model - All companies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#15.433 - Finance Lab\n",
    "#William Blair Team\n",
    "#Getting the necessary inputs for the Option Pricing Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In /Users/panqian/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/panqian/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/panqian/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In /Users/panqian/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/panqian/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/panqian/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/panqian/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/panqian/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "#Importing necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the provided files\n",
    "##Reading the Initial File\n",
    "Col_Names_1 = ['Date', 'Issuer Name', 'Ticker', 'Country Code', 'Country', 'Debt Security Name', 'Bid Price USD', 'Equity Security Name', 'Equity Price USD', 'Equity Total Return', 'Fiscal Date', 'Total Assets', 'Total Liabilities', 'Total Corporate Debt', 'Total Shareholders Equity', 'Shares Outstanding', 'Dividends per Share', 'Dividends Paid', 'Dividend Yield']\n",
    "Filepath_1 = 'MexicanSecurityData.csv'\n",
    "Type = {'Date':'object', 'Issuer Name':'object', 'Ticker':'object', 'Country Code':'object', 'Country':'object', 'Debt Security Name':'object', 'Bid Price USD':'float64', 'Equity Security Name':'object', 'Equity Price USD':'float64', 'Equity Total Return':'float64', 'Fiscal Date':'float64', 'Total Assets':'float64', 'Total Liabilities':'float64', 'Total Corporate Debt':'float64', 'Total Shareholders Equity':'float64', 'Shares Outstanding':'float64', 'Dividends per Share':'float64', 'Dividends Paid':'float64', 'Dividend Yield':'float64'}\n",
    "Mexican_Data = pd.read_csv(Filepath_1, header = 0, dtype = Type, names = Col_Names_1, na_values = ' NA')\n",
    "\n",
    "\n",
    "##Reading the Maturity File\n",
    "Col_Names_2 = ['Debt Name', 'Maturity']\n",
    "Filepath_2 = 'MexicanMaturities.xlsx'\n",
    "Mexican_Maturities = pd.read_excel(Filepath_2, header = 0, names = Col_Names_2, na_values = ' NA')\n",
    "\n",
    "\n",
    "##Reading the Treasury File\n",
    "Col_Names_3 = ['Date', '3M Libor', '1Y', '3Y', '5Y', '7Y', '10Y', '15Y', '30Y']\n",
    "Filepath_3 = 'USTRateData.csv'\n",
    "Treasury_Rate = pd.read_csv(Filepath_3, header = 0, names = Col_Names_3, na_values = ' NA')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BANORT', 'CEMEX', 'KOF', 'SCCO', 'TELVIS'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_list = Mexican_Data.Ticker.unique()\n",
    "company_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MX Banorte 5 3/4% due 31'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test\n",
    "Mexican_Maturities['Debt Name'].unique()[0][:-17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MX Cemex 5.2% due 30 Quoted Price Bid',\n",
       "       'MX Cemex 5.45% due 29 Quoted Price Bid',\n",
       "       'MX Cemex 5.7% due 25 Quoted Price Bid',\n",
       "       'MX Cemex 7 3/4% due 26 Quoted Price Bid',\n",
       "       'MX Cemex 7 3/8% due 27 Quoted Price Bid'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test\n",
    "a = Mexican_Data[Mexican_Data['Ticker'] == 'CEMEX']\n",
    "a['Debt Security Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test\n",
    "company = Mexican_Data[Mexican_Data['Ticker'] == 'CEMEX']\n",
    "company[company['Equity Total Return'].isna()]\n",
    "company = company.dropna(subset=['Equity Total Return'])\n",
    "company_bond = company[company['Debt Security Name'] == 'MX Cemex 5.2% due 30 Quoted Price Bid']\n",
    "\n",
    "#     print(company)\n",
    "MV_Assets, Annual_Volatility, Strike_Price, Expiration_Time, Risk_Free_Rate, Dividend_yield = info(company_bond, 'MX Cemex 5.2% due 30 Quoted Price Bid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1700"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test\n",
    "simu_liability_price_div = []\n",
    "for i in range(len(Annual_Volatility)):\n",
    "    simu_liability_price_div.append(Strike_Price[i] - \n",
    "                                    priceBS_div(MV_Assets[i], Strike_Price[i], int(Expiration_Time[i]), Risk_Free_Rate[i], \n",
    "                                                Annual_Volatility[i], Dividend_yield[i], 'P'))\n",
    "\n",
    "len(simu_liability_price_div)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9561a8f320>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test\n",
    "plt.figure(figsize=(16,9))\n",
    "x = range(1700)\n",
    "plt.plot(x, company_bond['Total Corporate Debt'], label = \"real value\")\n",
    "plt.plot(x, simu_liability_price_div, label = \"estimated value\")\n",
    "plt.xlabel('No. of Rows')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Banorte 5.2% due 30 Quoted Price Bid')\n",
    "plt.legend()\n",
    "# plt.savefig('CEMEX' + '5.2% due 30 Quoted Price Bid' + '.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/panqian/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:18: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
     ]
    }
   ],
   "source": [
    "for c in company_list: \n",
    "    company = Mexican_Data[Mexican_Data['Ticker'] == c]\n",
    "    company[company['Equity Total Return'].isna()]\n",
    "    company = company.dropna(subset=['Equity Total Return'])\n",
    "#     print(company)\n",
    "    bonds_list = company['Debt Security Name'].unique()\n",
    "    ind = 0\n",
    "    for b in bonds_list:\n",
    "#         print(b)\n",
    "        ind += 1\n",
    "        company_bond = company[company['Debt Security Name'] == b]\n",
    "        MV_Assets, Annual_Volatility, Strike_Price, Expiration_Time, Risk_Free_Rate, Dividend_yield = info(company_bond, b)\n",
    "        simu_liability_price_div = []\n",
    "        for i in range(len(Annual_Volatility)):\n",
    "            simu_liability_price_div.append(Strike_Price[i] - \n",
    "                                            priceBS_div(MV_Assets[i], Strike_Price[i], int(Expiration_Time[i]), Risk_Free_Rate[i], \n",
    "                                                        Annual_Volatility[i], Dividend_yield[i], 'P'))\n",
    "        plt.figure(figsize=(16,9))\n",
    "        x = range(len(simu_liability_price_div))\n",
    "        plt.plot(x, company_bond['Total Corporate Debt'], label = \"real value\")\n",
    "        plt.plot(x, simu_liability_price_div, label = \"estimated value\")\n",
    "        plt.xlabel('No. of Rows')\n",
    "        plt.ylabel('Value')\n",
    "        plt.title(c + ' ' + b)\n",
    "        plt.legend()\n",
    "        plt.savefig(c + ' ' + str(ind) + '.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def info(company, bond):\n",
    "    #First, we get the Total MV of Assets\n",
    "\n",
    "    ##Getting the necessary inputs with the correct data type\n",
    "    Total_Debt = pd.to_numeric(company['Total Corporate Debt'], errors = 'coerce').to_numpy()\n",
    "    Stock_Price = pd.to_numeric(company['Equity Price USD'], errors = 'coerce').to_numpy()\n",
    "    Shares_Outstanding = pd.to_numeric(company['Shares Outstanding'], errors = 'coerce').to_numpy()\n",
    "\n",
    "    ##Getting the MV of Equity\n",
    "    Equity = Stock_Price *  Shares_Outstanding\n",
    "\n",
    "    ##Getting the MV of Assets\n",
    "    MV_Assets = Total_Debt + Equity\n",
    "    \n",
    "    ##############################\n",
    "    #Second, we get the Assets' Volatility\n",
    "\n",
    "    ##We calculate the equity volatility\n",
    "    Lambda = 0.9075\n",
    "\n",
    "\n",
    "    Equity_Returns = pd.to_numeric(company['Equity Total Return'], errors = 'coerce').to_numpy()\n",
    "    l = len(Equity_Returns)\n",
    "    Equity_Volatility = np.zeros(l)\n",
    "    Equity_Volatility[1] = np.sqrt(Equity_Returns[0]**2)\n",
    "\n",
    "\n",
    "    ## Using EWMA, assume we are using last 250 trading days (one year)\n",
    "    EWMA1 = []\n",
    "    n = len(Equity_Returns)-250\n",
    "    for i in range(n):\n",
    "        total = 0\n",
    "        for j in range(250):\n",
    "            total = total + (Lambda**(250-j)) * (Equity_Returns[i+j]**2)\n",
    "        EWMA1.append((total*(1-Lambda))**0.5)\n",
    "\n",
    "\n",
    "    ##Since we used 250 days as the time window, we cannot calculate the volatilities for the first 250 days. \n",
    "    ##For consistency in list lengths of other variables below, I filled EWMA with 250 NaN in the beginning of the list.\n",
    "\n",
    "    EWMA = [np.nan]*250+EWMA1\n",
    "    \n",
    "    ##We get the D/E ratio\n",
    "    DE_Ratio = Total_Debt / Equity\n",
    "\n",
    "    ##Finally, we calculate the Asset Volatility\n",
    "    #####Asset_Volatility = (1 / (1 + DE_Ratio)) * Equity_Volatility\n",
    "    Asset_Volatility = (1 / (1 + DE_Ratio)) * EWMA\n",
    "\n",
    "    ##Annuliaze Volatility\n",
    "    Annual_Volatility = Asset_Volatility*np.sqrt(252)\n",
    "\n",
    "    \n",
    "    ##############################\n",
    "    #Third, we get the strike price - straightforward\n",
    "    Strike_Price = Total_Debt\n",
    "    \n",
    "    ##############################  \n",
    "    #Fourth, we get the time to maturity\n",
    "\n",
    "    ##We get the maturity of the Banorte Bond from the Mexican Maturities File\n",
    "    bond_pattern = bond[:-17]\n",
    "    Bond_Maturity = Mexican_Maturities[Mexican_Maturities['Debt Name'].str.contains(bond_pattern)]\n",
    "    Bond_Maturity = pd.to_datetime(Bond_Maturity['Maturity'], format = '%Y%m%d').to_numpy()\n",
    "\n",
    "    ##We get the date of the bond from the intial file\n",
    "    Date = pd.to_datetime(company['Date'], format = '%Y/%m/%d').to_numpy()\n",
    "\n",
    "    #Finally, we take the time difference between the actual date and Maturity to get the time to expiration\n",
    "    Expiration_Time = ( Bond_Maturity - Date ) / (365 * 24 * 60 * 60 * 10**9)\n",
    "\n",
    "    \n",
    "    ##############################  \n",
    "    #Fifth, we get the risk-free rate for the US Yield Curve\n",
    "    Treasury_Date = pd.to_datetime(Treasury_Rate['Date'], format = '%Y/%m/%d').to_numpy()\n",
    "    \n",
    "\n",
    "    #For a specific trading day of the bond, we get the closest Treasury Date and for this Treasury Date the closest maturity\n",
    "    Mat = np.array([0.25, 1, 3, 5, 7, 10, 15, 30])\n",
    "    Risk_Free_Rate = []\n",
    "    m = len(Date)\n",
    "    for i in range(m):\n",
    "        #Specific_Date = Date[i]\n",
    "        #We get the closest day when treasury rates are reported\n",
    "        #should be a list of 2555 entries\n",
    "        day_difference = [abs(Date[i] - x) for x in Treasury_Date]\n",
    "\n",
    "        #Find the index of that closest bond issuing day \n",
    "        #Need to be fixed \n",
    "        Day_Index = day_difference.index(min(day_difference))\n",
    "\n",
    "        #We get the closest maturity for the specific date\n",
    "        # should be a list of 8 entries\n",
    "        maturity_difference = [abs(Expiration_Time[i].astype(np.int64) - x) for x in Mat]\n",
    "\n",
    "        #Find the index of that closest maturity \n",
    "        Maturity_Index = maturity_difference.index(min(maturity_difference))\n",
    "\n",
    "        Treasury_Specific_Rate = Treasury_Rate.iloc[Day_Index, Maturity_Index]\n",
    "        #We get the continuously-compounded rate\n",
    "        Rate = np.log(1 + Treasury_Specific_Rate)\n",
    "        Risk_Free_Rate.append(Rate) \n",
    "        \n",
    "    ##############################   \n",
    "    ## Dividend yield \n",
    "    Dividend_yield = pd.to_numeric(company['Dividend Yield'], errors = 'coerce').to_numpy()\n",
    "    Dividend_yield = Dividend_yield / 100\n",
    "    Dividend_yield[0:20]\n",
    "\n",
    "    Dividend_yield = np.log(1 + Dividend_yield)\n",
    "\n",
    "    \n",
    "    return MV_Assets, Annual_Volatility, Strike_Price, Expiration_Time, Risk_Free_Rate, Dividend_yield"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def priceBS_div(S0, K, T, r, sigma, q, Type):\n",
    "    d1 = (np.log(S0/K) + (r - q + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))\n",
    "    d2 = (np.log(S0/K) + (r - q - 0.5*sigma**2)*T) / (sigma*np.sqrt(T))\n",
    "    if Type == \"C\":\n",
    "        return S0*np.exp(-q*T)*stats.norm.cdf(d1,0,1) - K*np.exp(-r*T)*stats.norm.cdf(d2, 0, 1)\n",
    "    else: \n",
    "        return K*np.exp(-r*T)*stats.norm.cdf(-d2, 0, 1) - S0*np.exp(-q*T)*stats.norm.cdf(-d1,0,1)"
   ]
  },
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   "metadata": {},
   "outputs": [],
   "source": []
  }
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