{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "source": [ "## Data Source: FactSet, searching for all public company PE ratio, deleting row with missing observations, Could be significant bias introduced during deletion\n" ], "metadata": { "id": "TQXcsiSyMtj6" }, "id": "TQXcsiSyMtj6" }, { "cell_type": "code", "execution_count": 1, "id": "4efee966-1f45-44e6-9f0c-37ba284e366b", "metadata": { "id": "4efee966-1f45-44e6-9f0c-37ba284e366b", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3be270b1-f4f3-49f2-89ed-b305a61d1639" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", " import pandas.util.testing as tm\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from pylab import mpl, plt\n", "import statsmodels.formula.api as smf\n", "import statsmodels.api as sm\n", "plt.style.use('seaborn')\n", "mpl.rcParams['font.family'] = 'serif'\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "id": "e7c64e59-e05f-4c95-91fd-5734f40908ee", "metadata": { "id": "e7c64e59-e05f-4c95-91fd-5734f40908ee", "outputId": "01d4873a-5128-4f6f-e32a-08a9cacd8596", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'/content'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 2 } ], "source": [ "import os \n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 10, "id": "2bfd132b-f851-4651-9788-d04f8d19c569", "metadata": { "id": "2bfd132b-f851-4651-9788-d04f8d19c569", "outputId": "e8705e58-b12a-4655-e048-77bee2934fda", "colab": { "base_uri": "https://localhost:8080/", "height": 765 } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Identifier Name Revenue \\\n", "0 III-GB 3i Group plc 310.80 \n", "1 MMM-US 3M Company 35,355.00 \n", "2 MAERSK.B-DK A.P. Moller - Maersk A/S 61,802.00 \n", "3 SAGA.B-SE AB Sagax 376.95 \n", "4 ABBN-CH ABB Ltd. 28,949.00 \n", "... ... ... ... \n", "1595 ZM-US Zoom Video Communications, Inc. 4,099.86 \n", "1596 ZI-US ZoomInfo Technologies, Inc. 747.20 \n", "1597 ZS-US Zscaler, Inc. 673.10 \n", "1598 2057-HK ZTO Express (Cayman), Inc. 4,714.55 \n", "1599 ZURN-CH Zurich Insurance Group AG 69,591.00 \n", "\n", " Industry Country EV_EBITDA PE DY \\\n", "0 Investment Managers United Kingdom 5.71 6.00 3.34 \n", "1 Industrial Conglomerates United States 12.36 17.56 3.33 \n", "2 Marine Shipping Denmark 3.18 3.96 10.66 \n", "3 Real Estate Development Sweden 61.96 13.80 0.70 \n", "4 Electrical Products Switzerland 18.57 16.95 2.35 \n", "... ... ... ... ... ... \n", "1595 Packaged Software United States 34.88 34.31 0.00 \n", "1596 Internet Software/Services United States 117.64 216.53 0.00 \n", "1597 Packaged Software United States NaN NaN 0.00 \n", "1598 Air Freight/Couriers China 20.29 30.96 0.00 \n", "1599 Multi-Line Insurance Switzerland NaN 12.52 5.49 \n", "\n", " ROE D_EBITDA SalesGrowth EV_Sales IntCoverage Sales_Growth \\\n", "0 21.93 0.48 35.23 50.11 44.26 -15.75 \n", "1 42.42 1.85 9.85 3.33 16.47 2.57 \n", "2 47.77 0.67 49.62 1.23 23.30 16.15 \n", "3 36.64 10.53 9.81 48.63 5.68 11.86 \n", "4 30.14 1.33 8.15 2.71 22.22 -0.83 \n", "... ... ... ... ... ... ... \n", "1595 28.53 0.00 54.63 10.21 NaN 131.49 \n", "1596 9.34 5.54 56.91 35.16 3.49 73.00 \n", "1597 -51.70 NaN 56.07 46.75 -3.89 52.40 \n", "1598 9.75 0.54 29.34 4.58 37.26 20.68 \n", "1599 13.92 NaN 15.45 1.23 NaN 11.39 \n", "\n", " NAICS \n", "0 Finance and Insurance \n", "1 Manufacturing \n", "2 Transportation and Warehousing \n", "3 Real Estate and Rental and Leasing \n", "4 Manufacturing \n", "... ... \n", "1595 Information \n", "1596 Information \n", "1597 Information \n", "1598 Professional, Scientific, and Technical Services \n", "1599 Finance and Insurance \n", "\n", "[1600 rows x 15 columns]" ], "text/html": [ "\n", "
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IdentifierNameRevenueIndustryCountryEV_EBITDAPEDYROED_EBITDASalesGrowthEV_SalesIntCoverageSales_GrowthNAICS
0III-GB3i Group plc310.80Investment ManagersUnited Kingdom5.716.003.3421.930.4835.2350.1144.26-15.75Finance and Insurance
1MMM-US3M Company35,355.00Industrial ConglomeratesUnited States12.3617.563.3342.421.859.853.3316.472.57Manufacturing
2MAERSK.B-DKA.P. Moller - Maersk A/S61,802.00Marine ShippingDenmark3.183.9610.6647.770.6749.621.2323.3016.15Transportation and Warehousing
3SAGA.B-SEAB Sagax376.95Real Estate DevelopmentSweden61.9613.800.7036.6410.539.8148.635.6811.86Real Estate and Rental and Leasing
4ABBN-CHABB Ltd.28,949.00Electrical ProductsSwitzerland18.5716.952.3530.141.338.152.7122.22-0.83Manufacturing
................................................
1595ZM-USZoom Video Communications, Inc.4,099.86Packaged SoftwareUnited States34.8834.310.0028.530.0054.6310.21NaN131.49Information
1596ZI-USZoomInfo Technologies, Inc.747.20Internet Software/ServicesUnited States117.64216.530.009.345.5456.9135.163.4973.00Information
1597ZS-USZscaler, Inc.673.10Packaged SoftwareUnited StatesNaNNaN0.00-51.70NaN56.0746.75-3.8952.40Information
15982057-HKZTO Express (Cayman), Inc.4,714.55Air Freight/CouriersChina20.2930.960.009.750.5429.344.5837.2620.68Professional, Scientific, and Technical Services
1599ZURN-CHZurich Insurance Group AG69,591.00Multi-Line InsuranceSwitzerlandNaN12.525.4913.92NaN15.451.23NaN11.39Finance and Insurance
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1600 rows × 15 columns

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\n", " " ] }, "metadata": {}, "execution_count": 10 } ], "source": [ "data = pd.read_csv('https://raw.githubusercontent.com/cyrus723/my-first-binder/main/data/Factset_corp.csv', header=0) \n", "data" ] }, { "cell_type": "code", "execution_count": 11, "id": "28e4e30f-477a-4e66-81f9-518120d53f4e", "metadata": { "id": "28e4e30f-477a-4e66-81f9-518120d53f4e", "outputId": "648fa64f-0b07-45e6-f0ee-58a5978639d9", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 1600 entries, 0 to 1599\n", "Data columns (total 15 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Identifier 1600 non-null object \n", " 1 Name 1600 non-null object \n", " 2 Revenue 1600 non-null object \n", " 3 Industry 1600 non-null object \n", " 4 Country 1600 non-null object \n", " 5 EV_EBITDA 1271 non-null float64\n", " 6 PE 1482 non-null float64\n", " 7 DY 1516 non-null float64\n", " 8 ROE 1559 non-null float64\n", " 9 D_EBITDA 1303 non-null float64\n", " 10 SalesGrowth 1594 non-null float64\n", " 11 EV_Sales 1539 non-null float64\n", " 12 IntCoverage 1328 non-null float64\n", " 13 Sales_Growth 1579 non-null float64\n", " 14 NAICS 1600 non-null object \n", "dtypes: float64(9), object(6)\n", "memory usage: 187.6+ KB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": null, "id": "04ca0560-f5f0-40e1-8773-79f555fb104e", "metadata": { "id": "04ca0560-f5f0-40e1-8773-79f555fb104e" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "2740154c-e1f0-493c-b29f-eb62e4767d86", "metadata": { "id": "2740154c-e1f0-493c-b29f-eb62e4767d86" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": 12, "id": "9df95087-86a3-48c4-bb4f-b5bf8cf20f42", "metadata": { "id": "9df95087-86a3-48c4-bb4f-b5bf8cf20f42", "outputId": "6f2f8aad-1b67-4915-af3d-fbb2adb2a5f1", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Identifier 0\n", "Name 0\n", "Revenue 0\n", "Industry 0\n", "Country 0\n", "EV_EBITDA 329\n", "PE 118\n", "DY 84\n", "ROE 41\n", "D_EBITDA 297\n", "SalesGrowth 6\n", "EV_Sales 61\n", "IntCoverage 272\n", "Sales_Growth 21\n", "NAICS 0\n", "dtype: int64\n" ] } ], "source": [ "count_nan = data.isnull().sum()\n", "print(count_nan)" ] }, { "cell_type": "code", "execution_count": 13, "id": "547fa80b-485d-48eb-b871-7ecb4d27aedc", "metadata": { "id": "547fa80b-485d-48eb-b871-7ecb4d27aedc", "outputId": "49f3a0a6-3fce-454e-c309-5c9b42fda434", "colab": { "base_uri": "https://localhost:8080/", "height": 818 } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Identifier Name Revenue \\\n", "0 III-GB 3i Group plc 310.80 \n", "1 MMM-US 3M Company 35,355.00 \n", "2 MAERSK.B-DK A.P. Moller - Maersk A/S 61,802.00 \n", "3 SAGA.B-SE AB Sagax 376.95 \n", "4 ABBN-CH ABB Ltd. 28,949.00 \n", "... ... ... ... \n", "1591 2899-HK Zijin Mining Group Co., Ltd. 34,366.65 \n", "1593 ZBH-US Zimmer Biomet Holdings, Inc. 7,836.20 \n", "1594 ZTS-US Zoetis Inc. 7,776.00 \n", "1596 ZI-US ZoomInfo Technologies, Inc. 747.20 \n", "1598 2057-HK ZTO Express (Cayman), Inc. 4,714.55 \n", "\n", " Industry Country EV_EBITDA PE DY \\\n", "0 Investment Managers United Kingdom 5.71 6.00 3.34 \n", "1 Industrial Conglomerates United States 12.36 17.56 3.33 \n", "2 Marine Shipping Denmark 3.18 3.96 10.66 \n", "3 Real Estate Development Sweden 61.96 13.80 0.70 \n", "4 Electrical Products Switzerland 18.57 16.95 2.35 \n", "... ... ... ... ... ... \n", "1591 Precious Metals China 8.59 12.88 2.49 \n", "1593 Medical Specialties United States 14.70 66.55 0.76 \n", "1594 Pharmaceuticals: Major United States 36.74 57.11 0.44 \n", "1596 Internet Software/Services United States 117.64 216.53 0.00 \n", "1598 Air Freight/Couriers China 20.29 30.96 0.00 \n", "\n", " ROE D_EBITDA SalesGrowth EV_Sales IntCoverage Sales_Growth \\\n", "0 21.93 0.48 35.23 50.11 44.26 -15.75 \n", "1 42.42 1.85 9.85 3.33 16.47 2.57 \n", "2 47.77 0.67 49.62 1.23 23.30 16.15 \n", "3 36.64 10.53 9.81 48.63 5.68 11.86 \n", "4 30.14 1.33 8.15 2.71 22.22 -0.83 \n", "... ... ... ... ... ... ... \n", "1591 25.03 2.42 40.66 1.28 9.11 29.27 \n", "1593 3.23 3.12 11.56 4.29 5.77 -0.41 \n", "1594 49.01 2.03 16.49 15.38 10.95 10.11 \n", "1596 9.34 5.54 56.91 35.16 3.49 73.00 \n", "1598 9.75 0.54 29.34 4.58 37.26 20.68 \n", "\n", " NAICS \n", "0 Finance and Insurance \n", "1 Manufacturing \n", "2 Transportation and Warehousing \n", "3 Real Estate and Rental and Leasing \n", "4 Manufacturing \n", "... ... \n", "1591 Mining, Quarrying, and Oil and Gas Extraction \n", "1593 Manufacturing \n", "1594 Manufacturing \n", "1596 Information \n", "1598 Professional, Scientific, and Technical Services \n", "\n", "[1106 rows x 15 columns]" ], "text/html": [ "\n", "
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IdentifierNameRevenueIndustryCountryEV_EBITDAPEDYROED_EBITDASalesGrowthEV_SalesIntCoverageSales_GrowthNAICS
0III-GB3i Group plc310.80Investment ManagersUnited Kingdom5.716.003.3421.930.4835.2350.1144.26-15.75Finance and Insurance
1MMM-US3M Company35,355.00Industrial ConglomeratesUnited States12.3617.563.3342.421.859.853.3316.472.57Manufacturing
2MAERSK.B-DKA.P. Moller - Maersk A/S61,802.00Marine ShippingDenmark3.183.9610.6647.770.6749.621.2323.3016.15Transportation and Warehousing
3SAGA.B-SEAB Sagax376.95Real Estate DevelopmentSweden61.9613.800.7036.6410.539.8148.635.6811.86Real Estate and Rental and Leasing
4ABBN-CHABB Ltd.28,949.00Electrical ProductsSwitzerland18.5716.952.3530.141.338.152.7122.22-0.83Manufacturing
................................................
15912899-HKZijin Mining Group Co., Ltd.34,366.65Precious MetalsChina8.5912.882.4925.032.4240.661.289.1129.27Mining, Quarrying, and Oil and Gas Extraction
1593ZBH-USZimmer Biomet Holdings, Inc.7,836.20Medical SpecialtiesUnited States14.7066.550.763.233.1211.564.295.77-0.41Manufacturing
1594ZTS-USZoetis Inc.7,776.00Pharmaceuticals: MajorUnited States36.7457.110.4449.012.0316.4915.3810.9510.11Manufacturing
1596ZI-USZoomInfo Technologies, Inc.747.20Internet Software/ServicesUnited States117.64216.530.009.345.5456.9135.163.4973.00Information
15982057-HKZTO Express (Cayman), Inc.4,714.55Air Freight/CouriersChina20.2930.960.009.750.5429.344.5837.2620.68Professional, Scientific, and Technical Services
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1106 rows × 15 columns

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\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ] }, "metadata": {}, "execution_count": 13 } ], "source": [ "\n", "data2 = data.dropna()\n", "data2" ] }, { "cell_type": "code", "execution_count": 14, "id": "052b2429-9350-49b8-af87-b793242b1d95", "metadata": { "id": "052b2429-9350-49b8-af87-b793242b1d95", "outputId": "be816164-ff34-4eda-fcb4-1b9f21b03df5", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 1106 entries, 0 to 1598\n", "Data columns (total 15 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Identifier 1106 non-null object \n", " 1 Name 1106 non-null object \n", " 2 Revenue 1106 non-null object \n", " 3 Industry 1106 non-null object \n", " 4 Country 1106 non-null object \n", " 5 EV_EBITDA 1106 non-null float64\n", " 6 PE 1106 non-null float64\n", " 7 DY 1106 non-null float64\n", " 8 ROE 1106 non-null float64\n", " 9 D_EBITDA 1106 non-null float64\n", " 10 SalesGrowth 1106 non-null float64\n", " 11 EV_Sales 1106 non-null float64\n", " 12 IntCoverage 1106 non-null float64\n", " 13 Sales_Growth 1106 non-null float64\n", " 14 NAICS 1106 non-null object \n", "dtypes: float64(9), object(6)\n", "memory usage: 138.2+ KB\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "(1106, 15)" ] }, "metadata": {}, "execution_count": 14 } ], "source": [ "data2.info()\n", "data2.shape" ] }, { "cell_type": "code", "execution_count": 15, "id": "2512d6c4-8e71-4f6b-9c5b-d2f606d646d7", "metadata": { "id": "2512d6c4-8e71-4f6b-9c5b-d2f606d646d7", "outputId": "a86267d2-e0a7-4bb8-fc0e-c1e3aeeb4ad1", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Identifier 0\n", "Name 0\n", "Revenue 0\n", "Industry 0\n", "Country 0\n", "EV_EBITDA 0\n", "PE 0\n", "DY 0\n", "ROE 0\n", "D_EBITDA 0\n", "SalesGrowth 0\n", "EV_Sales 0\n", "IntCoverage 0\n", "Sales_Growth 0\n", "NAICS 0\n", "dtype: int64\n" ] } ], "source": [ "count_nan = data2.isnull().sum()\n", "print(count_nan)" ] }, { "cell_type": "code", "execution_count": 16, "id": "f0966d18-20ab-4279-8e82-301074fa90e8", "metadata": { "id": "f0966d18-20ab-4279-8e82-301074fa90e8", "outputId": "3edd961f-5d7e-4ea4-9478-457c49c2c329", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: PE R-squared: 0.140\n", "Model: OLS Adj. R-squared: 0.135\n", "Method: Least Squares F-statistic: 25.61\n", "Date: Thu, 19 May 2022 Prob (F-statistic): 1.40e-32\n", "Time: 06:59:28 Log-Likelihood: -6991.6\n", "No. Observations: 1106 AIC: 1.400e+04\n", "Df Residuals: 1098 BIC: 1.404e+04\n", "Df Model: 7 \n", "Covariance Type: nonrobust \n", "===============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-------------------------------------------------------------------------------\n", "Intercept 17.3022 7.352 2.353 0.019 2.876 31.729\n", "EV_EBITDA 2.2773 0.191 11.910 0.000 1.902 2.652\n", "DY 1.4979 1.589 0.943 0.346 -1.619 4.615\n", "ROE -0.0073 0.015 -0.484 0.629 -0.037 0.022\n", "D_EBITDA -3.5974 0.575 -6.259 0.000 -4.725 -2.470\n", "SalesGrowth -0.0539 0.044 -1.223 0.222 -0.140 0.033\n", "EV_Sales -1.7662 0.729 -2.423 0.016 -3.197 -0.336\n", "IntCoverage -0.0075 0.011 -0.670 0.503 -0.029 0.014\n", "==============================================================================\n", "Omnibus: 2200.053 Durbin-Watson: 2.028\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 4265213.315\n", "Skew: 15.095 Prob(JB): 0.00\n", "Kurtosis: 305.726 Cond. No. 679.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "formula = 'PE ~ EV_EBITDA + DY + ROE + D_EBITDA + SalesGrowth + EV_Sales + IntCoverage'\n", "results = smf.ols(formula, data2).fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 17, "id": "3945b46c-c01d-4979-a230-21ef60b6fbbf", "metadata": { "id": "3945b46c-c01d-4979-a230-21ef60b6fbbf", "colab": { "base_uri": "https://localhost:8080/", "height": 332 }, "outputId": "6197c6f0-1806-4129-9870-c0703ea6cff9" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " EV_EBITDA PE DY ROE D_EBITDA SalesGrowth \\\n", "EV_EBITDA 1.000000 0.328878 -0.260691 -0.022547 0.576225 0.076867 \n", "PE 0.328878 1.000000 -0.079341 -0.019495 0.062110 -0.008015 \n", "DY -0.260691 -0.079341 1.000000 -0.023072 -0.021939 -0.017622 \n", "ROE -0.022547 -0.019495 -0.023072 1.000000 -0.019213 0.009836 \n", "D_EBITDA 0.576225 0.062110 -0.021939 -0.019213 1.000000 0.014906 \n", "SalesGrowth 0.076867 -0.008015 -0.017622 0.009836 0.014906 1.000000 \n", "EV_Sales 0.479353 0.149428 -0.237462 -0.024447 0.005065 0.097530 \n", "IntCoverage 0.017639 -0.010633 -0.038418 -0.004273 -0.041356 0.040734 \n", "Sales_Growth 0.189467 0.049153 -0.100704 -0.002051 -0.016838 0.734134 \n", "\n", " EV_Sales IntCoverage Sales_Growth \n", "EV_EBITDA 0.479353 0.017639 0.189467 \n", "PE 0.149428 -0.010633 0.049153 \n", "DY -0.237462 -0.038418 -0.100704 \n", "ROE -0.024447 -0.004273 -0.002051 \n", "D_EBITDA 0.005065 -0.041356 -0.016838 \n", "SalesGrowth 0.097530 0.040734 0.734134 \n", "EV_Sales 1.000000 0.095952 0.221300 \n", "IntCoverage 0.095952 1.000000 0.060372 \n", "Sales_Growth 0.221300 0.060372 1.000000 " ], "text/html": [ "\n", "
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EV_EBITDAPEDYROED_EBITDASalesGrowthEV_SalesIntCoverageSales_Growth
EV_EBITDA1.0000000.328878-0.260691-0.0225470.5762250.0768670.4793530.0176390.189467
PE0.3288781.000000-0.079341-0.0194950.062110-0.0080150.149428-0.0106330.049153
DY-0.260691-0.0793411.000000-0.023072-0.021939-0.017622-0.237462-0.038418-0.100704
ROE-0.022547-0.019495-0.0230721.000000-0.0192130.009836-0.024447-0.004273-0.002051
D_EBITDA0.5762250.062110-0.021939-0.0192131.0000000.0149060.005065-0.041356-0.016838
SalesGrowth0.076867-0.008015-0.0176220.0098360.0149061.0000000.0975300.0407340.734134
EV_Sales0.4793530.149428-0.237462-0.0244470.0050650.0975301.0000000.0959520.221300
IntCoverage0.017639-0.010633-0.038418-0.004273-0.0413560.0407340.0959521.0000000.060372
Sales_Growth0.1894670.049153-0.100704-0.002051-0.0168380.7341340.2213000.0603721.000000
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