# Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Materials and Methods

- (1)
- Selection and preparation of the data for the calculation.
- (2)
- Division of the data into training and testing data sets.
- (3)
- Creation of a bankruptcy model by means of an experiment using Mathematica Software.
- (4)
- Generation of NN using LSTM networks and other elementwise layers.
- (5)
- Evaluation of the performance of networks in the training and testing data sets, creation of a confusion matrix characterizing the correct classification of companies into “active” and “in liquidation”.
- (6)
- Description of the best NN and discussion on the success rate of the network.

#### 3.1. Data

- AKTIVACELK—total assets, i.e., the result of economic activities carried out in the past. This represents the future economic profit of the company.
- STALAA—fixed assets, i.e., long-term, fixed, non-current items, including property components, used for company activities over the long run (for more than one year) and consumed over time.
- OBEZNAA—current assets characterized by the operating cycle, i.e., they are in constant motion and change form. These include money, materials, semi-finished products, unfinished products, finished products, and receivables from customers.
- KP—short-term receivables with a maturity of less than 1 year, representing the right of the creditor to demand the fulfilment of a certain obligation from the other party. The receivable ceases to exist upon the fulfilment of the obligation.
- VLASTNIJM—equity, i.e., the company’s resources for financing assets in order to create capital. This primarily concerns the contributions of the founders (owners or partners) to the basic capital of the company and those components arising from the company’s activities.
- CIZIZDROJE—borrowed capital, i.e., company debts that have to be repaid within a specified period of time. This represents the company’s liabilities towards other entities.
- KZ—short-term liabilities, i.e., due within 1 year. Together with equity, they ensure the financing of the day-to-day activities of the company. These primarily include bank loans, liabilities to employees and institutions, debts to suppliers or taxes due.
- V—performance, i.e., the results of company activities which are characterized by the main activity of the company—production. This includes the goods and services used for satisfying demands.
- SLUZBY—services, i.e., those activities intended to meet human needs or the needs of a company by means of their execution.
- PRIDHODN—added value, i.e., trademarking, sales, changes in inventory through own activities, or activation reduced by power consumption. This includes both company margin and performance.
- ON—personnel costs, i.e., gross salaries and the employer’s compulsory social and health insurance contributions for each employee.
- PROVHOSP—operating results, i.e., the outcomes and products that reflect the ability of a company to transform production factors.
- NU—interest payable, i.e., the price of borrowed capital.
- HOSPVZUO—economic result for an accounting period, i.e., from operational, financial, and extraordinary activities.
- STAV—target situation, i.e., classification as “active” for companies able to survive potential financial distress, and “in liquidation” for companies that will go bankrupt.

#### 3.2. Methods

- Hyperbolic tangent (Tanh),
- Sinus (Sin),
- Ramp (referred to as ReLU),
- Logistic function (logistic sigmoid).

- Hyperbolic tangent (Tanh),
- Sinus (Sin),
- Ramp (referred to as ReLU),
- Logistic function (logistic sigmoid).

#### 3.3. Long-Short Term Memory Layer

_{t}: new state of the variable;

_{t}: forget gate;

_{t−1}: original state of the variable;

_{t}: input gate;

_{t}: memory gate.

_{ix}: input weight in input gate, matrix n × k;

_{t}: input variable, n × k matrix;

_{is}: weight in input gate, n × n matrix;

_{t−1}: previous state;

_{i}bias: vector size n.

_{t}: state of the variable;

_{t}: output gate;

_{ax}: input weight in output gate, n × k matrix;

_{as}: weight in output gate, n × n matrix;

_{o}bias: vector size n.

_{fx}: determines the forget gate input weigh, n × k matrix;

_{fs}: weight in forget gate, n × n matrix;

_{f}: vector size n.

_{mx}: input weight in memory gate, n × k matrix;

_{ms}: weight in memory gate, n × n matrix;

_{m}: vector size n.

#### 3.4. Elementwise Layer

- 1.
- Hyperbolic tangent (Tanh):

- 2.
- Sinus (Sin):

- 3.
- Ramp (referred to as ReLU):

- 4.
- Logistic function (logistic sigmoid):

#### 3.5. Evaluation of Network Performance

- The performance of the individual networks in the training and testing datasets.
- The confusion matrix characterizing the correct classification of companies into “active” and “in liquidation”. The confusion matrix was created for both the training and testing datasets.

## 4. Results

- The trained NN in the WLNet format is available from: https://ftp.vstecb.cz
- The training dataset in xlsx format is available from: https://ftp.vstecb.cz
- The testing dataset in xlsx format is available from: https://ftp.vstecb.cz

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Item | Training | Testing | ||||||
---|---|---|---|---|---|---|---|---|

Minimum | Maximum | Mean | Standard Deviation | Minimum | Maximum | Mean | Standard Deviation | |

Total Assets | 0 | 6,963,568 | 101,794.3 | 421,183.6 | 0 | 62,924,684 | 193,629.3 | 2,402,017 |

Fixed Assets | −19,065 | 4,558,816 | 49,269.92 | 251,519.4 | 0 | 30,832,576 | 90,484.69 | 1,204,674 |

Current Assets | 0 | 2,391,855 | 51,801.29 | 182,982.9 | 0 | 32,066,562 | 102,624.7 | 1,222,082 |

Short Term Receivables | −127 | 1,947,964 | 24,162.22 | 106,237.4 | 0 | 27,683,668 | 65,334.36 | 1,039,648 |

Equity | −9662 | 1,923,390 | 50,993.7 | 186,972.2 | −206,208 | 53,318,744 | 131,475.4 | 2,004,015 |

Borrowed Capital | 0 | 5,024,089 | 50,269.76 | 256,551.2 | −21 | 9,605,513 | 61,351.15 | 469,600.1 |

Short Term Liabilities | 0 | 4,906,382 | 31,905.76 | 204,347.7 | −96 | 7,970,386 | 41,665.4 | 366,137.2 |

Performance | −40 | 4,180,449 | 97,214.31 | 335,148.6 | −246 | 33,887,311 | 142,956.2 | 1,342,696 |

Services | 0 | 569,454 | 14,468.57 | 48,754.07 | 0 | 3,038,338 | 19,348.75 | 133,230.2 |

Added Value | −7788 | 824,345 | 26,734.39 | 76,957.12 | −20,284 | 3,457,583 | 30,516.86 | 167,441.7 |

Personnel Costs | 0 | 463,685 | 17,940.92 | 46,808.62 | −60 | 2,665,233 | 19,125.81 | 111,284.5 |

Operating Result | −150,310 | 852,991 | 7316.556 | 44,552.34 | −847,530 | 914,063 | 4213.27 | 63,193.46 |

Interest Payable | 0 | 187,218 | 744.4554 | 7525.506 | 0 | 52,798 | 494.3265 | 2912.81 |

Economic Result for Accounting Period (+/−) | −245,135 | 700,092 | 6354.041 | 43,196.58 | −645,493 | 673,179 | 5049.786 | 53,302.32 |

ID NN | Neural Network | Training Performance | Test Performance | ||||
---|---|---|---|---|---|---|---|

Active | Failed | Total | Active | Failed | Total | ||

1. | 14-940-Tanh-Tanh-2-1 | 0.978246 | 0.742838 | 0.899955 | 0.971253 | 0.752066 | 0.898491 |

2. | 14-1970-Ramp-Tanh-2-1 | 0.978586 | 0.722374 | 0.893376 | 0.967199 | 0.750331 | 0.899863 |

3. | 14-1980-Ramp-Sin-2-1 | 0.976547 | 0.718963 | 0.89088 | 0.973306 | 0.747934 | 0.898491 |

4. | 14-1990-Sin-Sin-2-1 | 0.973148 | 0.736016 | 0.894283 | 0.975359 | 0.747934 | 0.899863 |

5. | 14-1010-Tanh-Sin-2-1 | 0.980625 | 0.734652 | 0.89882 | 0.967146 | 0.772727 | 0.902606 |

Field | Type of Output | Size of Output |
---|---|---|

Input gate input weights | matrix | 940 × 14 |

Input gate state weights | matrix | 940 × 940 |

Input gate biases | vector | 940 |

Output gate input weights | matrix | 940 × 14 |

Output gate state weights | matrix | 940 × 940 |

Output gate biases | vector | 940 |

Forget gate input weights | matrix | 940 × 14 |

Forget gate state weights | matrix | 940 × 940 |

Forget gate biases | vector | 940 |

Memory gate input weights | matrix | 940 × 14 |

Memory gate state weights | matrix | 940 × 940 |

Memory gate biases | vector | 940 |

Field | Type of Output | Size of Output |
---|---|---|

Input gate input weights | matrix | 2 × 940 |

Input gate state weights | matrix | 2 × 2 |

Input gate biases | vector | 2 |

Output gate input weights | matrix | 2 × 940 |

Output gate state weights | matrix | 2 × 2 |

Output gate biases | vector | 2 |

Forget gate input weights | matrix | 2 × 940 |

Forget gate state weights | matrix | 2 × 2 |

Forget gate biases | vector | 2 |

Memory gate input weights | matrix | 2 × 940 |

Memory gate state weights | matrix | 2 × 2 |

Memory gate biases | vector | 2 |

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## Share and Cite

**MDPI and ACS Style**

Vochozka, M.; Vrbka, J.; Suler, P.
Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM. *Sustainability* **2020**, *12*, 7529.
https://doi.org/10.3390/su12187529

**AMA Style**

Vochozka M, Vrbka J, Suler P.
Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM. *Sustainability*. 2020; 12(18):7529.
https://doi.org/10.3390/su12187529

**Chicago/Turabian Style**

Vochozka, Marek, Jaromir Vrbka, and Petr Suler.
2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM" *Sustainability* 12, no. 18: 7529.
https://doi.org/10.3390/su12187529