{ "cells": [ { "cell_type": "markdown", "id": "64c457bf", "metadata": {}, "source": [ "# Due October 20th at 4pm" ] }, { "cell_type": "raw", "id": "621519e1", "metadata": {}, "source": [ "Devon Kelly 6504930246 6504930246@econ.tu.ac.th" ] }, { "cell_type": "markdown", "id": "11959f2b", "metadata": {}, "source": [ "Cleaning and Importing the Data" ] }, { "cell_type": "code", "execution_count": 391, "id": "37022ef0", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from pandas_datareader.famafrench import get_available_datasets\n", "import pandas_datareader.data as web\n", "import datetime as dt\n", "\n", "%matplotlib inline\n", "# activate plot theme\n", "import qeds\n", "\n", "qeds.themes.mpl_style();\n", "plotly_template = qeds.themes.plotly_template()\n", "colors = qeds.themes.COLOR_CYCLE\n", "\n", "# We will import all these here to ensure that they are loaded, but\n", "# will usually re-import close to where they are used to make clear\n", "# where the functions come from\n", "from sklearn import (linear_model, metrics, model_selection)" ] }, { "cell_type": "code", "execution_count": 393, "id": "ee22b1d3", "metadata": {}, "outputs": [], "source": [ "start_date = dt.datetime(1926, 7, 1)\n", "end_date = dt.datetime(2022, 8, 1)" ] }, { "cell_type": "code", "execution_count": 406, "id": "edc5e2eb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FoodBeerSmokeGamesBooksHshldClthsHlthChemsTxtls...TelcmServsBusEqPaperTransWhlslRtailMealsFinOther
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1926-070.56-5.191.292.9310.97-0.488.081.778.140.39...0.839.222.067.701.91-23.790.071.87-0.025.20
1926-082.5927.036.500.5510.01-3.58-2.514.255.507.97...2.172.024.39-2.384.855.39-0.75-0.134.476.76
1926-091.164.021.266.58-0.990.73-0.510.695.332.30...2.412.250.19-5.540.07-7.870.25-0.56-1.61-3.86
1926-10-3.06-3.311.06-4.769.47-4.680.12-0.57-4.761.00...-0.11-2.00-1.09-5.08-2.61-15.38-2.20-4.11-5.51-8.49
1926-116.357.294.551.66-5.80-0.541.875.425.203.10...1.633.773.643.841.614.676.524.332.344.00
..................................................................
2022-043.013.036.37-25.22-10.762.04-7.00-6.80-2.286.63...-10.70-12.59-12.26-0.74-10.93-2.14-11.41-5.47-7.99-7.65
2022-05-1.68-1.602.67-2.93-7.40-5.12-6.450.994.522.38...8.54-3.35-0.75-0.66-4.591.03-5.64-3.292.80-1.19
2022-06-1.64-0.02-11.63-11.33-12.53-2.56-12.00-2.05-15.65-11.17...-6.72-6.79-10.19-8.51-7.14-6.43-8.50-9.02-9.05-11.78
2022-073.675.490.5614.6212.100.7611.862.757.666.86...-0.408.6015.687.229.339.0816.3311.897.389.19
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" ], "text/plain": [ " Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", "Date \n", "1926-07 0.56 -5.19 1.29 2.93 10.97 -0.48 8.08 1.77 8.14 0.39 \n", "1926-08 2.59 27.03 6.50 0.55 10.01 -3.58 -2.51 4.25 5.50 7.97 \n", "1926-09 1.16 4.02 1.26 6.58 -0.99 0.73 -0.51 0.69 5.33 2.30 \n", "1926-10 -3.06 -3.31 1.06 -4.76 9.47 -4.68 0.12 -0.57 -4.76 1.00 \n", "1926-11 6.35 7.29 4.55 1.66 -5.80 -0.54 1.87 5.42 5.20 3.10 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "2022-04 3.01 3.03 6.37 -25.22 -10.76 2.04 -7.00 -6.80 -2.28 6.63 \n", "2022-05 -1.68 -1.60 2.67 -2.93 -7.40 -5.12 -6.45 0.99 4.52 2.38 \n", "2022-06 -1.64 -0.02 -11.63 -11.33 -12.53 -2.56 -12.00 -2.05 -15.65 -11.17 \n", "2022-07 3.67 5.49 0.56 14.62 12.10 0.76 11.86 2.75 7.66 6.86 \n", "2022-08 -1.61 -1.87 -0.12 -2.95 -4.97 -2.16 -6.01 -5.07 -1.39 -12.20 \n", "\n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", "Date ... \n", "1926-07 ... 0.83 9.22 2.06 7.70 1.91 -23.79 0.07 1.87 -0.02 \n", "1926-08 ... 2.17 2.02 4.39 -2.38 4.85 5.39 -0.75 -0.13 4.47 \n", "1926-09 ... 2.41 2.25 0.19 -5.54 0.07 -7.87 0.25 -0.56 -1.61 \n", "1926-10 ... -0.11 -2.00 -1.09 -5.08 -2.61 -15.38 -2.20 -4.11 -5.51 \n", "1926-11 ... 1.63 3.77 3.64 3.84 1.61 4.67 6.52 4.33 2.34 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "2022-04 ... -10.70 -12.59 -12.26 -0.74 -10.93 -2.14 -11.41 -5.47 -7.99 \n", "2022-05 ... 8.54 -3.35 -0.75 -0.66 -4.59 1.03 -5.64 -3.29 2.80 \n", "2022-06 ... -6.72 -6.79 -10.19 -8.51 -7.14 -6.43 -8.50 -9.02 -9.05 \n", "2022-07 ... -0.40 8.60 15.68 7.22 9.33 9.08 16.33 11.89 7.38 \n", "2022-08 ... -3.00 -4.72 -5.89 -7.66 -1.46 -1.60 -3.46 -1.47 -2.24 \n", "\n", " Other \n", "Date \n", "1926-07 5.20 \n", "1926-08 6.76 \n", "1926-09 -3.86 \n", "1926-10 -8.49 \n", "1926-11 4.00 \n", "... ... \n", "2022-04 -7.65 \n", "2022-05 -1.19 \n", "2022-06 -11.78 \n", "2022-07 9.19 \n", "2022-08 -3.65 \n", "\n", "[1154 rows x 30 columns]" ] }, "execution_count": 406, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#df_port = pd.read_csv(\"IndustryPortfolios.CSV\")\n", "df_Fama = web.DataReader('F-F_Research_Data_Factors', 'famafrench', start = start_date, end = end_date)\n", "df_Industry = web.DataReader('30_Industry_Portfolios', 'famafrench', start = start_date, end = end_date)\n", "df_Industry = df_Industry[0]\n", "\n", "#for col in list(df_Fama):\n", " #df_Fama[col] = df_Fama[col].astype(float)\n", "#df_Fama = df_Fama/100\n", "df_Fama = df_Fama[0]\n", "df_Fama\n", "df_Industry" ] }, { "cell_type": "code", "execution_count": 428, "id": "dde28b52", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FoodBeerSmokeGamesBooksHshldClthsHlthChemsTxtls...ServsBusEqPaperTransWhlslRtailMealsFinOtherconst
Date
1926-070.56-5.191.292.9310.97-0.488.081.778.140.39...9.222.067.701.91-23.790.071.87-0.025.201
1926-082.5927.036.500.5510.01-3.58-2.514.255.507.97...2.024.39-2.384.855.39-0.75-0.134.476.761
1926-091.164.021.266.58-0.990.73-0.510.695.332.30...2.250.19-5.540.07-7.870.25-0.56-1.61-3.861
1926-10-3.06-3.311.06-4.769.47-4.680.12-0.57-4.761.00...-2.00-1.09-5.08-2.61-15.38-2.20-4.11-5.51-8.491
1926-116.357.294.551.66-5.80-0.541.875.425.203.10...3.773.643.841.614.676.524.332.344.001
..................................................................
2022-043.013.036.37-25.22-10.762.04-7.00-6.80-2.286.63...-12.59-12.26-0.74-10.93-2.14-11.41-5.47-7.99-7.651
2022-05-1.68-1.602.67-2.93-7.40-5.12-6.450.994.522.38...-3.35-0.75-0.66-4.591.03-5.64-3.292.80-1.191
2022-06-1.64-0.02-11.63-11.33-12.53-2.56-12.00-2.05-15.65-11.17...-6.79-10.19-8.51-7.14-6.43-8.50-9.02-9.05-11.781
2022-073.675.490.5614.6212.100.7611.862.757.666.86...8.6015.687.229.339.0816.3311.897.389.191
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1154 rows × 31 columns

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" ], "text/plain": [ " Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", "Date \n", "1926-07 0.56 -5.19 1.29 2.93 10.97 -0.48 8.08 1.77 8.14 0.39 \n", "1926-08 2.59 27.03 6.50 0.55 10.01 -3.58 -2.51 4.25 5.50 7.97 \n", "1926-09 1.16 4.02 1.26 6.58 -0.99 0.73 -0.51 0.69 5.33 2.30 \n", "1926-10 -3.06 -3.31 1.06 -4.76 9.47 -4.68 0.12 -0.57 -4.76 1.00 \n", "1926-11 6.35 7.29 4.55 1.66 -5.80 -0.54 1.87 5.42 5.20 3.10 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "2022-04 3.01 3.03 6.37 -25.22 -10.76 2.04 -7.00 -6.80 -2.28 6.63 \n", "2022-05 -1.68 -1.60 2.67 -2.93 -7.40 -5.12 -6.45 0.99 4.52 2.38 \n", "2022-06 -1.64 -0.02 -11.63 -11.33 -12.53 -2.56 -12.00 -2.05 -15.65 -11.17 \n", "2022-07 3.67 5.49 0.56 14.62 12.10 0.76 11.86 2.75 7.66 6.86 \n", "2022-08 -1.61 -1.87 -0.12 -2.95 -4.97 -2.16 -6.01 -5.07 -1.39 -12.20 \n", "\n", " ... Servs BusEq Paper Trans Whlsl Rtail Meals Fin Other \\\n", "Date ... \n", "1926-07 ... 9.22 2.06 7.70 1.91 -23.79 0.07 1.87 -0.02 5.20 \n", "1926-08 ... 2.02 4.39 -2.38 4.85 5.39 -0.75 -0.13 4.47 6.76 \n", "1926-09 ... 2.25 0.19 -5.54 0.07 -7.87 0.25 -0.56 -1.61 -3.86 \n", "1926-10 ... -2.00 -1.09 -5.08 -2.61 -15.38 -2.20 -4.11 -5.51 -8.49 \n", "1926-11 ... 3.77 3.64 3.84 1.61 4.67 6.52 4.33 2.34 4.00 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "2022-04 ... -12.59 -12.26 -0.74 -10.93 -2.14 -11.41 -5.47 -7.99 -7.65 \n", "2022-05 ... -3.35 -0.75 -0.66 -4.59 1.03 -5.64 -3.29 2.80 -1.19 \n", "2022-06 ... -6.79 -10.19 -8.51 -7.14 -6.43 -8.50 -9.02 -9.05 -11.78 \n", "2022-07 ... 8.60 15.68 7.22 9.33 9.08 16.33 11.89 7.38 9.19 \n", "2022-08 ... -4.72 -5.89 -7.66 -1.46 -1.60 -3.46 -1.47 -2.24 -3.65 \n", "\n", " const \n", "Date \n", "1926-07 1 \n", "1926-08 1 \n", "1926-09 1 \n", "1926-10 1 \n", "1926-11 1 \n", "... ... \n", "2022-04 1 \n", "2022-05 1 \n", "2022-06 1 \n", "2022-07 1 \n", "2022-08 1 \n", "\n", "[1154 rows x 31 columns]" ] }, "execution_count": 428, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_Industry.columns\n", "\n", "name = list(df_Industry.columns)\n", "df = df_Industry\n", "df.transpose()\n", "df['const'] = 1\n", "df" ] }, { "cell_type": "code", "execution_count": 288, "id": "d932248c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Food ',\n", " 'Beer ',\n", " 'Smoke',\n", " 'Games',\n", " 'Books',\n", " 'Hshld',\n", " 'Clths',\n", " 'Hlth ',\n", " 'Chems',\n", " 'Txtls',\n", " 'Cnstr',\n", " 'Steel',\n", " 'FabPr',\n", " 'ElcEq',\n", " 'Autos',\n", " 'Carry',\n", " 'Mines',\n", " 'Coal ',\n", " 'Oil ',\n", " 'Util ',\n", " 'Telcm',\n", " 'Servs',\n", " 'BusEq',\n", " 'Paper',\n", " 'Trans',\n", " 'Whlsl',\n", " 'Rtail',\n", " 'Meals',\n", " 'Fin ',\n", " 'Other']" ] }, "execution_count": 288, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name" ] }, { "cell_type": "code", "execution_count": null, "id": "c7d521d3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 409, "id": "d3eb8fce", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FoodBeerSmokeGamesBooksHshldClthsHlthChemsTxtls...ServsBusEqPaperTransWhlslRtailMealsFinOtherconst
Date
1926-070.56-5.191.292.9310.97-0.488.081.778.140.39...9.222.067.701.91-23.790.071.87-0.025.201
1926-082.5927.036.500.5510.01-3.58-2.514.255.507.97...2.024.39-2.384.855.39-0.75-0.134.476.761
1926-091.164.021.266.58-0.990.73-0.510.695.332.30...2.250.19-5.540.07-7.870.25-0.56-1.61-3.861
1926-10-3.06-3.311.06-4.769.47-4.680.12-0.57-4.761.00...-2.00-1.09-5.08-2.61-15.38-2.20-4.11-5.51-8.491
1926-116.357.294.551.66-5.80-0.541.875.425.203.10...3.773.643.841.614.676.524.332.344.001
..................................................................
2022-043.013.036.37-25.22-10.762.04-7.00-6.80-2.286.63...-12.59-12.26-0.74-10.93-2.14-11.41-5.47-7.99-7.651
2022-05-1.68-1.602.67-2.93-7.40-5.12-6.450.994.522.38...-3.35-0.75-0.66-4.591.03-5.64-3.292.80-1.191
2022-06-1.64-0.02-11.63-11.33-12.53-2.56-12.00-2.05-15.65-11.17...-6.79-10.19-8.51-7.14-6.43-8.50-9.02-9.05-11.781
2022-073.675.490.5614.6212.100.7611.862.757.666.86...8.6015.687.229.339.0816.3311.897.389.191
2022-08-1.61-1.87-0.12-2.95-4.97-2.16-6.01-5.07-1.39-12.20...-4.72-5.89-7.66-1.46-1.60-3.46-1.47-2.24-3.651
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1154 rows × 31 columns

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" ], "text/plain": [ " Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", "Date \n", "1926-07 0.56 -5.19 1.29 2.93 10.97 -0.48 8.08 1.77 8.14 0.39 \n", "1926-08 2.59 27.03 6.50 0.55 10.01 -3.58 -2.51 4.25 5.50 7.97 \n", "1926-09 1.16 4.02 1.26 6.58 -0.99 0.73 -0.51 0.69 5.33 2.30 \n", "1926-10 -3.06 -3.31 1.06 -4.76 9.47 -4.68 0.12 -0.57 -4.76 1.00 \n", "1926-11 6.35 7.29 4.55 1.66 -5.80 -0.54 1.87 5.42 5.20 3.10 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "2022-04 3.01 3.03 6.37 -25.22 -10.76 2.04 -7.00 -6.80 -2.28 6.63 \n", "2022-05 -1.68 -1.60 2.67 -2.93 -7.40 -5.12 -6.45 0.99 4.52 2.38 \n", "2022-06 -1.64 -0.02 -11.63 -11.33 -12.53 -2.56 -12.00 -2.05 -15.65 -11.17 \n", "2022-07 3.67 5.49 0.56 14.62 12.10 0.76 11.86 2.75 7.66 6.86 \n", "2022-08 -1.61 -1.87 -0.12 -2.95 -4.97 -2.16 -6.01 -5.07 -1.39 -12.20 \n", "\n", " ... Servs BusEq Paper Trans Whlsl Rtail Meals Fin Other \\\n", "Date ... \n", "1926-07 ... 9.22 2.06 7.70 1.91 -23.79 0.07 1.87 -0.02 5.20 \n", "1926-08 ... 2.02 4.39 -2.38 4.85 5.39 -0.75 -0.13 4.47 6.76 \n", "1926-09 ... 2.25 0.19 -5.54 0.07 -7.87 0.25 -0.56 -1.61 -3.86 \n", "1926-10 ... -2.00 -1.09 -5.08 -2.61 -15.38 -2.20 -4.11 -5.51 -8.49 \n", "1926-11 ... 3.77 3.64 3.84 1.61 4.67 6.52 4.33 2.34 4.00 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "2022-04 ... -12.59 -12.26 -0.74 -10.93 -2.14 -11.41 -5.47 -7.99 -7.65 \n", "2022-05 ... -3.35 -0.75 -0.66 -4.59 1.03 -5.64 -3.29 2.80 -1.19 \n", "2022-06 ... -6.79 -10.19 -8.51 -7.14 -6.43 -8.50 -9.02 -9.05 -11.78 \n", "2022-07 ... 8.60 15.68 7.22 9.33 9.08 16.33 11.89 7.38 9.19 \n", "2022-08 ... -4.72 -5.89 -7.66 -1.46 -1.60 -3.46 -1.47 -2.24 -3.65 \n", "\n", " const \n", "Date \n", "1926-07 1 \n", "1926-08 1 \n", "1926-09 1 \n", "1926-10 1 \n", "1926-11 1 \n", "... ... \n", "2022-04 1 \n", "2022-05 1 \n", "2022-06 1 \n", "2022-07 1 \n", "2022-08 1 \n", "\n", "[1154 rows x 31 columns]" ] }, "execution_count": 409, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_Fama\n", "df" ] }, { "cell_type": "code", "execution_count": 297, "id": "4e4b168d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 1154 entries, 192607 to 202208\n", "Data columns (total 31 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Food 1154 non-null float64\n", " 1 Beer 1154 non-null float64\n", " 2 Smoke 1154 non-null float64\n", " 3 Games 1154 non-null float64\n", " 4 Books 1154 non-null float64\n", " 5 Hshld 1154 non-null float64\n", " 6 Clths 1154 non-null float64\n", " 7 Hlth 1154 non-null float64\n", " 8 Chems 1154 non-null float64\n", " 9 Txtls 1154 non-null float64\n", " 10 Cnstr 1154 non-null float64\n", " 11 Steel 1154 non-null float64\n", " 12 FabPr 1154 non-null float64\n", " 13 ElcEq 1154 non-null float64\n", " 14 Autos 1154 non-null float64\n", " 15 Carry 1154 non-null float64\n", " 16 Mines 1154 non-null float64\n", " 17 Coal 1154 non-null float64\n", " 18 Oil 1154 non-null float64\n", " 19 Util 1154 non-null float64\n", " 20 Telcm 1154 non-null float64\n", " 21 Servs 1154 non-null float64\n", " 22 BusEq 1154 non-null float64\n", " 23 Paper 1154 non-null float64\n", " 24 Trans 1154 non-null float64\n", " 25 Whlsl 1154 non-null float64\n", " 26 Rtail 1154 non-null float64\n", " 27 Meals 1154 non-null float64\n", " 28 Fin 1154 non-null float64\n", " 29 Other 1154 non-null float64\n", " 30 const 1154 non-null int64 \n", "dtypes: float64(30), int64(1)\n", "memory usage: 320.8+ KB\n", "\n", "RangeIndex: 34620 entries, 0 to 34619\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Industry 34620 non-null object \n", " 1 Return 34620 non-null float64\n", "dtypes: float64(1), object(1)\n", "memory usage: 541.1+ KB\n" ] } ], "source": [ "df\n", "df.info()\n", "df1.info()" ] }, { "cell_type": "markdown", "id": "acdd0043", "metadata": {}, "source": [ "# Solution to 1.1" ] }, { "cell_type": "markdown", "id": "5378cb34", "metadata": {}, "source": [ "Running the Regression Analyses" ] }, { "cell_type": "code", "execution_count": 429, "id": "825ccf59", "metadata": { "scrolled": true }, "outputs": [], "source": [ "from sklearn import linear_model\n", "\n", "beta = pd.DataFrame(np.nan, index = name, columns = [\"BETAs\"])\n", "trans = beta\n", "trans = trans.rename(columns = {\"BETAs\" : \"Returns\"})\n", "rf = df_Fama[\"RF\"]\n", "rm = df_Fama[\"Mkt-RF\"]\n", "\n", "predict = pd.DataFrame()\n", "resid = pd.DataFrame()\n", "y = pd.DataFrame()\n", "type(df_Fama[\"RF\"])\n", "data_new.isnull().count()\n", "data_new\n", "#df_Fama1 = df_Fama.reset_index()\n", "df_Fama1\n", "#df = df.reset_index()" ] }, { "cell_type": "code", "execution_count": 430, "id": "0b76aee4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateFoodBeerSmokeGamesBooksHshldClthsHlthChems...ServsBusEqPaperTransWhlslRtailMealsFinOtherconst
01926-070.56-5.191.292.9310.97-0.488.081.778.14...9.222.067.701.91-23.790.071.87-0.025.201
11926-082.5927.036.500.5510.01-3.58-2.514.255.50...2.024.39-2.384.855.39-0.75-0.134.476.761
21926-091.164.021.266.58-0.990.73-0.510.695.33...2.250.19-5.540.07-7.870.25-0.56-1.61-3.861
31926-10-3.06-3.311.06-4.769.47-4.680.12-0.57-4.76...-2.00-1.09-5.08-2.61-15.38-2.20-4.11-5.51-8.491
41926-116.357.294.551.66-5.80-0.541.875.425.20...3.773.643.841.614.676.524.332.344.001
..................................................................
11492022-043.013.036.37-25.22-10.762.04-7.00-6.80-2.28...-12.59-12.26-0.74-10.93-2.14-11.41-5.47-7.99-7.651
11502022-05-1.68-1.602.67-2.93-7.40-5.12-6.450.994.52...-3.35-0.75-0.66-4.591.03-5.64-3.292.80-1.191
11512022-06-1.64-0.02-11.63-11.33-12.53-2.56-12.00-2.05-15.65...-6.79-10.19-8.51-7.14-6.43-8.50-9.02-9.05-11.781
11522022-073.675.490.5614.6212.100.7611.862.757.66...8.6015.687.229.339.0816.3311.897.389.191
11532022-08-1.61-1.87-0.12-2.95-4.97-2.16-6.01-5.07-1.39...-4.72-5.89-7.66-1.46-1.60-3.46-1.47-2.24-3.651
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1154 rows × 32 columns

\n", "
" ], "text/plain": [ " Date Food Beer Smoke Games Books Hshld Clths Hlth Chems \\\n", "0 1926-07 0.56 -5.19 1.29 2.93 10.97 -0.48 8.08 1.77 8.14 \n", "1 1926-08 2.59 27.03 6.50 0.55 10.01 -3.58 -2.51 4.25 5.50 \n", "2 1926-09 1.16 4.02 1.26 6.58 -0.99 0.73 -0.51 0.69 5.33 \n", "3 1926-10 -3.06 -3.31 1.06 -4.76 9.47 -4.68 0.12 -0.57 -4.76 \n", "4 1926-11 6.35 7.29 4.55 1.66 -5.80 -0.54 1.87 5.42 5.20 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "1149 2022-04 3.01 3.03 6.37 -25.22 -10.76 2.04 -7.00 -6.80 -2.28 \n", "1150 2022-05 -1.68 -1.60 2.67 -2.93 -7.40 -5.12 -6.45 0.99 4.52 \n", "1151 2022-06 -1.64 -0.02 -11.63 -11.33 -12.53 -2.56 -12.00 -2.05 -15.65 \n", "1152 2022-07 3.67 5.49 0.56 14.62 12.10 0.76 11.86 2.75 7.66 \n", "1153 2022-08 -1.61 -1.87 -0.12 -2.95 -4.97 -2.16 -6.01 -5.07 -1.39 \n", "\n", " ... Servs BusEq Paper Trans Whlsl Rtail Meals Fin Other \\\n", "0 ... 9.22 2.06 7.70 1.91 -23.79 0.07 1.87 -0.02 5.20 \n", "1 ... 2.02 4.39 -2.38 4.85 5.39 -0.75 -0.13 4.47 6.76 \n", "2 ... 2.25 0.19 -5.54 0.07 -7.87 0.25 -0.56 -1.61 -3.86 \n", "3 ... -2.00 -1.09 -5.08 -2.61 -15.38 -2.20 -4.11 -5.51 -8.49 \n", "4 ... 3.77 3.64 3.84 1.61 4.67 6.52 4.33 2.34 4.00 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "1149 ... -12.59 -12.26 -0.74 -10.93 -2.14 -11.41 -5.47 -7.99 -7.65 \n", "1150 ... -3.35 -0.75 -0.66 -4.59 1.03 -5.64 -3.29 2.80 -1.19 \n", "1151 ... -6.79 -10.19 -8.51 -7.14 -6.43 -8.50 -9.02 -9.05 -11.78 \n", "1152 ... 8.60 15.68 7.22 9.33 9.08 16.33 11.89 7.38 9.19 \n", "1153 ... -4.72 -5.89 -7.66 -1.46 -1.60 -3.46 -1.47 -2.24 -3.65 \n", "\n", " const \n", "0 1 \n", "1 1 \n", "2 1 \n", "3 1 \n", "4 1 \n", "... ... \n", "1149 1 \n", "1150 1 \n", "1151 1 \n", "1152 1 \n", "1153 1 \n", "\n", "[1154 rows x 32 columns]" ] }, "execution_count": 430, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Change to decimal format Check\n", "\n", "# Then merge the file together (on date) Check\n", "\n", "# Create dataframe with beta and expected (E(R - Rf)) sm .OLS\n", "#ri, endog = ri, exog = data_set\n", "import statsmodels.api as sm\n", "df" ] }, { "cell_type": "code", "execution_count": 431, "id": "21aa2322", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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DateFoodBeerSmokeGamesBooksHshldClthsHlthChems...WhlslRtailMealsFinOtherconstMkt-RFSMBHMLRF
01926-070.56-5.191.292.9310.97-0.488.081.778.14...-23.790.071.87-0.025.2012.96-2.56-2.430.22
11926-082.5927.036.500.5510.01-3.58-2.514.255.50...5.39-0.75-0.134.476.7612.64-1.173.820.25
21926-091.164.021.266.58-0.990.73-0.510.695.33...-7.870.25-0.56-1.61-3.8610.36-1.400.130.23
31926-10-3.06-3.311.06-4.769.47-4.680.12-0.57-4.76...-15.38-2.20-4.11-5.51-8.491-3.24-0.090.700.32
41926-116.357.294.551.66-5.80-0.541.875.425.20...4.676.524.332.344.0012.53-0.10-0.510.31
..................................................................
11492022-043.013.036.37-25.22-10.762.04-7.00-6.80-2.28...-2.14-11.41-5.47-7.99-7.651-9.46-1.416.190.01
11502022-05-1.68-1.602.67-2.93-7.40-5.12-6.450.994.52...1.03-5.64-3.292.80-1.191-0.34-1.858.410.03
11512022-06-1.64-0.02-11.63-11.33-12.53-2.56-12.00-2.05-15.65...-6.43-8.50-9.02-9.05-11.781-8.432.09-5.970.06
11522022-073.675.490.5614.6212.100.7611.862.757.66...9.0816.3311.897.389.1919.572.81-4.100.08
11532022-08-1.61-1.87-0.12-2.95-4.97-2.16-6.01-5.07-1.39...-1.60-3.46-1.47-2.24-3.651-3.781.390.310.19
\n", "

1154 rows × 36 columns

\n", "
" ], "text/plain": [ " Date Food Beer Smoke Games Books Hshld Clths Hlth Chems \\\n", "0 1926-07 0.56 -5.19 1.29 2.93 10.97 -0.48 8.08 1.77 8.14 \n", "1 1926-08 2.59 27.03 6.50 0.55 10.01 -3.58 -2.51 4.25 5.50 \n", "2 1926-09 1.16 4.02 1.26 6.58 -0.99 0.73 -0.51 0.69 5.33 \n", "3 1926-10 -3.06 -3.31 1.06 -4.76 9.47 -4.68 0.12 -0.57 -4.76 \n", "4 1926-11 6.35 7.29 4.55 1.66 -5.80 -0.54 1.87 5.42 5.20 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "1149 2022-04 3.01 3.03 6.37 -25.22 -10.76 2.04 -7.00 -6.80 -2.28 \n", "1150 2022-05 -1.68 -1.60 2.67 -2.93 -7.40 -5.12 -6.45 0.99 4.52 \n", "1151 2022-06 -1.64 -0.02 -11.63 -11.33 -12.53 -2.56 -12.00 -2.05 -15.65 \n", "1152 2022-07 3.67 5.49 0.56 14.62 12.10 0.76 11.86 2.75 7.66 \n", "1153 2022-08 -1.61 -1.87 -0.12 -2.95 -4.97 -2.16 -6.01 -5.07 -1.39 \n", "\n", " ... Whlsl Rtail Meals Fin Other const Mkt-RF SMB HML RF \n", "0 ... -23.79 0.07 1.87 -0.02 5.20 1 2.96 -2.56 -2.43 0.22 \n", "1 ... 5.39 -0.75 -0.13 4.47 6.76 1 2.64 -1.17 3.82 0.25 \n", "2 ... -7.87 0.25 -0.56 -1.61 -3.86 1 0.36 -1.40 0.13 0.23 \n", "3 ... -15.38 -2.20 -4.11 -5.51 -8.49 1 -3.24 -0.09 0.70 0.32 \n", "4 ... 4.67 6.52 4.33 2.34 4.00 1 2.53 -0.10 -0.51 0.31 \n", "... ... ... ... ... ... ... ... ... ... ... ... \n", "1149 ... -2.14 -11.41 -5.47 -7.99 -7.65 1 -9.46 -1.41 6.19 0.01 \n", "1150 ... 1.03 -5.64 -3.29 2.80 -1.19 1 -0.34 -1.85 8.41 0.03 \n", "1151 ... -6.43 -8.50 -9.02 -9.05 -11.78 1 -8.43 2.09 -5.97 0.06 \n", "1152 ... 9.08 16.33 11.89 7.38 9.19 1 9.57 2.81 -4.10 0.08 \n", "1153 ... -1.60 -3.46 -1.47 -2.24 -3.65 1 -3.78 1.39 0.31 0.19 \n", "\n", "[1154 rows x 36 columns]" ] }, "execution_count": 431, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = df\n", "\n", "df4 = df3.merge(df_Fama1, on = \"Date\")\n", "df4" ] }, { "cell_type": "code", "execution_count": 432, "id": "7bb46ac9", "metadata": {}, "outputs": [], "source": [ "industries = name" ] }, { "cell_type": "code", "execution_count": 433, "id": "c2f6d28d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Food \n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.694\n", "Model: OLS Adj. R-squared: 0.693\n", "Method: Least Squares F-statistic: 868.1\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 7.81e-295\n", "Time: 15:39:53 Log-Likelihood: -2742.7\n", "No. Observations: 1154 AIC: 5493.\n", "Df Residuals: 1150 BIC: 5514.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.2050 0.078 2.640 0.008 0.053 0.357\n", "Mkt-RF 0.7475 0.015 48.252 0.000 0.717 0.778\n", "SMB -0.1404 0.026 -5.491 0.000 -0.191 -0.090\n", "HML 0.0523 0.022 2.354 0.019 0.009 0.096\n", "==============================================================================\n", "Omnibus: 149.225 Durbin-Watson: 1.846\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 815.171\n", "Skew: 0.455 Prob(JB): 9.72e-178\n", "Kurtosis: 7.016 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Beer \n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.494\n", "Model: OLS Adj. R-squared: 0.493\n", "Method: Least Squares F-statistic: 374.3\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 1.42e-169\n", "Time: 15:39:54 Log-Likelihood: -3507.6\n", "No. Observations: 1154 AIC: 7023.\n", "Df Residuals: 1150 BIC: 7043.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.2612 0.151 1.733 0.083 -0.034 0.557\n", "Mkt-RF 0.8667 0.030 28.837 0.000 0.808 0.926\n", "SMB 0.1964 0.050 3.960 0.000 0.099 0.294\n", "HML 0.1334 0.043 3.096 0.002 0.049 0.218\n", "==============================================================================\n", "Omnibus: 593.932 Durbin-Watson: 2.033\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 19337.825\n", "Skew: 1.753 Prob(JB): 0.00\n", "Kurtosis: 22.746 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Smoke\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.344\n", "Model: OLS Adj. R-squared: 0.342\n", "Method: Least Squares F-statistic: 201.1\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 7.52e-105\n", "Time: 15:39:54 Log-Likelihood: -3424.5\n", "No. Observations: 1154 AIC: 6857.\n", "Df Residuals: 1150 BIC: 6877.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.4375 0.140 3.121 0.002 0.162 0.713\n", "Mkt-RF 0.6494 0.028 23.219 0.000 0.594 0.704\n", "SMB -0.2169 0.046 -4.698 0.000 -0.307 -0.126\n", "HML 0.0966 0.040 2.410 0.016 0.018 0.175\n", "==============================================================================\n", "Omnibus: 112.385 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 766.527\n", "Skew: -0.052 Prob(JB): 3.55e-167\n", "Kurtosis: 6.991 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Games\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.711\n", "Model: OLS Adj. R-squared: 0.710\n", "Method: Least Squares F-statistic: 941.7\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 4.26e-309\n", "Time: 15:39:54 Log-Likelihood: -3447.5\n", "No. Observations: 1154 AIC: 6903.\n", "Df Residuals: 1150 BIC: 6923.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.1589 0.143 -1.111 0.267 -0.440 0.122\n", "Mkt-RF 1.2875 0.029 45.128 0.000 1.232 1.344\n", "SMB 0.4089 0.047 8.684 0.000 0.317 0.501\n", "HML 0.1382 0.041 3.381 0.001 0.058 0.218\n", "==============================================================================\n", "Omnibus: 316.297 Durbin-Watson: 1.896\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 4880.976\n", "Skew: -0.830 Prob(JB): 0.00\n", "Kurtosis: 12.937 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Books\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.715\n", "Model: OLS Adj. R-squared: 0.715\n", "Method: Least Squares F-statistic: 963.0\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 4.52e-313\n", "Time: 15:39:54 Log-Likelihood: -3188.9\n", "No. Observations: 1154 AIC: 6386.\n", "Df Residuals: 1150 BIC: 6406.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.1875 0.114 -1.640 0.101 -0.412 0.037\n", "Mkt-RF 1.0125 0.023 44.401 0.000 0.968 1.057\n", "SMB 0.3725 0.038 9.897 0.000 0.299 0.446\n", "HML 0.1862 0.033 5.696 0.000 0.122 0.250\n", "==============================================================================\n", "Omnibus: 120.867 Durbin-Watson: 2.059\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 864.987\n", "Skew: -0.131 Prob(JB): 1.48e-188\n", "Kurtosis: 7.233 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Hshld\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.674\n", "Model: OLS Adj. R-squared: 0.673\n", "Method: Least Squares F-statistic: 793.4\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 1.82e-279\n", "Time: 15:39:54 Log-Likelihood: -3011.9\n", "No. Observations: 1154 AIC: 6032.\n", "Df Residuals: 1150 BIC: 6052.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.0750 0.098 0.765 0.445 -0.117 0.267\n", "Mkt-RF 0.9063 0.020 46.332 0.000 0.868 0.945\n", "SMB -0.0882 0.032 -2.731 0.006 -0.152 -0.025\n", "HML -0.0354 0.028 -1.262 0.207 -0.090 0.020\n", "==============================================================================\n", "Omnibus: 132.839 Durbin-Watson: 1.993\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1163.603\n", "Skew: 0.034 Prob(JB): 2.12e-253\n", "Kurtosis: 7.919 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Clths\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.562\n", "Model: OLS Adj. R-squared: 0.561\n", "Method: Least Squares F-statistic: 492.8\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 7.72e-206\n", "Time: 15:39:54 Log-Likelihood: -3259.3\n", "No. Observations: 1154 AIC: 6527.\n", "Df Residuals: 1150 BIC: 6547.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.0917 0.122 0.755 0.451 -0.147 0.330\n", "Mkt-RF 0.7615 0.024 31.419 0.000 0.714 0.809\n", "SMB 0.4221 0.040 10.552 0.000 0.344 0.501\n", "HML -0.0600 0.035 -1.728 0.084 -0.128 0.008\n", "==============================================================================\n", "Omnibus: 327.709 Durbin-Watson: 1.833\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 10214.310\n", "Skew: -0.649 Prob(JB): 0.00\n", "Kurtosis: 17.517 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Hlth \n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.666\n", "Model: OLS Adj. R-squared: 0.665\n", "Method: Least Squares F-statistic: 763.0\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 6.42e-273\n", "Time: 15:39:54 Log-Likelihood: -2979.1\n", "No. Observations: 1154 AIC: 5966.\n", "Df Residuals: 1150 BIC: 5986.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.2999 0.095 3.147 0.002 0.113 0.487\n", "Mkt-RF 0.8789 0.019 46.228 0.000 0.842 0.916\n", "SMB -0.0857 0.031 -2.730 0.006 -0.147 -0.024\n", "HML -0.1895 0.027 -6.953 0.000 -0.243 -0.136\n", "==============================================================================\n", "Omnibus: 77.950 Durbin-Watson: 2.006\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 345.355\n", "Skew: -0.056 Prob(JB): 1.02e-75\n", "Kurtosis: 5.678 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Chems\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.782\n", "Model: OLS Adj. R-squared: 0.781\n", "Method: Least Squares F-statistic: 1372.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -2888.8\n", "No. Observations: 1154 AIC: 5786.\n", "Df Residuals: 1150 BIC: 5806.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.0787 0.088 0.893 0.372 -0.094 0.252\n", "Mkt-RF 1.0681 0.018 60.755 0.000 1.034 1.103\n", "SMB -0.1515 0.029 -5.222 0.000 -0.208 -0.095\n", "HML 0.0262 0.025 1.039 0.299 -0.023 0.076\n", "==============================================================================\n", "Omnibus: 119.449 Durbin-Watson: 1.944\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 559.456\n", "Skew: 0.362 Prob(JB): 3.28e-122\n", "Kurtosis: 6.333 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Txtls\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.692\n", "Model: OLS Adj. R-squared: 0.692\n", "Method: Least Squares F-statistic: 863.1\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 7.84e-294\n", "Time: 15:39:54 Log-Likelihood: -3328.5\n", "No. Observations: 1154 AIC: 6665.\n", "Df Residuals: 1150 BIC: 6685.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.2266 0.129 -1.756 0.079 -0.480 0.027\n", "Mkt-RF 0.9827 0.026 38.186 0.000 0.932 1.033\n", "SMB 0.5633 0.042 13.261 0.000 0.480 0.647\n", "HML 0.3730 0.037 10.111 0.000 0.301 0.445\n", "==============================================================================\n", "Omnibus: 333.882 Durbin-Watson: 1.953\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 7153.968\n", "Skew: 0.799 Prob(JB): 0.00\n", "Kurtosis: 15.093 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Cnstr\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.840\n", "Model: OLS Adj. R-squared: 0.839\n", "Method: Least Squares F-statistic: 2011.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -2817.6\n", "No. Observations: 1154 AIC: 5643.\n", "Df Residuals: 1150 BIC: 5663.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.1445 0.083 -1.744 0.081 -0.307 0.018\n", "Mkt-RF 1.1183 0.017 67.658 0.000 1.086 1.151\n", "SMB 0.2477 0.027 9.081 0.000 0.194 0.301\n", "HML 0.1078 0.024 4.551 0.000 0.061 0.154\n", "==============================================================================\n", "Omnibus: 82.802 Durbin-Watson: 2.026\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 394.794\n", "Skew: -0.025 Prob(JB): 1.87e-86\n", "Kurtosis: 5.865 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Steel\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.750\n", "Model: OLS Adj. R-squared: 0.750\n", "Method: Least Squares F-statistic: 1151.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -3317.6\n", "No. Observations: 1154 AIC: 6643.\n", "Df Residuals: 1150 BIC: 6663.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.3495 0.128 -2.734 0.006 -0.600 -0.099\n", "Mkt-RF 1.2597 0.025 49.412 0.000 1.210 1.310\n", "SMB 0.2320 0.042 5.515 0.000 0.149 0.315\n", "HML 0.3804 0.037 10.411 0.000 0.309 0.452\n", "==============================================================================\n", "Omnibus: 163.176 Durbin-Watson: 1.979\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 787.320\n", "Skew: 0.562 Prob(JB): 1.09e-171\n", "Kurtosis: 6.887 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "FabPr\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.855\n", "Model: OLS Adj. R-squared: 0.854\n", "Method: Least Squares F-statistic: 2251.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -2808.0\n", "No. Observations: 1154 AIC: 5624.\n", "Df Residuals: 1150 BIC: 5644.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0790 0.082 -0.961 0.337 -0.240 0.082\n", "Mkt-RF 1.1734 0.016 71.581 0.000 1.141 1.206\n", "SMB 0.2615 0.027 9.667 0.000 0.208 0.315\n", "HML 0.1120 0.023 4.766 0.000 0.066 0.158\n", "==============================================================================\n", "Omnibus: 104.749 Durbin-Watson: 1.901\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 392.289\n", "Skew: 0.370 Prob(JB): 6.54e-86\n", "Kurtosis: 5.759 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "ElcEq\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.815\n", "Model: OLS Adj. R-squared: 0.815\n", "Method: Least Squares F-statistic: 1690.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -3008.0\n", "No. Observations: 1154 AIC: 6024.\n", "Df Residuals: 1150 BIC: 6044.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.0322 0.098 0.329 0.742 -0.160 0.224\n", "Mkt-RF 1.2955 0.019 66.455 0.000 1.257 1.334\n", "SMB -0.0355 0.032 -1.102 0.271 -0.099 0.028\n", "HML -0.0006 0.028 -0.022 0.982 -0.055 0.054\n", "==============================================================================\n", "Omnibus: 33.771 Durbin-Watson: 2.069\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 55.494\n", "Skew: 0.242 Prob(JB): 8.90e-13\n", "Kurtosis: 3.959 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Autos\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.686\n", "Model: OLS Adj. R-squared: 0.685\n", "Method: Least Squares F-statistic: 838.1\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 9.06e-289\n", "Time: 15:39:54 Log-Likelihood: -3416.7\n", "No. Observations: 1154 AIC: 6841.\n", "Df Residuals: 1150 BIC: 6862.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.0068 0.139 0.049 0.961 -0.266 0.280\n", "Mkt-RF 1.2477 0.028 44.917 0.000 1.193 1.302\n", "SMB 0.0551 0.046 1.202 0.230 -0.035 0.145\n", "HML 0.1886 0.040 4.738 0.000 0.111 0.267\n", "==============================================================================\n", "Omnibus: 418.150 Durbin-Watson: 1.891\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 4814.371\n", "Skew: 1.333 Prob(JB): 0.00\n", "Kurtosis: 12.645 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Carry\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.719\n", "Model: OLS Adj. R-squared: 0.718\n", "Method: Least Squares F-statistic: 979.9\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 3.42e-316\n", "Time: 15:39:54 Log-Likelihood: -3254.3\n", "No. Observations: 1154 AIC: 6517.\n", "Df Residuals: 1150 BIC: 6537.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0463 0.121 -0.383 0.702 -0.284 0.191\n", "Mkt-RF 1.0976 0.024 45.482 0.000 1.050 1.145\n", "SMB 0.2181 0.040 5.477 0.000 0.140 0.296\n", "HML 0.3281 0.035 9.485 0.000 0.260 0.396\n", "==============================================================================\n", "Omnibus: 70.890 Durbin-Watson: 2.000\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 189.199\n", "Skew: 0.308 Prob(JB): 8.24e-42\n", "Kurtosis: 4.885 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Mines\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.461\n", "Model: OLS Adj. R-squared: 0.460\n", "Method: Least Squares F-statistic: 327.7\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 9.28e-154\n", "Time: 15:39:54 Log-Likelihood: -3578.6\n", "No. Observations: 1154 AIC: 7165.\n", "Df Residuals: 1150 BIC: 7185.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0280 0.160 -0.174 0.862 -0.342 0.286\n", "Mkt-RF 0.8426 0.032 26.364 0.000 0.780 0.905\n", "SMB 0.2587 0.053 4.905 0.000 0.155 0.362\n", "HML 0.1456 0.046 3.179 0.002 0.056 0.236\n", "==============================================================================\n", "Omnibus: 44.690 Durbin-Watson: 2.014\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 104.546\n", "Skew: 0.184 Prob(JB): 1.99e-23\n", "Kurtosis: 4.428 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Coal \n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.451\n", "Model: OLS Adj. R-squared: 0.449\n", "Method: Least Squares F-statistic: 314.3\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 5.51e-149\n", "Time: 15:39:54 Log-Likelihood: -4059.4\n", "No. Observations: 1154 AIC: 8127.\n", "Df Residuals: 1150 BIC: 8147.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.2331 0.243 -0.959 0.338 -0.710 0.244\n", "Mkt-RF 1.0842 0.048 22.362 0.000 0.989 1.179\n", "SMB 0.4854 0.080 6.066 0.000 0.328 0.642\n", "HML 0.6733 0.069 9.690 0.000 0.537 0.810\n", "==============================================================================\n", "Omnibus: 274.247 Durbin-Watson: 1.809\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1903.951\n", "Skew: 0.909 Prob(JB): 0.00\n", "Kurtosis: 9.024 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Oil \n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.581\n", "Model: OLS Adj. R-squared: 0.580\n", "Method: Least Squares F-statistic: 530.8\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 2.01e-216\n", "Time: 15:39:54 Log-Likelihood: -3279.5\n", "No. Observations: 1154 AIC: 6567.\n", "Df Residuals: 1150 BIC: 6587.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.1151 0.124 0.931 0.352 -0.128 0.358\n", "Mkt-RF 0.8788 0.025 35.627 0.000 0.830 0.927\n", "SMB -0.1792 0.041 -4.402 0.000 -0.259 -0.099\n", "HML 0.2901 0.035 8.205 0.000 0.221 0.359\n", "==============================================================================\n", "Omnibus: 114.285 Durbin-Watson: 1.842\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 555.916\n", "Skew: 0.317 Prob(JB): 1.93e-121\n", "Kurtosis: 6.341 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Util \n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.591\n", "Model: OLS Adj. R-squared: 0.590\n", "Method: Least Squares F-statistic: 553.1\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 1.89e-222\n", "Time: 15:39:54 Log-Likelihood: -3083.2\n", "No. Observations: 1154 AIC: 6174.\n", "Df Residuals: 1150 BIC: 6195.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.0508 0.104 0.487 0.626 -0.154 0.255\n", "Mkt-RF 0.7555 0.021 36.308 0.000 0.715 0.796\n", "SMB -0.1669 0.034 -4.860 0.000 -0.234 -0.100\n", "HML 0.2597 0.030 8.709 0.000 0.201 0.318\n", "==============================================================================\n", "Omnibus: 76.329 Durbin-Watson: 1.894\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 335.642\n", "Skew: -0.008 Prob(JB): 1.31e-73\n", "Kurtosis: 5.642 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Telcm\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.602\n", "Model: OLS Adj. R-squared: 0.601\n", "Method: Least Squares F-statistic: 578.8\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 3.26e-229\n", "Time: 15:39:54 Log-Likelihood: -2870.2\n", "No. Observations: 1154 AIC: 5748.\n", "Df Residuals: 1150 BIC: 5769.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.1372 0.087 1.582 0.114 -0.033 0.307\n", "Mkt-RF 0.6950 0.017 40.174 0.000 0.661 0.729\n", "SMB -0.1317 0.029 -4.611 0.000 -0.188 -0.076\n", "HML -0.0367 0.025 -1.482 0.139 -0.085 0.012\n", "==============================================================================\n", "Omnibus: 53.445 Durbin-Watson: 1.934\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 160.437\n", "Skew: 0.116 Prob(JB): 1.45e-35\n", "Kurtosis: 4.812 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Servs\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.346\n", "Model: OLS Adj. R-squared: 0.344\n", "Method: Least Squares F-statistic: 202.4\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 2.06e-105\n", "Time: 15:39:54 Log-Likelihood: -3827.5\n", "No. Observations: 1154 AIC: 7663.\n", "Df Residuals: 1150 BIC: 7683.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.4999 0.199 2.514 0.012 0.110 0.890\n", "Mkt-RF 0.8321 0.040 20.984 0.000 0.754 0.910\n", "SMB 0.3686 0.065 5.632 0.000 0.240 0.497\n", "HML -0.5058 0.057 -8.899 0.000 -0.617 -0.394\n", "==============================================================================\n", "Omnibus: 715.042 Durbin-Watson: 2.200\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 81810.763\n", "Skew: 1.918 Prob(JB): 0.00\n", "Kurtosis: 44.070 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "BusEq\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.783\n", "Model: OLS Adj. R-squared: 0.783\n", "Method: Least Squares F-statistic: 1385.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -2962.9\n", "No. Observations: 1154 AIC: 5934.\n", "Df Residuals: 1150 BIC: 5954.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.2803 0.094 2.982 0.003 0.096 0.465\n", "Mkt-RF 1.1212 0.019 59.804 0.000 1.084 1.158\n", "SMB 0.1533 0.031 4.955 0.000 0.093 0.214\n", "HML -0.4557 0.027 -16.960 0.000 -0.508 -0.403\n", "==============================================================================\n", "Omnibus: 70.809 Durbin-Watson: 2.067\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 285.566\n", "Skew: 0.056 Prob(JB): 9.77e-63\n", "Kurtosis: 5.434 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Paper\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.749\n", "Model: OLS Adj. R-squared: 0.748\n", "Method: Least Squares F-statistic: 1144.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -2881.2\n", "No. Observations: 1154 AIC: 5770.\n", "Df Residuals: 1150 BIC: 5791.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.0753 0.088 0.860 0.390 -0.097 0.247\n", "Mkt-RF 0.9528 0.017 54.552 0.000 0.919 0.987\n", "SMB -0.0577 0.029 -2.001 0.046 -0.114 -0.001\n", "HML 0.0454 0.025 1.812 0.070 -0.004 0.094\n", "==============================================================================\n", "Omnibus: 79.644 Durbin-Watson: 1.931\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 355.308\n", "Skew: 0.081 Prob(JB): 7.01e-78\n", "Kurtosis: 5.714 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Trans\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.795\n", "Model: OLS Adj. R-squared: 0.794\n", "Method: Least Squares F-statistic: 1483.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -2982.0\n", "No. Observations: 1154 AIC: 5972.\n", "Df Residuals: 1150 BIC: 5992.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.2353 0.096 -2.462 0.014 -0.423 -0.048\n", "Mkt-RF 1.0380 0.019 54.459 0.000 1.001 1.075\n", "SMB 0.1635 0.031 5.196 0.000 0.102 0.225\n", "HML 0.4544 0.027 16.633 0.000 0.401 0.508\n", "==============================================================================\n", "Omnibus: 81.776 Durbin-Watson: 1.915\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 384.621\n", "Skew: -0.027 Prob(JB): 3.02e-84\n", "Kurtosis: 5.828 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Whlsl\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.690\n", "Model: OLS Adj. R-squared: 0.689\n", "Method: Least Squares F-statistic: 853.8\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 5.95e-292\n", "Time: 15:39:54 Log-Likelihood: -3251.7\n", "No. Observations: 1154 AIC: 6511.\n", "Df Residuals: 1150 BIC: 6532.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.2105 0.121 -1.744 0.081 -0.447 0.026\n", "Mkt-RF 0.9730 0.024 40.409 0.000 0.926 1.020\n", "SMB 0.5502 0.040 13.844 0.000 0.472 0.628\n", "HML 0.0682 0.035 1.977 0.048 0.001 0.136\n", "==============================================================================\n", "Omnibus: 303.643 Durbin-Watson: 2.053\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 10510.009\n", "Skew: -0.496 Prob(JB): 0.00\n", "Kurtosis: 17.751 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Rtail\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.753\n", "Model: OLS Adj. R-squared: 0.753\n", "Method: Least Squares F-statistic: 1171.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -2893.6\n", "No. Observations: 1154 AIC: 5795.\n", "Df Residuals: 1150 BIC: 5815.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.1568 0.089 1.771 0.077 -0.017 0.330\n", "Mkt-RF 0.9800 0.018 55.509 0.000 0.945 1.015\n", "SMB 0.0416 0.029 1.429 0.153 -0.016 0.099\n", "HML -0.1315 0.025 -5.196 0.000 -0.181 -0.082\n", "==============================================================================\n", "Omnibus: 48.752 Durbin-Watson: 1.832\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 136.982\n", "Skew: -0.112 Prob(JB): 1.80e-30\n", "Kurtosis: 4.673 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Meals\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.625\n", "Model: OLS Adj. R-squared: 0.624\n", "Method: Least Squares F-statistic: 638.9\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 2.48e-244\n", "Time: 15:39:54 Log-Likelihood: -3230.1\n", "No. Observations: 1154 AIC: 6468.\n", "Df Residuals: 1150 BIC: 6488.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.1512 0.118 1.276 0.202 -0.081 0.384\n", "Mkt-RF 0.8969 0.024 37.955 0.000 0.851 0.943\n", "SMB 0.2895 0.039 7.423 0.000 0.213 0.366\n", "HML -0.0306 0.034 -0.902 0.367 -0.097 0.036\n", "==============================================================================\n", "Omnibus: 117.928 Durbin-Watson: 1.819\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 710.326\n", "Skew: -0.233 Prob(JB): 5.68e-155\n", "Kurtosis: 6.815 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Fin \n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.865\n", "Model: OLS Adj. R-squared: 0.865\n", "Method: Least Squares F-statistic: 2456.\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.00\n", "Time: 15:39:54 Log-Likelihood: -2687.7\n", "No. Observations: 1154 AIC: 5383.\n", "Df Residuals: 1150 BIC: 5404.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.1124 0.074 -1.518 0.129 -0.258 0.033\n", "Mkt-RF 1.1215 0.015 75.939 0.000 1.093 1.151\n", "SMB -0.0575 0.024 -2.360 0.018 -0.105 -0.010\n", "HML 0.3152 0.021 14.891 0.000 0.274 0.357\n", "==============================================================================\n", "Omnibus: 73.836 Durbin-Watson: 1.900\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 276.599\n", "Skew: -0.168 Prob(JB): 8.66e-61\n", "Kurtosis: 5.375 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "Other\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.716\n", "Model: OLS Adj. R-squared: 0.715\n", "Method: Least Squares F-statistic: 965.7\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 1.40e-313\n", "Time: 15:39:54 Log-Likelihood: -3113.6\n", "No. Observations: 1154 AIC: 6235.\n", "Df Residuals: 1150 BIC: 6255.\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.2119 0.107 -1.979 0.048 -0.422 -0.002\n", "Mkt-RF 0.9995 0.021 46.789 0.000 0.958 1.041\n", "SMB 0.2831 0.035 8.029 0.000 0.214 0.352\n", "HML 0.0183 0.031 0.599 0.549 -0.042 0.078\n", "==============================================================================\n", "Omnibus: 171.185 Durbin-Watson: 2.003\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1680.770\n", "Skew: 0.326 Prob(JB): 0.00\n", "Kurtosis: 8.876 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "const\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.007\n", "Model: OLS Adj. R-squared: 0.004\n", "Method: Least Squares F-statistic: 2.687\n", "Date: Thu, 20 Oct 2022 Prob (F-statistic): 0.0453\n", "Time: 15:39:54 Log-Likelihood: -42.324\n", "No. Observations: 1154 AIC: 92.65\n", "Df Residuals: 1150 BIC: 112.9\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.7321 0.007 97.865 0.000 0.717 0.747\n", "Mkt-RF 0.0031 0.001 2.094 0.036 0.000 0.006\n", "SMB 0.0026 0.002 1.065 0.287 -0.002 0.007\n", "HML -0.0028 0.002 -1.332 0.183 -0.007 0.001\n", "==============================================================================\n", "Omnibus: 199.038 Durbin-Watson: 0.057\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 327.203\n", "Skew: -1.116 Prob(JB): 8.89e-72\n", "Kurtosis: 4.349 Cond. No. 5.69\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", "/var/folders/n7/0psm04t971g6575br78tr0sc0000gn/T/ipykernel_53145/2027425925.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n" ] } ], "source": [ "industries_betas = pd.DataFrame(columns = [\"Industry\", \"beta\", \"E(R - Rf)\"])\n", "\n", "\n", "\n", "for industry in industries:\n", " ri = df4[industry] - df4[\"RF\"]\n", " regressed = sm.OLS(endog = ri, exog = df4[['const','Mkt-RF','SMB', 'HML']], missing = 'drop')\n", " print(industry)\n", " result = regressed.fit()\n", " print(result.summary())\n", " expected_ri = ri.mean()\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", " " ] }, { "cell_type": "code", "execution_count": 434, "id": "3819095d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.733708838821491" ] }, "execution_count": 434, "metadata": {}, "output_type": "execute_result" } ], "source": [ "expected_ri" ] }, { "cell_type": "markdown", "id": "d0cc2005", "metadata": {}, "source": [ "# Solution to 1.2" ] }, { "cell_type": "markdown", "id": "73471ce5", "metadata": {}, "source": [ "Plotting the Betas" ] }, { "cell_type": "code", "execution_count": 435, "id": "66b78b3a", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'numpy.ndarray'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Input \u001b[0;32mIn [435]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m industries_betas\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindustries_betas\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpected_ri\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 12\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBeta\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 13\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mExcess Return\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/pyplot.py:2807\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 2802\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[1;32m 2803\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[1;32m 2804\u001b[0m x, y, s\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, c\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 2805\u001b[0m vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, linewidths\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m 2806\u001b[0m edgecolors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, plotnonfinite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2807\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2808\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmarker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2812\u001b[0m sci(__ret)\n\u001b[1;32m 2813\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/__init__.py:1412\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1409\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1414\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1415\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1416\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_axes.py:4363\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4249\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4250\u001b[0m \u001b[38;5;124;03mA scatter plot of *y* vs. *x* with varying marker size and/or color.\u001b[39;00m\n\u001b[1;32m 4251\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4360\u001b[0m \n\u001b[1;32m 4361\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4362\u001b[0m \u001b[38;5;66;03m# Process **kwargs to handle aliases, conflicts with explicit kwargs:\u001b[39;00m\n\u001b[0;32m-> 4363\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43my\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4364\u001b[0m \u001b[38;5;66;03m# np.ma.ravel yields an ndarray, not a masked array,\u001b[39;00m\n\u001b[1;32m 4365\u001b[0m \u001b[38;5;66;03m# unless its argument is a masked array.\u001b[39;00m\n\u001b[1;32m 4366\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mravel(x)\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py:2521\u001b[0m, in \u001b[0;36m_AxesBase._process_unit_info\u001b[0;34m(self, datasets, kwargs, convert)\u001b[0m\n\u001b[1;32m 2519\u001b[0m \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[1;32m 2520\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mhave_units():\n\u001b[0;32m-> 2521\u001b[0m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2522\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 2523\u001b[0m \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[1;32m 2524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/axis.py:1449\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 1447\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[1;32m 1448\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m converter\n\u001b[0;32m-> 1449\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/category.py:116\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[0;34m(data, axis)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 116\u001b[0m axis\u001b[38;5;241m.\u001b[39mset_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 118\u001b[0m axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/category.py:192\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 192\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/category.py:225\u001b[0m, in \u001b[0;36mUnitData.update\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;66;03m# check if convertible to number:\u001b[39;00m\n\u001b[1;32m 224\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 225\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mOrderedDict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 226\u001b[0m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[1;32m 227\u001b[0m _api\u001b[38;5;241m.\u001b[39mcheck_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value\u001b[38;5;241m=\u001b[39mval)\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[1;32m 229\u001b[0m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n", "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "industries_betas\n", "#industries_betas = industries_betas.drop([0,30])\n", "industries_betas\n", "industries_betas.plot()\n", "\n", "industries_betas[\"E(R - Rf)\"] = industries_betas[\"E(R - Rf)\"]*100\n", "industries_betas\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(industries_betas, expected_ri)\n", "plt.xlabel('Beta')\n", "plt.ylabel('Excess Return')\n", "plt.title('Industry Betas and Excess Returns')\n", "plt.ylim(0.5,1)\n", "plt.xlim(0.5,1.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e5818a56", "metadata": {}, "source": [ "Observations and Conclusions" ] }, { "cell_type": "markdown", "id": "4024fdbc", "metadata": {}, "source": [ "Do you observe the SML? What do you conclude?\n", "SML is the security market line." ] }, { "cell_type": "markdown", "id": "01d694f4", "metadata": {}, "source": [ "Although mine is a bit skewed, I sort of do or know that I should observe the SML or security market line. This shows the beta or the basis of the risk and returns, for the entirety of the market." ] }, { "cell_type": "markdown", "id": "36c62a3e", "metadata": {}, "source": [ "# Solution to 1.3" ] }, { "cell_type": "markdown", "id": "f177be5e", "metadata": {}, "source": [ "Conducting cross-validation" ] }, { "cell_type": "code", "execution_count": 389, "id": "ae290005", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'c'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'c'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "Input \u001b[0;32mIn [389]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m err_mae_test \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 6\u001b[0m mkt \u001b[38;5;241m=\u001b[39m df4[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMkt-RF\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto_numpy()\n\u001b[0;32m----> 7\u001b[0m ri_rf \u001b[38;5;241m=\u001b[39m (\u001b[43mdf4\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindustry\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m-\u001b[39mdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRF\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m.\u001b[39mto_numpy()\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train,test \u001b[38;5;129;01min\u001b[39;00m kf\u001b[38;5;241m.\u001b[39msplit(mkt):\n\u001b[1;32m 9\u001b[0m lr\u001b[38;5;241m=\u001b[39mlinear_model\u001b[38;5;241m.\u001b[39mLinearRegression()\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3507\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3623\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3625\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3626\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3627\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3628\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", "\u001b[0;31mKeyError\u001b[0m: 'c'" ] } ], "source": [ "from sklearn.model_selection import KFold\n", "\n", "for i in range(len(industries)):\n", " err_rmse_test = 0\n", " err_mae_test = 0\n", " mkt = df4['Mkt-RF'].to_numpy()\n", " ri_rf = (df4[industry[i]]-df['RF']).to_numpy()\n", " for train,test in kf.split(mkt):\n", " lr=linear_model.LinearRegression()\n", " reg=lr.fit(mkt[train].reshape(-1, 1),ri_rf[train])\n", " ri_rf_pred_test =reg.predict(mkt[test].reshape(-1, 1))\n", " e_test = ri_rf[test]-ri_rf_pred_test\n", " err_rmse_test += np.sqrt(np.mean(e_test*e_test))\n", " err_mae_test += np.mean(np.abs(e_test))\n", " rmse_10cv_test = err_rmse_test/10\n", " mae_10cv_test = err_mae_test/10\n", " metric.iloc[i][0] = rmse_10cv_test\n", " metric.iloc[i][1] = mae_10cv_test" ] }, { "cell_type": "markdown", "id": "d3c4b02e", "metadata": {}, "source": [ "Calculating the average of RMSE and MAE" ] }, { "cell_type": "code", "execution_count": null, "id": "768ab6b0", "metadata": {}, "outputs": [], "source": [ "r2_cv = r2_score(y, p)\n", " MAE_test = mean_absolute_error(y[test],p[test])\n", " print('MAE on training: {}'.format(MAE_train))\n", " print('MAE on 10-fold CV: {}'.format(MAE_test))\n", " \n", "for train,test in kf.split(x):\n", " lasso=las.fit(x[train],y[train])\n", " y_pred =lasso.predict(x[test])\n", " e = y[test]-y_pred\n", " err += np.sum(e*e) \n", "rmse_10cv = np.sqrt(err/len(x))\n", "print('RMSE on 10-fold CV: {}'.format(rmse_10cv))" ] }, { "cell_type": "markdown", "id": "2ecd9ded", "metadata": {}, "source": [ "# Solution to 1.4" ] }, { "cell_type": "markdown", "id": "883b05d8", "metadata": {}, "source": [ "Using SMB and HML to see improvements in CAPM performance" ] }, { "cell_type": "code", "execution_count": 309, "id": "e62b0b38", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "199007 0.53\n", "199008 -1.51\n", "199009 1.32\n", "199010 -7.58\n", "199011 1.44\n", " ... \n", "202204 -0.30\n", "202205 -1.21\n", "202206 -0.09\n", "202207 0.27\n", "202208 0.42\n", "Name: SMB, Length: 386, dtype: float64" ] }, "execution_count": 309, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SMB = df2[\"SMB\"]\n", "HML = df2[\"HML\"]" ] }, { "cell_type": "markdown", "id": "45d41fa1", "metadata": {}, "source": [ "Running new regression analyses" ] }, { "cell_type": "code", "execution_count": null, "id": "c3c6dc17", "metadata": {}, "outputs": [], "source": [ "industries_betas = pd.DataFrame(columns = [\"Industry\", \"beta\", \"E(R - Rf)\"])\n", "\n", "\n", "\n", "for industry in industries:\n", " ri = df4[industry] - df4[\"RF\"]\n", " regressed = sm.OLS(endog = ri, exog = df4[['const','Mkt-RF','SMB', 'HML']], missing = 'drop')\n", " print(industry)\n", " result = regressed.fit()\n", " print(result.summary())\n", " expected_ri = ri.mean()\n", " industries_betas = industries_betas.append({\"Industry\" : industry, \"beta\" : result.params[1], 'E(R - Rf)': expected_ri}, ignore_index = True)\n", " " ] }, { "cell_type": "markdown", "id": "ea2c44e4", "metadata": {}, "source": [ "# Solution to 1.5" ] }, { "cell_type": "markdown", "id": "2ffbeed5", "metadata": {}, "source": [ "Plotting graphs" ] }, { "cell_type": "code", "execution_count": null, "id": "ad6e0c7a", "metadata": {}, "outputs": [], "source": [ "industries_betas\n", "#industries_betas = industries_betas.drop([0,30])\n", "industries_betas\n", "industries_betas.plot()\n", "\n", "industries_betas[\"E(R - Rf)\"] = industries_betas[\"E(R - Rf)\"]*100\n", "industries_betas\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(industries_betas, expected_ri)\n", "plt.xlabel('Beta')\n", "plt.ylabel('Excess Return')\n", "plt.title('Industry Betas and Excess Returns')\n", "plt.ylim(0.5,1)\n", "plt.xlim(0.5,1.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "85749973", "metadata": {}, "source": [ "Observations and Answers to questions" ] }, { "cell_type": "markdown", "id": "6f144ec2", "metadata": {}, "source": [ "# Solution to 1.6" ] }, { "cell_type": "markdown", "id": "d1321bd6", "metadata": {}, "source": [ "Conducting cross-validation" ] }, { "cell_type": "code", "execution_count": null, "id": "5e00de82", "metadata": {}, "outputs": [], "source": [ "lasso = linear_model.LassoCV(cv=10).fit(X_train,y_train)\n", "-np.log10(lasso.alpha_)\n", "\n", "# Or \n", "\n", " kf = KFold(n_splits=10)\n", " kf.get_n_splits(x)\n", " p = np.zeros_like(y)\n", " for train, test in kf.split(x):\n", " met.fit(x[train], y[train])\n", " p[test] = met.predict(x[test])" ] }, { "cell_type": "markdown", "id": "0f89afc8", "metadata": {}, "source": [ "Calculating the average of RMSE and MAE" ] }, { "cell_type": "code", "execution_count": null, "id": "f401bfc3", "metadata": {}, "outputs": [], "source": [ "r2_cv = r2_score(y, p)\n", " MAE_test = mean_absolute_error(y[test],p[test])\n", " print('MAE on training: {}'.format(MAE_train))\n", " print('MAE on 10-fold CV: {}'.format(MAE_test))\n", " \n", "for train,test in kf.split(x):\n", " lasso=las.fit(x[train],y[train])\n", " y_pred =lasso.predict(x[test])\n", " e = y[test]-y_pred\n", " err += np.sum(e*e) \n", "rmse_10cv = np.sqrt(err/len(x))\n", "print('RMSE on 10-fold CV: {}'.format(rmse_10cv))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }