System for estimating distance-to-default credit risk

A method, computer system, and computer program product are provided for assessing a credit risk of a set of companies. A computer system creates a training data set from distance-to-default values for a first set of companies. The computer system builds a set of predictive models based on the training data set, linking the observed distance-to-default to market capitalization and total liabilities. The computer system forecasts estimated new distance-to-default values for a second set of companies, based on their current distance-to-default (obtained from the Merton approach), and a future change in market capitalization and/or change in total liabilities, according to the set of predictive models.

BACKGROUND INFORMATION

The present disclosure relates generally to an improved system and method, which can be embodied in an apparatus, computer system, or computer program product, for assessing a credit risk for a set of companies.

Economist Robert C. Merton proposed a model for assessing the structural credit risk of a company by modeling the company's equity as a call option on its assets. Analysts and investors utilize the Merton model to understand how capable a company is at meeting financial obligations, servicing its debt, and weighing the possibility that it will go into credit default.

The distance-to-default is a measure of credit risk that is based on Merton's model. Among market practitioners, it is widely agreed that the distance-to-default is a useful measure for assessing the credit risk of a nonfinancial corporation. since the firm defaults when its asset value falls below the face value of its debt, the strike price or default barrier is equal to the value of the liabilities. The concept of a distance measure of risk follows simply that the higher the value of the firm assets, VA, relative to the strike price or default barrier, D, the farther away from default the firm is.

Calculation of a publicly listed company's distance-to-default via the Merton approach requires a time series of daily stock returns and the iterative solution of two unknowns in a nonlinear system of equations. The calculation of a company's distance-to-default via the Merton approach is both time-consuming and computationally intensive. The computation becomes particularly time consuming when one needs to calculate how the current distance-to-default for each company within a big portfolio of thousands of firms changes due a change in each firm's market capitalization or total liabilities.

Various attempts have been made to speed up the estimation of public firms' Merton's distance-to-default under various scenarios. For example, Chen proposed a numerical approximation for estimating the change of a firm's distance-to-default due to a change of the underlying market capitalization or total liabilities by leveraging the partial derivations of asset value and asset volatility, which have a closed-form expression, with respect to the underlying market capitalization or total liabilities. Chen's estimated change in distance-to-default can be computed from the estimated change in asset value and asset volatility as indicated by the partial derivatives and the change in the underlying factors. However, this approximation approach works well only locally, for a small change in the underlying factors.

Therefore, it would be desirable to have a method and apparatus that take into account at least some of the issues discussed above, as well as other possible issues. For example, it would be desirable to have a method and apparatus that overcome a technical problem with determining company's distance-to-default that is more time-consuming and computationally intensive than desired.

SUMMARY

An embodiment of the present disclosure provides a credit evaluation system comprising a computer system and a risk estimator in the computer system. The risk estimator operates to create a training data set from distance-to-default values for a first set of companies; to build a set of predictive models based on the training data set; and to forecast an estimated change in distance-to-default values for a second set of companies according to the set of predictive models.

Another embodiment of the present disclosure provides a method for assessing a credit risk of a set of companies. A computer system creates a training data set from distance-to-default values for a first set of companies. The computer system builds a set of predictive models based on the training data set. The computer system forecasts an estimated change in distance-to-default values for a second set of companies according to the set of predictive models.

Still another embodiment of the present disclosure provides a computer program product for assessing a credit risk for a set of companies, the computer program product comprising a computer readable storage media with program code stored on the computer-readable storage media. The program code includes code for creating a training data set from distance-to-default values for a first set of companies; code for building a set of predictive models based on the training data set; and code for forecasting an estimated change in distance-to-default values for a second set of companies according to the set of predictive models.

DETAILED DESCRIPTION

With reference now to the figures and, in particular, with reference toFIG.1, a pictorial representation of a network of data processing systems is depicted in which illustrative embodiments may be implemented. Network data processing system100is a network of computers in which the illustrative embodiments may be implemented. Network data processing system100contains network102, which is the medium used to provide communications links between various devices and computers connected together within network data processing system100. Network102may include connections, such as wire, wireless communication links, or fiber optic cables.

In the depicted example, server computer104and server computer106connect to network102along with storage unit108. In addition, client devices110connect to network102. As depicted, client devices110include client computer112, client computer114, and client computer116. Client devices110can be, for example, computers, workstations, or network computers. In the depicted example, server computer104provides information, such as boot files, operating system images, and applications to client devices110. Further, client devices110can also include other types of client devices such as mobile phone118, tablet computer120, and smart glasses122. In this illustrative example, server computer104, server computer106, storage unit108, and client devices110are network devices that connect to network102in which network102is the communications media for these network devices. Some or all of client devices110may form an Internet-of-things (IoT) in which these physical devices can connect to network102and exchange information with each other over network102.

Client devices110are clients to server computer104in this example. Network data processing system100may include additional server computers, client computers, and other devices not shown. Client devices110connect to network102utilizing at least one of wired, optical fiber, or wireless connections.

Program code located in network data processing system100can be stored on a computer-recordable storage medium and downloaded to a data processing system or other device for use. For example, the program code can be stored on a computer-recordable storage medium on server computer104and downloaded to client devices110over network102for use on client devices110.

In this illustrative example, risk estimator126is located in server computer104. As depicted, risk estimator126operates to generate training data set128for training artificial intelligence system130to assess a credit risk based on an estimated change in distance-to-default value.

With reference now toFIG.2, a block diagram of a credit evaluation environment is depicted in accordance with an illustrative embodiment. In this illustrative example, credit evaluation environment200includes components that can be implemented in hardware such as the hardware shown in network data processing system100inFIG.1.

As depicted, credit evaluation environment200is an environment in which risk estimator202manages credit evaluation system204in computer system206to provide services for assessing a credit risk for a set of companies.

In this illustrative example, risk estimator202and computer system206form credit evaluation system204. In managing credit evaluation system204to provide services, risk estimator202can perform estimations of distance-to-default values208for use in assessing a credit risk of a set of companies.

Risk estimator202can include a number of different components for assessing a credit risk for a set of companies. As depicted, risk estimator202includes data generation210, data classification212, and artificial intelligence system214.

As depicted, risk estimator202uses data generation210to create a training data set216from distance-to-default values208for a first set of companies218. Risk estimator202builds a set of predictive models220based on the training data set216. Risk estimator202then forecasts estimated values222of the distance-to-default for a second set of companies224according to the set of predictive models220.

The predictive models220of risk estimator202provide a numerical calibration that directly approximates a change to the distance-to-default based on a change in the underlying factors, without the need to compute the change in asset value and asset volatility. Unlike other solutions, risk estimator202works for large changes in the underlying factors. Furthermore, because prediction of estimated values222for individual companies does not require the computational intensity of other solutions, risk estimator202is more efficient determining potential credit risk, enabling faster distance to default determinations, and parallelizable estimation of changes in the distance-to-default for thousands of companies.

The predictive models220of risk estimator202speed up the estimation of public firms' Merton's distance-to-default under various scenarios, for any initial distance-to-default value. A numerical calibration enables the quick and reasonably accurate estimate of the distance-to-default of thousands of public companies, under various scenarios (e.g., what happens if their market capitalization decreases by 10%, or their total liabilities double), without the need for time-consuming and data-intensive calculations performed at individual company level.

In this illustrative example, risk estimator202includes artificial intelligence system214that comprises one or more predictive models220. Artificial intelligence system214is a system that has intelligent behavior and can be based on function of the human brain. Artificial intelligence system214comprises at least one of an artificial neural network, an artificial neural network with natural language processing, a cognitive system, a Bayesian network, a fuzzy logic, an expert system, a natural language system, a cognitive system, or some other suitable system.

In one illustrative example, machine learning is used to train predictive models220of artificial intelligence system214. Machine learning involves inputting data to the process and allowing the process to adjust and improve the function of the artificial intelligence system. In one illustrative example, artificial intelligence system214operates to train one or more of predictive models220for use in predicting distance-to-default in a supervised learning process.

During a supervised learning, the values for the output are provided along with the training data (labeled dataset) for the model building process. The algorithm, through trial and error, deciphers the patterns that exist between the input training data and the known output values to create a model that can reproduce the same underlying rules with new data. Examples of supervised learning algorithms include regression analysis, decision trees, k-nearest neighbors, neural networks, and support vector machines.

The set of predictive models220is trained from training data set216, one or more of predictive models220are numerically calibrated based the distance-to-default of first set of companies218. Once trained, predictive models220enables risk estimator202to provide a quick and accurate estimate of credit risk, under various scenarios, without the need for time-consuming and data-intensive calculations of distance-to-default values208performed at individual company level. For example, using predictive models220, risk estimator202can quickly determine what happens to a company's credit risk if the company's market capitalization decreases by 10%, or the company's total liabilities double.

In one illustrative example, risk estimator202creates the training data set216by identifying market capitalizations226and total liabilities228for the first set of companies218. For each of the first set of companies218, risk estimator202determines the distance-to-default values208according to the market capitalization226and total liabilities of the first set of companies218.

In one illustrative example, risk estimator202uses the Merton model to determine distance-to-default values208for first set of companies218. In the case of the Merton model, where it is assumed that the asset value of the firm follows a geometric Brownian motion process, the distance-to-default values208for first set of companies218can be determined by:

VAis the market-implied value of the asset;

D is the default point (e.g., total liabilities);

μ is the asset return;

σAis the asset volatility; and

T is the time horizon.

Equation (1) simply states that the distance-to-default is the expected difference between the asset value of the firm relative to the default barrier, after correcting and normalizing for the volatility of assets.

The asset volatility σAis generally unobservable. Instead, asset volatility σAmust be determined indirectly from the observable equity volatility σE. Asset volatility σAis related to equity volatility σEby:

VEis the equity value;

VAis the asset value; and

ΔEis a measure of the sensitivity of equity value VEto the underlying asset value VA

Assuming that a company is publicly traded, the equity volatility σEof the firm can be calculated from the standard deviation of the firm's stock price returns. Equity volatility σEis a statistical measure of the dispersion of returns for a given security or market index. Equity volatility σEaffected by a firm's leverage and is not directly interchangeable with asset volatility σA.

The equity value VErepresents a residual claim on the firm's assets beyond the payoff of the debt principal at maturity. As such the equity value VEcan be considered a call option on the firm's assets. If the underlying asset value of the firm follows a stochastic process with constant drift and volatility, then equity value VEcan be priced using the standard formula:
VE=VAN(d1)−Ke−rT*N(d2)  Eq. 3
Where:

N is the cumulative standard normal distribution;

P is the principal due to the bondholders at maturity;

T is the time to maturity of the bond; and

r is the continuously compounded risk-free interest rate.

Solving from equation 3, asset value can be determined by:

As can be seen in the above equations, asset value and asset volatility are interdependent. Therefore, calculation of a publicly listed company's distance-to-default via the Merton approach requires a time series of daily stock returns and the iterative solution of two unknowns in a nonlinear system of equations:

In the illustrative examples, distance to default values208for the training data set216are calculated via the iterative approach first set of companies218on different assessment dates. In this illustrative example, first set of companies218are selected randomly such that a wide range of distance-to-default values208and interest rate values are covered.

In one illustrative example, risk estimator202creates a training data set216by generating a set of triplex values230for each of the first set of companies218. In this illustrative example, the training data set216comprises the set of triplex values230. Data generation210generates a set of triplex values230from a multiplier ratio232, the distance-to-default values208for the first set of companies218, and modified distance-to-default values234for the first set of companies218.

Multiplier ratio232is a ratio of multipliers236. Each multiplier236is a scale factor of the various underlying contributing factors of distance to default values on a different scale. For example, set of multipliers236may be applied to market capitalization and total liabilities to generate a modified market capitalization and modified total liabilities.

In one illustrative example, risk estimator202identifies a set of multipliers236for market capitalizations226and total liabilities228of the first set of companies218. Each multiplier ratio232is a ratio between one of the multipliers236for the market capitalizations226of the first set of companies218and one of the multipliers236for the total liabilities228of the first set of companies218.

In this illustrative example, risk estimator202generates a modified market capitalization238and modified total liabilities240according to the set of multipliers236. For example, for each of the first set of companies218, risk estimator202scales the market capitalization226and total liabilities228of all companies by the same multipliers, respectively, and recalculate the actual distance-to-default values208based on the modified market capitalization and modified total liabilities. Using the iterative approach as described above, risk estimator202determines a set of modified distance-to-default values234for each of the first set of companies218, according to the modified market capitalization and the modified total liabilities.

Risk estimator202repeats the determination with different values of multiplier236for the market capitalization226and total liabilities228to obtain a set of triplex values230. Each of triplex values230can be represented as:

m1is the multiplier for market capitalization;

m2is the multiplier for total liabilities;

DD0is the original distance-to-default values before applying the multipliers; and

DD1is the adjusted distance-to-default values after applying the multipliers.

In one illustrative example, risk estimator202uses data classification212to separate the sets of triplex values230into training data subsets242as part of building the set of predictive models. In this illustrative example, risk estimator202separates the set of triplex values230according to their multiplier ratio232and their distance-to-default values208. For example, risk estimator202splits the triplex data points into four (4) training data subsets groups:

In this example, artificial intelligence system214builds different ones of predictive models220based on each of the training data subsets242. For example, artificial intelligence system214can fit a parametric function for each training data subsets242of triplex values230.
=f(DD0,m,{right arrow over (θ)})  Eq. 8
Where:

is the estimated distance-to-default value; and

{right arrow over (θ)} is the trainable parameters.

Using the predictive models220trained with data set b, risk estimator202can forecast estimated values222of distance-to-default for the second set of companies224. In this example, artificial intelligence system214uses the set of predictive models220to forecast estimated values222of distance-to-default for the second set of companies224according to market capitalizations226and total liabilities228of the second set of companies224.

The predictive models220of risk estimator202speed up the estimation of public firms' Merton's distance-to-default under various scenarios, for any initial distance-to-default value. Training one or more predictive models220with training data set216enables risk estimator202to perform a quick and reasonably accurate estimate of the distance-to-default of thousands of public companies, under various scenarios, without the need for time-consuming and data-intensive calculations performed at individual company level.

In one illustrative example, one or more technical solutions are present that overcome a technical problem with the large amount of computational resources used in determining Merton's distance-to-default. As a result, one or more technical solutions can provide a technical effect of generating a training data set216for training one or more predictive models220that can accurately predict distance-to-default values using fewer computational resources as compared to systems that use previous techniques.

Computer system206can be configured to perform at least one of the steps, operations, or actions described in the different illustrative examples using software, hardware, firmware, or a combination thereof. As a result, computer system206operates as a special purpose computer system in which risk estimator202in computer system206enables a method for assessing a credit risk of a set of companies. In particular, risk estimator202transforms computer system206into a special purpose computer system as compared to currently available general computer systems that do not have risk estimator202.

In the illustrative example, the use of risk estimator202in computer system206integrates processes into a practical application for assessing a credit risk for a set of companies that increases the performance of computer system206in estimating distance-to-default values using predictive models220that were trained using training data set216.

FIG.3illustrates an example of a recurrent neural network in which illustrative embodiments can be implemented. RNN300might comprise part of artificial intelligence system214inFIG.2. RNNs are recurrent because they perform the same task for every element of a sequence, with the output being depended on the previous computations. RNNs can be thought of as multiple copies of the same network, in which each copy passes a message to a successor. Whereas traditional neural networks process inputs independently, starting from scratch with each new input, RNNs persistence information from a previous input that informs processing of the next input in a sequence.

RNN300comprises an input vector302, a hidden layer304, and an output vector306. RNN300also comprises loop308that allows information to persist from one input vector to the next. RNN300can be “unfolded” (or “unrolled”) into a chain of layers, e.g.,310,320,330to write out RNN300for a complete sequence. Unlike a traditional neural network, which uses different weights at each layer, RNN300shares the same weights U, W across all steps. By providing the same weights and biases to all the layers310,320,330, RNN300converts the independent activations into dependent activations.

The input vector312at time step t−1 is xt−1. The hidden state ht−1314at time step t−1, which is required to calculate the first hidden state, is typically initialized to all zeroes. The output vector316at time step t−1 is yt−1. Because of persistence in the network, at the next time step t, the state ht324of the layer320is calculated based on the hidden state ht−1314and the new input vector xt322. The hidden state acts as the “memory” of the network. Therefore, output yt326at time step t depends on the calculation at time step t−1. Similarly, output vector yt+1336at time step t+1 depends on hidden state ht+1334, calculated from hidden state ht324and input vector xt+1332.

Training a neural network is conducted with standard mini-batch stochastic gradient descent-based approaches, where the gradient is calculated with the standard backpropagation procedure. In addition to the neural network parameters, which need to be optimized during the learning procedure, there are the weights for different distributions, which also need to be optimized based on the underlying dataset. Since the weights are non-negative, they are mapped to the range [0,1] while simultaneously requiring them summed to be 1.

In machine learning, a cost function estimates how the model is performing. It is a measure of how wrong the model is in terms of its ability to estimate the relationship between input x and output y. This is expressed as a difference or distance between the predicted value and the actual value. The cost function (i.e., loss or error) can be estimated by iteratively running the model to compare estimated predictions against known values of y during supervised learning. The objective of a machine learning model, therefore, is to find parameters, weights, or a structure that minimizes the cost function.

Gradient descent is an optimization algorithm that attempts to find a local or global minimum of a function, thereby enabling the model to learn the gradient or direction that the model should take in order to reduce errors. As the model iterates, it gradually converges towards a minimum where further tweaks to the parameters produce little or zero changes in the loss. At this point the model has optimized the weights such that they minimize the cost function.

Neural networks are often aggregated into layers, with different layers performing different kinds of transformations on their respective inputs. A node layer is a row of nodes that turn on or off as input is fed through the network. Signals travel from the first (input) layer to the last (output) layer, passing through any layers in between. Each layer's output acts as the next layer's input.

Neural networks can be stacked to create deep networks. After training one neural network, the activities of its hidden nodes can be used as input training data for a higher level, thereby allowing stacking of neural networks. Such stacking makes it possible to efficiently train several layers of hidden nodes.

Turning next toFIGS.4-6, examples of actual DD1values and the values estimated by parametric functions are illustrated under different values of DD0and m. The estimated values illustrated inFIGS.4-6were predicted using parametric functions generated from a training data set, such as training data set216ofFIG.2.

As illustrated by the estimated values ofFIGS.4-6, parametric functions generated from a training data set as described above offer a good approximation of distance-to-default values that links the change in the distance-to-default directly to changes in the underlying factors, including market capitalization and total liabilities. Parametric functions generated from a training data set as described above work for a large change in the underlying factors. Furthermore, parametric functions generated from a training data set as described above, enable fast and parallelizable estimation of the change in the distance-to-default for thousands of companies, without the need to compute the change in asset value and asset volatility.

Turning next toFIG.7, a flowchart of a process for assessing a credit risk of a set of companies is depicted in accordance with an illustrative embodiment. The process inFIG.7can be implemented in hardware, software, or both. When implemented in software, the process can take the form of program code that is run by one or more processor units located in one or more hardware devices in one or more computer systems. For example, the process can be implemented in risk estimator202in computer system206inFIG.2.

The process begins by creating a training data set from distance-to-default values for a first set of companies (step710). The training data set can be training data set216ofFIG.2. In one illustrative example, the distance-to-default values for the first set of companies can be determined using the Merton model and an iterative solution of a nonlinear system of equations to determine the asset value VAand the asset volatility σA.

The process builds a set of predictive models based on the training data set (step720). The predictive models can be predictive models220ofFIG.2.

The process forecasts an estimated change in distance-to-default values for a second set of companies according to the set of predictive models (step730) and terminates thereafter. In one illustrative example, the estimated change in distance-to-default values for the second set of companies is forecast from the set of predictive models according to market capitalizations and total liabilities of the second set of companies.

Turning next toFIG.8, a flowchart of a process for creating a training data set from distance-to-default values for a first set of companies is depicted in accordance with an illustrative embodiment. The process ofFIG.8is an example of process step710ofFIG.7.

As depicted, process step710for creating the training data set further comprises identifying market capitalizations and total liabilities for the first set of companies (step810). For each of the first set of companies, the process determines the distance-to-default values according to the market capitalization and total liabilities of the first set of companies (step820). Thereafter, the process proceeds to step720ofFIG.7.

Turning next toFIG.9, a flowchart of a process for creating a training data set and building predictive models is depicted in accordance with an illustrative embodiment. The process ofFIG.9is an example of process steps710and720ofFIG.7.

In one illustrative example, step710for creating the training data set further comprises generating a set of triplex values from a multiplier ratio, the distance-to-default values for the first set of companies, and modified distance-to-default values for the first set of companies, wherein the training data set comprises the set of triplex values (step910). The set of triplex values can be triplex values230, shown in block form inFIG.2.

In one illustrative example, step910includes identifying a set of multipliers for market capitalizations and total liabilities of the first set of companies, wherein each multiplier ratio is a ratio between one of the multipliers for the market capitalizations of the first set of companies and one of the multipliers for the total liabilities of the first set of companies (step920). For each of the first set of companies, the process generates a modified market capitalization and modified total liabilities according to the set of multipliers (step930). For each of the first set of companies, the process determines a set of modified distance-to-default values according to the modified market capitalization and the modified total liabilities (step940).

In one illustrative example, step720for building the set of predictive models further comprises separating the set of triplex values into training data subsets, wherein the set of triplex values are separated according to the multiplier ratio and the distance-to-default values of the first set of companies (step950). The process builds predictive models based on each of the training data subsets (step960). Thereafter, the process proceeds to step730ofFIG.7.

Turning now toFIG.10, a block diagram of a data processing system is depicted in accordance with an illustrative embodiment. Data processing system1000can be used to implement server computer104, server computer106, client devices110, inFIG.1. Data processing system1000can also be used to implement computer system206inFIG.2.

In this illustrative example, data processing system1000includes communications framework1002, which provides communications between processor unit1004, memory1006, persistent storage1008, communications unit1010, input/output (I/O) unit1012, and display1014. In this example, communications framework1002takes the form of a bus system.

Processor unit1004serves to execute instructions for software that can be loaded into memory1006. Processor unit1004includes one or more processors. For example, processor unit1004can be selected from at least one of a multicore processor, a central processing unit (CPU), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a network processor, or some other suitable type of processor.

Memory1006and persistent storage1008are examples of storage devices1016. A storage device is any piece of hardware that is capable of storing information, such as, for example, without limitation, at least one of data, program code in functional form, or other suitable information either on a temporary basis, a permanent basis, or both on a temporary basis and a permanent basis. Storage devices1016may also be referred to as computer-readable storage devices in these illustrative examples. Memory1006, in these examples, can be, for example, a random-access memory or any other suitable volatile or non-volatile storage device. Persistent storage1008may take various forms, depending on the particular implementation.

For example, persistent storage1008may contain one or more components or devices. For example, persistent storage1008can be a hard drive, a solid-state drive (SSD), a flash memory, a rewritable optical disk, a rewritable magnetic tape, or some combination of the above. The media used by persistent storage1008also can be removable. For example, a removable hard drive can be used for persistent storage1008.

Communications unit1010, in these illustrative examples, provides for communications with other data processing systems or devices. In these illustrative examples, communications unit1010is a network interface card.

Input/output unit1012allows for input and output of data with other devices that can be connected to data processing system1000. For example, input/output unit1012may provide a connection for user input through at least one of a keyboard, a mouse, or some other suitable input device. Further, input/output unit1012may send output to a printer. Display1014provides a mechanism to display information to a user.

Instructions for at least one of the operating system, applications, or programs can be located in storage devices1016, which are in communication with processor unit1004through communications framework1002. The processes of the different embodiments can be performed by processor unit1004using computer-implemented instructions, which may be located in a memory, such as memory1006.

These instructions are referred to as program code, computer usable program code, or computer-readable program code that can be read and executed by a processor in processor unit1004. The program code in the different embodiments can be embodied on different physical or computer-readable storage media, such as memory1006or persistent storage1008.

Program code1018is located in a functional form on computer-readable media1020that is selectively removable and can be loaded onto or transferred to data processing system1000for execution by processor unit1004. Program code1018and computer-readable media1020form computer program product1022in these illustrative examples. In the illustrative example, computer-readable media1020is computer-readable storage media1024.

In these illustrative examples, computer-readable storage media1024is a physical or tangible storage device used to store program code1018rather than a medium that propagates or transmits program code1018. The term “non-transitory” or “tangible”, as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

Alternatively, program code1018can be transferred to data processing system1000using a computer-readable signal media. The computer-readable signal media can be, for example, a propagated data signal containing program code1018. For example, the computer-readable signal media can be at least one of an electromagnetic signal, an optical signal, or any other suitable type of signal. These signals can be transmitted over connections, such as wireless connections, optical fiber cable, coaxial cable, a wire, or any other suitable type of connection.

Further, as used herein, “computer-readable media” can be singular or plural. For example, program code1018can be located in computer-readable media1020in the form of a single storage device or system. In another example, program code1018can be located in computer-readable media1020that is distributed in multiple data processing systems. In other words, some instructions in program code1018can be located in one data processing system while other instructions in program code1018can be located in one data processing system. For example, a portion of program code1018can be located in computer-readable media1020in a server computer while another portion of program code1018can be located in computer-readable media1020located in a set of client computers.

The different components illustrated for data processing system1000are not meant to provide architectural limitations to the manner in which different embodiments can be implemented. The different illustrative embodiments can be implemented in a data processing system including components in addition to or in place of those illustrated for data processing system1000. Other components shown inFIG.10can be varied from the illustrative examples shown. The different embodiments can be implemented using any hardware device or system capable of running program code1018.

The description of the different illustrative embodiments has been presented for purposes of illustration and description and is not intended to be exhaustive or limited to the embodiments in the form disclosed. In some illustrative examples, one or more of the components may be incorporated in or otherwise form a portion of, another component. For example, memory1006, or portions thereof, may be incorporated in processor unit1004in some illustrative examples.

Thus, illustrative embodiments by method, apparatus, system, and computer program product for [assessing a credit risk of a set of companies. In one illustrative example, a computer system creates a training data set from distance-to-default values for a first set of companies. The computer system builds a set of predictive models based on the training data set. The computer system forecasts an estimated change in distance-to-default values for a second set of companies according to the set of predictive models. This method can be implemented in the illustrative example described forFIG.2in which risk estimator202operates to train predictive models220.

The predictive models220of risk estimator202speed up the estimation of public firms' Merton's distance-to-default under various scenarios, for any initial distance-to-default value. Training one or more predictive models220with training data set216enables risk estimator202to perform a quick and reasonably accurate estimate of the distance-to-default of thousands of public companies, under various scenarios, without the need for time-consuming and data-intensive calculations performed at individual company level.

In one illustrative example, one or more technical solutions are present that overcome a technical problem with the large amount of computational resources used in determining Merton's distance-to-default. As a result, one or more technical solutions can provide a technical effect of generating a training data set216for training one or more predictive models220that can accurately predict distance-to-default values using fewer computational resources as compared to systems that use previous techniques.

In the illustrative example, the use of risk estimator202in a computer system, such as computer system206ofFIG.2, integrates processes into a practical application for assessing a credit risk for a set of companies that increases the performance of computer system206in estimating distance-to-default values using predictive models220that were trained using training data set216.

A computer system that includes risk estimator202be configured to perform at least one of the steps, operations, or actions described in the different illustrative examples using software, hardware, firmware, or a combination thereof. As a result, a computer system that includes risk estimator202operates as a special purpose computer system in which risk estimator202in computer system206enables a method for assessing a credit risk of a set of companies. In particular, risk estimator202transforms computer system206into a special purpose computer system as compared to currently available general computer systems that do not have risk estimator202.

The different illustrative examples describe components that perform actions or operations. In an illustrative embodiment, a component may be configured to perform the action or operation described. For example, the component may have a configuration or design for a structure that provides the component an ability to perform the action or operation that is described in the illustrative examples as being performed by the component.

Further, different illustrative embodiments may provide different features as compared to other illustrative embodiments. The embodiment or embodiments selected are chosen and described in order to best explain the principles of the embodiments, the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.