{ "cells": [ { "cell_type": "code", "execution_count": 34, "id": "d92d7f3c", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import requests\n", "import seaborn as sns\n", "import pandas_datareader.data as web\n", "import datetime as dt" ] }, { "cell_type": "code", "execution_count": 33, "id": "011a48bf", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "['F-F_Research_Data_Factors',\n", " 'F-F_Research_Data_Factors_weekly',\n", " 'F-F_Research_Data_Factors_daily',\n", " 'F-F_Research_Data_5_Factors_2x3',\n", " 'F-F_Research_Data_5_Factors_2x3_daily',\n", " 'Portfolios_Formed_on_ME',\n", " 'Portfolios_Formed_on_ME_Wout_Div',\n", " 'Portfolios_Formed_on_ME_Daily',\n", " 'Portfolios_Formed_on_BE-ME',\n", " 'Portfolios_Formed_on_BE-ME_Wout_Div',\n", " 'Portfolios_Formed_on_BE-ME_Daily',\n", " 'Portfolios_Formed_on_OP',\n", " 'Portfolios_Formed_on_OP_Wout_Div',\n", " 'Portfolios_Formed_on_OP_Daily',\n", " 'Portfolios_Formed_on_INV',\n", " 'Portfolios_Formed_on_INV_Wout_Div',\n", " 'Portfolios_Formed_on_INV_Daily',\n", " '6_Portfolios_2x3',\n", " '6_Portfolios_2x3_Wout_Div',\n", " '6_Portfolios_2x3_weekly',\n", " '6_Portfolios_2x3_daily',\n", " '25_Portfolios_5x5',\n", " '25_Portfolios_5x5_Wout_Div',\n", " '25_Portfolios_5x5_Daily',\n", " '100_Portfolios_10x10',\n", " '100_Portfolios_10x10_Wout_Div',\n", " '100_Portfolios_10x10_Daily',\n", " '6_Portfolios_ME_OP_2x3',\n", " '6_Portfolios_ME_OP_2x3_Wout_Div',\n", " '6_Portfolios_ME_OP_2x3_daily',\n", " '25_Portfolios_ME_OP_5x5',\n", " '25_Portfolios_ME_OP_5x5_Wout_Div',\n", " '25_Portfolios_ME_OP_5x5_daily',\n", " '100_Portfolios_ME_OP_10x10',\n", " '100_Portfolios_10x10_ME_OP_Wout_Div',\n", " '100_Portfolios_ME_OP_10x10_daily',\n", " '6_Portfolios_ME_INV_2x3',\n", " '6_Portfolios_ME_INV_2x3_Wout_Div',\n", " '6_Portfolios_ME_INV_2x3_daily',\n", " '25_Portfolios_ME_INV_5x5',\n", " '25_Portfolios_ME_INV_5x5_Wout_Div',\n", " '25_Portfolios_ME_INV_5x5_daily',\n", " '100_Portfolios_ME_INV_10x10',\n", " '100_Portfolios_10x10_ME_INV_Wout_Div',\n", " '100_Portfolios_ME_INV_10x10_daily',\n", " '25_Portfolios_BEME_OP_5x5',\n", " '25_Portfolios_BEME_OP_5x5_Wout_Div',\n", " '25_Portfolios_BEME_OP_5x5_daily',\n", " '25_Portfolios_BEME_INV_5x5',\n", " '25_Portfolios_BEME_INV_5x5_Wout_Div',\n", " '25_Portfolios_BEME_INV_5x5_daily',\n", " '25_Portfolios_OP_INV_5x5',\n", " '25_Portfolios_OP_INV_5x5_Wout_Div',\n", " '25_Portfolios_OP_INV_5x5_daily',\n", " '32_Portfolios_ME_BEME_OP_2x4x4',\n", " '32_Portfolios_ME_BEME_OP_2x4x4_Wout_Div',\n", " '32_Portfolios_ME_BEME_INV_2x4x4',\n", " '32_Portfolios_ME_BEME_INV_2x4x4_Wout_Div',\n", " '32_Portfolios_ME_OP_INV_2x4x4',\n", " '32_Portfolios_ME_OP_INV_2x4x4_Wout_Div',\n", " 'Portfolios_Formed_on_E-P',\n", " 'Portfolios_Formed_on_E-P_Wout_Div',\n", " 'Portfolios_Formed_on_CF-P',\n", " 'Portfolios_Formed_on_CF-P_Wout_Div',\n", " 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'North_America_25_Portfolios_ME_BE-ME_daily',\n", " 'Developed_6_Portfolios_ME_OP',\n", " 'Developed_6_Portfolios_ME_OP_Daily',\n", " 'Developed_ex_US_6_Portfolios_ME_OP',\n", " 'Developed_ex_US_6_Portfolios_ME_OP_Daily',\n", " 'Europe_6_Portfolios_ME_OP',\n", " 'Europe_6_Portfolios_ME_OP_Daily',\n", " 'Japan_6_Portfolios_ME_OP',\n", " 'Japan_6_Portfolios_ME_OP_Daily',\n", " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_OP',\n", " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_OP_Daily',\n", " 'North_America_6_Portfolios_ME_OP',\n", " 'North_America_6_Portfolios_ME_OP_Daily',\n", " 'Developed_25_Portfolios_ME_OP',\n", " 'Developed_25_Portfolios_ME_OP_Daily',\n", " 'Developed_ex_US_25_Portfolios_ME_OP',\n", " 'Developed_ex_US_25_Portfolios_ME_OP_Daily',\n", " 'Europe_25_Portfolios_ME_OP',\n", " 'Europe_25_Portfolios_ME_OP_Daily',\n", " 'Japan_25_Portfolios_ME_OP',\n", " 'Japan_25_Portfolios_ME_OP_Daily',\n", " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_OP',\n", " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_OP_Daily',\n", " 'North_America_25_Portfolios_ME_OP',\n", " 'North_America_25_Portfolios_ME_OP_Daily',\n", " 'Developed_6_Portfolios_ME_INV',\n", " 'Developed_6_Portfolios_ME_INV_Daily',\n", " 'Developed_ex_US_6_Portfolios_ME_INV',\n", " 'Developed_ex_US_6_Portfolios_ME_INV_Daily',\n", " 'Europe_6_Portfolios_ME_INV',\n", " 'Europe_6_Portfolios_ME_INV_Daily',\n", " 'Japan_6_Portfolios_ME_INV',\n", " 'Japan_6_Portfolios_ME_INV_Daily',\n", " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_INV',\n", " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_INV_Daily',\n", " 'North_America_6_Portfolios_ME_INV',\n", " 'North_America_6_Portfolios_ME_INV_Daily',\n", " 'Developed_25_Portfolios_ME_INV',\n", " 'Developed_25_Portfolios_ME_INV_Daily',\n", " 'Developed_ex_US_25_Portfolios_ME_INV',\n", " 'Developed_ex_US_25_Portfolios_ME_INV_Daily',\n", " 'Europe_25_Portfolios_ME_INV',\n", " 'Europe_25_Portfolios_ME_INV_Daily',\n", " 'Japan_25_Portfolios_ME_INV',\n", " 'Japan_25_Portfolios_ME_INV_Daily',\n", " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_INV',\n", " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_INV_Daily',\n", " 'North_America_25_Portfolios_ME_INV',\n", " 'North_America_25_Portfolios_ME_INV_Daily',\n", " 'Developed_6_Portfolios_ME_Prior_12_2',\n", " 'Developed_6_Portfolios_ME_Prior_250_20_daily',\n", " 'Developed_ex_US_6_Portfolios_ME_Prior_12_2',\n", " 'Developed_ex_US_6_Portfolios_ME_Prior_250_20_daily',\n", " 'Europe_6_Portfolios_ME_Prior_12_2',\n", " 'Europe_6_Portfolios_ME_Prior_250_20_daily',\n", " 'Japan_6_Portfolios_ME_Prior_12_2',\n", " 'Japan_6_Portfolios_ME_Prior_250_20_daily',\n", " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_Prior_12_2',\n", " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_Prior_250_20_daily',\n", " 'North_America_6_Portfolios_ME_Prior_12_2',\n", " 'North_America_6_Portfolios_ME_Prior_250_20_daily',\n", " 'Developed_25_Portfolios_ME_Prior_12_2',\n", " 'Developed_25_Portfolios_ME_Prior_250_20_daily',\n", " 'Developed_ex_US_25_Portfolios_ME_Prior_12_2',\n", " 'Developed_ex_US_25_Portfolios_ME_Prior_250_20_daily',\n", " 'Europe_25_Portfolios_ME_Prior_12_2',\n", " 'Europe_25_Portfolios_ME_Prior_250_20_daily',\n", " 'Japan_25_Portfolios_ME_Prior_12_2',\n", " 'Japan_25_Portfolios_ME_Prior_250_20_daily',\n", " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_Prior_12_2',\n", " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_Prior_250_20_daily',\n", " 'North_America_25_Portfolios_ME_Prior_12_2',\n", " 'North_America_25_Portfolios_ME_Prior_250_20_daily',\n", " 'Developed_32_Portfolios_ME_BE-ME_OP_2x4x4',\n", " 'Developed_ex_US_32_Portfolios_ME_BE-ME_OP_2x4x4',\n", " 'Europe_32_Portfolios_ME_BE-ME_OP_2x4x4',\n", " 'Japan_32_Portfolios_ME_BE-ME_OP_2x4x4',\n", " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_OP_2x4x4',\n", " 'North_America_32_Portfolios_ME_BE-ME_OP_2x4x4',\n", " 'Developed_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n", " 'Developed_ex_US_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n", " 'Europe_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n", " 'Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n", " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n", " 'North_America_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n", " 'Developed_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n", " 'Developed_ex_US_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n", " 'Europe_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n", " 'Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n", " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n", " 'North_America_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n", " 'Emerging_5_Factors',\n", " 'Emerging_MOM_Factor',\n", " 'Emerging_Markets_6_Portfolios_ME_BE-ME',\n", " 'Emerging_Markets_6_Portfolios_ME_OP',\n", " 'Emerging_Markets_6_Portfolios_ME_INV',\n", " 'Emerging_Markets_6_Portfolios_ME_Prior_12_2',\n", " 'Emerging_Markets_4_Portfolios_BE-ME_OP',\n", " 'Emerging_Markets_4_Portfolios_OP_INV',\n", " 'Emerging_Markets_4_Portfolios_BE-ME_INV']" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pandas_datareader.famafrench.get_available_datasets()" ] }, { "cell_type": "code", "execution_count": 36, "id": "b3765e9d", "metadata": {}, "outputs": [], "source": [ "start_date = dt.datetime(1926, 7, 1)\n", "end_date = dt.datetime(2022, 8, 1)" ] }, { "cell_type": "code", "execution_count": 37, "id": "bfd31bdd", "metadata": {}, "outputs": [], "source": [ "fffactors = web.DataReader('F-F_Research_Data_Factors', 'famafrench', start = start_date, end = end_date)" ] }, { "cell_type": "code", "execution_count": 38, "id": "3f5916ab", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: Mkt-RF SMB HML RF\n", " Date \n", " 1926-07 2.96 -2.56 -2.43 0.22\n", " 1926-08 2.64 -1.17 3.82 0.25\n", " 1926-09 0.36 -1.40 0.13 0.23\n", " 1926-10 -3.24 -0.09 0.70 0.32\n", " 1926-11 2.53 -0.10 -0.51 0.31\n", " ... ... ... ... ...\n", " 2022-04 -9.46 -1.41 6.19 0.01\n", " 2022-05 -0.34 -1.85 8.41 0.03\n", " 2022-06 -8.43 2.09 -5.97 0.06\n", " 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The 1-month TBill return is from Ibbotson and Associates, Inc. Copyright 2022 Kenneth R. French\\n\\n 0 : (1154 rows x 4 cols)\\n 1 : Annual Factors: January-December (95 rows x 4 cols)'}" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fffactors" ] }, { "cell_type": "code", "execution_count": 39, "id": "db0ee619", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1154 rows × 4 columns

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" ], "text/plain": [ " Mkt-RF SMB HML RF\n", "Date \n", "1926-07 2.96 -2.56 -2.43 0.22\n", "1926-08 2.64 -1.17 3.82 0.25\n", "1926-09 0.36 -1.40 0.13 0.23\n", "1926-10 -3.24 -0.09 0.70 0.32\n", "1926-11 2.53 -0.10 -0.51 0.31\n", "... ... ... ... ...\n", "2022-04 -9.46 -1.41 6.19 0.01\n", "2022-05 -0.34 -1.85 8.41 0.03\n", "2022-06 -8.43 2.09 -5.97 0.06\n", "2022-07 9.57 2.81 -4.10 0.08\n", "2022-08 -3.78 1.39 0.31 0.19\n", "\n", "[1154 rows x 4 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fffactors_monthly = fffactors[0]\n", "fffactors_monthly" ] }, { "cell_type": "code", "execution_count": 43, "id": "89a46a6a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 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],\n", " 1: Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", " Date \n", " 1926-07 0.86 -5.36 1.67 2.31 26.28 2.45 -0.68 2.45 1.01 0.41 \n", " 1926-08 3.70 19.25 5.01 2.53 21.18 -2.86 4.79 6.11 3.02 2.53 \n", " 1926-09 1.38 5.63 3.10 4.13 -2.04 1.76 -6.16 0.80 2.40 -3.80 \n", " 1926-10 -4.70 -6.08 -1.63 -5.76 8.33 -2.98 -1.12 0.13 -8.77 1.87 \n", " 1926-11 6.74 11.29 9.58 0.28 -5.98 1.65 0.85 7.20 5.34 4.21 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2022-04 -3.40 2.55 1.70 -12.41 -8.07 -5.29 -7.80 -19.95 -6.26 -7.30 \n", " 2022-05 -1.63 -6.48 -1.09 -4.71 -4.08 -5.67 -2.52 -7.47 2.62 -1.94 \n", " 2022-06 -4.07 -0.04 -11.34 -10.10 -13.41 -7.11 -10.48 2.25 -14.10 -14.07 \n", " 2022-07 3.91 5.30 -2.71 7.74 11.58 6.50 9.07 7.24 10.56 9.10 \n", " 2022-08 0.05 -2.60 -0.56 -2.68 -4.67 1.17 -7.46 2.55 1.02 -13.11 \n", " \n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", " Date ... \n", " 1926-07 ... 1.31 7.76 3.13 1.29 -0.88 -20.23 1.78 1.69 -0.38 \n", " 1926-08 ... 1.08 4.20 1.20 4.00 5.05 9.42 -0.45 -0.90 8.54 \n", " 1926-09 ... 0.22 -6.28 -0.89 -6.64 -0.24 0.32 -0.87 -0.14 -3.35 \n", " 1926-10 ... -0.99 -4.10 -5.88 -4.90 -2.28 -6.21 -3.33 -2.05 -5.39 \n", " 1926-11 ... 1.48 -3.62 1.65 2.60 0.45 -0.26 1.34 1.92 0.25 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2022-04 ... -11.33 -13.22 -13.45 0.87 -10.13 -5.57 -7.00 -7.24 -7.70 \n", " 2022-05 ... 4.87 -5.25 0.67 -2.90 -3.51 1.68 -6.00 -5.91 1.80 \n", " 2022-06 ... -12.16 -8.63 -8.82 -8.96 -8.48 -7.10 -11.87 -10.01 -7.44 \n", " 2022-07 ... 1.77 8.27 15.11 6.44 13.66 8.28 8.10 13.93 6.30 \n", " 2022-08 ... 1.15 -1.06 -2.96 -0.15 -3.35 -3.15 2.13 0.54 -2.05 \n", " \n", " Other \n", " Date \n", " 1926-07 2.61 \n", " 1926-08 3.48 \n", " 1926-09 -0.18 \n", " 1926-10 -6.39 \n", " 1926-11 -0.41 \n", " ... ... \n", " 2022-04 -6.92 \n", " 2022-05 -3.70 \n", " 2022-06 -8.16 \n", " 2022-07 4.18 \n", " 2022-08 0.73 \n", " \n", " [1154 rows x 30 columns],\n", " 2: Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", " Date \n", " 1927 39.00 64.22 47.72 11.14 86.11 43.03 43.13 46.55 57.08 31.06 \n", " 1928 24.05 38.80 -2.30 46.45 54.52 59.07 7.74 47.21 56.52 -9.70 \n", " 1929 -15.00 -16.07 -7.17 -37.68 -10.15 -34.99 -28.47 -25.20 1.08 -50.66 \n", " 1930 -14.81 -53.28 -3.24 -14.84 -17.41 -42.26 -20.63 -14.62 -26.49 -38.28 \n", " 1931 -34.34 -43.07 -20.18 -74.04 -65.50 -36.51 -24.27 -15.06 -40.92 -28.56 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2017 7.55 22.40 14.42 34.53 9.40 16.07 23.86 22.20 33.85 35.51 \n", " 2018 -8.60 -10.87 -30.34 3.47 -25.01 -3.46 7.42 4.82 -15.43 -51.13 \n", " 2019 21.86 26.29 22.08 31.97 14.29 35.59 39.20 20.20 6.45 16.82 \n", " 2020 8.40 14.70 -2.05 38.76 12.10 17.62 24.04 18.31 16.74 -0.96 \n", " 2021 13.25 13.41 21.97 -1.88 31.09 20.43 15.54 13.86 25.27 28.23 \n", " \n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", " Date ... \n", " 1927 ... 25.31 30.47 43.33 53.67 22.77 -45.47 43.54 32.14 64.02 \n", " 1928 ... 20.28 19.41 41.71 32.66 15.70 24.16 55.26 28.12 32.70 \n", " 1929 ... 20.70 29.49 0.34 3.20 1.19 -49.56 -41.34 -4.90 -26.50 \n", " 1930 ... -9.81 -30.72 -26.49 -15.50 -29.25 -47.91 -35.10 -29.47 -47.16 \n", " 1931 ... -30.77 -65.43 -42.94 -38.20 -60.81 -60.86 -25.20 -46.16 -53.30 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2017 ... 3.27 35.23 37.10 23.30 21.39 9.46 25.41 27.78 23.98 \n", " 2018 ... -3.52 2.28 -3.09 -16.34 -10.69 -11.10 6.83 2.56 -9.51 \n", " 2019 ... 29.79 37.66 51.85 14.19 14.92 32.99 25.40 28.17 33.92 \n", " 2020 ... 12.33 44.30 47.98 8.30 23.06 14.05 42.45 12.84 2.17 \n", " 2021 ... -7.32 25.48 38.23 9.27 18.31 32.51 16.61 21.64 30.42 \n", " \n", " Other \n", " Date \n", " 1927 24.64 \n", " 1928 -8.47 \n", " 1929 -62.25 \n", " 1930 -33.41 \n", " 1931 -11.18 \n", " ... ... \n", " 2017 7.03 \n", " 2018 -6.26 \n", " 2019 19.11 \n", " 2020 5.56 \n", " 2021 21.91 \n", " \n", " [95 rows x 30 columns],\n", " 3: Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", " Date \n", " 1927 27.37 93.09 36.84 -6.46 48.90 25.37 31.61 56.54 41.61 -0.06 \n", " 1928 29.43 41.05 26.69 50.61 38.54 34.45 5.19 32.58 42.72 28.42 \n", " 1929 -31.64 -28.02 -43.55 -56.73 -18.28 -39.33 -38.93 -17.51 -10.48 -51.53 \n", " 1930 -27.19 -57.29 -22.07 -9.38 -16.88 -50.74 -34.78 -17.87 -39.95 -34.66 \n", " 1931 -32.19 -23.01 -9.71 -74.70 -56.52 -45.37 -42.81 -37.21 -41.31 -10.02 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2017 7.44 28.88 32.26 21.67 -0.10 4.06 25.62 25.49 11.48 21.22 \n", " 2018 -9.66 -12.47 -11.32 0.69 -21.76 -21.46 17.65 -22.48 -24.92 -44.16 \n", " 2019 17.28 8.79 22.42 11.04 -6.86 24.95 24.24 25.96 22.37 32.01 \n", " 2020 17.99 31.93 17.95 50.25 -1.45 53.50 13.06 63.63 33.82 20.52 \n", " 2021 11.54 24.07 10.55 65.27 80.06 19.19 20.85 -10.86 39.58 37.25 \n", " \n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", " Date ... \n", " 1927 ... 25.81 28.84 54.74 38.12 35.28 1.19 29.82 32.93 38.77 \n", " 1928 ... 21.54 44.09 34.52 4.84 15.43 68.35 54.53 27.09 49.41 \n", " 1929 ... 4.26 -6.73 0.07 -30.11 -17.58 -41.24 -47.59 -1.43 -15.12 \n", " 1930 ... -7.38 -30.84 -37.75 -33.62 -42.86 -61.38 -40.39 -19.35 -45.01 \n", " 1931 ... -36.71 -26.23 -54.06 -35.73 -56.51 -50.08 -41.31 -41.83 -50.72 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2017 ... 14.65 26.85 23.07 15.00 9.08 7.85 -3.34 3.81 13.50 \n", " 2018 ... -15.31 -2.29 -10.84 -26.82 -21.13 -16.72 -14.89 -5.14 -11.35 \n", " 2019 ... 6.32 24.23 37.98 16.01 10.27 19.63 14.12 15.09 26.03 \n", " 2020 ... 13.41 60.44 64.30 13.55 19.32 22.01 58.47 22.46 2.41 \n", " 2021 ... 26.31 18.94 24.53 18.34 30.44 49.20 53.71 30.81 36.60 \n", " \n", " Other \n", " Date \n", " 1927 22.49 \n", " 1928 12.18 \n", " 1929 -46.91 \n", " 1930 -20.09 \n", " 1931 -20.45 \n", " ... ... \n", " 2017 -0.06 \n", " 2018 -7.17 \n", " 2019 14.33 \n", " 2020 67.47 \n", " 2021 11.08 \n", " \n", " [95 rows x 30 columns],\n", " 4: Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", " Date \n", " 1926-07 43 3 16 7 2 8 12 7 17 14 \n", " 1926-08 43 3 16 7 2 8 12 7 17 14 \n", " 1926-09 43 3 16 7 2 8 12 7 17 14 \n", " 1926-10 43 3 16 7 2 8 12 7 17 14 \n", " 1926-11 43 3 16 7 2 8 12 7 17 14 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2022-04 61 9 3 70 13 39 31 804 61 7 \n", " 2022-05 61 9 3 69 13 39 31 800 61 7 \n", " 2022-06 61 9 3 69 13 39 31 797 61 7 \n", " 2022-07 69 13 3 74 13 46 32 868 63 7 \n", " 2022-08 69 13 3 73 13 45 32 862 63 7 \n", " \n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", " Date ... \n", " 1926-07 ... 5 3 7 6 75 2 33 6 13 \n", " 1926-08 ... 5 3 7 6 75 2 33 6 13 \n", " 1926-09 ... 5 3 7 6 75 2 33 6 13 \n", " 1926-10 ... 5 3 7 6 75 2 33 6 13 \n", " 1926-11 ... 5 3 7 6 75 2 33 6 13 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2022-04 ... 51 499 255 31 62 90 140 52 604 \n", " 2022-05 ... 51 496 255 31 62 89 140 51 601 \n", " 2022-06 ... 51 494 253 31 62 89 140 51 599 \n", " 2022-07 ... 54 575 259 31 64 90 151 56 628 \n", " 2022-08 ... 54 574 257 31 64 90 151 56 627 \n", " \n", " Other \n", " Date \n", " 1926-07 4 \n", " 1926-08 4 \n", " 1926-09 4 \n", " 1926-10 4 \n", " 1926-11 4 \n", " ... ... \n", " 2022-04 279 \n", " 2022-05 275 \n", " 2022-06 272 \n", " 2022-07 494 \n", " 2022-08 490 \n", " \n", " [1154 rows x 30 columns],\n", " 5: Food Beer Smoke Games Books Hshld Clths \\\n", " Date \n", " 1926-07 35.98 7.12 59.72 26.41 12.02 22.27 18.36 \n", " 1926-08 36.10 6.75 60.47 27.17 13.33 22.13 19.83 \n", " 1926-09 37.00 8.58 64.03 27.30 14.67 21.18 19.29 \n", " 1926-10 37.14 8.92 64.42 28.76 14.42 21.23 19.03 \n", " 1926-11 35.88 8.62 65.08 27.38 15.79 20.14 19.03 \n", " ... ... ... ... ... ... ... ... \n", " 2022-04 15359.62 36316.42 81350.95 6615.40 2383.69 17791.92 12155.46 \n", " 2022-05 15808.94 37417.56 86536.79 5013.98 2123.98 18082.09 11303.89 \n", " 2022-06 15518.16 36800.36 88850.78 4865.69 1964.76 17144.72 10569.45 \n", " 2022-07 13454.01 25256.54 76572.31 4137.88 1710.10 13611.48 7796.50 \n", " 2022-08 13936.96 26642.68 77001.67 4806.38 1914.26 13954.85 8719.06 \n", " \n", " Hlth Chems Txtls ... Telcm Servs BusEq \\\n", " Date ... \n", " 1926-07 25.52 57.59 5.78 ... 350.36 13.60 56.70 \n", " 1926-08 25.80 62.13 5.79 ... 353.27 14.75 57.74 \n", " 1926-09 26.73 65.53 6.25 ... 360.96 15.05 59.61 \n", " 1926-10 26.87 68.47 6.36 ... 364.16 15.30 59.52 \n", " 1926-11 26.54 65.06 6.38 ... 363.74 14.89 58.74 \n", " ... ... ... ... ... ... ... ... \n", " 2022-04 5127.73 8432.96 1828.15 ... 25917.35 18887.90 28409.98 \n", " 2022-05 4795.80 8237.99 1949.09 ... 23017.21 16561.04 24917.22 \n", " 2022-06 4849.79 8587.11 1995.53 ... 24972.48 16025.64 24779.59 \n", " 2022-07 4520.24 7017.59 1686.63 ... 22412.89 12922.50 21290.22 \n", " 2022-08 4664.16 7549.60 1802.26 ... 22203.94 14045.39 24796.67 \n", " \n", " Paper Trans Whlsl Rtail Meals Fin Other \n", " Date \n", " 1926-07 35.35 66.46 1.19 46.65 10.82 18.03 24.25 \n", " 1926-08 37.86 67.52 0.90 46.57 11.00 18.01 25.51 \n", " 1926-09 36.82 70.51 0.95 46.11 10.94 18.77 27.21 \n", " 1926-10 34.77 70.34 0.88 46.15 10.80 18.41 26.16 \n", " 1926-11 32.80 68.29 0.74 45.03 10.33 17.38 23.94 \n", " ... ... ... ... ... ... ... ... \n", " 2022-04 9213.01 15562.44 5276.26 27854.80 13580.72 10488.67 7472.11 \n", " 2022-05 9144.48 13861.43 5213.56 24661.01 13083.30 9673.57 6993.85 \n", " 2022-06 9037.38 13177.43 5259.87 23247.55 12632.45 9959.78 6976.29 \n", " 2022-07 8174.88 11736.95 4801.89 18485.56 10590.24 8533.15 3409.71 \n", " 2022-08 8764.38 12832.30 5233.81 21493.83 11846.62 9163.38 3751.40 \n", " \n", " [1154 rows x 30 columns],\n", " 6: Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", " Date \n", " 1926 0.64 2.54 0.79 0.91 1.25 0.38 0.90 0.68 0.72 2.14 \n", " 1927 0.54 1.81 0.69 1.08 0.80 0.36 0.80 0.51 0.51 1.13 \n", " 1928 0.43 0.62 0.69 0.85 0.34 0.35 1.14 0.22 0.39 1.15 \n", " 1929 0.39 0.52 0.67 0.56 0.35 0.27 1.28 0.26 0.23 1.11 \n", " 1930 0.49 1.70 1.30 0.87 0.66 0.33 0.86 0.42 0.41 2.19 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2018 0.33 0.14 0.14 0.14 0.59 0.24 0.16 0.19 0.42 0.43 \n", " 2019 0.31 0.14 0.17 0.15 0.72 0.18 0.16 0.19 0.48 0.61 \n", " 2020 0.36 0.17 0.09 0.15 0.80 0.14 0.16 0.19 0.42 0.98 \n", " 2021 0.30 0.13 0.03 0.10 0.39 0.12 0.10 0.16 0.29 0.57 \n", " 2022 0.30 0.15 0.26 0.29 0.69 0.14 0.20 0.22 0.38 0.87 \n", " \n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", " Date ... \n", " 1926 ... 0.77 0.92 0.53 0.97 1.41 2.35 0.44 0.66 0.77 \n", " 1927 ... 0.73 0.71 0.42 1.00 1.30 5.25 0.43 0.59 0.72 \n", " 1928 ... 0.64 0.68 0.35 0.65 1.26 0.65 0.34 0.55 0.51 \n", " 1929 ... 0.55 0.54 0.24 0.43 1.09 0.39 0.29 0.41 0.52 \n", " 1930 ... 0.54 2.66 0.36 0.63 1.39 1.36 0.59 0.74 1.15 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2018 ... 0.43 0.16 0.22 0.21 0.28 0.32 0.16 0.14 0.55 \n", " 2019 ... 0.44 0.15 0.19 0.27 0.28 0.33 0.16 0.10 0.56 \n", " 2020 ... 0.50 0.15 0.15 0.31 0.31 0.33 0.14 0.24 0.70 \n", " 2021 ... 0.39 0.11 0.11 0.20 0.16 0.22 0.12 0.11 0.49 \n", " 2022 ... 0.55 0.17 0.15 0.28 0.24 0.27 0.16 0.17 0.59 \n", " \n", " Other \n", " Date \n", " 1926 0.63 \n", " 1927 0.69 \n", " 1928 0.60 \n", " 1929 0.91 \n", " 1930 1.29 \n", " ... ... \n", " 2018 0.39 \n", " 2019 0.31 \n", " 2020 0.44 \n", " 2021 0.32 \n", " 2022 0.40 \n", " \n", " [97 rows x 30 columns],\n", " 7: Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", " Date \n", " 1926 0.71 1.98 0.84 1.13 1.52 0.34 0.75 0.73 0.73 1.46 \n", " 1927 0.57 1.92 0.70 0.96 0.91 0.42 0.91 0.57 0.59 1.00 \n", " 1928 0.46 0.73 0.62 0.92 0.47 0.33 1.23 0.32 0.42 1.19 \n", " 1929 0.38 0.67 0.62 0.55 0.35 0.25 1.12 0.25 0.32 0.94 \n", " 1930 0.48 1.00 1.28 1.06 0.60 0.31 0.77 0.39 0.39 2.16 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2018 0.30 0.12 0.11 0.15 0.59 0.22 0.19 0.20 0.40 0.34 \n", " 2019 0.34 0.16 0.16 0.18 0.78 0.23 0.19 0.21 0.44 0.75 \n", " 2020 0.33 0.14 0.07 0.12 0.63 0.13 0.11 0.19 0.35 0.71 \n", " 2021 0.33 0.14 0.04 0.12 0.54 0.12 0.11 0.17 0.34 0.76 \n", " 2022 0.31 0.15 0.19 0.17 0.49 0.11 0.12 0.19 0.32 0.60 \n", " \n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", " Date ... \n", " 1926 ... 0.87 0.82 0.53 0.93 1.38 1.25 0.37 0.58 0.70 \n", " 1927 ... 0.80 0.80 0.51 1.21 1.53 2.45 0.47 0.70 0.77 \n", " 1928 ... 0.71 0.65 0.37 0.77 1.29 0.63 0.37 0.57 0.56 \n", " 1929 ... 0.67 0.65 0.28 0.51 1.17 0.37 0.26 0.49 0.58 \n", " 1930 ... 0.60 1.65 0.33 0.63 1.24 1.07 0.51 0.72 1.22 \n", " ... ... ... ... ... ... ... ... ... ... ... \n", " 2018 ... 0.39 0.17 0.23 0.20 0.28 0.31 0.16 0.14 0.53 \n", " 2019 ... 0.52 0.19 0.24 0.29 0.32 0.37 0.16 0.13 0.65 \n", " 2020 ... 0.42 0.16 0.15 0.24 0.25 0.29 0.14 0.17 0.50 \n", " 2021 ... 0.41 0.13 0.12 0.23 0.18 0.27 0.14 0.13 0.62 \n", " 2022 ... 0.50 0.12 0.11 0.23 0.19 0.23 0.11 0.13 0.48 \n", " \n", " Other \n", " Date \n", " 1926 0.66 \n", " 1927 0.65 \n", " 1928 0.60 \n", " 1929 0.68 \n", " 1930 1.41 \n", " ... ... \n", " 2018 0.36 \n", " 2019 0.34 \n", " 2020 0.35 \n", " 2021 0.38 \n", " 2022 0.36 \n", " \n", " [97 rows x 30 columns],\n", " 'DESCR': '30 Industry Portfolios\\n----------------------\\n\\nThis file was created by CMPT_IND_RETS using the 202208 CRSP database. It contains value- and equal-weighted returns for 30 industry portfolios. The portfolios are constructed at the end of June. The annual returns are from January to December. Missing data are indicated by -99.99 or -999. Copyright 2022 Kenneth R. French\\n\\n 0 : Average Value Weighted Returns -- Monthly (1154 rows x 30 cols)\\n 1 : Average Equal Weighted Returns -- Monthly (1154 rows x 30 cols)\\n 2 : Average Value Weighted Returns -- Annual (95 rows x 30 cols)\\n 3 : Average Equal Weighted Returns -- Annual (95 rows x 30 cols)\\n 4 : Number of Firms in Portfolios (1154 rows x 30 cols)\\n 5 : Average Firm Size (1154 rows x 30 cols)\\n 6 : Sum of BE / Sum of ME (97 rows x 30 cols)\\n 7 : Value-Weighted Average of BE/ME (97 rows x 30 cols)'}" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "industry = web.DataReader('30_Industry_Portfolios', 'famafrench', start = start_date, end = end_date)\n", "industry" ] }, { "cell_type": "code", "execution_count": 44, "id": "9272ce64", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FoodBeerSmokeGamesBooksHshldClthsHlthChemsTxtls...TelcmServsBusEqPaperTransWhlslRtailMealsFinOther
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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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1926-10-3.24-0.090.700.32-3.06-3.311.06-4.769.47-4.68...-0.11-2.00-1.09-5.08-2.61-15.38-2.20-4.11-5.51-8.49
1926-112.53-0.10-0.510.316.357.294.551.66-5.80-0.54...1.633.773.643.841.614.676.524.332.344.00
..................................................................
2022-04-9.46-1.416.190.013.013.036.37-25.22-10.762.04...-10.70-12.59-12.26-0.74-10.93-2.14-11.41-5.47-7.99-7.65
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2022-079.572.81-4.100.083.675.490.5614.6212.100.76...-0.408.6015.687.229.339.0816.3311.897.389.19
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ClthsNaN
HlthNaN
ChemsNaN
TxtlsNaN
CnstrNaN
SteelNaN
FabPrNaN
ElcEqNaN
AutosNaN
CarryNaN
MinesNaN
CoalNaN
OilNaN
UtilNaN
TelcmNaN
ServsNaN
BusEqNaN
PaperNaN
TransNaN
WhlslNaN
RtailNaN
MealsNaN
FinNaN
OtherNaN
\n", "
" ], "text/plain": [ " BETAs\n", "Food NaN\n", "Beer NaN\n", "Smoke NaN\n", "Games NaN\n", "Books NaN\n", "Hshld NaN\n", "Clths NaN\n", "Hlth NaN\n", "Chems NaN\n", "Txtls NaN\n", "Cnstr NaN\n", "Steel NaN\n", "FabPr NaN\n", "ElcEq NaN\n", "Autos NaN\n", "Carry NaN\n", "Mines NaN\n", "Coal NaN\n", "Oil NaN\n", "Util NaN\n", "Telcm NaN\n", "Servs NaN\n", "BusEq NaN\n", "Paper NaN\n", "Trans NaN\n", "Whlsl NaN\n", "Rtail NaN\n", "Meals NaN\n", "Fin NaN\n", "Other NaN" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta = pd.DataFrame(np.nan,index=ind_name,columns=[\"BETAs\"])\n", "beta" ] }, { "cell_type": "code", "execution_count": 72, "id": "71df923d", "metadata": {}, "outputs": [], "source": [ "for i in range(len(ind_name)):\n", " model = linear_model.LinearRegression()\n", " model = model.fit(df[['Mkt-RF']], pd.DataFrame(df[ind_name[i]]-df['RF'])) #First input must be feature\n", " #step3: save data in the dataframe\n", " beta.iloc[i:] = model.coef_" ] }, { "cell_type": "code", "execution_count": 73, "id": "b50238e9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BETAs
Food0.729095
Beer0.924026
Smoke0.623424
Games1.385464
Books1.110920
Hshld0.884306
Clths0.831563
Hlth0.833813
Chems1.043679
Txtls1.145560
Cnstr1.181354
Steel1.361442
FabPr1.239619
ElcEq1.288733
Autos1.286918
Carry1.188716
Mines0.913507
Coal1.278337
Oil0.889536
Util0.763865
Telcm0.664651
Servs0.823919
BusEq1.080205
Paper0.948937
Trans1.138238
Whlsl1.086695
Rtail0.967677
Meals0.946614
Fin1.158976
Other1.055502
\n", "
" ], "text/plain": [ " BETAs\n", "Food 0.729095\n", "Beer 0.924026\n", "Smoke 0.623424\n", "Games 1.385464\n", "Books 1.110920\n", "Hshld 0.884306\n", "Clths 0.831563\n", "Hlth 0.833813\n", "Chems 1.043679\n", "Txtls 1.145560\n", "Cnstr 1.181354\n", "Steel 1.361442\n", "FabPr 1.239619\n", "ElcEq 1.288733\n", "Autos 1.286918\n", "Carry 1.188716\n", "Mines 0.913507\n", "Coal 1.278337\n", "Oil 0.889536\n", "Util 0.763865\n", "Telcm 0.664651\n", "Servs 0.823919\n", "BusEq 1.080205\n", "Paper 0.948937\n", "Trans 1.138238\n", "Whlsl 1.086695\n", "Rtail 0.967677\n", "Meals 0.946614\n", "Fin 1.158976\n", "Other 1.055502" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta" ] }, { "cell_type": "markdown", "id": "48aadbd0", "metadata": {}, "source": [ "1.2" ] }, { "cell_type": "code", "execution_count": 95, "id": "cb5b75a3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Average Excess Return
Food0.699220
Beer0.929593
Smoke0.866482
Games0.835659
Books0.631750
Hshld0.655269
Clths0.664896
Hlth0.807920
Chems0.777340
Txtls0.675537
Cnstr0.694168
Steel0.677374
FabPr0.800849
ElcEq0.896854
Autos0.923614
Carry0.850295
Mines0.640659
Coal0.828094
Oil0.773960
Util0.618354
Telcm0.566352
Servs0.953033
BusEq0.903787
Paper0.721256
Trans0.655191
Whlsl0.575069
Rtail0.777964
Meals0.800208
Fin0.742305
Other0.522088
\n", "
" ], "text/plain": [ " Average Excess Return\n", "Food 0.699220\n", "Beer 0.929593\n", "Smoke 0.866482\n", "Games 0.835659\n", "Books 0.631750\n", "Hshld 0.655269\n", "Clths 0.664896\n", "Hlth 0.807920\n", "Chems 0.777340\n", "Txtls 0.675537\n", "Cnstr 0.694168\n", "Steel 0.677374\n", "FabPr 0.800849\n", "ElcEq 0.896854\n", "Autos 0.923614\n", "Carry 0.850295\n", "Mines 0.640659\n", "Coal 0.828094\n", "Oil 0.773960\n", "Util 0.618354\n", "Telcm 0.566352\n", "Servs 0.953033\n", "BusEq 0.903787\n", "Paper 0.721256\n", "Trans 0.655191\n", "Whlsl 0.575069\n", "Rtail 0.777964\n", "Meals 0.800208\n", "Fin 0.742305\n", "Other 0.522088" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "excess_return = pd.DataFrame(data = np.nan, index = ind_name, columns = ['Average Excess Return'])\n", "for i in range(len(ind_name)):\n", " excess_return.iloc[i] = np.sum((df[ind_name[i]]-df['RF'])/df.shape[0])\n", " \n", "excess_return" ] }, { "cell_type": "code", "execution_count": 96, "id": "b722b9cf", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(beta, excess_return)\n", "plt.xlabel('Beta')\n", "plt.ylabel('Excess Return')\n", "plt.title('1.2 Plot')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f0833ea2", "metadata": {}, "source": [ "Yes, we can observe SML" ] }, { "cell_type": "markdown", "id": "ba2c13ce", "metadata": {}, "source": [ "1.3" ] }, { "cell_type": "code", "execution_count": 85, "id": "2792842f", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import KFold" ] }, { "cell_type": "code", "execution_count": 86, "id": "a8683a7d", "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "KFold(n_splits=10, random_state=None, shuffle=False)" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kf = KFold(n_splits=10,shuffle=False)\n", "kf" ] }, { "cell_type": "code", "execution_count": 103, "id": "c13cc144", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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OtherNaNNaN
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" ], "text/plain": [ " RMSE MAE\n", "Food NaN NaN\n", "Beer NaN NaN\n", "Smoke NaN NaN\n", "Games NaN NaN\n", "Books NaN NaN\n", "Hshld NaN NaN\n", "Clths NaN NaN\n", "Hlth NaN NaN\n", "Chems NaN NaN\n", "Txtls NaN NaN\n", "Cnstr NaN NaN\n", "Steel NaN NaN\n", "FabPr NaN NaN\n", "ElcEq NaN NaN\n", "Autos NaN NaN\n", "Carry NaN NaN\n", "Mines NaN NaN\n", "Coal NaN NaN\n", "Oil NaN NaN\n", "Util NaN NaN\n", "Telcm NaN NaN\n", "Servs NaN NaN\n", "BusEq NaN NaN\n", "Paper NaN NaN\n", "Trans NaN NaN\n", "Whlsl NaN NaN\n", "Rtail NaN NaN\n", "Meals NaN NaN\n", "Fin NaN NaN\n", "Other NaN NaN" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model1_metric = pd.DataFrame(np.nan,index=ind_name,columns=[\"RMSE\", 'MAE'])\n", "model1_metric" ] }, { "cell_type": "code", "execution_count": 118, "id": "c8fdeb97", "metadata": {}, "outputs": [], "source": [ "for i in range(len(ind_name)):\n", " err_rmse_test = 0\n", " err_mae_test = 0\n", " mkt = df['Mkt-RF'].to_numpy()\n", " ri_rf = (df[ind_name[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", " model1_metric.iloc[i][0] = rmse_10cv_test\n", " model1_metric.iloc[i][1] = mae_10cv_test" ] }, { "cell_type": "code", "execution_count": 119, "id": "0b3b24fe", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RMSEMAE
Food2.5381091.898086
Beer4.6365143.384528
Smoke4.5265413.521301
Games4.8741973.623334
Books3.8618752.956903
Hshld3.2134062.409892
Clths4.2350363.093844
Hlth3.2297282.437323
Chems2.9053012.200431
Txtls4.6536653.403551
Cnstr2.8296982.153414
Steel4.4171773.314934
FabPr2.8171302.186627
ElcEq3.2744922.547459
Autos4.5721823.280924
Carry4.2242563.154163
Mines5.2550094.125605
Coal8.1143245.838560
Oil4.1392663.138144
Util3.5190192.705937
Telcm2.9016312.226987
Servs5.6320453.950464
BusEq3.4218392.596730
Paper2.8396792.148962
Trans3.5156332.602099
Whlsl3.7471442.795816
Rtail2.9542272.257817
Meals3.9975523.018501
Fin2.6914082.016012
Other3.4833932.651443
\n", "
" ], "text/plain": [ " RMSE MAE\n", "Food 2.538109 1.898086\n", "Beer 4.636514 3.384528\n", "Smoke 4.526541 3.521301\n", "Games 4.874197 3.623334\n", "Books 3.861875 2.956903\n", "Hshld 3.213406 2.409892\n", "Clths 4.235036 3.093844\n", "Hlth 3.229728 2.437323\n", "Chems 2.905301 2.200431\n", "Txtls 4.653665 3.403551\n", "Cnstr 2.829698 2.153414\n", "Steel 4.417177 3.314934\n", "FabPr 2.817130 2.186627\n", "ElcEq 3.274492 2.547459\n", "Autos 4.572182 3.280924\n", "Carry 4.224256 3.154163\n", "Mines 5.255009 4.125605\n", "Coal 8.114324 5.838560\n", "Oil 4.139266 3.138144\n", "Util 3.519019 2.705937\n", "Telcm 2.901631 2.226987\n", "Servs 5.632045 3.950464\n", "BusEq 3.421839 2.596730\n", "Paper 2.839679 2.148962\n", "Trans 3.515633 2.602099\n", "Whlsl 3.747144 2.795816\n", "Rtail 2.954227 2.257817\n", "Meals 3.997552 3.018501\n", "Fin 2.691408 2.016012\n", "Other 3.483393 2.651443" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model1_metric" ] }, { "cell_type": "markdown", "id": "7e48c3e7", "metadata": {}, "source": [ "1.4" ] }, { "cell_type": "code", "execution_count": 89, "id": "3aeaedd9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BETAsSMBHML
FoodNaNNaNNaN
BeerNaNNaNNaN
SmokeNaNNaNNaN
GamesNaNNaNNaN
BooksNaNNaNNaN
HshldNaNNaNNaN
ClthsNaNNaNNaN
HlthNaNNaNNaN
ChemsNaNNaNNaN
TxtlsNaNNaNNaN
CnstrNaNNaNNaN
SteelNaNNaNNaN
FabPrNaNNaNNaN
ElcEqNaNNaNNaN
AutosNaNNaNNaN
CarryNaNNaNNaN
MinesNaNNaNNaN
CoalNaNNaNNaN
OilNaNNaNNaN
UtilNaNNaNNaN
TelcmNaNNaNNaN
ServsNaNNaNNaN
BusEqNaNNaNNaN
PaperNaNNaNNaN
TransNaNNaNNaN
WhlslNaNNaNNaN
RtailNaNNaNNaN
MealsNaNNaNNaN
FinNaNNaNNaN
OtherNaNNaNNaN
\n", "
" ], "text/plain": [ " BETAs SMB HML\n", "Food NaN NaN NaN\n", "Beer NaN NaN NaN\n", "Smoke NaN NaN NaN\n", "Games NaN NaN NaN\n", "Books NaN NaN NaN\n", "Hshld NaN NaN NaN\n", "Clths NaN NaN NaN\n", "Hlth NaN NaN NaN\n", "Chems NaN NaN NaN\n", "Txtls NaN NaN NaN\n", "Cnstr NaN NaN NaN\n", "Steel NaN NaN NaN\n", "FabPr NaN NaN NaN\n", "ElcEq NaN NaN NaN\n", "Autos NaN NaN NaN\n", "Carry NaN NaN NaN\n", "Mines NaN NaN NaN\n", "Coal NaN NaN NaN\n", "Oil NaN NaN NaN\n", "Util NaN NaN NaN\n", "Telcm NaN NaN NaN\n", "Servs NaN NaN NaN\n", "BusEq NaN NaN NaN\n", "Paper NaN NaN NaN\n", "Trans NaN NaN NaN\n", "Whlsl NaN NaN NaN\n", "Rtail NaN NaN NaN\n", "Meals NaN NaN NaN\n", "Fin NaN NaN NaN\n", "Other NaN NaN NaN" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta2 = pd.DataFrame(np.nan,index=ind_name,columns=[\"BETAs\", 'SMB', 'HML'])\n", "beta2" ] }, { "cell_type": "code", "execution_count": 93, "id": "9a11c821", "metadata": {}, "outputs": [], "source": [ "for i in range(len(ind_name)):\n", " model = linear_model.LinearRegression()\n", " model = model.fit(df[['Mkt-RF', 'SMB', 'HML']], pd.DataFrame(df[ind_name[i]]-df['RF'])) #First input must be feature\n", " #step3: save data in the dataframe\n", " beta2.iloc[i:] = model.coef_" ] }, { "cell_type": "code", "execution_count": 94, "id": "edcc29d6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BETAsSMBHML
Food0.747453-0.1403840.052259
Beer0.8667320.1964420.133388
Smoke0.649355-0.2168730.096605
Games1.2875350.4089230.138250
Books1.0124980.3724910.186158
Hshld0.906274-0.088170-0.035378
Clths0.7615060.422109-0.060045
Hlth0.878894-0.085659-0.189481
Chems1.068119-0.1515140.026175
Txtls0.9827310.5632680.372973
Cnstr1.1183410.2477420.107824
Steel1.2596590.2320490.380399
FabPr1.1733770.2615500.111985
ElcEq1.295485-0.035460-0.000616
Autos1.2477080.0550860.188627
Carry1.0975550.2181240.328079
Mines0.8426470.2587310.145621
Coal1.0841630.4853780.673339
Oil0.878785-0.1792070.290080
Util0.755451-0.1669030.259715
Telcm0.694991-0.131654-0.036739
Servs0.8321290.368626-0.505840
BusEq1.1211670.153322-0.455735
Paper0.952825-0.0576840.045355
Trans1.0380010.1634730.454423
Whlsl0.9729690.5501520.068217
Rtail0.9799820.041639-0.131479
Meals0.8969390.289515-0.030568
Fin1.121536-0.0575390.315218
Other0.9995470.2830970.018335
\n", "
" ], "text/plain": [ " BETAs SMB HML\n", "Food 0.747453 -0.140384 0.052259\n", "Beer 0.866732 0.196442 0.133388\n", "Smoke 0.649355 -0.216873 0.096605\n", "Games 1.287535 0.408923 0.138250\n", "Books 1.012498 0.372491 0.186158\n", "Hshld 0.906274 -0.088170 -0.035378\n", "Clths 0.761506 0.422109 -0.060045\n", "Hlth 0.878894 -0.085659 -0.189481\n", "Chems 1.068119 -0.151514 0.026175\n", "Txtls 0.982731 0.563268 0.372973\n", "Cnstr 1.118341 0.247742 0.107824\n", "Steel 1.259659 0.232049 0.380399\n", "FabPr 1.173377 0.261550 0.111985\n", "ElcEq 1.295485 -0.035460 -0.000616\n", "Autos 1.247708 0.055086 0.188627\n", "Carry 1.097555 0.218124 0.328079\n", "Mines 0.842647 0.258731 0.145621\n", "Coal 1.084163 0.485378 0.673339\n", "Oil 0.878785 -0.179207 0.290080\n", "Util 0.755451 -0.166903 0.259715\n", "Telcm 0.694991 -0.131654 -0.036739\n", "Servs 0.832129 0.368626 -0.505840\n", "BusEq 1.121167 0.153322 -0.455735\n", "Paper 0.952825 -0.057684 0.045355\n", "Trans 1.038001 0.163473 0.454423\n", "Whlsl 0.972969 0.550152 0.068217\n", "Rtail 0.979982 0.041639 -0.131479\n", "Meals 0.896939 0.289515 -0.030568\n", "Fin 1.121536 -0.057539 0.315218\n", "Other 0.999547 0.283097 0.018335" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta2" ] }, { "cell_type": "markdown", "id": "a95d67dd", "metadata": {}, "source": [ "1.5" ] }, { "cell_type": "code", "execution_count": 98, "id": "b7ab50d0", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(beta2['BETAs'], excess_return)\n", "plt.xlabel('Beta')\n", "plt.ylabel('Excess Return')\n", "plt.title('Beta1 Plot')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 99, "id": "11bcc39f", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(beta2['SMB'], excess_return)\n", "plt.xlabel('SMB')\n", "plt.ylabel('Excess Return')\n", "plt.title('Beta2 Plot')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 100, "id": "e3ef87cc", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ], "text/plain": [ " RMSE MAE\n", "Food NaN NaN\n", "Beer NaN NaN\n", "Smoke NaN NaN\n", "Games NaN NaN\n", "Books NaN NaN\n", "Hshld NaN NaN\n", "Clths NaN NaN\n", "Hlth NaN NaN\n", "Chems NaN NaN\n", "Txtls NaN NaN\n", "Cnstr NaN NaN\n", "Steel NaN NaN\n", "FabPr NaN NaN\n", "ElcEq NaN NaN\n", "Autos NaN NaN\n", "Carry NaN NaN\n", "Mines NaN NaN\n", "Coal NaN NaN\n", "Oil NaN NaN\n", "Util NaN NaN\n", "Telcm NaN NaN\n", "Servs NaN NaN\n", "BusEq NaN NaN\n", "Paper NaN NaN\n", "Trans NaN NaN\n", "Whlsl NaN NaN\n", "Rtail NaN NaN\n", "Meals NaN NaN\n", "Fin NaN NaN\n", "Other NaN NaN" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model2_metric = pd.DataFrame(np.nan,index=ind_name,columns=[\"RMSE\", 'MAE'])\n", "model2_metric" ] }, { "cell_type": "code", "execution_count": 122, "id": "65d2b348", "metadata": {}, "outputs": [], "source": [ "for i in range(len(ind_name)):\n", " err_rmse_test = 0\n", " err_mae_test = 0\n", " threef = df[['Mkt-RF','SMB','HML']].to_numpy()\n", " ri_rf = (df[ind_name[i]])-df['RF'].to_numpy()\n", " for train, test in kf.split(mkt):\n", " lr = linear_model.LinearRegression()\n", " reg = lr.fit(threef[train], ri_rf[train])\n", " ri_rf_pred_test = reg.predict(threef[test])\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", " model2_metric.iloc[i][0] = rmse_10cv_test\n", " model2_metric.iloc[i][1] = mae_10cv_test" ] }, { "cell_type": "code", "execution_count": 123, "id": "2126c285", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RMSEMAE
Food2.5839921.950460
Beer4.7273823.441337
Smoke4.5346373.511960
Games4.8222353.520520
Books3.7259512.855786
Hshld3.2353312.434792
Clths4.1971402.975686
Hlth3.1977432.386017
Chems2.9439882.243567
Txtls4.2551043.106636
Cnstr2.8077132.112981
Steel4.1921683.179183
FabPr2.6903312.090893
ElcEq3.3341032.592332
Autos4.5648393.317047
Carry4.1023793.079451
Mines5.2098534.070646
Coal7.6838885.632237
Oil4.0870163.117979
Util3.3893652.590805
Telcm2.9089262.232419
Servs5.1439933.536210
BusEq3.1471242.411187
Paper2.9245362.204255
Trans3.2014442.472775
Whlsl3.5813112.605731
Rtail2.9522582.250535
Meals4.0325273.011346
Fin2.5149291.901121
Other3.4804902.631941
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" ], "text/plain": [ " RMSE MAE\n", "Food 2.583992 1.950460\n", "Beer 4.727382 3.441337\n", "Smoke 4.534637 3.511960\n", "Games 4.822235 3.520520\n", "Books 3.725951 2.855786\n", "Hshld 3.235331 2.434792\n", "Clths 4.197140 2.975686\n", "Hlth 3.197743 2.386017\n", "Chems 2.943988 2.243567\n", "Txtls 4.255104 3.106636\n", "Cnstr 2.807713 2.112981\n", "Steel 4.192168 3.179183\n", "FabPr 2.690331 2.090893\n", "ElcEq 3.334103 2.592332\n", "Autos 4.564839 3.317047\n", "Carry 4.102379 3.079451\n", "Mines 5.209853 4.070646\n", "Coal 7.683888 5.632237\n", "Oil 4.087016 3.117979\n", "Util 3.389365 2.590805\n", "Telcm 2.908926 2.232419\n", "Servs 5.143993 3.536210\n", "BusEq 3.147124 2.411187\n", "Paper 2.924536 2.204255\n", "Trans 3.201444 2.472775\n", "Whlsl 3.581311 2.605731\n", "Rtail 2.952258 2.250535\n", "Meals 4.032527 3.011346\n", "Fin 2.514929 1.901121\n", "Other 3.480490 2.631941" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model2_metric" ] }, { "cell_type": "code", "execution_count": null, "id": "bf18ed56", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "d7133584", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }