficient to cover the thirty year old individual&#39;s lowest value of the expected expense range  66  for long-term medical care. However, in determining the peak individual need  68  and the optimal sequence  112  of instructions for allocating investment inputs  76 , the expected expense range  66  for long-term inpatient care can be discounted by the low combined likelihood that the thirty year old individual will (1) survive to age eighty; (2) require long-term inpatient care at age eighty; and (3) survive ten years beyond entry into long-term care. Thus, the optimal sequence  112  of instructions for allocating investment inputs  76  can aim for moderate risk and reliable returns while still meeting a reasonably discounted value of the individual need  68  for long term care expenses. 
         [0044]    By performing similar probability analyses and estimates of expected expense ranges  66 , individual needs  68 , and instructions for allocating investment inputs  76  for each possible life event  62  in a large set of possible life events  62 , the process  10  can produce a likely-scenario recommendation for an optimum sequence  112  of instructions for allocating investment inputs  76  in the form of a report such as shown in  FIGS. 7 and 8 . 
         [0045]    One advantage of the present invention is that the process  10  provides a non-speculative basis for recommending diverse financial products that are most likely to meet the individual&#39;s present and future financial needs. 
         [0046]    Another advantage of the present invention is that the investment vehicles  70  can be selected from a diverse and comprehensive list of asset types including fixed annuities, tax-deferred investments, mutual funds, direct equity investments, secured lending instruments, variable annuities and other sorts of “longevity insurance”, variable universal life insurance, term life insurance, property and casualty insurance, and umbrella liability insurance. For example, before reaching peak individual need  68 , assets may be held primarily in mutual funds or equity investments. After peak individual need  68 , assets may be realigned to cash-similar investments such as money market accounts or short-term certificates of deposit. Thus, investment assets can continually be re-aligned to investment vehicles appropriate to an individual&#39;s present and anticipated financial needs. By performing similar probability analyses and estimates of expected economic and social conditions, individual and family needs, and business needs, the process can also produce a recommendations for an optimum sequence of property and casualty coverage allocations such as between home, renters, automobile, umbrella, small business, commercial, etc. for the varying lifetime stages and events. 
         [0047]    A further advantage of the present invention is that the system  12  can periodically and automatically repeat steps of the process  10  at predefined time intervals to provide automated review and modification of the optimal sequence  112  of instructions for allocating investment inputs  76 . The system  12  also can issue instructions for buying, selling, or redeeming investment vehicles or portions of investment vehicles, according to the optimal sequence  112  of instructions for allocating investment inputs  76 . Thus, the present invention enables an individual&#39;s overall portfolio to be automatically continually or periodically realigned, at predefined time intervals, to achieve a mix of investment vehicles  70  tailored to meet the individual&#39;s present and peak individual need  68 . 
         [0048]    Although this invention has been shown and described with respect to the detailed embodiments thereof, it will be understood by those skilled in the art that various changes in form and detail thereof may be made without departing from the spirit and the scope of the invention. 
         [0049]    For example, the Monte Carlo method is just one of many possible sampling methods that may be used to illustrate how random variation, lack of knowledge, or error affects the sensitivity, performance, or reliability of the long term recommendations envisioned. As another example, while the preferred embodiment envisions use of data inputs that closely match publicly available data, proprietary demographic data and non-public market factors analyses can be incorporated into the process without departing from the broad concept of the present invention.