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Safran SA
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== Appendix == === Financial statements === ==== Income Statement (In €) ==== {| class="wikitable" |+ !Year end date !TTM !12/31/2022 !12/31/2021 !12/31/2020 !12/31/2019 |- |Total Revenue |17,039,000 |19,523,000 |15,133,000 |16,631,000 |25,098,000 |- |Cost of Revenue |8,628,000 |10,145,000 |7,855,000 |9,016,000 |13,581,000 |- |'''Gross Profit''' |8,411,000 |9,378,000 |7,278,000 |7,615,000 |11,517,000 |- |'''Operating Expense''' |6,456,000 |6,663,000 |6,098,000 |6,193,000 |7,943,000 |- |'''Operating Income''' |1,955,000 |2,715,000 |1,180,000 |1,422,000 |3,574,000 |- |Net Non Operating Interest Income Expense | -80,000 | -39,000 | -103,000 | -2,000 | -19,000 |- |Pretax Income | -5,722,000 | -3,120,000 |268,000 |570,000 |3,474,000 |- |Tax Provision | -1,356,000 | -694,000 |200,000 |184,000 |962,000 |- |'''Net Income Common Stockholders''' | -4,393,000 | -2,459,000 |43,000 |352,000 |2,447,000 |- |Diluted NI Available to Com Stockholders | -4,393,000 | -2,459,000 |43,000 |352,000 |2,447,000 |- |Basic EPS | - | -5.76 |0.10 |0.83 |5.69 |- |Diluted EPS | - | -5.76 |0.10 |0.80 |5.63 |- |Basic Average Shares | - |426,681 |426,650 |426,036 |429,723 |- |Diluted Average Shares | - |440,160 |440,087 |440,460 |434,977 |- |Total Operating Income as Reported |1,584,000 | - |864,000 |927,000 |3,837,000 |- |Total Expenses |15,084,000 |16,808,000 |13,953,000 |15,209,000 |21,524,000 |- |Net Income from Continuing & Discontinued Operation | -4,393,000 | -2,459,000 |43,000 |352,000 |2,447,000 |- |Normalized Income |804,692 | -1,833,568 |304,660 |787,440 |2,421,692 |- |Interest Income |20,000 |67,000 |15,000 |22,000 |48,000 |- |Interest Expense |85,000 |94,000 |109,000 |108,000 |120,000 |- |Net Interest Income | -80,000 | -39,000 | -103,000 | -2,000 | -19,000 |- |EBIT | -5,637,000 | -3,026,000 |377,000 |678,000 |3,594,000 |- |EBITDA | -3,771,000 | - | - | - | - |- |Reconciled Cost of Revenue |8,628,000 |10,145,000 |7,855,000 |9,016,000 |13,581,000 |- |Reconciled Depreciation |1,866,000 |1,615,000 |1,642,000 |1,565,000 |1,589,000 |- |Net Income from Continuing Operation Net Minority Interest | -4,393,000 | -2,459,000 |43,000 |352,000 |2,447,000 |- |Total Unusual Items Excluding Goodwill | -6,812,000 | -804,000 | -356,000 | -643,000 |35,000 |- |Total Unusual Items | -6,812,000 | -804,000 | -356,000 | -643,000 |35,000 |- |Normalized EBITDA |3,041,000 | -607,000 |2,375,000 |2,886,000 |5,148,000 |- |Tax Rate for Calcs |0 |0 |0 |0 |0 |- |Tax Effect of Unusual Items | -1,614,308 | -178,568 | -94,340 | -207,560 |9,692 |} ==== Balance Sheet (In €) ==== {| class="wikitable" |+ !Year end date !12/31/2022 !12/31/2021 !12/31/2020 !12/31/2019 |- |'''Total Assets''' |46,828,000 |41,716,000 |39,531,000 |42,808,000 |- |'''Total Liabilities Net Minority Interest''' |35,962,000 |28,446,000 |26,781,000 |30,060,000 |- |'''Total Equity Gross Minority Interest''' |10,866,000 |13,270,000 |12,750,000 |12,748,000 |- |Total Capitalization |15,610,000 |17,750,000 |16,363,000 |15,507,000 |- |Common Stock Equity |10,411,000 |12,841,000 |12,349,000 |12,371,000 |- |Capital Lease Obligations |587,000 |609,000 |608,000 |729,000 |- |Net Tangible Assets | -2,679,000 | -609,000 | -1,387,000 | -2,307,000 |- |Working Capital | -2,479,000 |755,000 | -838,000 | -2,804,000 |- |Invested Capital |16,781,000 |19,373,000 |18,758,000 |18,926,000 |- |Tangible Book Value | -2,679,000 | -609,000 | -1,387,000 | -2,307,000 |- |Total Debt |6,957,000 |7,141,000 |7,017,000 |7,284,000 |- |Net Debt | - |1,285,000 |2,662,000 |3,923,000 |- |Share Issued |427,246 |427,242 |427,236 |427,234 |- |Ordinary Shares Number |424,559 |426,786 |426,917 |424,684 |- |Treasury Shares Number |2,687 |456.165 |319.284 |2,550 |} ==== Cash Flow (In €) ==== {| class="wikitable" |+ !Year end date !TTM !12/31/2022 !12/31/2021 !12/31/2020 !12/31/2019 |- |Operating Cash Flow |3,478,000 |3,545,000 |2,436,000 |1,866,000 |3,145,000 |- |Investing Cash Flow | -870,000 | -1,288,000 | -738,000 | -799,000 | -1,105,000 |- |Financing Cash Flow | -368,000 | -815,000 | -268,000 |68,000 | -1,740,000 |- |End Cash Position |6,167,000 |6,687,000 |5,247,000 |3,747,000 |2,632,000 |- |Changes in Cash |2,240,000 |1,442,000 |1,430,000 |1,135,000 |300,000 |- |Effect of Exchange Rate Changes |41,000 |2,000 |70,000 | -20,000 |2,000 |- |Beginning Cash Position |3,927,000 |5,247,000 |3,747,000 |2,632,000 |2,330,000 |- |Other Cash Adjustment Outside Change in Cash | -4,000 | -4,000 | - | - | - |- |Capital Expenditure | -834,000 | -879,000 | -756,000 | -793,000 | -1,162,000 |- |Issuance of Capital Stock |0 |0 |1,000 | - | - |- |Issuance of Debt |509,000 |510,000 |2,146,000 |1,595,000 |24,000 |- |Repayment of Debt | -613,000 | -671,000 | -1,367,000 | -778,000 | -875,000 |- |Repurchase of Capital Stock |0 | -270,000 | -73,000 | - | - |- |Free Cash Flow |2,644,000 |2,666,000 |1,680,000 |1,073,000 |1,983,000 |} === '''Discounted Cashflow model (DCF)''' === ==== Key inputs ==== {| class="wikitable" !Description !Value !Commentary |- |Discount rate (%) | 8.2% |The application of the weighted average cost of capital (WACC) formula was used to compute the discount rate for future cash flows. |- |Long term growth rate (%) |1.7% |We used the growth rate of you Dividend Discount Model. |- |Corporate Tax Rate (%) |25% |According to the new French legislation, the Corporate Tax Rate should be at 25% as from 1 January 2022.<ref>https://www.economie.gouv.fr/files/files/Actus2018/dp_plf2019.pdf</ref> |} For this model, we use the WACC model to compute the discount rate for the future cash flows. {| class="wikitable" !Cost of equity |8.68% |- !Cost of debt |5.00% |- !Tax rate |25.00% |- !Debt |6,957 |- !Equity (Current market cap) |65,010 |- !D/(D+E) |10% |- !E/(D+E) |90% |- !WACC |'''8.20%''' |} ==== Safran Unlevered Free Cash Flows ==== {| class="wikitable" |- !Period (t) !2019A !2020A !2021A !2022A !2023P !2024P !2025P !2026P !2027P |- | colspan="10" | |- !EBITDA |5830 |2962 |2825 |3,498 |3,673 |3,857 |4,049 |4,252 |4,464 |- !EBIT |4,695 |1,706 |1,489 |1,958 |1,950 |1,928 |1,892 |1,838 |1,763 |- !Tax rate |26.30% |27.50% |34.40% |31.4% |25.0% |25.0% |25.0% |25.0% |25.0% |- | colspan="10" | |- !EBIT (1-t) |3,460 |1,237 |977 |1,343 |1,462 |1,446 |1,419 |1,378 |1,322 |- !D&A |1135 |1256 |1336 |1,540 |1,723 |1,928 |2,157 |2,414 |2,701 |- !NWC |2,804 |838 |(755) |2,479 |2,256 |2,053 |1,868 |1,700 |1,547 |- !Capital expenditures |(1,162) |(793) |(756) |(879) |(919) |(960) |(1,003) |(1,048) |(1,095) |- !Unlevered free cash flows (UFCF) |6,237 |2,538 |802 |4,483 |4,523 |4,467 |4,441 |4,444 |4,475 |- | colspan="10" | |- !Discount rate (r) | | | | |8.7% |8.7% |8.7% |8.7% |8.7% |- !PV of UFCFs | | | | |4,180 |3,816 |3,506 |3,242 |3,018 |- | colspan="10" | |- !Stage 1: Sum of present values |'''17,762''' | | | | | | | | |} ==== Terminal value - growth in perpetuity approach ==== {| class="wikitable" !Long term growth rate |1.7% |- !2022 FCF x (1+g) |4,551 |- !Terminal value in 2022 |69,979 |- !Stage 2: PV of TV |47,187 |- | | |- !Enterprise value (stage 1 + 2) |'''64,949''' |} ==== Terminal value - EBITDA multiple approach ==== {| class="wikitable" !EBITDA multiple |14.0x |- !Terminal value in 2022 |62,413 |- !Stage 2: PV of TV |42,085 |- | | |- !Enterprise value (stage 1 + 2) |'''59,847''' |} ==== Net debt ==== {| class="wikitable" |Data as of: |29/07/2023 |- !Net debt |'''(14)''' |} ==== Equity valuations ==== {| class="wikitable" | !Perpetuity approach !EBITDA approach |- !Equity value |'''152.32 €''' |'''140.35 €''' |} ==== Sensitivity analysis ==== {| class="wikitable" | colspan="2" rowspan="2" | ! colspan="5" |Constant Growth rate |- |''0.7%'' |''1.2%'' |'''''1.7%''''' |''2.2%'' |''2.7%'' |- ! rowspan="7" |WACC |''7.0%'' |162.61 € |173.56 € |186.49 € |202.31 € |221.70 € |- |''7.4%'' |152.93 € |162.42 € |173.51 € |186.89 € |203.02 € |- |''7.8%'' |144.35 € |152.64 € |162.22 € |173.66 € |187.26 € |- |'''''8.2%''''' |136.68 € |143.97 € |'''152.32 €''' |162.20 € |173.80 € |- |''8.6%'' |129.79 € |136.24 € |143.58 € |152.17 € |162.16 € |- |''9.0%'' |123.56 € |129.30 € |135.78 € |143.32 € |151.99 € |- |''9.4%'' |117.90 € |123.04 € |128.80 € |135.45 € |143.04 € |} === Trading Comparables (Comps) === As mentioned earlier, the negative income and EBITDA figures make it impractical to use the P/E or EV/EBITDA ratios in this case. Different valuation approaches and more comprehensive analyses are needed to accurately assess the company's financial health and potential. For that matter we decided to use the Price-to-Book (PB) ratio, we collected the PB ratio for similar companies operting in the aerospace and defense sector. Rolls-Royce having a negative PB ratio, we left it out of our analysis. {| class="wikitable" !Company Name !PB ratio |- |Airbus SE (EPA: AIR) |7.32 |- |Thales Group (EPA: HO) |3.86 |- |<s>Rolls-Royce Holdings plc (LSE: RR.)</s> |<s>-2.75</s> |- |General Electric Company (NYSE: GE) |3.8 |- !Average |4.99 |- !Median |'''3.86''' |} We can then deduce the price per share using the median PB ratio. {| class="wikitable" ! colspan="6" |Calculating the price based on PB ratio |- |Price |Shares Outstanding |Book value |Total assets |Intangible assets |Total liabilities |- |'''101.87 €''' |426,500,000 |11,256,000,000.00 € |46,828,000,000.00 € |8,096,000,000.00 € |27,476,000,000.00 € |} Based on this straightforward analysis, it becomes evident that the company is deemed overvalued, given its current market value per share of 144.82€ in contrast to the value of 101.87€ calculated through our analysis. However, if we solely rely on the Airbus PB (Price-to-Book) ratio, the estimated price per share would be 193.19 €, indicating an even higher level of overvaluation in this instance. This demonstrates the limited usefulness of the Comps analysis in accurately determining the company's true value and highlights the need for incorporating multiple valuation metrics and methodologies to gain a more comprehensive understanding of its market worth. === Dividend Discount Model (DDM) === ==== Equity valuation ==== In the following table, we present the key values used in a DDM (Dividend Discount Model) analysis, including the CAPM model, risk-free rate of return, beta, market rate of return, and the corresponding cost of equity. {| class="wikitable" !CAPM model |Values |- |Risk-free rate of return (Euro short-term rate (€STR)) |3.41% |- |Beta (5Y Monthly, levered) |1.35 |- |Market rate of return (European Stocks Portfolio) |7.31% |- |Cost of equity |8.7% |} The graphical representation below illustrates the dividend history of Safran over the past years, showcasing the annual dividends paid, including the notable occurrence of zero dividends in the year 2019. [[File:Dividends.png|left|thumb|600x600px]] {| class="wikitable" !Year !2011 !2012 !2013 !2014 !2015 !2016 !2017 !2018 !2019 !2020 !2021 !2022 |- !Dividend per share |0.62 |0.96 |1.12 |1.20 |1.38 |1.52 |1.60 |1.82 |0.00 |0.43 |0.50 |1.35 |} To evaluate Safran's valuation, we applied the Gordon Growth Model (GGM), projecting the historical dividend growth rate from 2011 to 2018 to estimate the current growth rate starting from 2022. By doing so, we can make an estimation of the 2023 dividend. {| class="wikitable" !Period |/ !2011- 2012 !2012-2013 !2013-2014 !2014-2015 !2015-2016 !2016-2017 !2017-2018 |- !Dividend Growth Rate |/ |0.55 |0.17 |0.07 |0.15 |0.10 |0.05 |0.14 |- !Average Growth Rate |0.18 | colspan="7" rowspan="3" | |- !CAGR |0.14 |- !Constant growth rate |'''0.017''' |} The DDM value of '''19.67 €''' suggests that, according to the model's assumptions, the stock is overvalued relative to its intrinsic worth. On the other hand, the market value of 144.82 € reflects the collective perception of investors in the open market, which may be influenced by a variety of short-term and long-term factors. {| class="wikitable" !Value per Share (DDM) | ''' 19.67 €''' |- !Market value per share | 144.82 € |} We also performed a sensitivity analysis. {| class="wikitable" | ! colspan="8" |Constant Growth rate |- ! rowspan="8" |Cost of equity | |''1.1%'' |''1.3%'' |''1.5%'' |''1.7%'' |''1.9%'' |''2.1%'' |''2.3%'' |- |''6.5%'' |25.37 € |26.40 € |27.51 € |28.72 € |30.04 € |31.47 € |33.04 € |- |''7.5%'' |21.39 € |22.13 € |22.91 € |23.75 € |24.65 € |25.62 € |26.66 € |- |''8.5%'' |18.49 € |19.05 € |19.63 € |20.25 € |20.91 € |21.60 € |22.35 € |- |''8.7%'' |18.01 € |18.53 € |19.08 € |'''19.67 €''' |20.29 € |20.95 € |21.65 € |- |''8.9%'' |17.54 € |18.04 € |18.57 € |19.12 € |19.71 € |20.33 € |20.99 € |- |''9.9%'' |15.54 € |15.94 € |16.35 € |16.78 € |17.24 € |17.72 € |18.22 € |- |''10.9%'' |13.96 € |14.27 € |14.61 € |14.96 € |15.32 € |15.70 € |16.10 € |} ==== Inverse problem ==== In this section, our objective is to calculate the necessary growth rate of Safran's stock to achieve its market value, assuming market efficiency. To achieve this, we will simply reverse the GGM formula to derive the cost of equity. {| class="wikitable" | |Inverse problem |- !Market value per share |144.82 € |- !Expected dividend per share |1.37 € |- !Dividend growth rate |1.70% |- !Cost of equity |'''2.64%''' |} The cost of equity of 2.64% for Safran represents the expected return or required rate of return that investors demand for holding the company's stock. A low cost of equity, such as 2.64%, indicates that investors perceive Safran as a relatively safe and stable investment. === Monte Carlo Simulations for Stock Price Predictions - Python Code === <syntaxhighlight lang="python"> """ Created on Thu Jul 27 18:31:35 2023 @author: Jérémy Archier """ import csv import matplotlib.pyplot as plt import numpy as np from datetime import datetime, timedelta from scipy.stats import norm # Step 1: Load data from the CSV file csv_file_path = "SAF.PA.csv" dates = [] opens = [] with open(csv_file_path, newline="") as csvfile: csv_reader = csv.DictReader(csvfile) for row in csv_reader: # Check if the 'Open' value is 'null' or empty or 0 if row['Open'].strip() == '' or row['Open'].lower() == 'null' or float(row['Open']) == 0: # Skip the data point if there is no valid 'Open' value continue # Convert the 'Open' value to a float open_value = float(row['Open']) # Convert the 'Date' value to a datetime object date_value = datetime.strptime(row['Date'], '%Y-%m-%d') # Append the 'Date' and 'Open' values to their respective lists dates.append(date_value) opens.append(open_value) # Step 2: Compute the logarithmic returns of the stock log_returns = np.log(np.array(opens[1:]) / np.array(opens[:-1])) # Plot the previous prices plt.figure(figsize=(10, 5)) plt.plot(dates, opens, linestyle='-', color='b') plt.xlabel('Date') plt.ylabel('Open Value') plt.title('Open Value as a Function of Dates') plt.xticks(rotation=45) plt.grid(True) plt.tight_layout() plt.show() # Plot the log returns plt.figure(figsize=(6, 5)) plt.hist(log_returns, bins=30, edgecolor='k') plt.xlabel("Logarithmic Returns") plt.ylabel("Frequency") plt.title("Distribution of Logarithmic Returns") plt.grid(True) plt.tight_layout() plt.show() # Step 3: Compute the Drift u = log_returns.mean() var = log_returns.var() drift = u - (0.5 * var) # Step 4: Compute the Variance and Daily Returns stdev = log_returns.std() days = 50 trials = 1000000 # Reduce the number of trials to 100 for faster computation Z = norm.ppf(np.random.rand(days, trials)) # days, trials daily_returns = np.exp(drift + stdev * Z) # Step 5: Calculating the stock price for every trial price_paths = np.zeros_like(daily_returns) price_paths[0] = opens[-1] for t in range(1, days): price_paths[t] = price_paths[t - 1] * daily_returns[t] # Step 6: Calculate the probability of reaching the target price target_price = 150.0 # Replace this with the desired target price # Count how many times the stock price reaches or exceeds the target price in each trial success_count = np.sum(price_paths[-1, :] >= target_price) # Calculate the probability as the ratio of successful trials to total trials probability = success_count / trials # Print the probability print(f"The probability of the stock reaching or exceeding {target_price} is: {probability:.2%}") # Step 7: Plot the histogram of final stock prices final_prices = price_paths[-1, :] plt.figure(figsize=(10, 6)) plt.hist(final_prices, bins=30, edgecolor='k', density=True) plt.axvline(x=target_price, color='r', linestyle='dashed', linewidth=2, label=f'Target Price ({target_price})') plt.xlabel('Stock Price') plt.ylabel('Probability Density') plt.title('Probability Distribution of Final Stock Prices') plt.legend() plt.grid(True) plt.tight_layout() plt.show() # Step 8: Plot the price paths for each trial plt.figure(figsize=(10, 6)) for trial in range(trials): trial_dates = [dates[-1] + timedelta(days=i) for i in range(days)] plt.plot(trial_dates, price_paths[:, trial], linewidth=0.5) plt.xlabel('Date') plt.ylabel('Stock Price') plt.title('Monte Carlo Simulation of Stock Price') plt.tight_layout() plt.grid(True) plt.show() </syntaxhighlight>
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