diff --git a/DFC/DFC_skarozis.ipynb b/DFC/DFC_skarozis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f3c65b02a15561815f9a1c4219690d208aef3ad4 --- /dev/null +++ b/DFC/DFC_skarozis.ipynb @@ -0,0 +1,8002 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DFC foracst\n", + "### Author: Karozis Stelios\n", + "### Date: 12/4/2021\n", + "### Source: https://codingandfun.com/discounted-cash-flow-with-python-valuing-a-company/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Libs import" + ] + }, + { + "cell_type": "code", + "execution_count": 743, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pandas_datareader.data as web\n", + "import datetime\n", + "import plotly.graph_objects as go\n", + "pd.options.display.float_format = '{:,.2f}'.format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## User inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 684, + "metadata": {}, + "outputs": [], + "source": [ + "y_ahead=5\n", + "date_start = [2019,3,1]\n", + "timeframe=0 #analysis for current year: 0, analysis for previous year: 1 etc\n", + "company = 'GOOG' #'VWS.DE' #VESTAS\n", + "demo = '34c9e7a6a6fcfb7b1d531f1267eac987'" + ] + }, + { + "cell_type": "code", + "execution_count": 652, + "metadata": {}, + "outputs": [], + "source": [ + "IS = requests.get('https://financialmodelingprep.com/api/v3/income-statement/'+company+'?apikey='+demo).json()" + ] + }, + { + "cell_type": "code", + "execution_count": 653, + "metadata": {}, + "outputs": [], + "source": [ + "BS = requests.get('https://financialmodelingprep.com/api/v3/balance-sheet-statement/'+company+'?apikey='+demo).json()" + ] + }, + { + "cell_type": "code", + "execution_count": 654, + "metadata": {}, + "outputs": [], + "source": [ + "FR = requests.get('https://financialmodelingprep.com/api/v3/ratios/'+company+'?apikey='+demo).json()" + ] + }, + { + "cell_type": "code", + "execution_count": 655, + "metadata": {}, + "outputs": [], + "source": [ + "profile = requests.get('https://financialmodelingprep.com/api/v3/company/profile/'+company+'?apikey='+demo).json()" + ] + }, + { + "cell_type": "code", + "execution_count": 729, + "metadata": {}, + "outputs": [], + "source": [ + "historical_prices = requests.get('https://financialmodelingprep.com/api/v3/historical-price-full/'+company+'?apikey='+demo).json()" + ] + }, + { + "cell_type": "code", + "execution_count": 741, + "metadata": {}, + "outputs": [], + "source": [ + "history = requests.get('https://financialmodelingprep.com/api/v3/historical-price-full/'+company+'?apikey='+demo).json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FCF forecast" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 594, + "metadata": {}, + "outputs": [], + "source": [ + "def income_perc_years_ahead(IS,timeframe, y_ahead, revenue_g):\n", + " #Get net income\n", + " #net_income = dataframe[0]['netIncome']\n", + " #get income statement as % of revenue for future predictions and forecast 5 next IS years\n", + " income_statement = pd.DataFrame.from_dict(IS[timeframe],orient='index')\n", + "\n", + "\n", + " #income_statement = income_statement[6:27]\n", + " income_statement.columns = ['current_year']\n", + " income_statement['as_ratio_of_revenue'] = np.nan\n", + " for i in income_statement.index:\n", + " try:\n", + " #print(type(income_statement['current_year'][i]), income_statement['current_year'][i], type(net_income),net_income)\n", + " income_statement['as_ratio_of_revenue'][i] = float(income_statement['current_year'][i]) / float(income_statement['current_year']['revenue'])\n", + " except:\n", + " pass\n", + " \n", + " \n", + " for y in range(1,y_ahead+1):\n", + " if y==1:\n", + " cl_nm='next'+str(y)+'year'\n", + " prev='current_year'\n", + " else:\n", + " cl_nm='next'+str(y)+'year'\n", + " prev='next'+str(y-1)+'year'\n", + " #print(cl_nm)\n", + " income_statement[cl_nm] = np.nan\n", + " #for i in income_statement.index:\n", + " income_statement[cl_nm] = (income_statement[prev]['revenue'] * (1+revenue_g)) \\\n", + " * income_statement['as_ratio_of_revenue'] \n", + " \n", + " return income_statement" + ] + }, + { + "cell_type": "code", + "execution_count": 568, + "metadata": {}, + "outputs": [], + "source": [ + "def balance_perc_years_ahead(BS, timeframe, income_statement, y_ahead):\n", + " #Get Balance sheet as a percentage of revenue\n", + " balance_sheet = pd.DataFrame.from_dict(BS[timeframe],orient='index')\n", + " balance_sheet.columns = ['current_year']\n", + " balance_sheet['as_ratio_of_revenue'] = np.nan\n", + " for i in balance_sheet.index:\n", + " try:\n", + " balance_sheet['as_ratio_of_revenue'][i] = float(balance_sheet['current_year'][i]) \\\n", + " / float(income_statement['current_year']['revenue'])\n", + " except:\n", + " pass\n", + " \n", + " for y in range(1,y_ahead+1):\n", + " cl_nm='next'+str(y)+'year'\n", + " #print(cl_nm)\n", + " balance_sheet[cl_nm] = income_statement[cl_nm]['revenue'] \\\n", + " * balance_sheet['as_ratio_of_revenue'] \n", + " \n", + " return balance_sheet" + ] + }, + { + "cell_type": "code", + "execution_count": 602, + "metadata": {}, + "outputs": [], + "source": [ + "def CF_forecast_perc_years_ahead(income_statement, balance_sheet, y_ahead):\n", + " CF_forecast = {}\n", + " \n", + " for y in range(1,y_ahead+1):\n", + " if y==1:\n", + " cl_nm='next'+str(y)+'year'\n", + " prev='current_year'\n", + " else:\n", + " cl_nm='next'+str(y)+'year'\n", + " prev='next'+str(y-1)+'year'\n", + " #print(cl_nm)\n", + " CF_forecast[cl_nm] = {}\n", + " CF_forecast[cl_nm]['netIncome'] = income_statement[cl_nm]['netIncome']\n", + " CF_forecast[cl_nm]['inc_depreciation'] = income_statement[cl_nm]['depreciationAndAmortization'] - income_statement[prev]['depreciationAndAmortization']\n", + " CF_forecast[cl_nm]['inc_receivables'] = balance_sheet[cl_nm]['netReceivables'] - balance_sheet[prev]['netReceivables']\n", + " CF_forecast[cl_nm]['inc_inventory'] = balance_sheet[cl_nm]['inventory'] - balance_sheet[prev]['inventory']\n", + " CF_forecast[cl_nm]['inc_payables'] = balance_sheet[cl_nm]['accountPayables'] - balance_sheet[prev]['accountPayables']\n", + " CF_forecast[cl_nm]['CF_operations'] = CF_forecast[cl_nm]['netIncome'] + CF_forecast[cl_nm]['inc_depreciation'] + (CF_forecast[cl_nm]['inc_receivables'] * -1) + (CF_forecast[cl_nm]['inc_inventory'] *-1) + CF_forecast[cl_nm]['inc_payables']\n", + " CF_forecast[cl_nm]['CAPEX'] = balance_sheet[cl_nm]['propertyPlantEquipmentNet'] - balance_sheet[prev]['propertyPlantEquipmentNet'] + income_statement[cl_nm]['depreciationAndAmortization']\n", + "\n", + " CF_forecast[cl_nm]['FCF'] = CF_forecast[cl_nm]['CAPEX'] + CF_forecast[cl_nm]['CF_operations']\n", + " \n", + " #add the forecasted cash flows into a Pandas\n", + " CF_forec = pd.DataFrame.from_dict(CF_forecast,orient='columns')\n", + "\n", + " #add below option to format the dataframe with thousand separators\n", + " #pd.options.display.float_format = '{:,.2f}'.format\n", + " \n", + " return CF_forec" + ] + }, + { + "cell_type": "code", + "execution_count": 740, + "metadata": {}, + "outputs": [], + "source": [ + "def historical_prices(history):\n", + "\n", + " historical_prices = history['historical']\n", + " price_data = {}\n", + "\n", + " for item in historical_prices:\n", + " price_data[item['date']] = {}\n", + " price_data[item['date']]['date'] = item['date']\n", + " price_data[item['date']]['open']= item['open']\n", + " price_data[item['date']]['high'] = item['high']\n", + " price_data[item['date']]['low'] = item['low']\n", + " price_data[item['date']]['adjClose'] = item['adjClose']\n", + "\n", + " price_DF = pd.DataFrame.from_dict(price_data)\n", + " price_DF = price_DF.T\t\n", + " fig = go.Figure(data=[go.Candlestick(x=price_DF['date'],\n", + " open=price_DF['open'],\n", + " high=price_DF['high'],\n", + " low=price_DF['low'],\n", + " close=price_DF['adjClose'])])\n", + "\n", + " fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculations" + ] + }, + { + "cell_type": "code", + "execution_count": 744, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "close": [ + 723.15, + 708.14, + 705.84, + 691.02, + 693.01, + 698.21, + 692.36, + 695.7, + 701.43, + 711.12, + 712.9, + 723.18, + 715.29, + 713.31, + 710.83, + 716.49, + 706.23, + 706.63, + 700.32, + 709.74, + 704.24, + 720.09, + 725.27, + 724.12, + 732.66, + 735.72, + 734.15, + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "historical_prices(history)" + ] + }, + { + "cell_type": "code", + "execution_count": 690, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.12770532012826136\n", + "1 0.1830008989979462\n", + "2 0.23421586757475982\n", + "Revenue variation of the last year: 12.77 %\n" + ] + } + ], + "source": [ + "count = 0\n", + "#get revenue growth to estimate future sales\n", + "revenue_g = []\n", + "for item in IS:\n", + " if count < 4:\n", + " #print(item)\n", + " revenue_g.append(item['revenue'])\n", + " count = count + 1\n", + "for i in range(len(revenue_g)-1):\n", + " print(i,(revenue_g[i] - revenue_g[i+1]) /revenue_g[i+1])\n", + "\n", + "revenue_g = (revenue_g[timeframe] - revenue_g[timeframe+1]) /revenue_g[timeframe+1] \n", + "print('Revenue variation of the last year:',round(revenue_g*100,2),'%')" + ] + }, + { + "cell_type": "code", + "execution_count": 691, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/share/apps/gnu/8.2.0/soft/python-3.5.7/lib/python3.5/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \n" + ] + } + ], + "source": [ + "income_statement = income_perc_years_ahead(IS,timeframe, y_ahead, revenue_g)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 692, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/share/apps/gnu/8.2.0/soft/python-3.5.7/lib/python3.5/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " if __name__ == '__main__':\n" + ] + } + ], + "source": [ + "balance_sheet=balance_perc_years_ahead(BS,timeframe, income_statement, y_ahead)" + ] + }, + { + "cell_type": "code", + "execution_count": 598, + "metadata": {}, + "outputs": [], + "source": [ + "#balance_sheet\n", + "#income_statement" + ] + }, + { + "cell_type": "code", + "execution_count": 693, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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next1yearnext2yearnext3yearnext4yearnext5year
CAPEX27,828,487,609.4331,382,333,528.2835,390,024,477.8839,909,518,883.1845,006,176,768.32
CF_operations43,773,617,100.2849,363,740,885.2455,667,753,217.7262,776,821,463.2170,793,755,544.81
FCF71,602,104,709.7180,746,074,413.5291,057,777,695.60102,686,340,346.39115,799,932,313.12
inc_depreciation1,749,179,769.801,972,559,332.262,224,465,653.262,508,541,751.622,828,895,879.07
inc_inventory92,969,473.05104,842,169.37118,231,072.17133,329,809.10150,356,735.05
inc_payables713,745,034.20804,894,072.28907,683,327.451,023,599,317.361,154,318,395.86
inc_receivables4,007,903,766.914,519,734,400.505,096,928,529.015,747,833,418.486,481,862,325.23
netIncome45,411,565,536.2451,210,864,050.5857,750,763,838.2065,125,843,621.8173,442,760,330.16
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" + ], + "text/plain": [ + " next1year next2year next3year \\\n", + "CAPEX 27,828,487,609.43 31,382,333,528.28 35,390,024,477.88 \n", + "CF_operations 43,773,617,100.28 49,363,740,885.24 55,667,753,217.72 \n", + "FCF 71,602,104,709.71 80,746,074,413.52 91,057,777,695.60 \n", + "inc_depreciation 1,749,179,769.80 1,972,559,332.26 2,224,465,653.26 \n", + "inc_inventory 92,969,473.05 104,842,169.37 118,231,072.17 \n", + "inc_payables 713,745,034.20 804,894,072.28 907,683,327.45 \n", + "inc_receivables 4,007,903,766.91 4,519,734,400.50 5,096,928,529.01 \n", + "netIncome 45,411,565,536.24 51,210,864,050.58 57,750,763,838.20 \n", + "\n", + " next4year next5year \n", + "CAPEX 39,909,518,883.18 45,006,176,768.32 \n", + "CF_operations 62,776,821,463.21 70,793,755,544.81 \n", + "FCF 102,686,340,346.39 115,799,932,313.12 \n", + "inc_depreciation 2,508,541,751.62 2,828,895,879.07 \n", + "inc_inventory 133,329,809.10 150,356,735.05 \n", + "inc_payables 1,023,599,317.36 1,154,318,395.86 \n", + "inc_receivables 5,747,833,418.48 6,481,862,325.23 \n", + "netIncome 65,125,843,621.81 73,442,760,330.16 " + ] + }, + "execution_count": 693, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CF_forecast = CF_forecast_perc_years_ahead(income_statement, balance_sheet, y_ahead)\n", + "CF_forecast" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WACC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 576, + "metadata": {}, + "outputs": [], + "source": [ + "#Interest coverage ratio = EBIT / interest expenses\n", + "\n", + "def interest_coverage_and_RF(IS,timeframe, st_date=[]):\n", + " EBIT= IS[timeframe]['ebitda'] - IS[timeframe]['depreciationAndAmortization'] \n", + " interest_expense = IS[timeframe]['interestExpense']\n", + " interest_coverage_ratio = EBIT / interest_expense\n", + "\n", + " #RF\n", + " start = datetime.datetime(st_date[0], st_date[1], st_date[2])\n", + "\n", + " #end= datetime.datetime.today().strftime('%Y-%m-%d')\n", + " end= datetime.datetime(st_date[0]+1, st_date[1], st_date[2]).strftime('%Y-%m-%d')\n", + " \n", + " Treasury = web.DataReader(['TB1YR'], 'fred', start, end)\n", + " \n", + " RF = float(Treasury.iloc[-1])\n", + " RF = RF/100\n", + " \n", + " return [RF,interest_coverage_ratio]" + ] + }, + { + "cell_type": "code", + "execution_count": 577, + "metadata": {}, + "outputs": [], + "source": [ + "#Cost of debt\n", + "def cost_of_debt(RF,interest_coverage_ratio):\n", + " if interest_coverage_ratio > 8.5:\n", + " #Rating is AAA\n", + " credit_spread = 0.0063\n", + " if (interest_coverage_ratio > 6.5) & (interest_coverage_ratio <= 8.5):\n", + " #Rating is AA\n", + " credit_spread = 0.0078\n", + " if (interest_coverage_ratio > 5.5) & (interest_coverage_ratio <= 6.5):\n", + " #Rating is A+\n", + " credit_spread = 0.0098\n", + " if (interest_coverage_ratio > 4.25) & (interest_coverage_ratio <= 5.49):\n", + " #Rating is A\n", + " credit_spread = 0.0108\n", + " if (interest_coverage_ratio > 3) & (interest_coverage_ratio <= 4.25):\n", + " #Rating is A-\n", + " credit_spread = 0.0122\n", + " if (interest_coverage_ratio > 2.5) & (interest_coverage_ratio <= 3):\n", + " #Rating is BBB\n", + " credit_spread = 0.0156\n", + " if (interest_coverage_ratio > 2.25) & (interest_coverage_ratio <= 2.5):\n", + " #Rating is BB+\n", + " credit_spread = 0.02\n", + " if (interest_coverage_ratio > 2) & (interest_coverage_ratio <= 2.25):\n", + " #Rating is BB\n", + " credit_spread = 0.0240\n", + " if (interest_coverage_ratio > 1.75) & (interest_coverage_ratio <= 2):\n", + " #Rating is B+\n", + " credit_spread = 0.0351\n", + " if (interest_coverage_ratio > 1.5) & (interest_coverage_ratio <= 1.75):\n", + " #Rating is B\n", + " credit_spread = 0.0421\n", + " if (interest_coverage_ratio > 1.25) & (interest_coverage_ratio <= 1.5):\n", + " #Rating is B-\n", + " credit_spread = 0.0515\n", + " if (interest_coverage_ratio > 0.8) & (interest_coverage_ratio <= 1.25):\n", + " #Rating is CCC\n", + " credit_spread = 0.0820\n", + " if (interest_coverage_ratio > 0.65) & (interest_coverage_ratio <= 0.8):\n", + " #Rating is CC\n", + " credit_spread = 0.0864\n", + " if (interest_coverage_ratio > 0.2) & (interest_coverage_ratio <= 0.65):\n", + " #Rating is C\n", + " credit_spread = 0.1134\n", + " if interest_coverage_ratio <= 0.2:\n", + " #Rating is D\n", + " credit_spread = 0.1512\n", + "\n", + " cost_of_debt = RF + credit_spread\n", + " \n", + " return cost_of_debt" + ] + }, + { + "cell_type": "code", + "execution_count": 615, + "metadata": {}, + "outputs": [], + "source": [ + "def costofequity(company, profile, RF, st_date=[]):\n", + "\n", + "\n", + " #Start end dates\n", + " start = datetime.datetime(st_date[0], st_date[1], st_date[2])\n", + " #end= datetime.datetime.today().strftime('%Y-%m-%d')\n", + " end= datetime.datetime(st_date[0]+1, st_date[1], st_date[2]).strftime('%Y-%m-%d')\n", + "\n", + "#Beta\n", + "\n", + " beta = float(profile['profile']['beta'])\n", + " stock_cur = float(profile['profile']['price'])\n", + "\n", + " #Market Return\n", + " SP500 = web.DataReader(['sp500'], 'fred', start, end)\n", + " #Drop all Not a number values using drop method.\n", + " SP500.dropna(inplace = True)\n", + "\n", + " SP500yearlyreturn = (SP500['sp500'].iloc[-1]/ SP500['sp500'].iloc[-252])-1\n", + " \n", + " cost_of_equity = RF+(beta*(SP500yearlyreturn - RF))\n", + " \n", + " return cost_of_equity, stock_cur" + ] + }, + { + "cell_type": "code", + "execution_count": 613, + "metadata": {}, + "outputs": [], + "source": [ + "#effective tax rate and capital structure\n", + "def wacc(company, BS, FR, timeframe, ke, kd):\n", + "\n", + " ETR = FR[timeframe]['effectiveTaxRate']\n", + "\n", + "# \n", + " Debt_to = BS[timeframe]['totalDebt'] / (BS[timeframe]['totalDebt'] + BS[timeframe]['totalStockholdersEquity'])\n", + " equity_to = BS[timeframe]['totalStockholdersEquity'] / (BS[timeframe]['totalDebt'] + BS[timeframe]['totalStockholdersEquity'])\n", + "\n", + " WACC = (kd*(1-ETR)*Debt_to) + (ke*equity_to)\n", + " \n", + " return WACC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculations" + ] + }, + { + "cell_type": "code", + "execution_count": 694, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wacc of GOOG is 4.980497654294513%\n" + ] + } + ], + "source": [ + "RF_and_IntCov = interest_coverage_and_RF(IS,timeframe, st_date=date_start) \n", + "RF = RF_and_IntCov[0]\n", + "interest_coverage_ratio = RF_and_IntCov[1]\n", + "ke, stock_cur = costofequity(company, profile, RF, st_date=date_start)\n", + "kd = cost_of_debt(RF,interest_coverage_ratio)\n", + "wacc_company = wacc(company, BS, FR, timeframe, ke, kd)\n", + "print('wacc of ' + company + ' is ' + str((wacc_company*100))+'%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Net Present Value of the Forecasted Free Cash Flows" + ] + }, + { + "cell_type": "code", + "execution_count": 695, + "metadata": {}, + "outputs": [], + "source": [ + "#FCF List of CFs for each year\n", + "FCF_List = CF_forecast.loc[['FCF']].values[0].tolist()\n", + "npv = np.npv(wacc_company,FCF_List)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Terminal Value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The net present value obtained in the section F covers only the next 5 years of the company cash flows. But what happen after these forecasted years?\n", + "\n", + "We need to calculate the terminal value of the company. The terminal value captures the value of the company after the forecasting period. The terminal value will constitute at least 50% of the forecasted value. Therefore, it is super important to pay attention to the assumptions that we make here. \n", + "\n", + "We will assume 2% perpetuity rate since it is a common practice to be in line with the long-term inflation rate which is usually around 2%. Then, we discount the value using the WACC rate." + ] + }, + { + "cell_type": "code", + "execution_count": 696, + "metadata": {}, + "outputs": [], + "source": [ + "#Terminal value\n", + "LTGrowth = 0.02\n", + "\n", + "Terminal_value = (CF_forecast['next'+str(y_ahead)+'year']['FCF'] * (1+ LTGrowth)) /(wacc_company - LTGrowth)\n", + "\n", + "Terminal_value_Discounted = Terminal_value/(1+wacc_company)**4\n", + "#Terminal_value_Discounted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Target Price" + ] + }, + { + "cell_type": "code", + "execution_count": 697, + "metadata": {}, + "outputs": [], + "source": [ + "target_equity_value = Terminal_value_Discounted + npv\n", + "debt = balance_sheet['current_year']['totalDebt']\n", + "target_value = target_equity_value - debt\n", + "numbre_of_shares = requests.get('https://financialmodelingprep.com/api/v3/enterprise-values/'+company+'?apikey='+demo).json()\n", + "numbre_of_shares = numbre_of_shares[0]['numberOfShares']\n", + "\n", + "target_price_per_share = target_value/numbre_of_shares\n", + "#target_price_per_share" + ] + }, + { + "cell_type": "code", + "execution_count": 698, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GOOG current price per stock is 2300.01\n", + "GOOG forecasted price per stock is 5272.227316237697\n", + "The forecast is based on the following assumptions:\n", + " Revenue growth: 12.77 %\n", + " Cost of Capital: 4.98 %\n", + " Perpetuity growth: 2.0 %\n" + ] + } + ], + "source": [ + "print(company + ' current price per stock is ' + str(stock_cur) )\n", + "print(company + ' forecasted price per stock is ' + str(target_price_per_share) )\n", + "print('The forecast is based on the following assumptions:')\n", + "print(' Revenue growth: ',round(revenue_g*100,2),'%')\n", + "print(' Cost of Capital: ', round(wacc_company*100,2),'%' )\n", + "print(' Perpetuity growth: ', round(LTGrowth*100,2),'%')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}