Debt Restructuring Forecast of PT. X During Covid-19 Pandemic Using Monte Carlo Simulation
Jeremy Lee1*, Ruslan Prijadi1
1 Faculty of Economic and Business, University of Indonesia, Jakarta, Indonesia
*Corresponding Author: [email protected]
Accepted: 15 August 2021 | Published: 1 September 2021
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Abstract: The Covid-19 pandemic has led to the worst economic crisis in Indonesian history.
The economic crisis has negatively influenced the financial condition of the majority of the companies operating in Indonesia. PT. X as an Indonesian mining contractor is currently facing financial distress due to its incapability to sustain its healthy cash flow. Debt restructuring is one method for PT. X to escape and restore its financial position during the pandemic. This paper offers managerial recommendations for debt restructuring alternatives by forecasting the Net Present Values using Monte Carlo Simulation with cash flow volatility as the basis of analysis. Three alternatives, such as debt to equity swap, asset liquidation, and debt rescheduling, are considered as the most viable options for PT. X. The results of the simulation showed that Debt to Equity Swap generates the highest Net Present Value of Rp 23 billion, whereas rescheduling and asset liquidation only yields Rp 22 billion and Rp 21 billion respectively. Furthermore, to look for the best outcome in the future, other restructuring methods should be taken into consideration according to the capabilities of the company.
Keywords: Covid-19 Pandemic, Financial Distress, Debt Restructuring, Monte Carlo, Cash flow volatility
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1. Introduction
Coronavirus Disease or often referred to as Covid-19 is a new form of virus that was initially discovered on December 31, 2019 in Wuhan, China. (WHO, Rolling updates on coronavirus disease (Covid-19), 2020). This virus is related to the SARS (Severe Acute Respiratory Syndrome) virus family and several other types of influenza (UNICEF, 2020). Covid-19 attacks the human respiratory system with symptoms such as fever, dry cough, fatigue, and if severe it can cause acute inflammation of the respiratory tract (WHO, Coronavirus Disease (Covid-19), 2020). Due to its high transmission rate, on January 30, 2020, the Director-General of WHO declared Covid-19 as a public health emergency of international concern (PHEIC) (WHO, Rolling updates on coronavirus disease (Covid-19), 2020). Hundreds of countries issued various kinds of rules and regulations to control the transmission of Covid-19 and including in Indonesia. The Ministry of Health of the Republic of Indonesia issued 5M health program instructions to the Indonesian people, namely mandatory wearing masks rule, maintaining physical distance, washing hands regularly, avoiding crowds, and reducing mobility (KEMENKES, 2021).
With the strict rules and regulations for controlling this black swan phenomenon, Indonesia's economic sector has experienced a very drastic decline, people's mobility is hampered and as a result, the wheels of economy in Indonesia have stalled. Total losses are unavoidable by all people and there have been layoffs of around ≥ 1.5 million workers (Indraini, 2020; Yamali &
Putri, 2020), a decline in the price of Indonesian shares with a significance value of 0.00 < 0, (Nurmasari, 2020), the decline in Indonesian imports in the first quarter of 2020 was 3.7%
(Yamali & Putri, 2020), with inflation of 2.96% (Yamali & Putri, 2020) and various industries suffered major losses. The Minister of Finance of the Republic of Indonesia, Sri Mulyani, said that the Covid-19 crisis was far more complicated than the 1997 and 1998 crises because the root cause could not be contained (Victoria, 2020). Prime Minister of Malaysia Dr. Mahathir Mohamad also said that this pandemic was worse than the financial crises, and was a major blow to the entire world economy (Bloomberg, 2020). Various kinds of Indonesian industries have been adversely affected by Covid-19, more specifically for the mining construction sector.
PT. X is a private company in a mining contractor business line, also suffered losses when Covid-19 entered Indonesia. Job layoffs have been carried out for hundreds of workers to reduce losses due to large-scale social restrictions imposed by the government which resulted in complete operational layoffs. Therefore, various strategies to restore the financial condition of PT. X has become a critical consideration for the management of the company. Apart from termination of employment rights to employees, debt restructuring is the best alternative that can be done by PT X at this time to avoid bankruptcy or financial distress. This paper offers managerial recommendations for debt restructuring to PT. X by projecting the best debt restructuring alternatives for PT. X using the Monte Carlo simulation and cash flow volatility as the basis for the simulation.
2. Literature Review
Restructuring Purpose
According to (Johanputro, 2004) in (Ardiprawiro, 2016), company restructuring has the main objective of improving and maximizing company performance. For companies that have initial public offerings in Indonesia, this can be seen from the high and low price of the company's shares so that the company value is maximized and the price can be maintained at the highest level. The increase in share prices is not the result of the actions of market participants or speculators of stock speculation, but rather reflects investors' expectations of the company's future. Together with issuers or have gone public, the maximum value of the company can be seen from the selling price of the company's shares.
However, in urgent situations such as the 1997 monetary crisis or the Covid-19 pandemic, restructuring is used to avoid financial distress for companies. Financial distress occurs when a company's operating cash flow is insufficient to satisfy its present liabilities. (interest costs, trade credit) and with the hope that the company will take recovery action in response to the situation (Ross, Westerfield, Jaffe, & Jordan, 2016).
Determination of the Company's Financial Distress Conditions
The Altman Z-Score approach, which is commonly used to predict firm insolvency, can be used to determine whether the company is in financial distress. (Fadrul & Ridawati, 2020).
This approach, developed by Edward I. Altman in 1968, is a formula that uses the five basic financial ratios to determine a company's financial health. Calculating the Z-score using the equation below (Altman, 1968; Heine, 2000; Patunrui & Yati, 2017).
𝑍 = 1,2 (𝑋1) + 1,4 (𝑋2) + 3,3 (𝑋3) + 0,6 (𝑋4) + 1 (𝑋5) (1)
Where, X1 is Net Working Capital to Total Assets, X2 is Retained Earnings to Total Assets, X3 is Earnings Before Interest and Tax to Total Assets, X4 is Market Value of Equity to Book
Value of Debt, X5 is Sales to Total Assets (Patunrui & Yati, 2017). The Z result from the above equation will give the meaning of a healthy company if Z > 2.99; potentially bankrupt if Z
<1.81; and gray area or gray area if 1.81 < Z < 2.99 (Patunrui & Yati, 2017; Fadrul & Ridawati, 2020; Ernawati, 2007). With an accuracy rate of 80% - 90% (Heine, 2000), the Altman Z-Score method was chosen as the determinant of the company's bankruptcy conditions.
Apart from that, based on (Fadrul & Ridawati, 2020; Setiadi, 2011), the bankruptcy of a company can be classified into two parts, the first one is bankruptcy due to economic failure.
This situation occurs when the company has lost money to cover the company's operational costs. Second, financial failure, which is defined as the insolvency of the company's liabilities in excess of its total assets. In addition, there are other internal and external variables to consider in influencing the bankruptcy of a company (Fadrul & Ridawati, 2020). For internal factors, company management is no longer able to run efficiently and is no longer able to fulfill its obligations. Then for external factors such as changes in customer desires that the company cannot anticipate, excessive accounts receivable (Fadrul & Ridawati, 2020) and in this study the emergence of the Covid-19 pandemic.
Debt Restructuring
Debt restructuring, a part of financial restructuring, has the main objective of improving and planning a debt strategy that is acceptable to creditors (Ichwan, 2003; Soemarso, 1998).
Improving the financial framework in order to increase value for managers and shareholders and transfer ownership of assets for more effective and maximum use is an outline of the implementation of debt restructuring.
However, based on the Bank of Indonesia Decree dated November 12, 1998 No. 31/150 / KEP / DIR regarding credit restructuring of the directors of Bank Indonesia, explained that credit restructuring is a business undertaken by a bank in its pre-credit business activities so that debtors can carry out their obligations, through:
1) Lowering credit interest rates;
2) Reducing interest on arrears;
3) Reducing the principal of the loan;
4) Extending the credit period;
5) Additional lines of credit;
6) Obtaining debtor's assets in accordance with applicable regulations;
7) Converting debt into temporary equity of the debtor entity.
As for the company, things that can be done for debt restructuring are generally as follows (Brunner & Krahnen, 2001; Barker, 2020):
1) Debt to Asset Swap - debt is settled using the asset. The debtor must have assets available for payment from the loan. Even though an asset swap eliminates debt, total assets and total debt will decrease. In the end the total source of operational funding will decrease. In addition, liquidity and solvency in the long run will suffer because the decline in total assets will affect the ability of the business to commit in the future (Manaligod, 2008).
2) Debt to Equity Swap - Debt payments are settled in the form of shares or ownership of the debtor (Manaligod, 2008).
3) Haircut or debt reduction which refers to interest payable - another restructuring method to eliminate part of the debt. This is usually because the creditor believes that the debtor will not be able to pay off the debt in full through cash settlement or asset compensation (Mitchell, 2020).
4) Recapitalization - Inviting new capital to enter in order to reduce loss of control and to inject new capital.
5) Asset Liquidation - The sale of company assets that are no longer productive.
6) Splitting the company into different business components
7) Partial Selling / Divestiture - The sale of a company division that causes bankruptcy, usually a subsidiary selling its shares.
8) Stand Still - Suspend the payment of principal and interest on the loan for the purpose of renegotiating the loan contract.
9) Rescheduling or debt rescheduling - is an alternative to restructuring by extending the timeframe or changing the schedule for debt repayment. The lender believes that the debtor's current financial situation will not be able to fulfill his obligations, but it is hoped that the debtor's performance in the future will improve. Therefore, the creditor decides to reschedule the debtor's debt by extending the period or simply rescheduling the debtor's debt.
Monte Carlo Simulation
First discovered when the Manhattan project in America by Stanislaw Ulam, the Monte Carlo (MC) Simulation has been used using analog computers (Metropolis, 1987). This simulation gets its name from the Monte Carlo Casino, Monaco, where Stanislaw Ulam's uncle often gambled (Metropolis, 1987).
Monte Carlo Simulation is a sort of simulation that calculates outcomes by repeating random sampling and statistical analysis. (Raychaudhuri, 2008). In general, MC simulation is a method that uses random numbers to perform calculations (Beisbart & Norton, 2012), where this simulation uses data from the past and predicts the movement of risk factors that refer to a predetermined probability distribution (Pesiwarissa, 2006). This simulation technique is widely employed in a wide range of fields, including finance, project management, energy, manufacturing, engineering, research and development, insurance, oil and gas, transportation, and the environment. (Palisade, 2018).
MC simulation shows extreme possibilities and in Finance, the modeled scenario (forecast) can give the result of an important output, for example, net profit or gross expense. In addition, according to (Agarwal, 2019) simulations that uses the stochastic method (random input sampling) to solve statistical problems and virtual simulations that represent problems.
The use of the MC method in this study requires several parts before being able to run the MC simulation, namely:
1) Historical stochastic process of data from PT. X to calculate cash flow volatility, market prices and correlation values that will be randomly simulated to determine losses or gains.
2) The results of losses or gains will be recapitulated in order to obtain a distribution pattern and standard deviation. Then project the results of the NPV from cash flow for the next three years for PT. X.
In implementing the Monte Carlo simulation, it is necessary to determine probability distribution methods such as uniform, triangular, PERT, normal and Erlang distribution (Salling, 2007).
Triangular Distribution
Triangular distribution was selected for the probability distribution in the Monte Carlo simulation. Seeing that the available data is sufficient for the implementation of this probability distribution. In essence, the triangular distribution determines an upper and lower bound to
estimate how the model will work (Salling, 2007). When the link between variables is known but the data is restricted, this method is used to simulate the process. The triangular distribution has the following model (Salling, 2007);
𝑓( 𝑥 ∣ 𝑎, 𝑏, 𝑐 ) = {
2. (𝑥 − 𝑎) (𝑏 − 𝑎). (𝑐 − 𝑎)
2. (𝑏 − 𝑥) (𝑏 − 𝑎). (𝑏 − 𝑐)}
𝐹𝑜𝑟 𝑎 ≤ 𝑥 ≤ 𝑏,
𝐹𝑜𝑟 𝑐 < 𝑥 ≤ 𝑏 (2)
Cash flow Volatility
Volatility has a definition to measure the spread of returns from a security or market index (Kuepper, 2020). According to (Hudiyono & Husodo, 2013), volatility is often used as a direct approach in estimating the total risk of financial assets and an indirect approach in evaluating information. The standard deviation or variance of returns emanating from the same security or market index is sometimes referred to as volatility (Kuepper, 2020). Calculating the volatility of a company, especially from its cash flow, is very useful to use as an assessment of how the company will perform in the future. However, determining the variable for volatility is very difficult as it is when determining input parameters (Pamplona, et al., 2013).
In obtaining input parameters from cash flow, many methods can be used. However, based on the available real options, using a consolidated approach of uncertainty is the best method (Pamplona, et al., 2013). This method is often used when calculating the volatility of assets with cash flows, and has several advantages, namely, it includes the ability to accommodate negative flows, and, in applying more in-depth research, it always provides a more accurate estimate of asset volatility analysis. and conservative (Pamplona, et al., 2013).
Based on (Dudley & James, 2015), in measuring volatility there are three more categories, the first is the rolling window approach. Where volatility is measured as the standard deviation of cash flow in a constant period from several periods ago. The advantage of this approach is the simplicity of its implementation. However, this approach has several drawbacks, namely, it places the same burden on the profitability of the former company as it is now. This results in persistence and delays in adapting when there is a process of volatility in cash flows. The second approach, the variance of prior stock returns is a measure of volatility used in the capital structure literature (Frank & Goyal, 2009; Faulkender & Petersen, 2006). Then, the third approach is based on (Leary & Roberts, 2005), which makes a model of the absolute change in income from the prior period is used to calculate cash flow volatility. The limitation of this approach is that all the burdens are placed on the innovation of the company's recent profits regardless of the volatility of the previous time (t-1).
The standard deviation of operational cash flow divided by the average sales in a period can be used to assess cash flow volatility in this study (Harper, 2017). This measurement method is the standardization of getting cash flow volatility.
The standard deviation itself is the difference in the value of the mean of the data, so that it can represent the variation of the data (Lee, In, & Lee, 2015). If an observed data is distributed densely around the mean value, the variance value as well as the standard deviation will be low. However, the variance can be confused in interpreting the data because the variance is computed by rooting the units of the observed value. Therefore, the standard deviation using the same units as the mean can be expressed by the following equation.
Mean (X̅) = Σ𝑖=1𝑛 𝜒𝑖
𝑛 (3)
Variance = Σ𝑖=1𝑛 (X̅ − 𝑋𝑖)2
𝑛 − 1 (4)
Standard Deviation = √Σ𝑖=1𝑛 (X̅ − 𝑋𝑖)2
𝑛 − 1 (5)
Company Profile of PT. X
PT. X was founded in 2005 and is engaged in mining and mining contracting. Since 2005, PT.
X has contributed to the Quarries mining process and land grading (Cut and fill) ownership of PT. Indocement Tunggal Prakarsa Tbk. PT. X employs 130 people, all of whom are trained workers in their specific fields. PT. X has a vision to become a known mining and land grading company in Indonesia that is committed to providing customer satisfaction through the best performance. Based on the financial statements from PT. X from 2016 - 2019, showing the number of Net Profit Margin (net profit margin) at an average number of 0.07 which is based on (Luwiyanto, 2016) which is a very reasonable number where the industry has an average 0.06. The debt to equity ratio of PT. X which is at 3.10 is also a good number compared to the results (Luwiyanto, 2016), which means that liquidated capital can cover debts 3.10 times. A summary for PT. X's financial ratios can be seen in the table below.
Table I: Summary of PT. X Financial Ratios
2016 2017 2018 2019 2020
Gross Profit Margin 13.17% 10.28% 1.24% 11.38% 17.07%
Operating Profit Margin 10.73% 8.08% 8.63% 8.69% 14%
EBITDA Margin 23.77% 21.37% 24.89% 25.64% 49.20%
Net Profit Margin 8.33% 6.18% 6.56% 6.59% 11.07%
Average Return on Equity 6.88% 8.02% 11.98% 7.09% 5.43%
Average Return on Asset 6.06% 3.09% 5.24% 5.56% 5.18%
Debt to Equity 0.14 1.59 1.28 0.28 0.05
Debt to Asset 0.12 0.61 0.94 0.19 0.05
Current Ratio 4.01 0.50 0.43 1.26 7.22
The crisis experienced by PT. X can be seen in the income statement, in 2019, sales reached Rp 33.9 billion and Rp 35.7 billion in 2018. However, sales were only recorded at Rp 16.3 billion in 2020 due to a decrease in production from 120,000 tons/month, reduced to only 30,000 tons/month. Then the cost for employee salaries is Rp 11.1 billion and in 2020 it is only Rp 6.1 billion. This shows a very significant employee efficiency of PT. X so that salary costs can be cut in half. Then the usage costs in 2020 only cost 10% compared to 2019 and 9%
compared to 2018. This proves that the operations of PT. X was stopped due to the PSBB carried out by the government. Summary of the income statement of PT. X from 2018-2020 can be seen below.
Table II: Summary of PT. X Income Statement (in million Rupiah)
2018 2019 2020
Sales 35,745.99 33,997.23 16,370.56
Usage 14,110.23 12,395.67 6,723.59
Salary 10,522.47 11,153.66 6,169.68
Maintenance and Fixing Cost 558.75 321.11 38.47
Transportation Cost 189.86 191.13 62.66
Cost of Goods Sold 31,302.15 30,128.00 19,125.81
Gross Profit (Loss) 4,443.84 3,869.23 (2,755.26)
Operating Net Profit 3,085.12 2,953.26 (3,299.12)
Earnings Before Tax 3,086.34 2,954.60 (3,297.83)
Income Tax 740.01 713.56 -
Net Income 2,346.33 2,241.04 (3,297.83)
Due to the quick decision from the management to layoff employees as soon as possible, the catastrophic effect of the pandemic could be minimized. However, the drastic fall from the operational costs of PT. X indicates unhealthy activity in 2020. Apart from that, measurements are required to determine whether PT. X is included in the category of healthy companies or threatened with bankruptcy using the Altman Z-Score discriminant analysis method (Altman, 1968; Heine, 2000; Patunrui & Yati, 2017). The following are the results of the calculation of Z on PT. X.
Table III: Altman Z-Score of PT. X
Ratio 2018 2019 2020
X1 -0.319 0.056 0.146
X2 0.341 0.439 0.669
X3 0.069 0.073 -0.111
X4 0.778 3.631 0.463
X5 0.799 0.844 0.549
Z-Score 1.588 3.946 1.573
Category Distress Zone Safe Zone Distress Zone
Looking at the results of the Z value above, in 2018 PT. X is in the distress zone and in 2019 managed to improve its performance and increased to the safe zone area, however, in 2020 it dropped drastically to the distress zone again which indicates the severity of the situation.
Looking at the summary of the financial statements of PT. X, it may be established that PT.
X's financial difficulties is the result of external sources. As mentioned in the previous section, there are significant changes in the wishes of the company's consumers that the company cannot anticipate and also the existence of government regulations that restrict the company's operations. These factors are in line with what has been written by (Taleb, 2007) regarding the Black Swan phenomenon, or black swan. Where the book tells the story of many people thinking that the black swan did not exist before the black swan was found alive. Just like Covid-19, before this pandemic, people would never have thought this existed or would happen. But when it happens, it has a tremendous effect on the whole world.
Therefore, in this study, several debt restructuring options were used that could be implemented by PT. X to improve the company's financial health and in the face of the Covid-19 pandemic, including:
1) Debt to Equity Swap. This alternative can be done by PT. X because one of the creditors from PT. X is a sister company which in 2019 has also done this process before. Therefore, the possibility of implementing this method again becomes higher.
According to (Manaligod, 2008), it has no impact on financial statements, specifically on total assets. Because there are no assets used to settle debts, liquidity and solvency will remain the same. However, total debt will decrease which is the impact of debt payments and shareholder equity will increase.
2) Asset Liquidation. After consulting with PT. X, the management had planned to carry out this restructuring process due to unproductive equipment would be sold. The sale of these
unproductive assets will have an impact on the company's cash which can ultimately be used to cover debt.
3) Rescheduling. This strategy is a debt restructuring process carried out by PT. United Tractors Tbk. Judging from the similarity of the line of business, it would be favorable for PT. X to follow the steps of the company and uses it as a benchmark in making decisions.
In this alternative, PT. X gave the decision that it would split the rescheduled debt. Most of the short-term debt will be rescheduled and the rest will remain within the same deadline.
Looking at the three alternatives, each will be simulated and projected for the next five years using the MC simulation method by including relevant assumptions. The results of the simulated NPV would show which alternative is the most profitable for PT. X.
3. Discussion and Conclusion Calculation of Cash flow Volatility
Table IV: Cash flow and Cash flow Volatility of PT. X (in million Rupiah)
2017 2018 2019 2020
Operating Cash Flow Rp (15,865.33) Rp 7,633.40 Rp 24,498.80 Rp 15,232.58 Investing Cash Flow Rp (24,574.70) Rp (8,676.96) Rp (1,120.25) -
Financing Cash Flow - - Rp 15,514.96 Rp (5,151.08)
Net Cash Flow Rp (40,440.02) Rp (1,043.56) Rp 38,893.51 Rp 10,081.50
Sales Rp 22,369.42 Rp 35,745.99 Rp 33,997.23 Rp 16,370.56 Sales Average Rp 21,528.44
Cash Flow Volatility -0.717 0.389 1.171 0.728
Standard Deviation of Volatility 0.78
In 2017 and 2018, PT. X purchased a rather expensive machine which increased volatility value in those four years. With the volatility value of 0.78, PT. X also has the possibility to carry out major investment activities in the coming years, considering the fact that PT. X is in the business life cycle of business growth or is developing. However, in this simulation model, this possibility is eliminated from the assumptions as discussed below.
Monte Carlo Simulation Establishment Assumptions for Simulation
In carrying out the MC simulation, several assumptions are needed to maximize the probability of the simulation results. The assumptions made are applied to all simulation scenarios in order to obtain measurable results. The following assumptions are set in the simulation;
1) The discount rate in calculating the NPV is set at 9%. This figure is obtained after calculating the WACC from 2017 to 2019 and taking the average value of the three years. WACC (Table VII) in 2020 was ignored due to the negative value, and in that year PT. X was exposed to the Covid-19 pandemic. (Mitra, 2011; Hargrave, 2021; Corporate Finance Institute, 2020).
2) The value of 0.78 of the cash flow volatility is set as the upper and lower limits in the cash flow projections for the following year. The results of the standard deviation of volatility provide a range of numbers that cash flows in the following year can increase or decrease with the upper and lower limits at 0.78. This follows a simulation step with a triangular probability distribution (Back, Boles, & Fry, 2000; Harper, 2017; Fairchild, Misra, & Shi, 2016).
3) Not conducting any financing or investment activities by PT. X for the next 5 years. The next five years are an estimate of the length of Indonesia's economic recovery needed to return to pre-Covid-19 conditions. So that PT. X will limit excessive spending and save funds to survive during Covid-19 and a backup plan for the second wave of the pandemic (CNN Indonesia, 2021; Putri, 2021).
4) The year 2020 is set as year 0 in the NPV calculation or set as the initial year.
Implementation of Monte Carlo Simulation for All Alternatives
The implementation of the DTE Swap simulation begins with the debt of PT. X amounting to Rp 527 million which will be exchanged into share capital for PT. X. Changes in current liabilities as much as exchanged will change the net cash flow of PT. X in 2021 due to the recording of additional capital as a result of the process. However, the operating cash flow will start to be simulated by multiplying the upper and lower limits of 0.78, according to the volatility of the cash flow of PT. X. After calculating and establishing a cash flow (Table VI) model from 2020 to 2025, the NPV from the first simulation was Rp 26 billion.
The liquidation of assets will be carried out by PT. X, where PT. X plans to liquidate the machine for Rp 8 billion. After establishing a cash flow (Table VI) model from 2020 to 2025, the NPV from the first simulation was Rp 18,638 billion.
For the third alternative, PT. X will reschedule their debt of IDR 700 million, of which 30%
will still be paid in 2021 and 70% will be scheduled to be paid in 2022 assuming there is no interest fee in rescheduling. After calculating and establishing a cash flow (Table VI) model from 2020 to 2025, the NPV from the first simulation was Rp 22,571 billion.
From all three results of the NPV, the Monte Carlo simulation is run again for 1000 times, to find the distribution of the probability and the results of the NPV that come out most often from the simulation is the NPV of the simulation. The 1000x simulation data produces the values below.
Table V: Monte Carlo Simulation Result for All Alternatives (in million Rupiah) DTE Swap Asset Liquidation Rescheduling
Mean 23,204.30 21,838.24 22,107.33
Median 23,273.54 21,878.87 22,071.37
Min 16,131.55 15,132.43 15,866.06
Max 30,100.23 29,022.25 28,742.70
Std. Deviation 2,428.11 2,394.42 2,358.43
From the three simulations, it can be seen in the mean from the table above, that the Debt to Equity Swap gives the highest NPV among the three alternatives. Debt to Equity Swap has several advantages other than being a financial savior of the company, being able to increase the company's capital from the debt without reducing the credit score due to the company's default (CFI, 2020), a shortcut to closing debt when unable to get new loan capital, and being able to repair debt. company's financial statements by reducing the amount of debt and increasing equity (Turner & Benjamin, 2020). However, the Debt to Equity Swap will affect the ownership structure of the company, where the possibility of conflict of interest increases.
Nevertheless, after looking at the amount of the debt owed, when DTE swap is implemented, it would affect as much as 2% of the PT. X ownership for its own stock. This would provide PT. X a still major decision maker without having any risk from major conflict of interest.
Looking at the second-best option from the three alternatives based on the Monte Carlo simulation, Rescheduling produces the second highest NPV. In addition to significantly
reducing short-term debt, the rescheduling alternative will not significantly change the balance sheet position. Only changes in the amount of short-term debt and long-term debt only.
However, rescheduling in this case can be taken considering that long-term debt scheduling is not subject to additional interest fees.
This research provides an overview and learning of the actions that must be taken by companies with characteristics such as PT. X when facing the Black Swan phenomenon, or, in this case, the Covid-19 pandemic. Debt restructuring is one way out for PT. X, in maintaining their financial health through the Covid-19 pandemic. Debt to Equity Swap, Asset Liquidation, and Rescheduling are three alternative options to be implemented by PT. X. With the help of Monte Carlo simulation in finding NPV profit, Debt to Equity Swap gives the largest NPV result, followed by Rescheduling and Asset Liquidation respectively. PT. X is advised to take the Debt to Equity Swap alternative due to its provision of the largest NPV, the creditor for the debt to be swapped is lent by a sister company from PT. X. Thus, this will not interfere with the composition of ownership of PT. X. Nevertheless, PT. X can also take the Rescheduling alternative. Since there will be no additional interest from the bank, this option will not change the composition of PT. X and the financial structure of PT. X significantly. Rescheduling may be considered a Safe Haven PT. X in maintaining the company's financial health. Therefore, if PT. X sees from the high NPV profit, it is recommended to take Debt to Equity Swap alternative as the low impact on the stock ownership would not mean any significant risk in the future of decision making.
In order to deepen the learning on the use of Monte Carlo simulation for debt restructuring in the future, several recommendations such as the formation of a model if certain assumptions are not met and the addition of alternative debt restructuring for companies with different characteristics should be considered. Additional external factor assumptions should be taken into considerations for the simulation model as well.
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Appendices
Table VI: First Simulated Projected Cash Flow of PT. X for All Alternatives (in million rupiah)
DTE
Year 2020 2021 2022 2023 2024 2025
OCF 4,666.88 5,152.23 6,706.30 7,859.02 7,653.68 6,458.96 Net Cash Flow - 812.11 5,680.14 6,706.30 7,859.02 7,653.68 6,458.96
NPV 26,016.33 Asset
Liquidation
OCF 4,666.88 5,152.23 4,797.55 6,230.28 4,694.88 5,427.58 Net Cash Flow - 812.11 11,920.44 4,797.55 6,230.28 4,694.88 5,427.58
NPV 18,638.63 Rescheduling
OCF 4,667.06 5,152.23 4,606.39 7,910.67 5,073.09 8,284.03 Net Cash Flow - 812.11 5,152.23 4,113.67 7,910.67 5,073.09 8,284.03
NPV 22,571.78
Table VII: Weighted Average Capital Cost of PT. X 2017 - 2020
WACC 2020 2019 2018 2017
-22.12% 10% 9% 7%
WACC Average (2019 – 2017) 9%
