A computer system configured to evaluate a portfolio comprising instruments, comprising a computer memory configured to store, for each instrument, an instrument value for each portfolio scenario in an n-dimensional matrix, a first constraint and a second constraint; and a computer processor configured to transpose the n-dimensional matrix, to determine a first solution by maximizing the product of transpose of the n-dimensional matrix and the first constraint, determine whether the first solution is within an accepted range of an acceptable risk, if the expected first solution is not within an accepted range of an acceptable risk, process the second constraint with the first solution to obtain a second solution, and determine whether the second solution is within the accepted range of the acceptable risk.

A technical problem facing those interested in evaluating large portfolios of financial instruments is that the evaluation problems are very complex and the current technical solutions for evaluating them are not efficient to permit a timely analysis. Evaluation of the portfolios are defined by many permutations, constraints, and variables, such that when a significant number of financial instruments need to be evaluated, the complexity of the problem is too large to solve in a timely and efficient matter. Current technical solutions are inefficient because the problems are not efficiently solved to permit evaluation of the financial instruments in a timely manner to react to different potential scenarios that may arise.

One specific application where this problem arises is in the evaluation of insurance portfolios. Insurance portfolios generally comprise a significant number of financial instruments, such as insurance policies. Each financial instrument within the portfolio has an expected value for a given scenario. A scenario is, for example, a different event, such as a hurricane, earthquake, flood, wind storm, hail storm, ice storm, snow event, fire, having a certain severity. Because the events vary in severity, there are many different scenarios for each type of event. It is helpful to evaluate a portfolio of insurance financial instruments under the many different events at different severity levels to determine the value of the financial instrument under each event and each severity. It is further helpful to determine how to maximize or minimize values associated with the portfolio. For example, it is desirable to evaluate how to maximize the profits of the financial instruments for a given risk, or how to determine the risk for a given desired profit. Generally, the current technical solutions for doing so are not efficient and timely to permit timely decisions based on evaluating this data. The inventions disclosed herein address that problem by providing new technical solutions that permit the timely and efficient evaluation of the portfolio such that it can be optimized. The timely evaluation permits consideration of which instruments to sell, buy, adjust pricing, or adjust policy terms.

This invention includes a computer system configured to evaluate a portfolio comprising instruments, comprising a computer memory configured to store, for each instrument, an instrument value for each portfolio scenario in an n-dimensional matrix, a first constraint and a second constraint; and a computer processor configured to transpose the n-dimensional matrix, to determine a first solution by maximizing the product of transpose of the n-dimensional matrix and the first constraint, determine whether the first solution is within an accepted range of an acceptable risk, if the expected first solution is not within an accepted range of an acceptable risk, process the second constraint with the first solution to obtain a second solution, and determine whether the second solution is within the accepted range of the acceptable risk.

This invention further includes a computer-readable storage medium having embodied thereon a program, the program being executable by a computer processor to perform a method for optimizing a portfolio, the method transposing an n-dimensional matrix to determine a first solution by maximizing a product of the transpose of a n-dimensional matrix comprising for a plurality of instruments, an instrument value for a plurality of scenarios, and a first constraint; determining whether the first solution is within an accepted range of an acceptable risk; if the expected first solution is not within an accepted range of an acceptable risk, processing a second constraint with the first solution to obtain a second solution; and determining whether the second solution is within the accepted range of the acceptable risk.

A detailed description of illustrative embodiments will now be described with reference to the various figures. Although this description provides a detailed example of possible implementations, it should be noted that the details are intended to be exemplary and in no way limit the scope of the application.

**100** in which one or more disclosed embodiments may be implemented. The computer system **100** may be a stand alone computer, tablet, laptop, or a mobile computer. **100** processor **118** in which one or more disclosed embodiments may be implemented. The processor **118** may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Array (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor **118** may perform signal coding, data processing, power control, input/output processing, and/or any other functionality that enables the system to operate in a wireless environment. The processor **118** may be coupled to the transceiver **120**, which may be coupled to the transmit/receive element **122**. While **118** and the transceiver **120** as separate components, it will be appreciated that the processor **118** and the transceiver **120** may be integrated together in an electronic package or chip.

The processor **118** may be coupled to, and may receive user input data from, an input device such as a speaker/microphone **124**, keypad **126**, and/or a display/touchpad **128** (e.g., a liquid crystal display (LCD) display unit or organic light-emitting diode (OLED) display unit). The processor **118** may also output user data to the speaker/microphone **124**, the keypad **126**, and/or the display/touchpad **128**. In addition, the processor **118** may access information from, and store data in, any type of suitable memory, for example the non-removable memory **130** and/or the removable memory **132**. The non-removable memory **130** may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory **132** may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In an embodiment, the processor **118** may access information from, and store data in, memory that is not physically located in the computing system, for example on another server or a home computer (not shown).

The processor **118** may receive power from the power source **134**, and may be configured to distribute and/or control the power to the other components in the computing system **102**. The power source **134** may be any suitable device for powering the computing system. For example, the power source **134** may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, and the like.

The processor **118** may also be coupled to the GPS chipset **136**, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the computer system **100**. In addition to, or in lieu of, the information from the GPS chipset **136**, the computer system **100** may receive location information over the air interface **115**/**116**/**117** from a base station and/or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the computer system **100** may acquire location information by way of any suitable location-determination method while remaining consistent with an embodiment.

The processor **118** may further be coupled to other peripherals **138**, which may include one or more software and/or hardware modules that provide additional features, functionality and/or wired or wireless connectivity. For example, the peripherals **138** may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, and the like.

The processor **118** may be programmed to evaluate a portfolio with the algorithms as shown in **130**, **132** as executable instructions. A portfolio of financial instruments can be inputted into the computing system, received and processed by the processor, and stored in the memory along with a number of scenarios. A nonlimiting example of a financial instrument is an insurance or reinsurance policy. A scenario comprises an insurance event such as a hurricane, earthquake, flood, typhoon, mud slide, avalanche, wind storm, hail storm, ice storm, snow event, or fire and a severity for the event. The severity may indicate the amount of expected damage. Each financial instrument in the portfolio can have an expected value for each scenario. For some scenarios, the value is negative due to a catastrophic loss associated with the scenario. However, the expected instrument value is typically positive because the premiums collected are more than the expected loss for a scenario. This value for each instrument for a particular event can be inputted into the computing system, received and processed by the processor, and stored in the memory or calculated by the processor with the input device. The processor can form a scenario matrix Y having n columns with each column corresponding to a particular financial instrument in the portfolio, and having J rows with each row corresponding to a different scenario. Each entry in the matrix represents the value of the financial instrument in that column for a particular scenario. The sum of the columns represents the expected value of the instruments for different scenarios. The sum of each row represents the portfolio value for each scenario. The processor may be programmed to calculate and store in memory an outcome vector that comprises the sum of the rows; the processor can store the row summations in memory in matrix denoted as y. An example of the matrix is shown in

The processor can be programmed with an algorithm **300** to determine the risk of the portfolio by using the “Conditional Value-at-Risk (CVaR)” also called the “Tail Value-at-Risk (TVaR), as described in Pavlo Krokhmal, Jonas Palmquist, and Stanislav Uryasev, Portfolio Optimization with Conditional Value-at-Risk Objective and Constraints, *Journal of Risk, *4:11-27 (2002), as shown in **300** can determine the CVaR for a return period p (provided that the number of scenarios J is a multiple of the return period ρ) by executing the following steps:

1. Retrieving the return period “ρ” from memory at **301**,

2. Obtaining the outcome vector “o” by calculating the matrix product of Y and x at **302**

3. Reordering the entries of o in increasing fashion to obtain the ordered outcome vector “o” at **303**,

4. store the vector o′ in memory at **304**,

5. Calculating the risk vector “r” for each scenario as follows at **305**:

Here “J” embodies the number of scenarios.

6. At **306**, the processor can then determine the CVaR by calculating the matrix product of the transpose of r and o′, which is provided as rTo′.

The processor can be programmed with the algorithm **400** in **401**, a constraint C is defined and stored in memory. The constraint C can be input into the computing system with the input mechanism or determined by the processor. In a preferred example, the constraint C reflects how much the shares in insurance policies can realistically be varied by. If the share in a particular policy can be expected to be grown by up to 20% or shrunk by up to 10%, the constraint would be represented as: 0.8 less than or equal to x less than or equal to 1.2, where x embodies the position for this policy. There are many other constraints, but in this algorithm, only one constraint above is selected at step **401**. The preferred embodiment of the other constraints is an acceptable risk level. At step **402**, the processor can be programmed to determine the vector column sums of the matrix Y and this summation can be represented by “p.” The product (p^{T}x) of the transpose of p multiplied by the position vector x constraint yields the expected profit of a position vector x. This vector x can be calculated in the following step **403**, the processor can be programmed to maximize the product of the transpose of p multiplied by the constraint (p^{T}x). By doing so a position vector x is determined. The techniques to perform this step are well known and referred to as linear programming. A preferred example to perform this step is the open source software Ip_solve 5.5: http://lpsolve.sourceforge.net/.

The processor **400** can be programmed to compare the risk R to an acceptable risk level. The risk error tolerance can be denoted δ and can be inputted with the input mechanism, processed by the computer and stored in memory, or determined by the processor. At step **404**, the processor can calculate the achieved risk R*. This can be done by calculating the CVaR as described in steps 1 to 5 set forth above . At step **405**, the processor can compare the risk error tolerance level δ to determine whether the risk error tolerance δ is greater than or equal to the difference between the determined risk R* and R. If the risk R is within the risk error tolerance (e.g., δ is greater than or equal the difference of R* and R), a solution has been achieved that maximizes the profit for that risk. The processor can then at **406** determine the output of the algorithm being the solution x for the achieved risk and determine the profit s=p^{T}x.

If the risk R is not within an acceptable risk error level, the processor can at step **408** add a second constraint π. An example of a second constraint is a constraint that enforces the acceptable risk level R of the portfolio on the particular position vector x at hand. The processor can then return to step **403** and continue to calculate new position vectors x and continue to add further constraints until a solution is achieved.

The inventions disclosed herein significantly reduce processing time for evaluating the portfolio matrix values for expected profit levels for a given risk and the potential number of constraints, thereby enabling portfolio managers the ability to make timely decisions on handling of financial instruments within a portfolio. In other preferred embodiments, the constraints are Transaction Cost Balance Constraints, Value Constrains, and Liquidity Constraints. Other constraints can be used.

The algorithms of this invention can be implemented in a variety of software programs. An exemplary program is provided below in the R version 3.0.2 of 1pSolve software which is based on 1p_solve 5.5.

Although features and elements are described above in particular combinations, one of ordinary skill in the art will appreciate that each feature or element may be used alone or in any combination with the other features and elements. In addition, the methods described herein may be implemented in a computer program, software, or firmware incorporated in a computer-readable medium for execution by a computer or processor. Examples of computer-readable media include electronic signals (transmitted over wired or wireless connections) and computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, a read only memory (ROM), a random access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, optical media such as CD-ROM disks, and digital versatile disks (DVDs). Examples of computing systems are personal computers, laptop computers, tablets, and smart phones.