Asset and liability modeling tool
A method for modeling financial variables describing a client over a time period. The method may comprise the step of generating a first simulation of the time period. Generating the first simulation may comprise the steps of assigning the client to a first health-related state and advancing the first simulation from a first interval of the time period to a second interval of the time period. A probability that the client will transition from the first health-related state to a second health-related state may be calculated, the client may be randomly assigned to either the first health-related state or the second health-related state considering the probability. According to various embodiments, the methods may also comprise the steps of calculating a client income for the second interval; and calculating a plurality of client expenses for the second interval. Also, the various health-related states may include one or more of a healthy state, a long term care (LTC) state, a disabled state and a dead state.
This application is a continuation-in-part of U.S. application Ser. No. 11/389,962 filed on Mar. 27, 2006.
STATEMENT UNDER 37 C.F.R. §1.84(a)(2)The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.
BACKGROUNDThere is a present need in asset management to move towards “liability-led investing” at the individual and/or household level. Currently, there is an observable trend for governments and corporations to push the responsibility for pension and healthcare liability management and financing back to the individual because existing arrangements for their funding are either unaffordable or the financing risk is too high. Consequently, it has become increasingly important for individuals to accurately model their financial condition into the future in order to plan for multiple goals such as retirement, college education for children, etc.
Such modeling is a challenging task. Most individuals have multiple future goals, some flexibility in the timing and acceptable spend of those future goals (e.g., they can retire earlier or later; retire on a higher or lower retirement income) and dynamic goal priorities (e.g., will trade-off goals differently depending on the likely level of spend). Dependencies between these goals go forwards and backwards in time. For example, if an individual (or household) spends more on their children's education, he or she will have less to support retirement. On the other hand, if the individual retires later, he or she may be able to afford to spend more on their children's education today.
On top of this wide array of goals and related choices, individuals (and households) face uncertainty about their future income, future expenses, and even how long they are going to live. For example, there is uncertainty about an individual's future earned income, social security receipts, Medicare benefits and returns on their savings and investments. There is also uncertainty about an individual's future expenditures on healthcare, nursing care, residential care, etc. There is a risk the individual may die young. Under those circumstances, they want to be sure their family is provided for. There is also a risk the individual may live a long time. In that case, they want to be sure that they have enough assets to support them through a very long retirement, where healthcare costs may be high.
When considering assets, liabilities, goals, and the uncertainties of life, people care about every outcome. There are, however, too many variables for any individual, no matter how intelligent, to solve the problem in all of its complexities and find comprehensive answers. Most individuals solve the problem serially. For example, if they have enough money, they will send their children to a particular school without a thorough understanding of how that would affect their retirement age or spend, or how it would impact their family if they were to die unexpectedly.
There are existing tools for modeling an individual's financial situation, however, they do not fully address the problem. In general, existing tools focus on funding individual goals and ignore the interactions and time dependencies between cashflows and their priorities. They ignore unforeseen events such as health events or the need for long-term care. These existing tools assume that the only risk decision to be made by the individual is how much risk to take in the investment portfolio. They therefore focus the individual on expected portfolio returns, or the degree of investment risk needed to meet that single goal. They do not help the individual assess the nature of the risks to their meeting their set of goals, nor help them understand the nature and consequences of the choices they have, of which investment risk is but one.
BRIEF SUMMARY OF THE INVENTIONIn one general aspect, the present invention is directed to a method for displaying the results of a financial model to allow visual assessment of a client's future financial condition in an interactive, timely content-rich manner. The financial model may be generated by a financial modeling tool that captures a comprehensive set of future financial variables for the individual (e.g., cashflows, liabilities etc.), and the uncertainty and range of possible outcomes for each. The financial model is displayed in a manner that provides a way of visualizing the possible outcomes that enable the individual to understand that range, and understand the consequences on the outcomes of different choices they have. In so doing, the model may focus individuals on risk management with respect to their future goals, not simply on investment returns. In addition, the financial model, in various embodiments, may make financial advising more attractive to individuals by allowing for the generation of an approximate model based on abbreviated input data, for example, a set of input data that may be entered on a single screen. The abbreviated input model enables the individual to provide accurate input data whilst engaged in reviewing the results, as opposed to providing comprehensive input data prior to reviewing the output. This may provide an incentive for individuals to engage in the often complex and time consuming process of creating a full financial model.
The financial model models the client's future financial condition over a given time period (e.g., a life, the life of a household, etc.) by generating a likely range of forecasted values over the time period for one or more financial variables describing the client. Exemplary financial variables include, net worth, liquid assets, investable assets, outflow, annual cashflow, available net worth (i.e., balance sheet and cashflow items), etc. In various embodiments, the financial model may include a number of computer simulations of the time period. Each computer simulation may generate a set of possible forecasted values for the financial variable or variables over the time period. The aggregate of the sets of values from all of the simulations forms the results of the financial model (e.g., a distribution of possible outcomes for the client).
The results of the financial model may be displayed as a graphical representation, which may display results for some or all of the modeled financial variables so that the client can gain a visual understanding of their prospective financial condition and gain insight as to the consequences of different available choices. The graphical representation may take the form of a topographical chart positioned on a plane defined by a time axis and a value axis, such that values of the financial variables over time may be plotted on the time and value axis. Each coordinate set on the axes may correspond to a point on the topographical chart. The height of the points on the topographical chart indicates the likelihood that the displayed financial variable will take the value and time indicated by the corresponding coordinate sets. For example, the height of the points may indicate the number of simulations that result in a value and time of the displayed financial variable or variables represented by the corresponding coordinate set. In various embodiments, the color of points on the topographical chart also indicates the likelihood that the displayed financial variable will take the value and time of the corresponding coordinate sets (e.g., how many of the simulations result in the corresponding coordinate sets). For example, more intense colors may indicate a larger portion of the simulations. In that way, the height, color, and intensity of points on the topographical chart may be indicative of the probability that the forecasted variables will have the value and time of the corresponding coordinate sets. The color of points on the topographical chart may also indicate whether the represented value of the financial variable is positive or negative. For example, negative values of the financial variable or variables (e.g., values indicating that the client will lack financial means) may be red while positive values may be green.
The graphical representation may also comprise representations of one or more goals of the client at a point or points in time. Each goal may represent an expenditure or other financial event that the client would like to achieve in the future. Each representation indicates a portion of the simulations where the goal is achieved. Also, selecting the representation of the goal may allow detailed information about the goal to be viewed and/or edited. Where the goal is a retirement goal, the time after retirement may be partitioned into a plurality of time blocks. The success of the retirement goal, (e.g., whether the client has enough assets and/or income to meet desired consumption levels) may be indicated for each time block. The user may be able to drag a goal on the topographical chart so as to adjust the time horizon for the goal. The simulations may be accordingly regenerated to determine the likelihood of achieving the goal given the revised horizon. The user may also be able to analyze the graphical representation. For example, placing a cursor over the representation at various points in time may display information over the various simulations (e.g., the distribution of possible outcomes at that point in time, states of the client, etc.) In various embodiments, the user may manipulate the viewing angle of the graphical representation using navigation buttons.
In another general aspect, the present invention may be directed to a method for modeling financial variables describing a client over a time period. The method may comprise the step of generating a first simulation of the time period. Generating the first simulation may comprise the steps of assigning the client to a first health-related state and advancing the first simulation from a first interval of the time period to a second interval of the time period. A probability that the client will transition from the first health-related state to a second health-related state may be calculated, the client may be randomly assigned to either the first health-related state or the second health-related state considering the probability. According to various embodiments, the methods may also comprise the steps of calculating a client income for the second interval; and calculating a plurality of client expenses for the second interval. Also, the various health-related states may include one or more of a healthy state, a long term care (LTC) state, a disabled state and a dead state.
In yet another general aspect, the present invention may be directed to methods of simulating the finances of a client over a time period. The methods may comprise the step of receiving a description of a first goal. The description of the first goal may comprise a priority of the first goal, a date for the first goal, a minimum amount for the first goal and a desired amount for the first goal. The methods may also comprise the step of receiving a description of a second goal, which may also comprise a date for the second goal, a minimum amount for the first goal a desired amount for the second goal, and a priority of the second goal, which may be lower than that of the first goal. The client income and expenses over a first interval of the time period may be calculated, with the client expenses categorized into discretionary and non-discretionary expenses. The non-discretionary expenses may be funded with at least a portion of the client income. If any client income remains, at least a portion of the remaining client income may be allocated to a first goal account and a second goal account. This allocating may comprise the steps of assigning a minimum amount to the first goal account; if any client income remains, assigning a minimum amount to the second goal account; and if any client income remains, assigning a desired amount to the first goal account.
The user of the financial modeling tool may be the client itself (e.g., a business entity, individual, or household), or in various embodiments, may be a financial advisor or other representative working on behalf of the client.
Embodiments of the present invention are described herein, by way of example, in conjunction with the following figures, wherein:
Embodiments of the present invention are directed in general to a financial modeling software tool that allows a user to generate a financial model of the possible future financial condition of an individual or household (e.g., a client) based on current data and assumptions about the future. The financial model models the clients' future financial condition by forecasting the values of one or more financial variables such as, for example, net worth, liquid assets, investable assets, outflow, annual cashflow, available net worth, etc. The model may be generated using any suitable modeling method or methods for simulating asset returns and stochastic liabilities (e.g., a Monte Carlo method) and may be based on input data and assumptions specific to the client. Results of the model may be presented to a user of the financial modeling tool and/or clients in a visual, interactive and content-rich manner. For example, the results may be displayed on a topographical chart, as shown in
Various input data for developing the model may be stored in databases 114, 116, 118, 120. The input data may be received from a user, for example, via plan module 108, or may be received from an outside source, such as a subscription service, etc. Plan database 118 may store financial information about the client including, for example, income, asset, and liability information. The plan database 118 may also store goal information for the client, as described in more detail below. In various embodiments, the plan database 118 may include data about a client entered through the plan module 108 in a current or previous session, as well as generic or stock data that may be applied to multiple clients. Assumption database 116 may include data describing assumptions that may be used when developing the model. Exemplary assumption data includes, the loss tolerance of the client (e.g., the client's tolerance for one year losses on their investment portfolio), the tax status of the client, the transaction costs for buying and selling assets, etc. Other examples of assumption data may include actuary tables or other data for modeling states of the client (e.g., date of death, disability state, etc.). Assumption data may be entered through the plan module 108 or may be default data that may be applied to multiple clients, such as, for example, default transaction costs, default retirement consumption adjustments, etc.
An economic database 114 may include data describing historical economic performance that may be considered in generating the model. For example, the database 114 may include data describing the historic trends of the stock market, interest rates and the related expected asset returns, volatilities and covariances, etc. Regulation database 120 may include information describing various laws and regulations that may affect the model including, for example, tax codes, securities laws and regulations, etc. Data stored at the economic and regulation databases 114, 120 may be received from one or more data subscription services.
The simulation module 112 may model the possible range of the client's forecasted financial condition over the chosen time period considering the input data stored in databases 114, 116, 118, 120. The time period may be, for example, the client's expected life. In various embodiments, the simulation module 112 may model the range of the client's forecasted financial condition according to a Monte Carlo technique, for example, by simulating both asset returns and stochastic liabilities, in the context of desired future cashflows (e.g., the amounts required for the client to meet future goals and expenses). For each simulation, the simulation module 112 may generate output values for the financial variables over one instance of the time period, considering the input data, assumptions and other constraints.
In various embodiments, each simulation may generate values of the financial variables based on assumptions regarding states of the client, and/or the economy at large. For example, each simulation may generate a date of the client's death; whether and, if so, when the client experiences a disability; whether and, if so, when the client requires long term care; etc. Each simulation may also assume future economic trends regarding, for example, the stock market, interest rates, etc. The states assumed by any particular simulation may be randomly generated, but based on the likelihood of the states occurring, for example, as shown by actuary tables, assumptions, or other input data/constraints. For example, if the statistical data indicates that there is a 5% chance that the client will experience a disability at age 40 and a 10% chance that the client will experience a disability at age 50, then approximately 5% of the total number of simulations may assume a disability at age 40 and approximately 10% will assume a disability at age 50.
It will be appreciated that, in various embodiments, the values for the financial variables generated during each simulation may reflect the dependency and/or covariance of the financial variables on each other. For example, the values for asset returns, interest rates and inflation rates generated by any given simulation may allow for the historic co-variance of those variables. Also, the simulations may generate values of future earned income that covary with the financial variables representing economic state and inflation rates. In addition, each simulation may model actuarial co-variances (e.g., a different likelihood of needing long-term care if disabled; different life expectancy depending on health status).
The aggregate of the sets of values from all of the simulations may be displayed to the user and/or the client as a topographical chart or other graphical representation, for example, shown by user interfaces 900 and 1000 of
It will be appreciated that in various embodiments, some or all of the software program of the financial modeling tool 100 may be executed by components of the network 110 other than the server 106. For example, user machines 102 may contain some or all of the software of the financial modeling tool 100. In such embodiments, the user machines 102 may access the databases 114, 116, 118, 120 via server 106, or may, in various embodiments, each include local copies of the databases 114, 116, 118, 120. It will also be appreciated that the databases 114, 116, 118, 120 may be implemented using any number of physical or logical storage devices.
From step 202, the client, and/or the user of the financial modeling tool 100, may choose to enter an abbreviated plan at box 204 or a full plan at box 206. Entering a full plan at box 206 may include entering detailed plan and assumption information about the client including the client's income, assets, liabilities, goals, etc., for example, at user interface 700 shown at
At step 208, the simulation module 112 may run one or a series of simulations of the client's life based on the information received at steps 202, 204 and/or 206. Results of the simulations may be displayed at box 210, for example, as one or more of the graphical representations shown at interface 900 in
Clients are sometimes reluctant to enter or provide a user (e.g., a financial analyst) with enough financial and/or personal information for the simulation module 112 to generate its most accurate simulations. For one thing, gathering and entering the sheer volume of desirable information may take a long time. For another, the client may be hesitant to provide detailed information to a financial analyst with whom they may not have an established relationship, or who may not manage all of their assets. Accordingly, the financial modeling tool 100 may be used to motivate the client to disclose and/or enter as much information as possible. For example, the client may enter and/or disclose to a user only the information necessary for an abbreviated plan. Based on the outcomes of the simulations from the abbreviated plan, however, the client may become quickly engaged by the resulting graphical representation of the output and may be more motivated to provide additional and/or more accurate information as required to further understand the impacts on the output. It will be appreciated that subsequent outputs and graphical representations based on the additional and/or more accurate information may themselves be more accurate. The client and/or the user may then begin to create or supplement a full plan by entering the additional data and re-running the financial model. In this way, the financial modeling tool 100 may move away from the linear logic of requiring a client to provide and/or enter all input information before the client can become engaged in the model's output.
New plans may be generated by selecting one of the icons 631 and 635. The user may have the option to create and/or use a full plan by selecting icon 635 or an abbreviated plan by selecting icon 631. As described above, a full plan allows the user to enter detailed plan and assumption information about the client including the client's income, assets, liabilities, goals, etc. This information is then considered by the simulation module 112 in generating a model of the client's future financial condition. An abbreviated plan allows the user to enter less detailed financial information about the client, which may require substantially less time and effort. In various embodiments, data for the abbreviated plan may be entered in a single user interface screen such as, for example, user interface 300 shown in
In
In
For each category and sub-category listed in expense column 766 corresponding entries may exist in the minimum amount column 768 and the maximum amount column 770. The user may list the minimum and maximum amounts that the client expects to spend on a particular category in its corresponding entries in columns 768 and 770, respectively. In that way, the client's expenses can be bracketed between expected minimum and maximum amounts. It will be appreciated that, in various embodiments, the user may forgo entering values for all sub-categories and may instead enter minimum and maximum amounts only for total annual expenses 773, 775 and/or a portion of the categories.
Each of the columns 768 and 770 may include a respective Other item 769, 771. The Other items 769, 771 may allow a user to categorize some, but not all, of the client's expenses under one or more of the nested categories discussed above. For example, the Other items 769, 771 may display the difference between the total annual expenses 773, 775 of the client and the sum of the amounts classified in the respective categories. For example, referring to Other item 769, if the minimum amount of total household expenses is $25,000 and no other expenses are categorized, then, the amount of the Other item 769 would be $25,000 so that the total minimum annual expenses value 773 would remain at $50,000. As the sum of the categorized expenses increases (e.g., as more expenses are categorized), the value of the Other item 769 decreases until the sum of the categorized expenses equals the total minimum annual expenses 773. At that point, if the sum of the categorized expenses were to increase further, the Other item 769 would remain at zero and the total expenses 773 would increase. It will be appreciated that the categorized expenses, Other item 771 and total maximum annual expenses 775 of Maximum Amount column 770 may behave in a similar manner.
Detailed information about current goals listed in field 790 may be viewed and/or edited at field 792 by selecting the representation of the goal from field 790. For example, the start date and the minimum and maximum costs of the goal may be entered and/or viewed. In various embodiments, the goal may be assigned a priority. For example, using slide-bar 795. The priority of the goal may be considered by the simulation module 112 as described herein. The funding source for the goal may also be viewed and/or edited, for example, by selecting button 794. The priority and funding source or sources for a goal may be considered by the simulation module 112 when simulations are conducted. Selecting button 794 may cause field 796 to appear, as shown in
If a loan is selected as an asset for funding a goal, the field 791 may allow viewing and/or editing of detailed information about the loan, for example, as shown in
In addition to, or instead of, the funding sources designated at field 796, it will be appreciated that the various simulations may implement an automatic funding hierarchy. For example, if there is not enough cash-on-hand to fund a year's expenditures (e.g., consumption, goals, etc.), the simulation may apply a set of funding rules. The funding rules may set forth a sequence for selling different types of assets and borrowing to finance expenditures. The funding rules may also specify a point at which further expenditure is disallowed (e.g., when credit is exhausted, or a predetermined amount of assets have been sold).
The values of the financial variables versus time generated over the various simulations may be aggregated and plotted on axes 912, 914, resulting in topographical chart 918. Each coordinate set on the axes 912, 914 corresponds to a point on the topographical chart 918. The height (or depth) of any particular point on the topographical chart indicates the number, or percentage, of simulations where the displayed financial variable (e.g., net worth) took the value and time (e.g., $1.5 million in 2021) of the corresponding coordinate set on axes 912, 914. Thus, in one embodiment, peaks on the topographical chart 918 represent outcomes with relatively high probability and topographical points below the peaks (including valleys) represent outcomes with relatively lower probabilities.
The chart 918 may also be color coded, with the color of a plotted point representing the frequency of the occurrence of its corresponding coordinate set. For example, the intensity of a color may indicate the frequency of occurrence (e.g., the probability) of its corresponding coordinate set. Points having colors that are more intense may have occurred in more simulations, while points having colors that are less intense may have occurred in relatively fewer simulations. In various embodiments, the color of points on the topographical chart 918 may also indicate the desirability (from the point of view of the client) of the corresponding coordinate sets. For example, points on the topographical chart 918 corresponding to undesirable coordinate sets (e.g., those indicating that the client lacks sufficient assets and/or income to maintain desired consumption levels, etc.) may be assigned one color, such as red, while points corresponding to desirable coordinate sets may be assigned another color, such as green or brown. It will be appreciated that this color coding may focus the client and/or user on the downside risks they face, and the choices they have to mitigate those risks. It will be appreciated that various shades of color or even additional colors may be used to illustrate gradations between degrees of desirability.
The legend 910 may allow the user to select which financial variable or variables are displayed in topographical chart 918. For example, the user may select a financial variable by actuating its corresponding button in legend 910. It will be appreciated that more than one financial variable at a time may be selected from legend 910 and viewed in field 902. In various embodiments, the legend 910 may be toggled on and off using Legend button 956. Also, the user may navigate the topographical chart 918, for example, using navigation buttons 916. By making the appropriate selection from buttons 916, the user may manipulate the axes 912, 914 of topographical chart 918 to thereby acquire different views of the chart 918, for example, as shown in
The interface 900 may also include various tools for examining the topographical chart 918. For example, a cursor 904 in
The simulation module 112 may also compute a likelihood of the client achieving desirable and acceptable levels of goal spend (e.g., the amount available to spend on a goal considering other goals and expenses) and the expected distribution of goal spend outcomes. The success or failure of each goal may be indicated at display field 902. For example, a representation or icon for each goal may be positioned along the time axis 912. The exemplary chart in
Selecting the representation 920, 922, 924 of a goal may cause additional detailed information about the goal to be displayed, as shown in pop-up field 921 in
It will be appreciated that details of the selected goal may be modified at details field 908 and/or pop-up field 921. Also, the time horizon of the goal may be moved by selecting the representation for the goal and dragging it along the time axis 912. Additional goals may be added to the financial model, for example, by selecting a goal icon from field 906 and/or by selecting and dragging an icon from field 906 to field 902. It will be appreciated that different types of insurance, levels of borrowing and changes to asset allocation may also be added to the financial model. In other words, the client and/or user can test the implications of incurring different levels of risk in different ways (e.g., insuring or self insuring a risk; accepting a higher risk of failing to meet a goal versus taking more risk in the investment portfolio, etc.). It will be appreciated that adding additional goals, or modifying details and/or the time horizon of the selected goal causes the simulation module 112 to regenerate the simulations based on the modified goal. In that way, dynamic adjustments to the client's forecast can be achieved by modifying a goal.
It will be appreciated that the topographical chart 918 may be configured to display results for multiple financial variables simultaneously, if desired. For example,
In various embodiments, other chart types may be used in addition to or instead of the topographical chart 918. For example,
The interface 900 may provide various additional options for manipulating the view and/or simulations. Referring to
Embodiments of the present invention are also directed to apparatuses and methods for implementing a financial model. As described above, the financial model may generate a likely range of forecasted values of one or more financial variables describing a client over a given time period (e.g., the life of a client, the life of the client's household, etc.). Executing the financial model may involve generating a number of computer simulations of the time period. Each simulation may represent one simulated life of the client and, in various embodiments, may also include a simulated life of a member or members of the client's household, such as a spouse. For each simulated life, Monte Carlo methods may be used to estimate various health-related states of the client and resulting values for the financial variables over the course of the simulated life. The results of all simulated lives may be aggregated to generate an indication of the likelihood of various values of the financial variables into the future.
The financial model is described below as implemented by the computer system 110 described above, including simulation module 112. It will be appreciated, however, that the financial model may also be implemented by any other suitable computer system. Also, the financial model, as described below may receive input according to a user interface such as user interfaces 300 and 600 described above, or according to any other suitable user interface or interfaces. In addition, the financial model described below may present its output in the forms shown by user interfaces 600, 900 described above, or according to any other suitable forms.
The state of the client (and any modeled members of the client's household) may be successively recalculated, with each recalculation representing the passage of an interval or period of time in the simulated life. For example, each recalculation may represent the passage one month, one quarter, one year, etc. At each interval, the values of the financial variables may also be recalculated given the assets, income and expenses available to the client and considering the effects of the new state on expenses and income. In this way, the state of the client and its influence on the financial variables may be estimated at the various intervals throughout the simulated life. Steps 4704, 4706, 4708, 4710 and 4712 below illustrate one exemplary method of recalculating the clients state and the client's financial variables.
The simulation module 112 may calculate the probabilities of the client transitioning from his or her current state to some or all of the other allowable states (4704). According to the exemplary state diagram 4800 and assuming that the client is currently in the Healthy state 4802, this may involve calculating probabilities that the client will remain in the Healthy state 4802, that client will transition from the Healthy state 4802 to the LTC state 4804; that the client will transition from the Healthy state 4802 to the Disabled state 4806 and that the client will transition from the Healthy state 4802 to the Dead state 4808. In embodiments where other members of the client's household are also modeled, similar probabilities may also be found for these other members. Exemplary methods of calculating these probabilities for the client and any household members are discussed in more detail below.
When the probabilities are calculated, the simulation module 112 may randomly select a new state for the client, based on the calculated probabilities (4706). For example, if the probability of the client transitioning from the healthy state 4802 to the LTC state 4804 is 10%, then there may be a 10% chance that the simulation module 112 will randomly select the LTC state 4804 as the new state. Likewise, if there is a 50% chance that the client will remain in the healthy state 4802, then there may be a 50% chance that the simulation module 112 may randomly select the healthy state 4802 as the new state. New states may also be found in a similar way for other modeled members of the client's household, if any.
When a new state is determined for the client and for any other modeled members of the client's household, the simulation module 112 may calculate the consequences of the new state and new interval to the financial variables. For example, the simulation module 112 may calculate the client's income and assets given the new state and new interval (4708). The client's assets may carry over from a previous interval and may, according to various embodiments, be based on the assets provided as input to the financial model, as well as any assets accumulated during the simulated life due to interest, purchase, etc.
The client's income in the new interval may include any incomes from the client's salary, the salary of other household members, investments, etc. According to various embodiments, new income may initially be assigned to a cash account. The client's new state as well as the new states of any modeled household members may affect the amount of income available. For example, if the client, or the client's spouse, has transitioned into a disabled state, such as LTC state 4804, Disabled state 4806, or Disabled in LTC state 4810, then the affected party's income from salary may cease. In that case, provided that the client has appropriate disability or long term care insurance, the salary may be replaced with an insurance payment. If the client or client's household member has transitioned into the Dead state 4808, their income will also cease, however life insurance, if available, will be payable to the client's estate or surviving household members. Also, if the new interval indicates the onset of retirement for the client, then the client's salary income may cease and may be replaced with various pension/Social Security income, depending on availability and eligibility.
The simulation module 112 may also calculate the client's expenses at the new interval and state. According to various embodiments, expenses may be classified according to an expense hierarchy including discretionary and non-discretionary expenses. Non-discretionary expenses may include, for example, taxes, minimum levels of basic expenses, health care expenses, insurance premium expenses, etc. The amount of health care expense attributed to the client during each interval may be stochastically determined, for example, as described below. It will be appreciated that some non-discretionary expenses may depend on the new state. For example, if the client or a member of the client's household enters a disability state, such as LTC state 4804, Disabled state 4806 or Disabled in LTC state 4810, additional expenses may be necessary to maintain the client or household member. Discretionary expenses may include, for example, contributions to retirement accounts (which may be classified as discretionary or non-discretionary), contributions toward the client's goals, basic expenses above minimum levels, and other investments.
At each interval, the simulation module 112 may allocate the available income and assets to the new expenses. Income and assets may first be applied to non-discretionary expenses, and then to discretionary expenses. According to various embodiments, the amount of available income and assets may be determined according to a funding hierarchy. The expense hierarchy and funding hierarchy may be utilized together to match income and assets to current expenses. Any suitable hierarchies could be used. In one example, however, cash assets and current income (e.g., from the cash account) may be applied first to non-discretionary expenses. If any non-discretionary expenses remain then liquid assets may be utilized. If liquid assets are exhausted before non-discretionary expenses are met, then various loans may be used (e.g., home equity loans, mortgage loans, qualified account loans, etc.). Loans themselves may be prioritized according to any suitable method. Finally, if non-discretionary expenses are still not met, other measures may be implemented, such as, for example, credit card financing, liquidating of physical assets, and liquidating qualified (e.g., retirement) accounts.
According to various embodiments, funding options above a given point on the funding hierarchy may not be used to fund discretionary expenses. For example, although the simulation module 112 may simulate loans and asset liquidations to meet non-discretionary expenses, these funding options may not be used to fund discretionary expenses. According to various embodiments, an exception to this general practice may exist for goal funding. For example, the user may specify that certain types of loans may be used to finance goals, as described above with respect to
According to various embodiments, the simulation module 112 may allocate available funding amount different goals at different levels. For example, prior to the target date, the simulation module 112 may begin a set-aside fund for each goal, based on the minimum and desired spend of the goal as well as the amount of time remaining between the current interval and the desired start date of the goal. For example, during each relevant interval, each goal may be expensed at two different levels. A first minimum level may represent an amount necessary to allow the client to meet a specified minimum goal spend. A second desirable level may represent an amount necessary to allow the client to meet a desired goal spend. Also, according to various embodiments, the user may have provided a priority for each goal, for example, as described above with respect to
According to various embodiments, the simulation module 112 may fund expenses directly from the cash account. Under some circumstances, it may be necessary for the simulation module 112 to rebalance the cash account. For example, if the cash account has a zero balance, the simulation module 112 may rebalance the account, by taking loans, selling assets, etc., for example, as set forth in the funding hierarchy. If all allowable loans and sales have been made, then the simulation module 112 may institute a crisis funding process, where long-term physical assets may be liquidated, for example, at reduced valuations. The cash account may also be rebalanced if its balance exceeds a given threshold (e.g., $10,000) or a given proportion of assets (e.g., if the cash account's forms a portion of total assets that is shifted by more than 500 bps). In these cases, the simulation module 112 may use the cash account funds to purchase assets according to an asset mix, which may be specified by the user or determined by the simulation module 112.
As shown by the process flow 4700, steps 4704, 4706, 4708, 4710 and 4712 may be continuously repeated until the client and all relevant members of the client's household reach the Dead state 4808. According to various embodiments, the simulation may end upon the deaths of the client and the client's spouse. Turning now to the exemplary state diagram 4800, the allowable states and state transitions will be examined. The Healthy state 4802 may represent a state where the client is in relatively good health. From the Healthy state 4802, four transitions are shown: 4850 to the LTC state 4804; 4852 to the Dead state 4808; 4854 to the Disabled state 4806; and 4856 back to the Healthy state. Each of the paths 4852, 4854, 4856 may have an associated probability representing the likelihood that a client will move along the respective path. For example, path 4850 between the Healthy state 4802 and the LTC state 4804 may be associated with the probability that the client will develop a need for long term care. The probabilities for each path may be taken and/or derived from an actuarial table, for example, as described below.
The LTC state 4804 may represent a state where the client is in need of long term care. This may be due to an injury, illness, etc. According to the exemplary state diagram 4800, a client in the LTC state 4804 is permitted to transition to the Healthy state 4802 along path 4858, to the Dead state along path 4860 or back to the LTC state 4804 along path 4862. The Disabled state 4806 may represent a state where the client has suffered a disability that prevents the client from working. From the Disabled state, the client may transition to the Disabled in LTC state 4810 along path 4861, to the Dead state 4808 along path 4864 or back to the Disabled state along path 4806. In the exemplary state diagram 4800, the Disabled state 4806 is assumed to permanent, hence no path is provided from the disabled state 4806 to the healthy state 4802.
The Disabled in LTC state 4810 may represent a state where the client is both disabled and in long term care. From the Disabled in LTC state 4810, the client may transition to the Disabled state 4806 along path 4868. In this case, the client may retain his or her disability, but may no longer require long term care. Also, from the Disabled in LTC state 4810, the client may transition to the Dead state 4808 along path 4872 or back to the Disabled in LTC state along path 4870. In the exemplary state diagram 4800, no transitions are allowed from the dead state 4808.
The exemplary state diagram 4800 shows just one set of allowable states and state transitions. It will be appreciated, however, that the allowable states and state transitions may vary based on the parameters of the financial model. For example,
The probabilities of transitioning between the various states may be taken from actuarial data and/or relationships. For example, a mortality rate may be used in determining the probability of progressing to the Dead state 4808 (e.g., along paths 4852, 4860, 4864 or 4872). According to various embodiments, the mortality rates used may be a function of age, sex and health status, although it will be appreciated that more dependencies may be found and utilized if desired. For any given client, sex may be specified by the input data. Age may be an indication of temporal position within the simulated life. The health status used to find the mortality rate for any given client may be determined according to various methods. For example, it may be determined based on the state of health reported by the client and may also depend on the clients state. For example, a client in the Disabled state 4806 may not exist in the same state of health as a client in the Healthy state 4802.
According to various embodiments, a correction factor may be applied to the mortality rate to adjust for increases in life expectancy that occur with time. For example, a set of correction factors may be derived. Each correction factor may correspond to an age of the client. The actual mortality rate may then be found, for example, according to Equation 1 below:
M=Mu×(1−F)t (1)
In Equation 1, M represents the actual mortality rate; Mu represents the unadjusted mortality rate, F represents the age-specific adjustment factor; and t represents length of time between the time at which the mortality rate is being found and the present time.
A long term disability rate may be used in determining the probability of transitioning to the Disabled state 4806 (e.g., along path 4854). Like the mortality rates, the long term disability rate for any given client may be a function of various factors including, for example, the client's age, sex and health status. Several long term care (LTC) related rates may be used in determining the probabilities of entering and exiting LTC state 4804 and Disabled in LTC state 4810. For example, an LTC incidence rate may described the probability that a client will enter LTC state 4804 and/or Disabled in LTC state 4810. The LTC incidence rate may be a function of various factors including, for example, marital status, age and health status. Other relevant LTC related rate is the LTC persistence rate and the LTC exit by death rate. The LTC persistence rate may describe the probability that a client in LTC will remain in LTC. This rate may be used to find the probabilities of transitioning along paths 4850 or 4862. According to various embodiments, the LTC persistence rate may be a function of age and the duration of the client's stay in the LTC state 4810 or 4804. For example, a client who has been in LTC for an extended period of time may be less likely to leave LTC. The LTC exit by death rate may describe the chance that when the client leaves an LTC state 4804 or 4810 by death, or returns to another state. The LTC exit by death rate may be used, for example, in conjunction with the LTC persistence rate, to determine the probabilities that a client will transition along paths 4860 or 4872.
As described above, morbidity, or the rate of healthcare spend, is another expense that may be considered by the financial model. According to various embodiments, morbidity may be characterized as a given amount of spend over a given period of time. This period of time may be chosen to correspond to the state recalculation interval described above. In this way, incorporating morbidity into the financial model may involve adding the calculated morbidity to the total expenses for each interval. According to various embodiments, morbidity may be expressed as a function of, for example, age, sex and health status. For example, morbidity may be represented as a set of health care spend levels, with each level associated with a probability that a person of a given age, sex and health status would incur healthcare related expenses of up to that level over the given period of time. The actual morbidity used at any given point in the financial model may be found by randomly selecting a spend level, given a particular client. For example, if actuarial data suggests that 1.08% of healthy males between the ages of 45 and 49 should expect to incur monthly healthcare expenses of $905, then approximately 1.08% of all clients existing as healthy men aged 46-49 would experience morbidity resulting in expenses of $905.
As used herein: the term, “client” refers to an individual or household that is the subject of a financial modeling tool; and the term, “user” refers to an operator of the financial modeling tool. In various embodiments, the user may also be a client or an employee of a client. In other non-limiting embodiments, the user may be a financial advisor providing advice to the client.
As used herein, “storing” when used in reference to a computer or computer system refers to any suitable type of storing operation including, for example, storing a value to memory, storing a value to cache memory, storing a value to a processor register, storing a value to a non-volatile data storage device, etc.
As used herein, a “computer” or “computer system” may be, for example and without limitation, either alone or in combination, a personal computer (PC), server-based computer, main frame, server, microcomputer, minicomputer, laptop, games console, personal data assistant (PDA), cellular phone, pager, processor, including wireless and/or wireline varieties thereof, and/or any other computerized device capable of configuration for processing data for standalone application and/or over a networked medium or media. Computers and computer systems disclosed herein may include operatively associated memory for storing certain software applications used in obtaining, processing, storing and/or communicating data. It can be appreciated that such memory can be internal, external, remote or local with respect to its operatively associated computer or computer system. Memory may also include any means for storing software or other instructions including, for example and without limitation, a hard disk, an optical disk, floppy disk, ROM (read only memory), RAM (random access memory), PROM (programmable ROM), EEPROM (extended erasable PROM), and/or other like computer-readable media.
The various modules 108, 112 of the system 110 may be implemented as software code to be executed by a processor(s) of the system 110 or any other computer system using any type of suitable computer instruction type. The software code may be stored as a series of instructions or commands on a computer readable medium.
While several embodiments of the invention have been described, it should be apparent that various modifications, alterations and adaptations to those embodiments may occur to persons skilled in the art with the attainment of some or all of the advantages of the present invention. It is therefore intended to cover all such modifications, alterations and adaptations without departing from the scope and spirit of the present invention as defined by the appended claims.
Claims
1. A method for modeling financial variables describing a client over a time period, the method comprising:
- generating a first simulation of the time period, wherein generating the first simulation comprises: assigning the client to a first health-related state; advancing the first simulation from a first interval of the time period to a second interval of the time period; calculating a first probability that the client will transition from the first health-related state to a second health-related state; randomly assigning the client to at least one of the first health-related state and the second health-related state considering the first probability; calculating a client income for the second interval; and calculating a plurality of client expenses for the second interval.
2-32. (canceled)
Type: Application
Filed: Jul 12, 2010
Publication Date: Nov 11, 2010
Inventors: Trevor Samuel Harris (Closter, NJ), Richard Graham Foster (Haslemere), Marc Daniel Donner (NewYork, NY), Matthew P. Thomas (Rye, NY), Jonathan Stanley Roach (Cambridgeshire), Malcolm H. Lansell (Wellingborough)
Application Number: 12/804,013
International Classification: G06Q 40/00 (20060101);