Abstract:
A system and method for modifying a portfolio having respective quantities of a plurality of different meat cut types (MCT) of an animal comprising: accessing portfolio information of the portfolio stored in a memory, the portfolio information including a first quantity of a first MCT and a second quantity of a second MCT, the first MCT having a first predicted future price for a first future time period (FTP) and the second MCT having a second predicted future price for a second FTP, the first and second MCTs being part of the plurality of different MCTs; determining first comparison data based on historical first market prices of the first MCT and historical second market prices of the second MCT for a selected historical time period; determining a correlation value between the first MCT and the second MCT based on the first comparison data and the second first comparison data and the first quantity and the second quantity; analyzing the correlation value and adjusting a parameter of a price model used in determining the first predicted future price in order to modify a value of a first price premium associated with the first predicted future price, the first price premium representing compensation in exchange for assuming a risk that the first predicted future price may be different from the eventual market price of the first MCT when reaching the first FTP; and sending the modified value of the first price premium for presentation to a member of the portfolio for promoting either an increase or a decrease in the quantity of the first MCT in the portfolio.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    The present invention relates to optimizing a portfolio of products, and more particularly to optimizing a portfolio of cuts of meat. 
         [0002]    Entities that guarantee payments to customers when an event occurs are exposed to significant financial risk. Examples of such entities are insurance providers or financial services companies that guarantee the forward price of a commodity such as gold or particular cuts of meat. To reduce this financial risk, entities charge an insurance premium which is designed to protect the entity from financial exposure should a series of unexpected negative events occur. 
         [0003]    In addition, some entities have developed hedging strategies that involve purchasing securities that are thought to be related to the market price of an underlying product for which the entities secure a forward price to a customer. It is well known in the art, for example, for gold mining companies to buy gold futures when the gold mining company guarantees the future price of a quantity of gold. Holding gold futures operates to minimize the financial exposure of the company should the price that is guaranteed for the quantity of gold not correspond to the market price of gold over time. A disadvantage to the above technique is that the assets in the portfolio (i.e. units of gold) change prices in the same direction and at the same rate as the assets are substantially correlated, which minimizes the ability of the entity to construct a portfolio of assets with offsetting correlations. 
         [0004]    Another technique to minimize risk is to diversify the types of assets in a portfolio. Insurance companies, for example, often create a portfolio of insurance policies of different risk levels. An automobile insurance company, for example, may wish to have a portfolio with a suitable mix of high-risk drivers and low-risk drivers to mitigate the risk that a catastrophic incident from a single insured person will cause financial hardship to the company while still enabling the insurance company to collect high insurance premiums from the high-risk group. An insurance company will often design the content of the portfolio based on historical data, however, because there is no evidence that the groups of insured individuals are substantially correlated in a positive or negative relationship, techniques for limiting the risk of an insurance portfolio while maximizing profit are limited. 
       SUMMARY OF THE INVENTION 
       [0005]    Accordingly, it is an object to provide a system and method for modifying content of a portfolio having different meat cuts to obviate or mitigate at least one of the above-mentioned disadvantages. 
         [0006]    According to a first aspect, there is provided a method for modifying a portfolio having respective quantities of a plurality of different meat cut types (MCT) of an animal, the method comprising the steps implemented on a processor of: accessing portfolio information of the portfolio stored in a memory, the portfolio information including a first quantity of a first MCT and a second quantity of a second MCT, the first MCT having a first predicted future price for a first future time period (FTP) and the second MCT having a second predicted future price for a second FTP, the first and second MCTs being part of the plurality of different MCTs; determining first comparison data based on historical first market prices of the first MCT and historical second market prices of the second MCT for a selected historical time period; determining a correlation value between the first MCT and the second MCT based on the first comparison data and the second first comparison data and the first quantity and the second quantity; analyzing the correlation value and adjusting a parameter of a price model used in determining the first predicted future price in order to modify a value of a first price premium associated with the first predicted future price, the first price premium representing compensation in exchange for assuming a risk that the first predicted future price may be different from the eventual market price of the first MCT when reaching the first FTP; and sending the modified value of the first price premium for presentation to a member of the portfolio for promoting either an increase or a decrease in the quantity of the first MCT in the portfolio. 
         [0007]    According to another aspect, there is provided a system for modifying a portfolio having respective quantities of a plurality of different meat cut types (MCT) of an animal, the system comprising: a processor; a memory for storing portfolio information including a first quantity of a first MCT and a second quantity of a second MCT, the first MCT having a first predicted future price for a first future time period (FTP) and the second MCT having a second predicted future price for a second FTP, the first and second MCTs being part of the plurality of different MCTs; an optimization module for determining first comparison data based on historical first market prices of the first MCT and historical second market prices of the second MCT for a selected historical time period; a correlation module for determining a correlation value between the first MCT and the second MCT based on the first comparison data and the second first comparison data and the first quantity and the second quantity; a model module for analyzing the correlation value and adjusting a parameter of a price model used in determining the first predicted future price in order to modify a value of a first price premium associated with the first predicted future price, the first price premium representing compensation in exchange for assuming a risk that the first predicted future price may be different from the eventual market price of the first MCT when reaching the first FTP; and a presentation module for sending the modified value of the first price premium for presentation to a member of the portfolio for promoting either an increase or a decrease in the quantity of the first MCT in the portfolio. 
         [0008]    According to yet another aspect, there is provided a method for modifying a portfolio having respective quantities of a plurality of different meat cut types (MCT) of an animal, the method comprising the steps implemented on a processor of: accessing portfolio information of the portfolio stored in a memory, the portfolio information including a first quantity of a first MCT, the first MCT having a first predicted future price for a first future time period (FTP), the first MCTs being part of the plurality of different MCTs; selecting a second quantity of a second MCT, the second MCT not being part of the plurality of different MCTs; determining first comparison data based on historical first market prices of the first MCT and historical second market prices of the second MCT for a selected historical time period; determining a correlation value between the first MCT and the second MCT based on the first comparison data and the second first comparison data and the first quantity and the second quantity; analyzing the correlation value and adjusting a parameter of a price model used in determining the second predicted future price in order to determine an adjusted value of a second price premium associated with the second predicted future price, the second price premium representing compensation in exchange for assuming a risk that the second predicted future price may be different from the eventual market price of the second MCT when reaching a second FTP associated with the second MCT; and sending the modified value of the first price premium for presentation to a member of the portfolio for promoting addition of the second MCT to the portfolio. 
     
    
     
       DESCRIPTION OF FIGURES 
         [0009]      FIG. 1  is a schematic of a data processing system that includes a portfolio management tool; 
           [0010]      FIG. 2  is a diagram of an exemplary electronic device used for interacting with the portfolio management tool of  FIG. 1 ; 
           [0011]      FIG. 3  is a component diagram of an embodiment of the portfolio management tool of  FIG. 1 ; 
           [0012]      FIG. 4  shows the membership levels of different users of the portfolio management tool of  FIG. 3 ; 
           [0013]      FIG. 5  is an exemplary log-in screen of the portfolio management tool of  FIG. 3 ; 
           [0014]      FIG. 6  shows a model module of the portfolio management tool  FIG. 3 ; 
           [0015]      FIG. 7  is a flow-chart of steps performed by the model module of  FIG. 6 ; 
           [0016]      FIG. 8  is a block diagram of a model of the tool of  FIG. 3 ; 
           [0017]      FIG. 9  shows a predictor module of the portfolio management tool of  FIG. 3 ; 
           [0018]      FIG. 10  is a flow-chart of the steps performed by the predictor module of  FIG. 9 ; 
           [0019]      FIG. 11   a  is an exemplary prediction table displayed to a user of the portfolio management tool of  FIG. 1 ; 
           [0020]      FIG. 11   b  is an alternative exemplary prediction table displayed to a user of the portfolio management tool of  FIG. 1 ; 
           [0021]      FIG. 12   a  is an exemplary visual representation displayed to a user of the portfolio management tool of  FIG. 1 ; 
           [0022]      FIG. 12   b  is an alternative exemplary visual representation displayed to a user of the portfolio management tool of  FIG. 1 ; 
           [0023]      FIG. 13  shows a purchasing module of the portfolio management tool of  FIG. 3 ; 
           [0024]      FIG. 14  is a flow-chart of the steps performed by the purchasing module of  FIG. 13 ; 
           [0025]      FIG. 15  is an alternative embodiment of a portfolio management tool of  FIG. 1 ; 
           [0026]      FIG. 16  shows an aggregate portfolio of the provider of the portfolio management tool of  FIG. 15 ; 
           [0027]      FIG. 17  is an optimization module of the portfolio management tool of  FIG. 15 ; 
           [0028]      FIG. 18  is an adjustment module of the portfolio management tool of  FIG. 15 ; 
           [0029]      FIG. 19  is a flow-chart of the steps performed by the optimization module and the adjustment module of the portfolio management tool of  FIG. 15 ; and 
           [0030]      FIG. 20  is a table illustrating the net financial benefit provided by the tool of  FIG. 15 ; 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
     Data Processing System  10   
       [0031]    Referring to  FIG. 1 , a data processing system  10  is presented for electronically predicting, quoting, managing and securing forward prices of cuts of meat, for example in a portfolio. Real-time derivative data, such as the current prices of live animal (e.g. cattle, hogs, etc.) futures contracts are provided to the electronic portfolio management tool  12  by a derivative data provider  18 , such as the Chicago Mercantile Exchange (CME), via the network  16 . A futures contract is a standardized contract, traded on a securities exchange  310 , to buy or sell a quantity of a specified commodity (such as live cattle) at a specified date in the future, at a specified price. The future price is determined by the instantaneous equilibrium between the forces of supply and demand among competing buy and sell orders on the exchange  310  at the time of the purchase or sale of the contract. The future date is called the delivery date or final settlement date. The official price of the futures contract at the end of a day&#39;s trading session on the exchange  310  is called the settlement price for that day of business on the exchange  310 . Periodically, the portfolio management tool  12  receives (either automatically or by request) prices (e.g. averaged) of a specific cut of meat from a meat data provider  20 , such as the daily prices reported for the specific cut of meat by the United States Department of Agriculture (USDA). The portfolio management tool  12  communicates with applications  14  to generate a dynamic and interactive visual representation  28  that is presented to the member  8 . As will be appreciated, applications  14  may be distributed across a public or private network  22  or may reside on the same electronic device  18  (not shown) of the electronic portfolio management tool  12 . It will also be appreciated that the meat data provider  20  and the derivative data provider  18  may be accessible by the tool  12  via the network  16  as shown, or via a direct communications link. The tool  12  is operable to access a trading system  308  for buying and selling futures contracts on a futures market  310 , such as the CME. The trading system  308  may be an electronic component of a futures market  310  or may be an electronic service offered by a third party for executing instructions related to the buying and selling of securities. 
         [0032]    In one embodiment of the invention, the customer or the member  8  can use various electronic or digital devices  18 , such as but not limited to cell phones, mobile computers, home computers, pagers and PDAs, to interact with the portfolio management tool  12 . For example, the member  8  may choose to view predicted prices (i.e. in a future time period as compared to the present time period in which the prices are viewed) and secure predicted prices of a particular cut of meat. The member  8  communicates with the portfolio management tool  12  by sending requests  9  via the network  16  that are generated when the member  8  interacts with a visual interface  28  (such as but not limited to a web page) that is presented to the member  8  on the digital device  18 . As will be appreciated, in one embodiment, the portfolio management tool  12  may be in communication with applications  14  to process requests  9  submitted by the member  8  and to dynamically generate and update the visual representation  28  that is presented to the user  8 . As shown, the portfolio management tool  12  is operable to access data in the tables  24  and to store data in the tables  24 . The tables  24  are in electronic communication with the tool  12 . It will be appreciated that the networks  16 ,  22  may include public or private networks, a group of wireless and/or wired networks or any other medium that facilitates communication between electronic devices  18 . 
       Electronic Device  18   
       [0033]    Referring to  FIG. 2 , the generic electronic device  18  can include input devices  38 , such as a keyboard, microphone, mouse and/or touch screen by which the member  8  interacts with the visual interface  28 . It will also be appreciated that the tool  12  resides on an electronic device  18  which may include similar components to the electronic device  18  employed by the user  8 . A processor  42  can co-ordinate through applicable software the entry of data and requests  9  into the memory  40  and then display the results on a screen as visual representation  28 . A storage medium  46  can also be connected to device  18 , wherein software instructions and/or member data is stored for use by the tool  12 . As shown, the storage medium  46  includes tables  24  wherein member data is stored as well as data received from the derivative data provider  18  and the meat data provider  20 . 
         [0034]    The software instructions can comprise code and/or machine readable instructions for implementing predetermined functions/operations including those of an operating system, the tool  12 , or other information processing system, for example, in response to commands or inputs provided by a user  8  of the tool  12 . The processor  42  (also referred to as module(s) for specific components of the tool  12 ) as used herein is a configured device and/or set of machine-readable instructions for performing operations as described by example above. 
         [0035]    As used herein, the processor/modules in general may comprise any one or combination of, hardware, firmware, and/or software. The processor/modules act upon information by manipulating, analyzing, modifying, converting or transmitting information for use by an executable procedure or an information device, and/or by routing the information with respect to an output device. The processor/modules may use or comprise the capabilities of a controller or microprocessor, for example. Accordingly, any of the functionality provided by the systems and processes of  FIGS. 1-20  may be implemented in hardware, software or a combination of both. Accordingly, the use of a processor/modules as a device and/or as a set of machine readable instructions is hereafter referred to generically as a processor/module for sake of simplicity. 
         [0036]    It will be understood by a person skilled in the art that the memory  40  storage described herein is the place where data is held in an electromagnetic or optical form for access by a computer processor. In one embodiment, storage  40  means the devices and data connected to the computer through input/output operations such as hard disk and tape systems and other forms of storage not including computer memory and other in-computer storage. In a second embodiment, in a more formal usage, storage  40  is divided into: (1) primary storage, which holds data in memory (sometimes called random access memory or RAM) and other “built-in” devices such as the processor&#39;s L1 cache, and (2) secondary storage, which holds data on hard disks, tapes, and other devices requiring input/output operations. Primary storage can be much faster to access than secondary storage because of the proximity of the storage to the processor or because of the nature of the storage devices. On the other hand, secondary storage can hold much more data than primary storage. In addition to RAM, primary storage includes read-only memory (ROM) and L1 and L2 cache memory. In addition to hard disks, secondary storage includes a range of device types and technologies, including diskettes, Zip drives, redundant array of independent disks (RAID) systems, and holographic storage. Devices that hold storage are collectively known as storage media. 
         [0037]    Referring to  FIG. 2   b,  the portfolio management tool  12  resides on and is implemented by one or more generic electronic devices  18 . Generic device  18  may be a server that makes available the tool  12  to the member  8  over the network  16 . As known, device  18  may include input devices  38 , such as a keyboard, microphone, mouse and/or touch screen by which the provider of the tool  12  interacts with the tool  12  via the visual interface  28 . A processor  42  can co-ordinate through applicable software the entry of data and requests  9  into the memory  40  and then display/present the results on a screen as visual representation  28 . It will be understood that the visual representation  28  displayed to the provider of the tool  12  may be different than the visual representation displayed to a member  8 . Further, it is recognised that the visual representation  28  can be presented (as a result of operation of the tool  12 ) to the member  8  on their client (e.g. of the tool  12  implemented on a networked server) electronic device  18  via the network  16 . A storage medium  46  can also be connected to device  18 , wherein software instructions, applications  14 , member data, and other data is stored for use by the tool  12 . As shown, the storage medium  46  includes tables  24  wherein member data is stored as well as data received from the derivative data provider  18 , the meat data provider  20 , and the exchange  310  amongst other information. 
         [0038]    The software instructions may comprise code and/or machine readable instructions for implementing predetermined functions/operations including those of an operating system, the tool  12 , or other information processing system, for example, in response to commands or inputs provided by a user  8  and/or the provider of the tool  12 . The processor  42  (also referred to as module(s) for specific components of the tool  12 ) as used herein is a configured device and/or set of machine-readable instructions for performing operations as described by example above. Some or all of the modules of the tool  12  may be distributed across a network as applications  14  or reside on the electronic device  18 . As is understood, some or all of the modules of the tool  12  may also be downloadable to the electronic device  18  of the member  8  and will be in communication with other modules of the tool  12  on the electronic device  18  of the provider of the tool  12 . 
         [0039]    As used throughout, the processor/modules on the device  18  of the tool  12  in general may comprise any one or combination of, hardware, firmware, and/or software. The processor/modules act upon information by manipulating, analyzing, modifying, converting or transmitting information for use by an executable procedure or an information device, and/or by routing the information with respect to an output device. The processor/modules may use or comprise the capabilities of a controller or microprocessor, for example. Accordingly, any of the functionality provided by the systems and processes of  FIGS. 1-20  may be implemented in hardware, software or a combination of both. Accordingly, the use of a processor/modules as a device and/or as a set of machine readable instructions is referred to generically as a processor/module for sake of simplicity. 
         [0040]    It will be understood by a person skilled in the art that the memory  40  of the electronic device  18  of the tool  12  described herein is the place where data is held in an electromagnetic or optical form for access by a computer processor. In one embodiment, storage  40  means the devices and data connected to the computer  18  through input/output operations such as hard disk and tape systems and other forms of storage not including computer memory and other in-computer storage. In a second embodiment, in a more formal usage, storage  40  is divided into: (1) primary storage, which holds data in memory (sometimes called random access memory or RAM) and other “built-in” devices such as the processor&#39;s L1 cache, and (2) secondary storage, which holds data on hard disks, tapes, and other devices requiring input/output operations. Primary storage can be much faster to access than secondary storage because of the proximity of the storage to the processor or because of the nature of the storage devices. On the other hand, secondary storage can hold much more data than primary storage. In addition to RAM, primary storage includes read-only memory (ROM) and L1 and L2 cache memory. In addition to hard disks, secondary storage includes a range of device types and technologies, including diskettes, Zip drives, redundant array of independent disks (RAID) systems, and holographic storage. Devices that hold storage are collectively known as storage media. 
       Database  24   
       [0041]    A database or tables  24  is a further embodiment of memory  40  as a collection of information that is organized so that it can easily be accessed, managed, and updated. In one view, databases can be classified according to types of content: bibliographic, full-text, numeric, and images. In computing, databases are sometimes classified according to their organizational approach. As well, a relational database is a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A distributed database is one that can be dispersed or replicated among different points in a network. An object-oriented programming database is one that is congruent with the data defined in object classes and subclasses. 
         [0042]    Computer databases  24  typically contain aggregations of data records or files, such as sales transactions, product catalogs and inventories, and customer profiles. Typically, a database manager provides users the capabilities of controlling read/write access, specifying report generation, and analyzing usage. Databases and database managers are prevalent in large mainframe systems, but are also present in smaller distributed workstation and mid-range systems such as the AS/400 and on personal computers. SQL (Structured Query Language) is a standard language for making interactive queries from and updating a database such as IBM&#39;s DB2, Microsoft&#39;s Access, and database products from Oracle, Sybase, and Computer Associates. 
         [0043]    Memory  40  storage is the electronic holding place for instructions and data that the computer&#39;s microprocessor  42  can reach quickly. When the computer  18  is in normal operation, its memory  40  usually contains the main parts of the operating system and some or all of the application programs and related data that are being used. Memory  40  is often used as a shorter synonym for random access memory (RAM). This kind of memory is located on one or more microchips that are physically close to the microprocessor in the computer. 
       Portfolio Management Tool  12   
       [0044]    Reference is next made to  FIG. 3 , which shows a component diagram of a portfolio management tool  12 . As shown, the tool  12  includes a receipt module  30  for managing user requests  9  and for directing user requests  9  to one or more of the appropriate modules  32 ,  200 ,  300  and/or  560 . As the member  8  interacts with visual representation  28  via their input devices  38 , the receipt module  30  co-ordinates the responsibilities and tasks of the other modules  32 ,  200 ,  300  and/or  560  of the management tool  12 . The management tool  12  communicates with a data manager  34  which is operable to retrieve data from and send data to the tables  24  upon instruction from the receipt module  30  and/or the other components of the tool  12 . The data manager  34  is also in electronic communication with the derivative data provider  18  and the meat data provider  20 . The visual manager/module  36  is instructed by the components of the management tool  12  to recreate and redraw the visual representation  28  for viewing and further interaction by the user or member  8 . It is recognised that the visual manager/module  36  can present the representation  28  on the display of the tool  12  and/or send the representation  28  over the network  16  for presentation on the display of the member&#39;s networked device  18   
         [0045]    The tool  12  includes a Model Module  560  for creating particular model(s)  204  of the portfolio management tool  12 . As described below, the model module  560  is operable to create a model  204  for each meat cut (e.g. and for associated delivery time period—e.g. month) that is offered to the member  8  by the tool  12 . The model module  560  instructs the data manager  34  to store each model  204  in the tables  24  or directly into memory  40  of the electronic device  18  for use by the other components of the tool  12 . The tool  12  also includes a Predictor Module  200  which is operable to generate the future predicted prices of a cut of meat for a selected future time period upon selection by the user  8 . The predictor module  200  is in communication with and retrieves the appropriate model  204  from the model module  560  (or directly from tables  24  or memory  40 ). The predictor module  200  uses the model  204  to create a prediction table  220  and instructs the visual manager  36  to render the prediction table  220  to the visual interface  28  for viewing and interaction by the member  8  (either locally and/or remotely to the tool  12 ). The tool  12  also includes a Purchasing Module  300  for buying and selling live animal futures contracts on a futures market  310 . The purchasing module  300  retrieves a hedge ratio  906  from the model module  560  and determines the number of live animal futures to buy. Once the number of live futures is determined by the purchasing module  300 , components of the purchasing module buy the number of live futures and store the information regarding the trade in the tables  24  via the data manager  34 . The tool  12  also includes a subscription manager/module  32  for managing the member accounts of the members  8  and for instructing the visual manager  36  the information to display to the member  8  and the types of interaction that the member  8  is allowed to perform with the tool  12 . 
       Subscription Manager/Module  32   
       [0046]    Referring next to  FIG. 4 , it is shown that the tool  12  enables members  8   a,    8   b  to have different membership levels that are obtained from the provider of the portfolio management tool  12  through a subscription or membership. By different membership levels, it is meant that members  8   a,    8   b  are able to view different information and interact differently with the portfolio management tool  12 . As shown, member  8   a  interacts with the visual interface  28   a  on electronic device  18   a.  The member  8   a  is allowed to view a prediction table  220   a  for a selected cut of meat for the selected delivery period; however, the user  8   a  is unable to secure the forward price for the selected cut for a selected delivery period, because the member  8   a  does not have the required permission to secure forward prices. In addition, the member  8   a  is able to see the risk level associated with a predicted price of the selected cut of meat, but the member  8   a  is not able to see the premium that would be charged to the member  8   a  to eliminate this risk. Contrarily, the member  8   b  is able to view the predicted price of the selected cut, and is also able to secure the forward price for a desired quantity of the selected cut for a chosen delivery month. The member  8   b  is able to view the risk levels associated with the predicted future prices for the selected cut and is also able to see the premium that is charged to the member  8   b  in exchange for the provider of the tool  12  eliminating the risk (i.e. by the provider securing forward prices of the selected cut for the member  8   b ). As shown, the tool  12  is in communication with the futures market  310  and an associated trading system  308 . When the member  8   b  requests to secure forward prices, the tool  12  buys a certain number of live cattle futures contracts by accessing the trading system  308  to protect the provider of the tool  12  against market price movements in the particular cut of meat selected. It will be appreciated that the tool  12  is capable of having any number of membership levels for each member  8   a,    8   b  that has permission to access the tool  12 . For example, the tool  12  may allow one member  8   a  to secure prices for a selected cut of meat, and allow another member  8   b  (or the same member  8   a ) to secure prices for another commodity that is associated with a futures contract (e.g. pork bellies) that is traded on a public market  310 . 
       Logging on to the Portfolio Management Tool  12   
       [0047]    Reference is next made to  FIG. 5  which illustrates an exemplary log-in screen  360  for interacting with the electronic portfolio management tool  12 . The log-in screen  360  may be a web-page that is displayable on the device  18 . The member  8  accesses the log-in screen  360  in known manner by employing a web-browser and instructing the web-browser to display the log-in screen  360  (for example, by typing the web-address of the log-in screen  360  into the browser or by performing a web search for the provider of the tool  12 ). As shown, the log-in screen  360  includes a user ID field  362  for receiving the user ID from the member  8 , a password field  364  for receiving a password from the member  8 , and a submit button  366  for submitting the user ID and password to the portfolio management tool  12  and for gaining access to the portfolio management tool  12  if the member  8  has permission. To gain access to the portfolio management tool  12 , the member  8  must enter a preselected user ID into the user ID field  362  and a preselected password into the password field  364 . As will be appreciated, the user ID and the password are unique to the member  8 . Once the user ID and password are entered, the member  8  selects the submit button  366 . The interactions of the user  8  are processed by the receipt module  30 . The receipt module  30  captures the user ID and password entered and communicates the data to the subscription manager  32  which is operable to determine if the user ID and password are valid and to manage the information and features that are available to the member  8 . The subscription manager  32  uses the entered user ID and password to construct a query and requests a data set from the data manager  34 . The data manager  34  processes the query and returns a data set to the subscription manager  32  that corresponds to the criteria of the query. The subscription manager  32  analyzes the data set and instructs the visual manager  36  to generate a visual representation  28  for display as a web page. If the subscription manager  32  determines that the member  8  has permission to access the tool  12 , the subscription manager  32  instructs the visual manager  36  to display a welcome screen as a web page to the member  8  and to display certain controls on the visual representation  28  that correspond to the permission level of the member  8 . Contrarily, if the subscription manager  32  determines that the member  8  does not have permission to access the tool  12  (or determines that the password and/or the user ID are incorrect), the subscription manager  32  instructs the visual manager  36  to display a web-page to the user  8  which informs the user  8  that the password and/or user ID are not recognized by the tool  12 . 
       Model Module  560   
       [0048]    Reference is next made to  FIG. 6 , which illustrates the model module  560  of the portfolio management tool  12 . The model module  560  is operable to create one or more model(s)  204  for each cut of meat and associated delivery future time period for which the provider of the tool  12  will secure forward prices (e.g. predicted future prices) to a member  8 . Each model  204  is operable to predict the forward/future price of a particular cut of meat based on the historical relationship between the market prices of the particular cut of meat and the prices of live animal futures contracts for the animal from which the cut of meat is derived. The model module  560  creates each model  204  by employing the steps outlined in  FIG. 7 , and instructs the data manager  34  to store each model  204  in the tables  24  or into memory  40 . When a user  8  requests a prediction for a particular cut of meat, the receipt module  30  instructs the model module  560  to create a model  204  that represents the selected cut of meat. In one embodiment, the model module  560  retrieves the appropriate model  204  from the data manager  34 . If the particular model  204  does not exist (i.e. it has not been created yet) the model module  560  creates a new model  204  by performing the operations illustrated in  FIG. 7 . If the particular model  204  exists, the model module  560  may update the model  204  using data from the data manager  34  that was not previously applied to the model  204 . Alternatively, the model module  560  may determine that the model  204  is up-to-date and will instruct the receipt module  30  that the model  204  is ready for use by the other components of the tool  12 . 
         [0049]    Reference is next made to  FIG. 7 , which illustrates the series of steps that the model module  560  performs to create each prediction model  204  of the tool  12 . As mentioned above, each cut of meat can have a model  204  that is created for each delivery future time period. At step  562 , the model module  560  is provided for representing the selected cut of meat and the selected delivery period from the receipt module  30 . The model module  560  retrieves the historic prices of the selected cut from the meat data provider  20  via the data manager  34  at step  564 . At step  566 , the model module  560  retrieves the historic prices of live animal futures contracts from the derivative data provider  18 . It will be appreciated that steps  564  and  566  may be performed in parallel or sequentially. Next, at step  568 , the model module  560  performs a curve fitting function (such as a regression analysis) on the historic prices and creates a curve of best fit which is stored in memory or in the tables  24  for further use by the tool  12 . At step  570 , the model module  560  retrieves error terms  206  from other models  204  for the selected cut, but for different delivery periods. For example, the model module  560  may retrieve error terms  206  for models  204  that correspond to the same cut, but for one month and/or two months before the delivery period desired by the user  8 . At step  572 , the module  560  modifies the curve of best fit to give the curve greater predictive abilities by applying the error terms  206  as properties of the curve. At step  574 , the model module  560  calculates the hedge ratio  906  for the selected cut which is used by the Purchasing Module  300 , as is described below. Finally, at step  576 , the model module  560  stores the curve as a model object  204  in memory  40  and/or in the tables  24  for further use by the tool  12 . 
         [0050]    In one embodiment, the prediction model  204  is an ordinary least squares regression model to predict future prices, though it will be appreciated that the portfolio management tool  12  may implement any model  204  that predicts the future prices of a selected cut of meat based on the available input data (i.e. the historic reported market prices of a product and the historic market prices of a security that relates to the product). An ordinary least squares regression model  204  is a method of finding the best curve fit for a data set, where the best fit is that instance of the model  204  for which the sum of the squared residuals is minimized. The residuals are the differences between an observed value and the value given by the model  204 . In one embodiment, the model  204  is also self-correcting. The model module  560  compares the predicted price of a selected cut for each delivery period to the actual market price of the selected cut for the same period. The market price of the selected meat cut is provided to the module  560  from the data manager  34  which accesses the meat data provider  20 . The module  560  calculates the difference between the predicted price and the market price of the selected cut (i.e. the error terms  206  in the predicted price) and modifies the internal structure of the model  204  by fitting a new curve for the model  204 . In an embodiment, the module  560  fits a new curve for the model  204  on a real-time basis, as the market price of a selected cut of meat is available from the meat data provider  20 . In yet another embodiment, the module  560  fits a new curve for model  204  at a pre-determined frequency (such as a month-by-month basis) by minimizing the squares of the error terms  206  that have become available since the last curve fit performed by the module  560  for the model  204 . 
         [0051]    In one embodiment, the model  204  takes the residuals (i.e. errors) from other models  204   a,b  as inputs. Such a model  204  is a hybrid auto-regressive model because lagged residual terms from models  204 , a,b  corresponding to previous months for the same cut of meat are included as a characteristic of the model  204 . An autoregressive trend is one in which current results are affected by past results. Although the residuals are generally white noise (i.e. containing no predictive information), lagged residual terms from the models  204   a,b  for previous months are reintroduced into the model  204  to form a hybrid auto-regressive multivariate model  204 . The inclusion of residuals from models  204   a,b  further develops the predictive ability of the model  204  by allowing the residuals from previous months to direct predictions for the price of the selected cut in the future. In this embodiment, inputting residuals into model  204  from models  204   a,b  that correspond to delivery months immediately before the delivery month for the same cut (as selected by the member  8 ) gives the model  204  greater predictive accuracy. The ordinary least squares regression analysis of the model  204  can be represented by the following equation: 
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         [0052]    In the above equations, Y can represents the observed historical prices of the selected cut of meat for the selected delivery period, X represents a matrix containing the intercept, the value of live futures contracts and at least one residual error term, and {circumflex over (β)} represents the vector of coefficient estimates. The vector of coefficient estimates contains the set of coefficients which detail how the predicted cash price is to be calculated given the observed data. The only variable in the above model  204  that is not controlled by the provider of the tool  12  is the value of the live animal futures contract. 
         [0053]    Reference is next made to  FIG. 8 , which illustrates in black-box form the inputs that are received by a model  204  and the outputs that are produced by the model  204  when the model  204  is employed by the components of the tool  12 . As mentioned above, the model  204  implements a linear regression analysis to predict the future price of a specific cut of meat for a selected future period. In its internal structure, the model  204  includes a curve  904  and a hedge relationship (e.g. ratio)  906 . The curve  904  and the hedge ratio  906  are characteristics of the model  204  that are created by the model module  560  as described above. The hedge ratio  906  is determined by the model module  560  by analyzing the historic live cattle futures prices provided by the derivative data provider  18  and historic prices for the selected cut of meat provided by the meat data provider  20 . The hedge ratio  906  is then used by the purchasing module  300  to determine the number of live futures to buy to minimize the risk of the provider of the tool  12  when the provider secures a future price for a selected cut of meat to the member  8 . In an embodiment, the hedge ratio  906  is a ratio of the relative movements in the prices of the live cattle futures and the reported prices of the selected cut of meat. For example, the model module  560  may determine that for every three dollar increase in the price of life cattle futures, the price of a selected cut of meat historically increases by one dollar. Likewise, the model module  560  may determine that for every three dollar decrease in the price of live cattle futures, the selected cut of meat historically decreases by one dollar in price. In this embodiment, the provider of the tool  12  may wish to buy three live cattle futures for each unit of the selected cut when the provider of the tool  12  secures a forward price for the member  8 . 
         [0054]    As shown in  FIG. 8 , the model  204  takes real-time live futures prices  908  as an input, which are provided by the data manager  34  which retrieves the live futures prices  908  from the derivative data provider  18 . The live futures prices  908  are used by the curve  904  to predict the future predicted prices  910  of the selected cut for the selected delivery period. The model  204  may also input at least one residual  206  as an input term as described above. The residuals  206  may be error terms for the model  204  for previously generated predictions  910  in the delivery month, or may be residuals  206  from other models  204   a,b  for the selected cut, but for previous delivery months. In an embodiment, the model  204  takes two residuals  206   a,b:  one residual  206   a  from a model  204   a  that corresponds to the selected cut but for a delivery period one month before the selected delivery period, and a second residual  206   b  from a model  204   b  that corresponds to the selected cut but for a delivery period two months before the selected delivery period. 
         [0055]    As shown, the model  204  also generates a value for the basis risk  912 . The term “basis risk” refers to the risk faced by the provider of the tool  12  and the member  8  that the actual market outcomes (i.e. the market price of a specific cut of meat) deviates from the future price  910  predicted by the tool  12 . The provider of the tool  12  assumes the basis risk  912  in return for a customer paying a basis risk premium  916 . The basis risk value  912  indicates the range of reasonably expected outcomes for analysis by the member  8  and the provider of the tool  12 . The basis risk  912  is a monetary value based on a percentage of the predicted price  910  of a cut of meat and is based on the basis risk confidence level  914 . The range is determined by the basis risk confidence value  914  which is inputted to the model  204  by the provider of the tool  12 . As an example, the provider of the tool  12  may set the basis risk confidence level  914  to 97.5%. This means that the predicted prices  910  of the selected cut are expected to statistically fall within the range of the basis risk values  912  at a rate of 97.5% of the time. The provider of the tool  12  may set the basis risk confidence level  914  to any level desired. When a member  8  subscribes to a “premium” membership level, the member  8  is allowed to view the basis risk values  912  when they submit a prediction request as well as the basis risk premium  916 . Members  8  that do not subscribe to the “premium” membership level are able to view the basis risk values  912  but not the associated premium  916  as these customers are not given permission to secure predicted prices in the future. 
         [0056]    The model  204  also generates a basis risk premium  916 . The basis risk premium  916  is the amount of money that the member  8  can pay to the provider for the provider of the tool  12  assuming the basis risk  912  when the provider secures a predicted future price  910  for the selected cut to the customer  8 . The amount of the basis risk  912  (and hence the amount of the basis risk premium  916 ) varies by cut of meat and by month. The basis risk premium  916  is a monetary value that corresponds to a percentage of the predicted price  910  and is dependent on the basis risk premium confidence level  918  that is chosen by the provider of the tool  12  that is an input to the model  204 . For example, the provider of the tool  12  may set the basis risk premium confidence level  918  to 80%. It is understood that the predicted future price  910  is on a 50% confidence level since there is a 50% chance that the actual price will be lower than the predicted price  910 , and a 50% chance that the actual price will be higher than the predicted future price  910 . The difference between the basis risk premium confidence level  918  and the confidence level of the predicted price  910  determines the amount of the basis risk premium  916 . In the above example (i.e. where the basis risk premium confidence level  918  is set to 80%) the member  8  can pay a basis risk premium  916  equal to a 30% risk off the forecasted price  910  and the provider of the tool  12  will assume the basis risk  912  on behalf of the customer  8 . 
         [0057]    Although the basis risk confidence level  914  and the basis risk premium confidence level  918  are inputs to the model  204 , is to be understood that these values are controllable by the provider of the tool  12 . For example, the provider of the tool  12  may manage its internal risk in guaranteeing the forward prices of cuts of meat to customers  8  by modifying the confidence levels  914 ,  918 . The only uncontrollable input to the model  204  is the price of live cattle futures  908  which gives the provider of the tool  12  significant flexibility in managing its risk over time by modifying the confidence levels  914 ,  918 . 
       Predictor Module  200   
       [0058]    Reference is next made to  FIG. 9 , which illustrates the Predictor Module  200  of the tool  12 . The Predictor Module  200  is operable to predict future prices  910  for a selected cut of meat for a selected delivery period as chosen by the member  8 . For example, the member  8  may wish to view the predicted prices  910  of a rib-eye steak for every month in the next 12-month period. Alternatively, the member  8  may wish to view the predicted price  910  of a pound of ground beef for a specific month, for example, December 2010. As shown, the Predictor Module  200  receives a request by the receipt module  30  and retrieves data from the meat data provider  20  and the derivate data provider  18  via the data manager  34 . The module  200  interfaces with a prediction model  204  which is a set of instructions for processing inputs to the model  204  and for generating a set of future prices  910  for the selected cut of meat. The module  200  applies the data received from the provider  18  an input to the model  204  and generates the basis risk  912 , predicted future prices  910  and the basis risk premium  916  as described above. 
         [0059]    It will be understood that there is a risk that the prediction model  204  will not perfectly predict the future price  910  of a selected cut of meat. When a member  8  chooses to secure a future price  910 , the provider of the tool  12  assumes the risk (i.e. the basis risk  912 ) that the predictions  910  will not be correct. The basis risk  912  will be different for each selected cut of meat and for each delivery period. In exchange for assuming the basis risk  912 , the provider of the tool  12  charges a basis risk premium  916 , which is similar to an insurance premium that is to be paid by the member  8 . Generally, the basis risk premium  916  is a fraction of the predicted price of a selected cut of meat, which gives the member  8  an opportunity to receive price stability for an additional price premium. 
         [0060]    When a user  8  requests a prediction  910  for a selected cut of meat for a selected delivery period, the receipt module  30  communicates the prediction request to the module  200  which employs the prediction model  204 . The member  8  selects the cut of meat and the delivery period by interacting with controls on the user interface  28  and by submitting the request to the portfolio management tool  12 . The receipt module  30  communicates the selected cut and delivery period to the module  200  which accesses the appropriate prediction model  204  from the data manager  34 . The model  204  may implement an ordinary least squares regression model, as described above, or the model  204  may implement any number of different numerical techniques. In one embodiment, the model  204  may be interchangeable and customizable based on the wishes of the member  8  and/or the provider of the tool  12 . The model  204  runs a set of instructions to generate prediction data and provides the prediction data to the module  200 . The module  200  communicates the prediction data to the receipt module  30  which instructs the visual manager  36  to render the prediction data in a prediction table  220  for further viewing and interaction by the member  8 . The module  200  may also communicate directly with the visual manager  36  to render the prediction table  220  to the user interface. 
         [0061]    The predictor module  200  may also have the capability to dynamically generate predictions at a predetermined frequency. In an embodiment, the predetermined frequency is customizable by the member  8 . For example, the predictor module  200  may generate prediction data for the selected cut and delivery period every minute, every second or on a real-time basis. The module  200  retrieves the applicable input data from the data manager  34  at the predetermined frequency and communicates the input data to the model  204 . The model  204  processes the input data based on a set of instructions and creates prediction data which is communicated to the module  200  for rendering as the prediction table  220  on the visual representation  28 . 
         [0062]    Reference is next made to  FIG. 10 , which illustrates the series of steps the predictor module  200  carries out when a prediction request is made by a member  8 . At step  700 , the predictor module  200  receives the prediction request via the receipt module  30 . At step  702 , the predictor module  200  retrieves the appropriate model  204  for the selected cut and delivery month via the data manager  34  or directly from memory  40 . At step  704 , the module  200  determines whether a model  204  was successfully retrieved at the previous step  702 . If a model  204  is successfully retrieved, the module  200  generates prediction data  910  at step  708  for presentation as the visual representation  28  by the visual manager  36 . If the model  204  is not successfully retrieved at step  702 , the module  200  instructs the model module  560  to generate a new model  204  for the selected cut at step  706  and the module  200  goes back to step  702 . At step  710 , the prediction module  200  obtains the permission or membership level of the member  8  from memory or the tables  24 . Finally, at step  712 , the prediction module  200  instructs the visual manager  36  to create a prediction table  220  that is customized to the permission level of the member  8  and to render the prediction table  220  on the visual interface  28  for viewing and possible further interaction by the member  8 . 
       Prediction Tables  220   
       [0063]    Reference is next made to  FIG. 11   a,  which illustrates a prediction table  220   a  which is displayable on the user interface  28 . The prediction table  220   a  is operable to display the prediction data created by the prediction model  204  in a convenient format for analysis by the member  8 . As shown, the prediction table  220   a  includes prediction rows  222  and columns  224 . Each row  222  represents the prediction price  910  for the selected cut of meat for each month  226  in the selected delivery period and associated information. In the exemplary prediction table  220   a,  the member  8  has requested to view prediction data for each month from April 2009 until January 2010 by interacting with controls (not shown) on the visual interface  28 . The predictor module  200  instructs the model  204  to create prediction data and communicates the prediction data to the visual manager  36  for rendering the results in the prediction table  220   a.  The table  220   a  is updated by the visual manager  36  at the predetermined frequency as chosen by the member  8  or in real-time. As is apparent, the member  8  can view the following data: the delivery month  226 , the price of live cattle futures contracts  908  for each month  226  in the delivery period, the predicted price  910  of the selected cut of meat for each month  226  in the delivery period, and the daily change  229  in the predicted price  910  for the selected cut of meat. The member  8  is restricted in which data is viewable on the visual representation  28  because the member  8  does not have full membership privileges as described above with reference to  FIG. 4 . In an embodiment, the member  8  is able to view additional information such as the basis risk  912  without having a premium membership as shown in  FIG. 12   a.    
         [0064]    Reference is next made to  FIG. 11   b,  which illustrates an exemplary prediction table  220   b  presented to the member  8  on the visual representation  28 . As shown, the prediction table  220   b  contains all of the information of the prediction table  220   a  in  FIG. 1  la, as well as other information that provides a valuable analytical tool to the member  8 . The member  8  makes a prediction request to the tool  12  by interacting with controls (not shown) on the visual representation  28 . Prediction data is generated by the predictor module  200  by employing a prediction model  204  and is presented on the visual representation  28  as prediction table  220   b  by the visual manager  36 . The prediction table  220   b  also includes information related to the basis risk  912  of the predicted prices  910  and the basis risk premium  916  (indicated by the text “BRP”) that the member  8  can choose to pay in exchange for securing the prices  910  for any delivery month  226 . In exchange for paying the basis risk premium  916 , the provider of the tool  12  assumes the financial risk for the basis risk  912  associated with the predictions  910  (i.e. the risk that the predicted future prices  910  are not correct). The prediction table  220   b  also includes the Up Market Risk  248  and the Down Market Risk  250 . As is apparent, the Up Market Risk  248  is the risk that the market price of the selected cut will be higher than the predicted prices  910  by the amount of the basis risk  912  at prevailing (i.e. current) live cattle futures values. The Down Market Risk  250  is the risk that the market price of the selected cut will be lower than the predicted prices  910  by the amount of the basis risk  912  at prevailing (i.e. current) live cattle futures values. The range between the Up Market Risk  248  and the Down Market Risk  250  (e.g. 3.8991−3.2662=$0.663 in row  241 ) is the risk that the provider of the tool  12  is willing to assume in exchange for the member  8  paying the associated basis risk premium  916 . As mentioned above, the provider of the tool  12  may adjust the basis risk confidence level  914  to any desired level to increase or decrease the risk represented by the range between the Up Market Risk  248  and the Down Market Risk  250 . It is recognised that the basis risk can be determined by a defined comparative relationship, such as but not limited to a difference. 
         [0065]    Reference is made to  FIG. 12   a,  which shows a visual representation  28  that is presented to a member  8  when the member  8  selects a cut of meat for which to generate predicted future prices  910 . As shown, the visual representation  28  includes a prediction table  220  for displaying the predicted prices  910  to the member  8 , a selection menu  502  for allowing the member  8  to select a cut of meat  506  for which to generate predicted future prices  910 , and a chart  504  for allowing the member  8  to analyse the risk inherent in the predicted future prices  910 . The member  8  has selected the cut  506  from the selection menu  502 , for example, by clicking on the cut  506  with a mouse cursor or using another input device  38 . Once the member  8  selects the cut  506 , the receipt module  30  instructs the predictor module  200  to generate the prediction table  220  and the chart  504 . As described above, the predictor module  200  retrieves the input data via the data manager  34  and creates a prediction data set. The predictor module  200  then instructs the visual manager  36  to create and display the visual representation  28  to the member  8 . As shown, the chart  504  includes a prediction line  508  which illustrates the predicted prices  910  of the selected cut  506  over the time axis  510 . Also, the chart  504  displays a risk band  512  to the user  8  which represents the range of reasonably expected outcomes of the predicted prices  910 . The risk band is a visual representation of the basis risk  912  in the predictions  910 . As described above, the basis risk  912  is defined by threshold values (i.e. the basis risk confidence level  914 ) that are customizable by the provider of the tool  12  and/or the member  8 . As an example, the risk band  512  may represent a 97.5% confidence level  914 , meaning that based on the internal structure of the model  204  (which has been created to represent the relationship between the historical prices of the selected cut  506  and the historical prices of live cattle futures  908 ), the predicted future prices  910  will fall within the risk band  512  97.5% of the time. The basis risk  912  can be modified to any confidence level  914  depending on the wishes of the provider and/or the member  8 . 
         [0066]    Reference is next made to  FIG. 12   b,  which illustrates a visual representation  28  which is presented to the member  8  when the member  8  selects a cut of meat  506  for which to generate predictions of the futures prices  910  of the selected cut  506 . As shown, the chart  504  includes a prediction line  508  for visualizing the predicted price  910  of the selected cut  506  over the time axis  510 . The chart  504  includes a risk band  512  for visualizing the range of reasonably expected future prices and also includes an insurance premium line  514  that enables the member  8  to view how much the basis risk premium  916  will cost the member  8  if the member  8  wishes to secure the future price  910  of the selected cut  506 . The basis risk premium  916  is determined by the model  204  to be the difference (or other appropriate comparative relationship) between a basis risk premium confidence level  918  and the 50% confidence level of the predicted future prices  910 . As described above, the basis risk premium confidence level  918  is an input to the model  204  that may be controlled by the provider of the tool  12 . As an example, the basis risk premium confidence level  918  can be set to 80% by the provider of the tool  12 . This means that the provider has determined that there is an 80% probability that the predicted future price  910  of the selected cut  506  will fall within the range represented by the basis risk premium confidence level  918 . In exchange for paying the basis risk premium  916 , the provider of the tool  12  assumes the risk that the predicted future prices  910  will not fall within the basis risk premium confidence level  918 . 
         [0067]    It is to be understood that in other embodiments of the invention, the tool  12  may show any or all of the information shown in the prediction tables  220  of  FIGS. 11   a,    11   b  and  FIGS. 12   a,    12   b  to a member  8 . For example, the provider of the tool  12  may wish to allow all members  8  to see the information in  FIG. 12   b.  However, for example in other embodiments, the provider of the tool  12  may wish to display only the predicted future prices  910  to a member  8 . 
       Purchasing Module  300   
       [0068]    Reference is next made to  FIG. 13 , which illustrates the Purchasing Module  300  of the Portfolio Management Tool  12 . As shown, the purchasing module  300  includes a purchasing manager  302  for managing purchase requests for a quantity of the selected cut of meat  506  and for managing the buying and selling of live animal futures contracts throughout the duration of the purchasing contracts. The purchasing module  300  allows a member  8  to secure a price  910  for a quantity of a desired cut  506  of meat. The member  8  is provided the predicted price  910  by the provider of the portfolio management tool  12  for the duration of the contract defined by the delivery period. A member  8  can initiate a purchase request by interacting with controls (not shown) on the visual interface  28 . The controls are operable to receive at least three types of information from the user  8 , namely, a cut of meat desired  506 , the delivery period and the quantity of the cut of meat. When the member  8  initiates the purchase request (for example, by clicking on a submit purchase request button (or by communicating instructions to the provider of the tool  12 ), the receipt module  30  communicates the request to the purchasing manager  302  for further processing. It will be appreciated that the member  8  may send written instructions in an email or in another format, or may communicate instructions verbally with the provider of the tool  12 . Additionally, the member  8  may have predetermined instructions for the provider of the tool  12  to take certain actions on the happening of certain events, such as when the predicted future prices  910  of a selected cut  506  are above or below a predetermined level (e.g. recorded in the memory of the tool  12 ). The purchasing manager  302  is in communication with the predictor module  200  and requests specific information from the module  200 . Specifically, the purchasing manager  302  may request information relating to the number of futures contracts to buy to manage the risk associated with providing the predicted price  910  of the selected cut  506  to the member  8 . The model  204  provides the information as a hedge ratio/relationship  906  on request to the receipt module  30  which relays the information back to the purchasing manager  302 . In another embodiment, the information is stored in the tables  24  and the purchasing manager  302  is able to retrieve the information via the data manager  34 . The purchasing manager  302  is operable to execute trades (i.e. to buy and sell) futures contracts on a trading system  308  of a futures market  310 . The trading system  308  may be provided by the operator of the futures market  310  or may be a system provided by a third party as is known. Upon receipt of the number of futures contracts to buy (information provided by the model  204  as described above) the purchasing manager  302  buys the required number of futures contracts. 
         [0069]    As described above, the prediction model  204  produces a hedge ratio  906 , which represents the relationship between the movement in the market price of a futures contract and the associated movement in the price of the selected cut of meat  506 . The hedge ratio  906  determines the amount of futures that the provider purchases to hedge against the risk associated with providing the predicted future predicted price  910  of the selected cut  506  to the member  8 . In one embodiment, the hedge ratio  906  is expressed in pounds (or another unit of weight). As an example, a provider of the tool  12  is willing to provide a future price to a customer on 100,000 lbs of rib-eye steaks and the model module  560  determines that the hedge ratio  906  of the model  204  (i.e. the model  204  for rib-eye steaks for the selected month) to be 2.8. In this example, the purchasing module  300  will purchase a quantity of live cattle futures contracts that corresponds to a weight of live cattle equal to 2.8 times the weight of rib-eyes (i.e. the selected cut of meat). As is known, there are 40,000 lbs of live cattle for every live cattle futures contract. In this example, the purchasing module  300  buys 7 live cattle futures contracts which correspond to 280,000 lbs of live cattle which provides a hedge ratio of 2.8 (i.e. 280,000 lbs of live cattle for 100,000 lbs of rib-eye steaks). 
         [0070]    In one embodiment, the purchasing module  300  includes a settlement manager/module  304  for selling or “unwinding” the provider&#39;s portfolio of futures contracts. The settlement manager  304  is operable to sell the futures contracts in an optimal fashion over the course of the delivery month. In an embodiment, the provider of the tool  12  buys live cattle futures that have the closest settlement month that is after the delivery month of the cut of meat. In practice, live cattle futures may only be available for even months (i.e. February, April, June, etc). If a customer  8  wants December rib-eyes, the provider buys futures for February of the next year. If the customer wants to buy March rib-eyes, however, the provider buys April live cattle futures. In an embodiment, the settlement manager  304  sells the quantity of futures contracts in a distributed fashion in periodic (e.g. weekly) increments over the course delivery period (e.g. month) of the cut of meat (for example not the settlement month of the live cattle futures). For example, if the provider of the tool  12  is holding  16  futures contracts to hedge against price movements for a cut of meat that has a particular delivery month, the provider may choose to sell  4  futures each week during each week of the delivery month to minimize the risk of selling the futures at a low point during the month. In another embodiment, the provider buys live cattle futures with a settlement month the same as the delivery month of the cut of meat, if available, and for the nearest settlement month after the delivery month if live cattle futures are not offered for the selected delivery month. 
         [0071]    The purchasing module  300  may also include a contract manager/module  306  for managing contracts between the member  8  and the provider of the portfolio management tool  12 . As will be appreciated, each time a member  8  chooses to secure the future prices  910  of a selected cut of meat  506 , the provider of the tool  12  agrees to provide the predicted future price  910  for the selected cut of meat and the selected delivery month which creates a contract between the member  8  and the provider of the tool  12 . The contract manager  306  is operable to manage the recordation and execution of the terms of the contract that is entered into between the member  8  and the provider of the tool  12 . It will also be appreciated that each individual member  8  may enter into a number of contacts with the provider of the tool  12 . The contract manager  306 , for example, is operable to store the terms of each contract and party-specific information in the tables  24 . The contract manager  306  is adapted to instruct the purchasing manager  302  to sell or unwind futures contracts in each delivery month. 
         [0072]    Reference is next made to  FIG. 14 , which illustrates the series of steps performed by the purchasing manager  302  when a member  8  chooses to lock in the predicted future prices  910  for a selected cut of meat  506  for a selected delivery period. At step  660 , the purchasing manager  302  retrieves the hedge ratio  906  from the prediction model  204  via the predictor module  200 . The hedge ratio  906  is used by the purchasing manager at step  662  to determine the quantity of live cattle futures to purchase to hedge against future price movements in the selected cut. At step  664 , the purchasing manager  302  accesses a trading system  308  on a futures market  310  and buys the appropriate number of futures at step  668 . Finally, at step  670 , the purchasing manager  302  instructs the data manager  34  to record the number of futures purchased, the price paid for the futures, the type and quantity of the cut of meat and the selected delivery month in the tables  24  or directly in memory  40 . 
       Alternative Embodiment of the Tool  12   
       [0073]    Reference is next made to  FIG. 15 , which shows a component diagram of an alternative embodiment of the portfolio management tool  12 . In addition to the functionality described above, the portfolio management tool  12  is operable to lower the overall risk of the aggregate portfolio  960  of the provider of the tool  12  as the provider guarantees forward prices of various cuts of meat to customers  8 . As shown, the management tool  12  includes a receipt module  30  for managing user requests  9  and for directing user requests to one or more of the appropriate modules  32 ,  200 ,  300 ,  400 ,  540  and/or  560 . As the member  8  interacts with the visual representation  28  via input devices  38 , the receipt module  30  co-ordinates the responsibilities and tasks of the other modules  32 ,  200 ,  300 ,  400 ,  540  and/or  560  of the management tool  12 . The management tool  12  communicates with a data manager  34  which is operable to retrieve data from and send data to the tables  24  upon instruction from the receipt module  30  and/or the other components of the tool  12 . The data manager  34  is also in electronic communication with the derivative data provider  18  and the meat data provider  20 . The visual manager  36  is instructed by the components of the management tool  12  to recreate and redraw the visual representation  28  for viewing and further interaction by the user or member  8 . The tool  12  includes a model module  560  for creating the model(s)  204  of the portfolio management tool  12 . As described below, the model module  560  is operable to create an models  204  for representing each cut of meat and each corresponding delivery time period that is offered to the member  8  by the tool  12 . The model module  560  instructs the data manager  34  to store each model  204  in the tables  24  or directly into memory  40  of the electronic device  18  for use by the other components of the tool  12 . The tool  12  also includes a predictor module  200  which is operable to generate the future predicted prices of a cut of meat for a selected period upon selection by the user  8 . The predictor module  200  is in communication with and retrieves the appropriate model from the model module  560 . The predictor module  200  uses the model  204  to create a prediction table  220  and instructs the visual manager  36  to render the prediction table  220  to the visual interface  28  for viewing and interaction by the member  8 . The tool  12  also includes a Purchasing Module  300  for buying and selling live cattle futures contracts on a futures market  310 . The purchasing module  300  retrieves a hedge ratio  906  from the model module  560  and determines the number of live cattle futures to buy. Once the number of live cattle futures is determined by the purchasing module  300 , components of the purchasing module  300  buy the appropriate number of live cattle futures determined by the hedge ratio  906  and store the information regarding the trade in the tables  24  via the data manager  34 . The tool also includes a subscription manager  32  for managing the member accounts of the members  8  and for instructing the visual manager  36  the type of information to display to the member  8  and the types of interaction that the member  8  has permission to perform. In addition, the tool  12  includes an optimization module  400 . The optimization module  400  is for calculating correlation factors for each pair of assets in the aggregate portfolio  960  and for creating optimization decisions  404 . The decisions  404  are used by the adjustment module  540  to alter the internal structure of the models  204  to make some cuts of meat more attractive (i.e. less expensive) relative to other cuts of meat (which may be made more expensive) for customers  8  and potential customers of the tool  12 . The adjustment module  540  may communicate adjustment parameters to the model module  560  which modifies the appropriate model  204  upon instruction by the adjustment module  540 . Alternatively, the adjustment module  540  directly modifies models  204  to put some cuts of meat on sale and to increase the prices of other cuts of meat. By continually generating optimization decisions  404  and modifying the internal structure of models  204 , the risk associated with guaranteeing forward prices of cuts of meat in an aggregate portfolio  960  is continually being monitored and optimized for the provider of the tool  12 . 
       Aggregate Portfolio  960   
       [0074]    Reference is next made to  FIG. 16 , which illustrates an aggregate portfolio  960  of the provider of the tool  12 . The aggregate portfolio  960  includes customer portfolios  962   a - n  which are held by the members  8   a - n.  The aggregate portfolio  960  therefore includes all cuts of meat for each delivery period for each customer  8  of the tool  12 . The provider of the tool  12  is exposed to risk by securing the forward prices  910  of the cuts of meat in the portfolios  962   a - n  for each of the customers  8   a - n.  It will be appreciated that the provider of the tool  12  is desirous of lowering its overall risk exposure of the aggregate portfolio  960  while still profiting from offering the services of the tool  12 . 
       Optimization Module  400   
       [0075]    Reference is next made to  FIG. 17 , which illustrates a Portfolio Optimization Module  400  of the management tool  12 . The portfolio optimization module  400  includes an Optimization manager  402  which is operable to generate optimization decisions  404  based on the aggregate portfolio content  960  resident in the tables  24 . The optimization manager  402  performs statistical analysis on the relationship between historic market prices of pairs of cuts of meat in the portfolio. The optimization decisions  404  are designed to reduce the risk of the provider of the portfolio management tool  12  in securing the predicted future prices  910  of selected cuts of meat  506  for selected delivery periods. For example, the manager  402  may calculate that based on historical data, one cut of meat is negatively correlated with another cut of meat in the same month, for example meaning that one cut of meat is found to historically increase in price while another cut of meat historically decreases, such that the variation in the historical prices represents price variation in the historical prices. This negative correlation also means that the price variation of one meat cuts is opposite to the price variation of the other meat cut (e.g. one meat cut price increases while the other meat cut price decreases). 
         [0076]    The manager  402  then generates decisions  404  that are implemented by modules of the tool  12  to increase the quantity of one or both cuts of meat in the portfolio  960 , thus lowering the overall risk of the portfolio  960  as is known. Likewise, the manager  402  may generate decisions  404  to decrease the quantity of positively correlated cuts in a portfolio  960 . The optimization manager  402  may periodically generate optimization decisions  404  at a predetermined frequency or the optimization manager  402  may dynamically generate decisions  404  on a real-time basis as the content of the aggregate portfolio  960  is modified (i.e. as members  8  choose to secure the future prices  910  of selected cuts  506  in exchange for paying the basis risk premium  916  and/or as the delivery periods of selected cuts  506  expire). In an embodiment, the manager  402  generates decisions  404  to alter the models  204 , for example, to lower the basis risk premium charged on a cut of meat, effectively putting the cut of meat on sale. In addition, the decisions  404  may be used by employees of the provider of the tool  12  to target certain customers that may be interested in certain cuts of meat. It is also recognised that that one of the meat cuts may be already present in the portfolio  960  while another of the meat cuts may not yet be included in the portfolio  960 . In this case, the portfolio  960  content may be optimized/adjusted by looking for and attempting to include those cuts of meat that have opposite price variations to those meat cuts already present in the portfolio  960 , for example. 
         [0077]    The optimization module  400  provides several advantages by generating optimization decisions  404 , such as:
       It reduces short-term exposure of the provider of the tool  12  to basis risk  912 . The more cuts in a portfolio  960 , the greater the likelihood that a high basis risk  912  on one cut will be offset by a low basis risk  912  on another cut,   It creates an opportunity for arbitrage by aggregating different customers with different individual exposures into a lower risk portfolio  960 , while still collecting basis risk premiums  916  priced to individual cuts. The weighted average sum of the basis risk premiums  916  charged on individual cuts is greater than the basis risk premium  916  that would be required for the overall portfolio  960  as demonstrated by table  420  and further described below.       
 
         [0080]    It is known that diversification in an aggregate portfolio  960  which includes different types of assets can be quantified by an intra-portfolio correlation. This is a statistical measurement between negative one and positive one that measures the degree to which the various assets in a portfolio  960  can be expected to perform in a similar fashion or not. A measure of −1 means that the assets within the portfolio  960  perform perfectly oppositely: whenever one asset goes up, the other goes down. A measure of 0 means that the assets fluctuate independently, i.e. that the performance of one asset cannot be used to predict the performance of the others. A measure of 1, on the other hand, means that whenever one asset goes up, so do the others in the portfolio. To eliminate diversifiable risk completely, one needs an intra-portfolio correlation of −1. Prices of cuts of meat may move in the same direction depending on the price of live cattle; however, if a prediction model  204  of a first cut generates a positive error or basis (i.e. the model predicts higher prices  910  than occur on the market), it has been found that the prediction model  204  of a second cut that is negatively correlated with the first cut will generate a negative basis. In this way, a provider of the tool  12  is able to offset any losses from the first cut by increasing the quantity of the second cut in the portfolio  960 . 
         [0081]    The price of live animal (e.g. cattle) can be a significant factor that influences the market prices of cuts of meat. The expected price of live cattle for a period in the future is reflected in the price of live cattle futures. When live cattle goes up in price by a significant percentage, most if not all cuts of meat will follow the trend in the price of live cattle. In times of relative price stability in live cattle or when there is no discernable trend in the price of live cattle, however, the demand for a particular cut of meat may be a more dominant factor that influences the price of a particular cut of meat than the price of live cattle. Market demand itself may be influenced by several factors such as the season of the year or the economic prosperity of a given market. When prices of live cattle are relatively stable a pair of cuts of meat may be negatively correlated with each other, such that one of the cuts of meat in the pair is not following the movement in the price of live cattle futures. The optimization manager  402  can calculate an intra-portfolio correlation Q for each pair of cuts of meat in the portfolio  960  that share the same delivery period. The provider of the tool  12  sets pre-determined thresholds of positive and negative correlations such that decisions  404  are created by the optimization manager when negative correlations are at or below a pre-determined threshold and the positive correlations are at or above a pre-determined threshold. For example, in times of relative stability in the price of live cattle, the optimization manager  402  may determine that many pairs of cuts of meat are negatively or positively correlated with each other (and below or above the pre-determined thresholds respectively) giving the provider of the tool many options to optimize the portfolio  960  based on the decisions  404  created by the manager  402 . As mentioned above, the models  204  that correspond to negatively correlated cuts of meat may be altered to effectively put the cuts of meat on sale and the models  204  that correspond to positively correlated cuts of meat may be altered to effectively make the cuts of meat more expensive. The adjustment module  540  may adjust the price of the basis risk premiums  916  Lip or down to make a cut more expensive or less expensive respectively. 
         [0082]    As mentioned above, when the price of live animal (e.g. cattle) increases by a significant percentage and/or when the price of live cattle has a discernable trend, most if not all cuts of meat will change prices in the same direction of the price of live cattle. In this situation, most if not all pairs of cuts of meat will be positively correlated. In another embodiment of the tool  12 , the optimization manager  402  can be operable to calculate a different intra-portfolio correlation factor Q than the one described above. The manager  402  analyzes the past predicted prices of each pair of cuts of meat for the same delivery period, for example, in the portfolio  960  and determines the basis (i.e. difference between actual market price and predicted price) in the predicted prices. The manager  402  then calculates an intra-portfolio correlation on the basis for the first cut of meat and the basis on the second cut of meat in the pair. The manager  402  creates decisions  404  that may be used by the adjustment module  540  when the correlation is above or below the pre-determined thresholds. For example, the manager  402  may determine that a first cut of meat historically has a negative basis for the delivery month and the second cut of meat historically has a positive basis for the delivery month (i.e. the model  204  of the first cut historically predicts prices above the respective market price and the model  204  of the second cut historically predicts prices below the respective market price). If the correlation between the first cut and the second cut is below the pre-determined threshold, the adjustment module  540  may adjust the models  204  (for example, by decreasing the basis risk premiums  916 ) corresponding to the first and second cuts to make the cuts less expensive. If the correlation between a first and second cut is positive and above a pre-determined threshold, the adjustment module  540  may adjust the models  204  (for example, by increasing the basis risk premiums  916 ) corresponding to the first and second cuts to make the cuts more expensive 
         [0083]    In an embodiment, the optimization module  400  employs a value-at-risk model that uses the technique known as intra-portfolio correlation to generate optimization decisions  404 . The optimization manager  402  retrieves portfolio content  960  from the tables  24  by sending a request to the data manager  34 . The aggregate portfolio content  960  is an aggregate collection of all prices, quantities, delivery periods and the types of meat for which the provider is providing future prices to members  8 , as well as perhaps other information. The optimization manager  402  analyzes the aggregate portfolio content  960  and calculates the positive or negative correlation for pairs of different cuts of meat in the provider&#39;s portfolio  960 . As is understood, optimizing a portfolio requires holding cuts of meat with poor (or offsetting) correlations. As mentioned above, if the prediction  910  of one cut has a positive basis then there is a higher likelihood that a poorly correlated cut will have a negative basis. When the provider of the tool  12  agrees to guarantee a forward price  910  for a particular cut to a customer  8 , the optimization manager  402  will analyze which cuts have a poor correlation and either target customers who would be interested in forward prices  910  on those cuts and/or adjust the basis risk premium confidence level on those cuts down (analogous to putting those cuts “on sale”). Optimization decisions  404  may be in the form of a list of options which the provider can select to optimize the aggregate portfolio  960 , and hence, lower the risk of the provider in providing forward prices  910 . For example, the list may include an option to increase the basis risk premium confidence level  918  to one of several values, or to leave the basis risk premium confidence level  918  at the same value. Alternatively, optimization decisions  404  may be executed directly by the optimization manager  402  to modify the basis risk premium confidence levels  918  of models  204  that correspond to cuts of meat that are poorly correlated. 
         [0084]    In one embodiment, the optimization module  400  employs an intra-portfolio correlation that is represented by the following formula: 
         [0000]    
       
         
           
             Q 
             = 
             
               
                 ( 
                 
                   
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                      
                     
                       
                         X 
                         i 
                       
                        
                       
                         X 
                         j 
                       
                        
                       
                         p 
                         ij 
                       
                     
                   
                 
                 ) 
               
               
                 ( 
                 
                   
                     ∑ 
                     i 
                   
                    
                   
                       
                   
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                       j 
                     
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         [0085]    Where Q is the intra-portfolio correlation, 
         [0086]    X i is the fraction invested in asset i, 
         [0087]    X j is the fraction invested in asset j, 
         [0088]    P ij is the correlation between assets i and j, 
         [0089]    The expression is only computed when i≈j 
         [0090]    In the above equation, it is noted that there is a unique intra-portfolio correlation for every pair of assets in a particular delivery period. For example, if the provider of the tool  12  is guaranteeing the forward prices  910  of rib-eyes for December, strip-loins for December and rib-eyes for February of the same year in its aggregate portfolio  960 , there will be a correlation factor Q only for the pair made up of rib-eyes for December and strip-loins for December. The optimization manager  402  does not compute a correlation factor for rib-eyes for February with the other assets because of the seasonal nature of cuts of meat. That is, it is not expected that rib-eyes for February are statistically correlated with cuts of meat for months other than February. It is to be understood that in other embodiments of the tool  12 , however, a year may be divided up into seasons instead of months. In this embodiment, there is a correlation factor Q for every cut of meat that shares a delivery season. For example, the manager  402  will calculate one correlation factor for rib-eyes with strip-loins that are to be delivered in Spring, but not for rib-eyes to be delivered in Spring with strip-loins to be delivered in Fall. To calculate the correlation factor Q for a given pair of assets in the same delivery period, the optimization manager  402  takes two vectors of historic data as inputs and calculates the correlation coefficient Pij: one vector represents data for the first cut of meat for the delivery period, the second vector represents data for the second cut of meat for the delivery period. For example, the provider of the tool may use  8  years of data for the delivery month to calculate Q. In this example, the first vector contains average prices of the first cut for the delivery month in each year and the second vector contains average prices of the second cut for the delivery month in each year. As is known, the correlation coefficient Pij indicates the strength and direction of a relationship between two random variables. The correlation coefficient implemented by the optimization manager  402  may be a known correlation coefficient that uses covariance or other statistical properties or may be a customized correlation coefficient developed by the provider that is specific to the tool  12 . In another embodiment of the tool  12 , in calculating the correlation factor Q, the first input vector contains the average daily prices for each day in the delivery month for the first cut, and the second vector contains the average daily prices for each day in the delivery month for the second cut. It will be understood that the input vectors may include sample daily prices for each day, one specific day or a group of days in the delivery month for the first and second cuts respectively. 
         [0091]    In operation, the optimization manager  402  retrieves the aggregate portfolio content  960  from the data manager  34  as an input. For each pair of assets in the portfolio (i.e. the cuts of meat in the same delivery month for which the provider has secured forward prices  910 ), the optimization manager  402  generates an intra-portfolio correlation factor. The correlation factor for the asset pair represents the relative movements of one cut of meat in the asset pair relative to the other cut of meat in the asset pair for the particular delivery month. The optimization manager  402  is operable to analyse the set of intra-portfolio correlations and generate optimization decisions  404 . In an embodiment, the manager  402  searches for intra-portfolio correlations that are negative and below a pre-determined threshold (−0.5 for example). The manager  402  produces decisions  404  that are designed to increase the content of the portfolio  960  made up of asset pairs below the negative pre-determined threshold. The manager  402  also searches for portfolio correlations that are positive and above a pre-determined threshold (+0.5 for example) and produces decisions  404  designed to decrease the content of the portfolio  960  devoted to the asset pairs above the pre-determined threshold. It is to be understood that the pre-determined thresholds are set by the provider of the tool  12  and may be changed by the provider at any time. As mentioned above, the decisions  404  may be used to modify the models  204  of cuts of meat such that asset pairs with negative correlations are put on sale and asset pairs with positive correlations are made more expensive. 
         [0092]    In one embodiment, the optimization manager  402  uses pre-determined setting to determine if both cuts of meat in a pair with offsetting correlations should be put on sale or if only one of the cuts and which one of the cuts of meat should be put on sale. The settings are customizable by the provider of the tool  12 . For example, a provider of the tool  12  may create electronic settings that instruct the manager  402  to put one cut of meat in the pair on sale at a discount equal to twice as much of the other cut of meat in the pair, either in absolute monetary terms or as a percentage of the predicted future price  910 . It is to be understood that the provider may create such settings for each cut of meat in the portfolio relative to each of the other cuts of meat in the portfolio. In another aspect, an employee of the provider of the tool makes this decision after prompting by the manager  402  on a user interface  28 . For example, the employee may wish to consider other information such as business trends, competitive strategy, short-term weather predictions or any other information relevant to the business of the provider of the tool  12 . 
         [0093]    In one embodiment, the optimization manager  402  communicates the optimization decisions  404  to an adjustment module  540  which directly adjusts the internal structure of models  204  to put certain cuts of meat on sale and to make other cuts of meat more expensive. 
       Adjustment Module  540   
       [0094]    Referring next to  FIG. 18 , an adjustment module  540  of the tool  12  is illustrated. The adjustment module  540  receives optimization decisions  404  from the optimization model  400 . Optimization decisions  404  may be executable electronic instructions to be executed by the adjustment module  540  to adjust models  204  or may be in the form of a data set that is retrievable by the adjustment module  540  via the data manager  34 . The adjustment module  540  reads and/or executes the optimization decisions  404  and modifies the internal structure of models  204 . For example, the optimization manager  402  may determine that a first cut of meat in the portfolio is well correlated with a certain cut, that a second cut of meat in the portfolio is poorly correlated with the certain cut and there is no correlation between the first cut and the second cut. The manager  402  creates optimization decisions  404  to decrease the quantity of the first cut in the portfolio  960  and to increase the quantity of the second cut in the portfolio  960 . The adjustment module  540  receives the optimization decisions  404  as an input and adjusts the models  204  that correspond to the first and second cuts respectively. Specifically, to reduce the overall risk of the portfolio  960 , the adjustment module  540  adjusts the model  204  of the first cut to provide a disincentive to prospective customers by increasing the basis risk premium  916   a  of the first cut. The provider has several options for increasing the basis risk premium  916   a  of the first cut. The adjustment module  540  may increase the basis risk premium confidence level  918   a  of the first cut from 80% to 90%, meaning that a customer must pay an amount of money equal to a 40% risk of the forecast to secure a predicted price  910 . Alternatively, the adjustment module  540  may directly increase the basis risk premium  916   a  of the first cut to a predefined level, making the first cut of meat more expensive than it was prior to modification by the adjustment module  540 . Additionally, the adjustment module  540  decreases the basis risk premium confidence level  918   b  of the second cut, which lowers the basis risk premium  916   a  of the second cut, effectively putting the second cut on sale. For example, the adjustment module  540  may decrease the basis risk premium confidence level  918   b  on the second cut to 70%, meaning that a member  8  must only pay an amount equal to a 20% risk of the forecast to secure a predicted price  910 . Alternatively, the adjustment module  540  may directly decrease the basis risk premium  916   b  on the second cut. It is expected that over time, putting cuts of meat with offsetting correlations on sale will decrease the overall risk that the provider of the tool  12  is exposed to in securing the predicted prices  910  of the cuts of meat in the aggregate portfolio  960 . Likewise, making cuts of meat with positive correlations more expensive will also decrease the risk exposure of the provider of the tool  12  in guaranteeing forward prices  910  for cuts of meat in the aggregate portfolio  960 . 
         [0095]    Reference is next made to  FIG. 19 , which illustrates the series of steps performed by the optimization manager  402  and the adjustment module  540  in optimizing the risk level associated with the portfolio  960  of the provider of the tool  12 . At step  800 , the optimization manager  402  calculates the intra-portfolio correlation of each pair of cuts of meat that share a delivery month in the portfolio  960 . At step  802 , the optimization manager  402  analyzes each correlation term and determines if the correlation terms are above or below a pre-determined threshold. For example, the provider may customize the optimization module  400  such that the optimization manager  402  creates optimization decisions  404  for each correlation term above +0.5 and below −0.5. At step  804 , the optimization manager  402  creates the appropriate optimization decisions  404  for each correlation term that is above or below the predetermined thresholds. The optimization decisions  404  are directed to the adjustment module  540  at step  806 . At step  808 , the adjustment module  540  retrieves the models  204  that correspond to the optimization decisions  404  and modifies the models  204  as described above, to either increase or decrease the basis risk premium  916  on the relevant cuts of meat. 
         [0096]    Reference is next made to  FIG. 20 , which illustrates the advantageous net effect of the operations performed by the optimization module  400  and the adjustment module  540  on the risk level of the aggregate portfolio  960  held by the provider of the portfolio management tool  12 . The table  940  shows the content of the portfolio  960  (i.e. by cut of meat  506  and the percentage  942  of the portfolio  960  made up of each cut), the individual basis risk premium  916  for each cut of meat, the weighted average basis risk premium  944  and the aggregate basis risk premium  946  of the aggregate portfolio  960 . The basis risk premium  944  is a weighted average of the basis risk premiums  916  as the selected cuts  506  were sold individually to different members  8 . As shown, the weighted average of the basis risk premiums  944  charged for guaranteeing future prices  910  of those cuts of meat to members  8  is $0.1569. That is, the provider of the tool  12  has collected an average basis risk premium  944  of $0.1569 per cut  506  as the provider has built up the portfolio  960 . However, the basis risk premium  946  that would have been required if the cuts of meat  506  were all sold to one customer is only $0.0995. The provider of the tool is able to realize a net benefit  948  of +$0.0574 on each cut of meat, which represents the benefit of executing the optimization decisions  404  of the optimization module  400 . The net benefit  948  to the provider of the tool  12  is realized by creating an aggregate portfolio  960  of pairs of assets with offsetting correlation values by adjusting the basis risk premium confidence levels  918  of the assets as described above. The optimization decisions  404  provide an incentive to members  8  to buy cuts of meat that lower the risk exposure of the provider and a disincentive to members  8  to buy cuts of meat that increase the risk exposure of the provider of the tool  12 . 
         [0097]    As an example, the percentage of a portfolio  960   a  (not shown) devoted to the cut ‘90s’ is 20% and the percentage of the portfolio  960   a  devoted to ‘50s’ is 10%. The remaining percentage of the portfolio  960   a  is made up of other cuts not relevant to the example. The optimization manager  402  determines that asset pairs ‘90s’, ‘50s’ are negatively correlated and below a pre-determined threshold. In portfolio  960   a,  the net benefit  948   a  is lower than the net benefit  948  of +$0.0574 in Table  940 . The manager  402  generates decisions to put ‘90s’ and ‘50s’ on sale by lowering the basis risk premium  916  on each of the cuts. The basis risk premium  916  on the other cuts of meat in the portfolio  960   a  may also be modified or may stay the same depending on the provider. In addition, because the provider of the tool knows the portfolio correlation between ‘90s’,‘50s’, the provider may target customers  8  that are interested in these cuts. Over time, members  8  buy more of‘90s’ and ‘50s’ eventually leading to the provider having the portfolio  960  in Table  940  made up of 32% of ‘90s’ and 18% of ‘50s’. The provider now realizes a greater net benefit  948  of +$0.574 in the portfolio  960  as compared to the net benefit  948   a  in the portfolio of  960   a.  The net benefit  948  of the portfolio  960  is greater than that of portfolio  960   a  because the portfolio  960  is less risky to the provider of the tool  12 , yet the provider of the portfolio  960  is able to charge individual basis risk premiums  916  that do not reflect the offsetting correlations in the cuts of meat in the portfolio  960 . 
         [0098]    It will be appreciated that another set of optimization decisions  404  may result in a higher or lower net benefit  948  to the provider of the tool  12  and that other diversification strategies are useable by the tool  12  in lowering the risk exposure in the aggregate portfolio  960 . For example, in another embodiment, the optimization decisions  404  are not directly executed by the adjustment module  540 , but instead, are used by the provider of the tool  12  for analysis only. The provider of the tool  12  may choose to implement the optimization decision  404  as described above or may choose to disregard the optimization decisions  404 . In yet another embodiment, the provider of the tool  12  can set a maximum percentage for each cut of meat that can be included in the aggregate portfolio  960 . For example, a provider of the tool  12  may wish to limit the content of the portfolio  960  devoted to rib-eyes to 20%, or any other percentage. Once 20% of the portfolio content is devoted to rib-eyes, customers  8  no longer have the ability to secure forward prices  910  of rib-eyes until rib-eyes make up less than 20% of the aggregate portfolio  960 .