{ "cells": [ { "cell_type": "markdown", "id": "55723145", "metadata": {}, "source": [ "# Phatcharida Amornborirak phatcharida.amo@st.econ.tu.ac.th" ] }, { "cell_type": "code", "execution_count": 1, "id": "4e542201", "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) ## For model evaluation" ] }, { "cell_type": "code", "execution_count": 2, "id": "17898986", "metadata": {}, "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", " 'Portfolios_Formed_on_D-P',\n", " 'Portfolios_Formed_on_D-P_Wout_Div',\n", " '6_Portfolios_ME_EP_2x3',\n", " '6_Portfolios_ME_EP_2x3_Wout_Div',\n", " '6_Portfolios_ME_CFP_2x3',\n", " '6_Portfolios_ME_CFP_2x3_Wout_Div',\n", " '6_Portfolios_ME_DP_2x3',\n", " '6_Portfolios_ME_DP_2x3_Wout_Div',\n", " 'F-F_Momentum_Factor',\n", " 'F-F_Momentum_Factor_daily',\n", " '6_Portfolios_ME_Prior_12_2',\n", " '6_Portfolios_ME_Prior_12_2_Daily',\n", " '25_Portfolios_ME_Prior_12_2',\n", " '25_Portfolios_ME_Prior_12_2_Daily',\n", " '10_Portfolios_Prior_12_2',\n", " '10_Portfolios_Prior_12_2_Daily',\n", " 'F-F_ST_Reversal_Factor',\n", " 'F-F_ST_Reversal_Factor_daily',\n", " '6_Portfolios_ME_Prior_1_0',\n", " '6_Portfolios_ME_Prior_1_0_Daily',\n", " '25_Portfolios_ME_Prior_1_0',\n", " '25_Portfolios_ME_Prior_1_0_Daily',\n", " '10_Portfolios_Prior_1_0',\n", " '10_Portfolios_Prior_1_0_Daily',\n", " 'F-F_LT_Reversal_Factor',\n", " 'F-F_LT_Reversal_Factor_daily',\n", " '6_Portfolios_ME_Prior_60_13',\n", " '6_Portfolios_ME_Prior_60_13_Daily',\n", " '25_Portfolios_ME_Prior_60_13',\n", " '25_Portfolios_ME_Prior_60_13_Daily',\n", " '10_Portfolios_Prior_60_13',\n", " '10_Portfolios_Prior_60_13_Daily',\n", " 'Portfolios_Formed_on_AC',\n", " '25_Portfolios_ME_AC_5x5',\n", " 'Portfolios_Formed_on_BETA',\n", " '25_Portfolios_ME_BETA_5x5',\n", " 'Portfolios_Formed_on_NI',\n", " '25_Portfolios_ME_NI_5x5',\n", " 'Portfolios_Formed_on_VAR',\n", " '25_Portfolios_ME_VAR_5x5',\n", " 'Portfolios_Formed_on_RESVAR',\n", " '25_Portfolios_ME_RESVAR_5x5',\n", " '5_Industry_Portfolios',\n", " '5_Industry_Portfolios_Wout_Div',\n", " '5_Industry_Portfolios_daily',\n", " '10_Industry_Portfolios',\n", " '10_Industry_Portfolios_Wout_Div',\n", " '10_Industry_Portfolios_daily',\n", " '12_Industry_Portfolios',\n", " '12_Industry_Portfolios_Wout_Div',\n", " '12_Industry_Portfolios_daily',\n", " '17_Industry_Portfolios',\n", " '17_Industry_Portfolios_Wout_Div',\n", " '17_Industry_Portfolios_daily',\n", " '30_Industry_Portfolios',\n", " '30_Industry_Portfolios_Wout_Div',\n", " '30_Industry_Portfolios_daily',\n", " '38_Industry_Portfolios',\n", " '38_Industry_Portfolios_Wout_Div',\n", " '38_Industry_Portfolios_daily',\n", " '48_Industry_Portfolios',\n", " '48_Industry_Portfolios_Wout_Div',\n", " '48_Industry_Portfolios_daily',\n", " '49_Industry_Portfolios',\n", " '49_Industry_Portfolios_Wout_Div',\n", " '49_Industry_Portfolios_daily',\n", " 'ME_Breakpoints',\n", " 'BE-ME_Breakpoints',\n", " 'OP_Breakpoints',\n", " 'INV_Breakpoints',\n", " 'E-P_Breakpoints',\n", " 'CF-P_Breakpoints',\n", " 'D-P_Breakpoints',\n", " 'Prior_2-12_Breakpoints',\n", " 'Developed_3_Factors',\n", " 'Developed_3_Factors_Daily',\n", " 'Developed_ex_US_3_Factors',\n", " 'Developed_ex_US_3_Factors_Daily',\n", " 'Europe_3_Factors',\n", " 'Europe_3_Factors_Daily',\n", " 'Japan_3_Factors',\n", " 'Japan_3_Factors_Daily',\n", " 'Asia_Pacific_ex_Japan_3_Factors',\n", " 'Asia_Pacific_ex_Japan_3_Factors_Daily',\n", " 'North_America_3_Factors',\n", " 'North_America_3_Factors_Daily',\n", " 'Developed_5_Factors',\n", " 'Developed_5_Factors_Daily',\n", " 'Developed_ex_US_5_Factors',\n", " 'Developed_ex_US_5_Factors_Daily',\n", " 'Europe_5_Factors',\n", " 'Europe_5_Factors_Daily',\n", " 'Japan_5_Factors',\n", " 'Japan_5_Factors_Daily',\n", " 'Asia_Pacific_ex_Japan_5_Factors',\n", " 'Asia_Pacific_ex_Japan_5_Factors_Daily',\n", " 'North_America_5_Factors',\n", " 'North_America_5_Factors_Daily',\n", " 'Developed_Mom_Factor',\n", " 'Developed_Mom_Factor_Daily',\n", " 'Developed_ex_US_Mom_Factor',\n", " 'Developed_ex_US_Mom_Factor_Daily',\n", " 'Europe_Mom_Factor',\n", " 'Europe_Mom_Factor_Daily',\n", " 'Japan_Mom_Factor',\n", " 'Japan_Mom_Factor_Daily',\n", " 'Asia_Pacific_ex_Japan_MOM_Factor',\n", " 'Asia_Pacific_ex_Japan_MOM_Factor_Daily',\n", " 'North_America_Mom_Factor',\n", " 'North_America_Mom_Factor_Daily',\n", " 'Developed_6_Portfolios_ME_BE-ME',\n", " 'Developed_6_Portfolios_ME_BE-ME_daily',\n", " 'Developed_ex_US_6_Portfolios_ME_BE-ME',\n", " 'Developed_ex_US_6_Portfolios_ME_BE-ME_daily',\n", " 'Europe_6_Portfolios_ME_BE-ME',\n", " 'Europe_6_Portfolios_ME_BE-ME_daily',\n", " 'Japan_6_Portfolios_ME_BE-ME',\n", " 'Japan_6_Portfolios_ME_BE-ME_daily',\n", " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_BE-ME',\n", " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_BE-ME_daily',\n", " 'North_America_6_Portfolios_ME_BE-ME',\n", " 'North_America_6_Portfolios_ME_BE-ME_daily',\n", " 'Developed_25_Portfolios_ME_BE-ME',\n", " 'Developed_25_Portfolios_ME_BE-ME_daily',\n", " 'Developed_ex_US_25_Portfolios_ME_BE-ME',\n", " 'Developed_ex_US_25_Portfolios_ME_BE-ME_daily',\n", " 'Europe_25_Portfolios_ME_BE-ME',\n", " 'Europe_25_Portfolios_ME_BE-ME_daily',\n", " 'Japan_25_Portfolios_ME_BE-ME',\n", " 'Japan_25_Portfolios_ME_BE-ME_daily',\n", " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_BE-ME',\n", " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_BE-ME_daily',\n", " 'North_America_25_Portfolios_ME_BE-ME',\n", " '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": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_available_datasets()" ] }, { "cell_type": "code", "execution_count": 6, "id": "a406cba8", "metadata": {}, "outputs": [], "source": [ "start_date = dt.datetime(1926, 7, 1)\n", "end_date = dt.datetime(2022, 8, 1)" ] }, { "cell_type": "code", "execution_count": 8, "id": "a0c2ad77", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "F-F Research Data Factors\n", "-------------------------\n", "\n", "This file was created by CMPT_ME_BEME_RETS using the 202208 CRSP database. 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)\n" ] } ], "source": [ "fama_factors = web.DataReader('F-F_Research_Data_Factors', 'famafrench', start = start_date, end = end_date)\n", "print(fama_factors['DESCR'])" ] }, { "cell_type": "code", "execution_count": 9, "id": "a0d897e5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Mkt-RFSMBHMLRF
Date
1926-072.96-2.56-2.430.22
1926-082.64-1.173.820.25
1926-090.36-1.400.130.23
1926-10-3.24-0.090.700.32
1926-112.53-0.10-0.510.31
...............
2022-04-9.46-1.416.190.01
2022-05-0.34-1.858.410.03
2022-06-8.432.09-5.970.06
2022-079.572.81-4.100.08
2022-08-3.781.390.310.19
\n", "

1154 rows × 4 columns

\n", "
" ], "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": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fama_factors_monthly = fama_factors[0]\n", "fama_factors_monthly" ] }, { "cell_type": "code", "execution_count": 10, "id": "42509ca0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30 Industry Portfolios\n", "----------------------\n", "\n", "This 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)\n" ] } ], "source": [ "indus_port = web.DataReader('30_Industry_Portfolios', 'famafrench', start = start_date, end = end_date)\n", "print(indus_port['DESCR'])" ] }, { "cell_type": "code", "execution_count": 11, "id": "6b020957", "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
2022-08-1.61-1.87-0.12-2.95-4.97-2.16-6.01-5.07-1.39-12.20...-3.00-4.72-5.89-7.66-1.46-1.60-3.46-1.47-2.24-3.65
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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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" ], "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": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta = pd.DataFrame(np.nan, index = name_indus, columns = ['BETAs'])\n", "beta" ] }, { "cell_type": "code", "execution_count": 28, "id": "2a58943f", "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": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import linear_model\n", "\n", "for i in range(len(name_indus)):\n", " model = linear_model.LinearRegression()\n", " model = model.fit(df[['Mkt-RF']], pd.DataFrame(df[name_indus[i]]-df['RF'])) \n", " # y = Ri - rf\n", " beta.iloc[i,:][0] = model.coef_\n", " \n", "beta" ] }, { "cell_type": "markdown", "id": "1a2191d3", "metadata": {}, "source": [ "# Q.2" ] }, { "cell_type": "code", "execution_count": 29, "id": "e0f1b613", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": [ " 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": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Exp_return = pd.DataFrame(np.nan, index = name_indus, columns = ['Return'])\n", "\n", "for i in range(len(name_indus)):\n", " Exp_return.iloc[i] = np.sum(df[name_indus[i]]-df['RF'])/df.shape[0]\n", " \n", "Exp_return" ] }, { "cell_type": "code", "execution_count": 30, "id": "377e0943", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(beta, Exp_return)\n", "plt.xlabel('Beta')\n", "plt.ylabel('Return')\n", "plt.title('Beta and Return')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6ef6b0cc", "metadata": {}, "source": [ "The SML from the graph can't be observe as we can't see the trend line in here as all the result is scattered.\n", "The beta might have bias or errors in the data as it has omitted vairiable problem." ] }, { "cell_type": "markdown", "id": "2ce4a8f3", "metadata": {}, "source": [ "# Q.3" ] }, { "cell_type": "code", "execution_count": 44, "id": "57e1cccf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RMSEMAE
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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": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val = pd.DataFrame(data = np.nan, index = name_indus, columns = ['RMSE','MAE'])\n", "cross_val" ] }, { "cell_type": "code", "execution_count": 64, "id": "2451015e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Beer4.9893643.360948
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Carry4.2729903.145406
Mines5.4278784.110851
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Oil4.2693713.128392
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BusEq3.5435632.546208
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Trans3.6168982.596783
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Meals4.0383133.003735
Fin2.7204371.987869
Other3.6918702.652401
\n", "
" ], "text/plain": [ " RMSE MAE\n", "Food 2.642453 1.881125\n", "Beer 4.989364 3.360948\n", "Smoke 4.727653 3.513285\n", "Games 4.977611 3.598645\n", "Books 4.048454 2.940202\n", "Hshld 3.291161 2.396703\n", "Clths 4.222359 3.003754\n", "Hlth 3.278073 2.430431\n", "Chems 2.984509 2.190558\n", "Txtls 4.793870 3.386041\n", "Cnstr 2.900702 2.142154\n", "Steel 4.551444 3.315325\n", "FabPr 2.892213 2.179287\n", "ElcEq 3.271249 2.525533\n", "Autos 4.699752 3.259967\n", "Carry 4.272990 3.145406\n", "Mines 5.427878 4.110851\n", "Coal 8.530871 5.837826\n", "Oil 4.269371 3.128392\n", "Util 3.642029 2.636504\n", "Telcm 2.933160 2.208827\n", "Servs 6.818082 3.794773\n", "BusEq 3.543563 2.546208\n", "Paper 2.938146 2.142701\n", "Trans 3.616898 2.596783\n", "Whlsl 4.341622 2.786228\n", "Rtail 2.998471 2.252243\n", "Meals 4.038313 3.003735\n", "Fin 2.720437 1.987869\n", "Other 3.691870 2.652401" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import KFold\n", "kf = KFold(n_splits=10,shuffle=False)\n", "\n", "for i in range(len(name_indus)):\n", " mae_err_test =0\n", " rmse_err_test =0\n", " re_mkt = df['Mkt-RF'].to_numpy()\n", " y = (df[name_indus[i]]-df['RF']).to_numpy()\n", " \n", " for train,test in kf.split(re_mkt):\n", " lr=linear_model.LinearRegression()\n", " reg=lr.fit(re_mkt[train].reshape(-1, 1),y[train])\n", " y_pred_test =reg.predict(re_mkt[test].reshape(-1, 1))\n", " e_test = y[test]-y_pred_test\n", " mae_err_test += np.mean(np.abs(e_test)) \n", " rmse_err_test += np.sqrt(np.mean(e_test*e_test))\n", " \n", " mae_test_10cv_model1 = mae_err_test/10 \n", " rmse_test_10cv_model1 = rmse_err_test/10 \n", " cross_val.iloc[i][0] = rmse_test_10cv_model1\n", " cross_val.iloc[i][1] = mae_test_10cv_model1\n", "\n", "cross_val" ] }, { "cell_type": "code", "execution_count": 67, "id": "1002493d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE (Model1:Linear): 4.0685\n", "RMSE (Model1:Linear): 2.8985\n" ] } ], "source": [ "mean_rmse = np.mean(cross_val['RMSE'])\n", "mean_mae = np.mean(cross_val['MAE'])\n", "print(f'MAE (Model1:Linear): {mean_rmse:.4f}')\n", "print(f'RMSE (Model1:Linear): {mean_mae:.4f}')" ] }, { "cell_type": "markdown", "id": "cc542504", "metadata": {}, "source": [ "# Q.4" ] }, { "cell_type": "code", "execution_count": 69, "id": "1c107306", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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MKTSMBHML
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": [ " MKT 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": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_fama = pd.DataFrame(data = np.nan, index = name_indus, columns = ['MKT', 'SMB', 'HML'])\n", "beta_fama" ] }, { "cell_type": "code", "execution_count": 77, "id": "54de2d76", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MKTSMBHML
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
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" ], "text/plain": [ " MKT 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": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import linear_model\n", "\n", "for i in range(len(name_indus)):\n", " model2 = linear_model.LinearRegression()\n", " model2 = model.fit(df[['Mkt-RF','SMB','HML']], pd.DataFrame(df[name_indus[i]]-df['RF']))\n", " # y = Ri - rf\n", " beta_fama.iloc[i] = model.coef_\n", " \n", "beta_fama" ] }, { "cell_type": "markdown", "id": "cfac1cff", "metadata": {}, "source": [ "# Q.5" ] }, { "cell_type": "code", "execution_count": 87, "id": "072e20bf", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(beta_fama['MKT'], Exp_return)\n", "plt.xlabel('Beta_fama_MKT')\n", "plt.ylabel('Return')\n", "plt.title('Beta_fama_MKT and Return')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 86, "id": "70496bff", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(beta_fama['SMB'], Exp_return)\n", "plt.xlabel('Beta_fama_SMB')\n", "plt.ylabel('Return')\n", "plt.title('Beta_fama_SMB and Return')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 85, "id": "f529c017", "metadata": {}, "outputs": [ { "data": { "image/png": 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/Nfs5StOmTUNaWpqq79LZ2Rlz5sxBXl4eduzYgSNHjsDf3x+urq6QSqW4e/cufv31V8ycOVM1R8TLywvu7u64cuUKVq9eDS8vL4jFYgwcOBDdunWDq6srwsLCcPjwYcyfPx/9+/dHbW0tzp8/D3d3d6P0j5kDJhYTe7QjXiwWw9nZGQMHDsQzzzyD4ODgBufPmTMH/fv3R3JyMi5duoTy8nLY29vDzc0NEyZMwMiRI9XOnz17NsRiMS5cuICrV69CLpcjNDQUAwcOhJubGz788EPExsbiypUrOH/+PDp16oTXXnsNQUFBBvuA7devH5YsWYL4+Hj8+OOPEIvF6NmzJ5YvX478/HyDJ5Zu3bphxYoV+Prrr3H27FnI5XJ07doV0dHRcHBwMLvE0lSN1SwCAwMbTSzGeo8iIiLg6uqKhIQEpKWloW3btggJCUFkZCRWr14tyDOU3NzcMH78eOzduxfx8fGYPXs27OzssGLFChw+fBjp6ek4efIkampq4OLigg4dOuDFF1/EsGHDVPcQi8VYtGgRtm7dirNnz+LYsWNQKBRwc3NT9RPNmzcPLi4uSE9PR3JyMlxdXTFixAhMmzbNrOaaGJKoqqpKYeogiIjIcrCPhYiIBMXEQkREgmIfC+nkxIkTDUYIaWJvb69aDsYSpKam6jRJz93d3Wgz4onMHRML6eTkyZM6TRx0d3e3qMTyww8/6DRvIyAggImF6P+1+s575bdRS98yl4jIWFhj+X+PL75ojsrKyuDo6GjqMATD8pgvSyoLwPIYinI7h8ex856IiATFxEJERIJiYiEiIkExsRARkaCYWIiISFAcFdZE+07nYk1SFvKk1ejoaouoSb0wcaCnqcMiIjI5JpYm2Hc6F4vjLqNa9nC/iFxpNRbHPZxEx+RCRK0dm8KaYE1SliqpKFXL5FiTlGWiiIiIzAcTSxPkSTVPptR2nIioNWFiaYKOrppnm2o7TkTUmjCxNEHUpF6wtVb/r7O1FiNqUi8TRUREZD7Yed8Eyg56jgojImqIiaWJJg70ZCIhItKATWFERCQoJhYiIhIUEwsREQmKfSxEFoLLDJG5YGIhsgBcZojMCZvCiCwAlxkic8IaC5EFaM4yQ5qa0Eb2Mf1+6tRyscZCZAGausyQsgktV1oNBf5oQquurTdAlNRaMLEQWYCmLjOkrQmtoqZO8Bip9WBTGJEFaOoyQ9qayurlGg8T6YSJhchCNGWZoY6utsjVkFwkbMugZuCPD1Erpq0Jzb4Nv3NS0/Gnh6gV09aEZmsjMXFk1JIxsRC1cpqa0MrKykwUDVkCNoUREZGgWGMxIq7lREStgckTy/79+5GQkACpVIouXbpg9uzZ8Pf313p+RkYGdu7cibt378LZ2RnPPvssnn/+eSNG3DRcy4mIWguTNoVlZGRg06ZNmDp1KtatW4c+ffpg6dKlKCws1Hj+2bNnsXr1aowbNw6ff/455s6di7179+L77783cuT641pORNRamDSxJCYmIiwsDOPGjUPnzp0xZ84cuLq6Ijk5WeP5R44cwcCBA/HMM8/Aw8MDAwYMwJQpU7B7924oFAojR6+f5qzlROZt3+lcjFqcjsKSGoxanI59p3NNHRKRSZksschkMty4cQPBwcFqx4ODg3H16lWt19jY2Kgds7Gxwf3797XWcsxFU9dyIvP26FpbwB9NnEwu1JqZrI+ltLQUcrkcLi4uasddXFxw8eJFjdeEhIRg06ZNOH/+PIKCgpCXl4fExEQAgFQqRYcOHdTOP3jwIFJSUhqNIzo6GoDhh1fGLwhCWVUdHq1XiQA4trXS+dkKhcKihoFaQnkCO9kgYWEIAOApB2sciO4P4OHM9ZZcNkt4bx7F8hiGra3mL8Ym77wXiUQ6nztu3Djk5+dj+fLlqKurg52dHSZNmoS4uDiIxQ0rX+Hh4QgPD2/0nsqajqOjYZcJdwRwUuOosHY636OsrMzgcRqTJZRnwD+Oq74sHIjujwkrzwJ4+KXh2ueN/+yZM0t4bx7F8hiXyRKLk5MTxGIxpFKp2vHi4uIGtRglkUiEyMhIvPjiiyguLoaTk5OqduPu7m7okJutKWs5kXnTttYWmzipNTNZH4u1tTV8fHyQmZmpdjwzMxN9+vRp9FqJRAI3NzdYW1vj2LFj8PX11ZqMiAypqcvVE1kykzaFRUREYM2aNejZsyf8/PyQnJyMoqIijB8/HgAQGxuLrKwsLF++HABQUlKC48ePIzAwEDKZDKmpqTh+/DhWrlxpymJQK/boWlsA4MmJr0SmTSzDhw9HaWkp4uPjUVRUBG9vb8TExKiatYqKipCfn692TVpaGrZu3QqFQgFfX1+sWLECvXrx2yGZjrKJs6ysDEeWjTR1OEQmJ6qqqjLvCSAGpuy8d3JyMnEkT2buHXb6YnnMlyWVBWB5DEXbqDAuQklERIJiYiEiIkExsRARkaCYWIiISFBMLEREJCiTL+lCRE/GTeKoJWFiITJz3CSOWho2hRGZOW4SRy0NEwuRmeMmcdTSMLEQmTluEkctDRMLkZnjCsrU0rDznsjMPbqCMkeFUUvAxELUAnCTOGpJmFiIyKxwzk7Lx8RCRGaDc3YsAzvvichscM6OZWBiISKzwTk7loGJhYjMBufsWAYmFiIyG5yzYxnYeU9kQVr6iCrO2bEMTCxEFsJSRlRxzk7Lx6YwIgvBEVVkLphYiCwER1SRuWBTGJGF6Ohqi1wNSYQjqkgTQ/bHscZCZCE4oop0peyPy5VWQ4E/+uP2nc4V5P5MLEQWYuJATyybEQBPV1uIAHi62mLZjAB2hFMDhu6PY1MYkQXhiCrShaH741hjISJqZQy9wgETCxFRK2Po/jg2hRERtTKGXuGAiYWIqBUyZH8cm8KIiEhQrLGQSbX0RROJqCEmFjKZ6tp6i1g0kYjUsSmMTKaipo6LJhJZIJPXWPbv34+EhARIpVJ06dIFs2fPhr+/v9bzz58/j7i4ONy5cwdWVlbw8/PDK6+8Ai8vLyNGTUKol2s+zkUTn4xNiGTOTFpjycjIwKZNmzB16lSsW7cOffr0wdKlS1FYWKjx/Pz8fCxbtgz+/v745JNPsGzZMtTU1OC9994zcuQkBImWnz5zWzRx3+lcjFqcDt/XD2LU4nTB1lNqTjyGXOeJqLlMmlgSExMRFhaGcePGoXPnzpgzZw5cXV2RnJys8fzs7GzU19fjpZdegqenJ7p3746//OUvyMvLQ0lJiZGjp+ayb2Nl9osmmuOHOPddIXNnssQik8lw48YNBAcHqx0PDg7G1atXNV7j4+MDiUSCQ4cOob6+HpWVlfjhhx/Qs2dPODs7GyNsEpCtjcTsF000xw9x7rtC5k5UVVWlMMWDHzx4gMjISKxcuRIBAQGq4zt27MDRo0exYcMGjdf98ssv+PDDD1FaWgqFQoHu3btj6dKlcHFxaXDuwYMHkZKS0mgc0dHRAACRSNT0whiJQqFoEXHqqiWUp7CkRutr7s5t1P5trPI8KKvR2D8lEQNujm0avtAELeG90QfLYxjt27fXeNzknff6/OdIpVJ8+umnCA0NxZ///GdUVVXhm2++wUcffYTly5dDLFavgIWHhyM8PLzReyr7cxwdHfUP3sjKyspaRJy6agnlmfTROY2bZ3m62uLIspFqx4xVnvSrZWrDtIGHTYgPa3vtBHlGS3hv9MHyGJfJmsKcnJwgFoshlUrVjhcXF2usfQAPR5DZ2trilVdeQY8ePRAQEIC33noLly9f1tp8RtQc5rh5FvddIXNnshqLtbU1fHx8kJmZiWHDhqmOZ2ZmYujQoRqvqampaVArUf5boTBJix5ZOEMv1tecuEwdA5E2Jm0Ki4iIwJo1a9CzZ0/4+fkhOTkZRUVFGD9+PAAgNjYWWVlZWL58OQCgf//+2Lt3L3bs2IERI0agsrIS27dvR7t27eDj42PKopAF44c4kX6alFjq6+tx4cIFFBQUoKysrEFtQSQSYdq0aU+8z/Dhw1FaWor4+HgUFRXB29sbMTExcHd3BwAUFRUhPz9fdX7fvn2xcOFC7N69GwkJCbCxsUHv3r3x3nvvwdbWvOY+EBG1VnqPCrt58yaWL1+O+/fva21+EolE2Lt3ryABGpqy897JycnEkTyZuXfY6YvlMV+WVBaA5TEUbV/o9a6xfPnll6iqqkJ0dDQCAgLg4ODQ7OCImsLclzUx9/iIDEXvxHLz5k3MmDEDgwcPNkQ8RDpRzog315WRzT0+IkPSe7ixi4sLJBKJIWIh0pk5zoh/lLnHZyrmtu4aGYbeiWXixIk4cuQI6urqDBEPkU7MfVkTc4/PFMxx3TUyDL2bwtzc3CAWi/G3v/0No0ePRvv27RvMLQEejvgiMpSOrrYaZ8Sby8rI5h6fKTRWi2PzoGXRO7F8/PHHqr9/9dVXGs8RiURMLGRQUZN6aVzWxFxWRjb3+EyBtbjWQ+/EopysSGRK5jojXsnc4zMF1uJaD70Si0wmw71799CpUyf06tV6v3mReTD3GfHmHp+xsRbXeujVeW9tbY3169fj5s2bhoqHiCwUF89sPfRuCvPy8mqwIjERkS5Yi2sd9B5uPG3aNOzfvx+//fabAcIhIqKWTu8ay8WLF+Hs7Iz58+ejT58+8PDwgI2Njdo5IpEIc+fOFSxIIiJqOfROLAcPHlT9/cqVK7hy5UqDc5hYiIhaL70TS1JSkiHiICIiC2GyrYmJiMgyMbEQEZGg9G4KmzRpEkQi0RPPaykbfRGR6XHvGsuid2KZNm1ag8RSX1+PgoICnDp1Cl5eXhg4cKBgARKRZePeNZZH78QyY8YMra89ePAAb731Fjp16tSsoIio9eCqx5ZH0D4WNzc3jB8/Hjt27BDytkRkwbjqseURvPPewcEB+fn5Qt+WiCyUttWNuepxyyVoYikpKUFKSgrc3d2FvC0RWbCoSb1ga63+UcRVj1s2vftYFi1apPF4eXk5cnJyUFdXh3feeafZgRFR68C9ayyP3olFLpc3GBUmEonQoUMHBAcHY+zYsfD05A8EEemOqx5bFr0Ty8qVKw0RBxERWQi9+1h27NiB27dva3399u3bHBVGRNSKNSmx3Lp1S+vrd+7cwbffftusoIgs1b7TuRi1OB2+rx/EqMXp2Hc619QhEQlO76awJ6msrIREIhH6tkQtHmeYU2uhU2K5deuWWi3lypUrkMvlDc4rLy/HgQMH4OXlJVyERBaCM8yptdApsZw8eVLVbyISiXDw4EG1Db8eZWdnh7feeku4CIksBGeYU2uhU2IZO3YsQkJCoFAo8M4772D69OkICQlRO0ckEqFNmzbw9PSEtbW1QYIlask6utoiV0MS4QxzsjQ6JRY3Nze4ubkBAJYvX47OnTvDxcXFkHERWZyoSb3U+lgAzjAny6R3531gYCAAoKamBjdu3EBxcTECAgLg7OwseHBEloQzzKm1aNKosKSkJMTFxaGqqgoA8P7776Nv374oKSnBnDlz8D//8z8YO3asoIESWQLOMKfWQO95LKmpqdi8eTNCQkLw97//HQqFQvWas7MzgoKCkJGRIWiQRETUcuhdY9m7dy8GDBiAd955B6WlpQ1e79GjB/bv36/z/fbv34+EhARIpVJ06dIFs2fPhr+/v8Zz4+LitM7q3759O/t9iIjMgN6J5e7du5gwYYLW152cnDQmHE0yMjKwadMmzJ07F35+fjhw4ACWLl2Kzz//XOPS+5MnT8b48ePVjq1atQoikYhJhVoU7vFOlkzvpjA7OztUVFRofT0vL0/njvzExESEhYVh3Lhx6Ny5M+bMmQNXV1ckJydrPL9t27ZwdXVV/amrq8OVK1fYn0MtinIGfq60Ggr8MQOfy7uQpdA7sfzpT39CamoqZDJZg9fu37+PlJQU9OvX74n3kclkuHHjBoKDg9WOBwcH4+rVqzrFcvjwYdjb2+Ppp5/WLXgiM9DYDHxdcL0xMnd6N4X99a9/xcKFC/Hmm29i2LBhEIlEOHv2LC5cuICUlBRIJBJMmzbtifcpLS2FXC5v0ITl4uKCixcvPvF6uVyOw4cPY9SoUVonZB48eBApKSmN3ic6OhoAUFZW9sRnmppCoWgRceqqtZZn82sBWl970vXVtfXw97RRu4cIwL0HxbC1EW6Nvtb63rQU5lIeW1vNk3v1Tiyenp746KOPsHHjRnz77bdQKBTYu3cvgIe1mXnz5sHV1VXn+z2+aZiuzp49i/v37zfaDBYeHo7w8PBG71NYWAgAcHR0bFIcxlRWVtYi4tRVay3PpI/OaZyB7+lqiyPLRjZ+7eL0Jl+rj9b63rQU5l6eJs1j6dy5Mz744AOUl5cjNzcXCoUCHh4esLOzw6FDh5CQkIAtW7Y0eg8nJyeIxWJIpVK148XFxTp1xB86dAh9+vSBt7d3U4pAZDLNmYHP9caoJdA5schkMpw+fRp5eXlwdHTEwIED4erqil69eqG6uhr79u1DUlISSkpK0LFjxyfez9raGj4+PsjMzMSwYcNUxzMzMzF06NBGr33w4AHOnDmDv//977qGT2Q2mjMDn+uNUUugU2J58OABoqOjUVBQoJoQuWXLFixZsgRisRirV6/GgwcP4Ovri3nz5mHw4ME6PTwiIgJr1qxBz5494efnh+TkZBQVFamGFMfGxiIrKwvLly9Xuy41NRW2trZqCYmoJWnqDHyuN0YtgU6JZfv27SgsLMTkyZMREBCAgoICfPvtt1i/fj2Ki4vh7e2Nt99+G35+fno9fPjw4SgtLUV8fDyKiorg7e2NmJgY1RyWoqIi5Ofnq12jUChw6NAhjBgxQmvHEZGl4npj1BKIqqqqFE86KTIyEv3798ff/vY31bHjx4/jo48+Qr9+/VQ1l5ZI2Xnv5ORk4kiezNw77PTF8pgvSyoLwPIYSrNGhRUXF6N3795qx3x9fQE83KulpSYVIrIcXM3AfOiUWORyeYO5Isp/29nZCR8VEZGO9p3OxbKdV1BcWac6plzNAACTiwnoPCqsoKAAWVl/zAxWLuuSk5ODtm3bNji/Vy92JhKRYSmXx3l8JQPgj9UMmFiMT+fEEhcXh7i4uAbHN27cqPZvhUIBkUikmjRJRGQompbHeRTn95iGToll/vz5ho6DiEhvT0ocnN9jGjollrCwMEPHQUSkN20TRgHO7zElDueiVo+rBbdcUZN6wda64ceYi701ls0IYP+KiTRprTAiS/F45y9HE7UsnDBqnphYqFVrbG8Ufji1DE1dHocMh01h1KpxtWAi4bHGQq3ak1YL5mzu1onve/OwxkKtmqbOX+VoIu5N3zrxfW8+JhZq1SYO9MSyGQHwdLWFCA93YlSOJmru3vTUMvF9bz42hVGrp63zl/0vrRPf9+ZjjYVIC22ztjmb27LxfW8+JhYiLRrrfyHLxfe9+dgURqQFJ9+1Tnzfm0+nHSQtGXeQNB2Wx3CaO1zWnMoiBJbHMJq1gyQRtRxcpoZMjX0sRBaGw2XJ1JhYiCwMh8uSqbEpjMjCPGmZmsdp6o8Z2cf07ffUcrHGQmRh9Bkuq235kuraeiNFS5aINRYiC6PPcFlt/TEVNXVob5RoyRIxsRBZIF33KNHW71Iv13iYSCdsCiNqxbT1u0j4yUDNwB8folZMW3+MfRs2ZlDT8aeHqBXT1h9jayNp0v24QRYBTCxErZ6m/piysjK978MZ/6TEpjAiEgRn/JMSEwsRCYIz/kmJiYWIBMENskiJiYWIBMENskiJnfdEJAhukEVKTCxEJBhdZ/yTZTN5Ytm/fz8SEhIglUrRpUsXzJ49G/7+/lrPVygUSEpKQnJyMgoKCuDo6IjQ0FBERkYaL2giItLKpIklIyMDmzZtwty5c+Hn54cDBw5g6dKl+Pzzz+Hu7q7xmi1btuDMmTN45ZVX0LVrV1RUVEAqlRo5ciIi0sakiSUxMRFhYWEYN24cAGDOnDk4d+4ckpOT8fLLLzc4PycnB99//z0+++wzdO7c2djhEhGRDkyWWGQyGW7cuIHJkyerHQ8ODsbVq1c1XnPq1Cl4eHjg3LlzeO+996BQKBAQEIBXXnkFLi4uDc4/ePAgUlJSGo0jOjoaQNNmGhubQqFoEXHqiuUxX5ZUFoDlMRRbW81DyU2WWEpLSyGXyxskBBcXF1y8eFHjNfn5+SgsLERGRgbefPNNiEQi/Oc//8EHH3yAjz/+GGKx+lDH8PBwhIeHNxpHYWEhAMDR0fx3zCsrK2sRceqK5TFfllQWgOUxNpPPYxGJRDqfq1AoIJPJEBUVhYCAAPj7+yMqKgpZWVm4fv26AaMkIiJdmSyxODk5QSwWN+h4Ly4u1tisBQCurq6QSCTw8vJSHfP09IREIsG9e/cMGS4REenIZInF2toaPj4+yMzMVDuemZmJPn36aLymT58+qK+vR15enupYfn4+6uvrtY4iIyIi4zJpU1hERAR++OEHpKSk4Pfff8fGjRtRVFSE8ePHAwBiY2OxaNEi1flBQUHo0aMH1q1bh+zsbGRnZ2PdunXo3bs3fHx8TFUMIiJ6hEmHGw8fPhylpaWIj49HUVERvL29ERMTo6p9FBUVIT8/X3W+WCzGu+++i40bNyI6Oho2NjYICgrCzJkzG3TcExGRaYiqqqoUpg7ClJSjwpycnEwcyZOZ+0gQfbE85suSygKwPIaibbgxv+YTEZGgTL5WGBFxr3iyLEwsRCbGveLJ0rApjMjEuFc8WRomFiIT417xZGnYFEakhbH6PTq62iJXQxLhXvHUUrHGQqSBst8jV1oNBf7o99h3OlfwZ3GveLI0TCxEGhiz32PiQE8smxEAT1dbiAB4utpi2YwAdtxTi8WmMCINjN3vwb3iyZKwxkKkgbb+DfZ7ED0ZEwuRBuz3IGo6NoURaaBsluJseCL9MbEQacF+D6KmYVMYEREJiomFiIgExcRCRESCYmIhIiJBMbEQEZGgmFiIiEhQTCxERCQoJhYiIhIUEwsREQmKiYWIiATFxEJERIJiYiEiIkFxEUoige07nctVkalVY2IhEtC+07lYHHdZta1xrrQai+MuAwCTC7UabAojEtCapCxVUlGqlsmxJinLRBERGR8TC5GA8qTVeh0nskRMLEQC6uhqq9dxIkvExEIkoKhJvWBrrf5rZWstRtSkXiaKiMj42HlPJCBlBz1HhVFrxsRCJLCJAz2ZSKhVY1MYEREJiomFiIgExcRCRESCYmIhIiJBiaqqqhSmDsKUCgsLTR0CEVGL5e7u3uAYayxERCSoVl9jaUkWLFiAtWvXmjoMwbA85suSygKwPMbGGgsREQmKiYWIiATFxEJERIJiYiEiIkExsRARkaCYWIiISFBMLEREJCgmFiIiEhQTSwsybtw4U4cgKJbHfFlSWQCWx9g4856IiATFGgsREQmKiYWIiATFxEJERIKyMnUApJ1MJsN//vMfHD16FLW1tejbty/mzp2Ldu3a6XT90aNHsXr1avTv3x8xMTEGjvbJmlKelJQUpKWl4c6dO1AoFOjevTteeOEF+Pv7GzFyYP/+/UhISIBUKkWXLl0we/bsRmP47bffsGHDBly/fh0ODg4IDw/HtGnTIBKJjBi1dvqU59KlS9i7dy+ysrJQUVEBT09PTJo0CWPGjDFy1Nrp+/4o5ebm4s0334RCocDOnTuNEOmT6VsWhUKBpKQkJCcno6CgAI6OjggNDUVkZKTxgn4MayxmbNOmTfjpp5/w9ttv48MPP0RlZSXef/991NfXP/Ha/Px8bN261egfwI1pSnkuXbqE4cOHY9myZVi9ejW8vLwQExOD3Nxco8WdkZGBTZs2YerUqVi3bh369OmDpUuXat0krrKyEkuWLIGLiwvWrFmDV199FXv27EFiYqLRYm6MvuW5evUqvL298Y9//AOff/45xo8fj/Xr1yM9Pd24gWuhb3mUZDIZVq1aZVa/I00py5YtW3DgwAFERkbiyy+/RExMDAICAowYdUNMLGaqoqIChw8fxiuvvILg4GD4+PggKioKv/32Gy5evNjotXV1dfj444/x4osvokOHDkaKuHFNLc/ChQvx7LPPokePHujUqRPmzZuHtm3b4ty5c0aLPTExEWFhYRg3bhw6d+6MOXPmwNXVFcnJyRrPT09PR01NDRYsWABvb288/fTT+K//+i8kJiZCoTD9IEx9yzN16lS8+OKL8PPzg4eHByZMmIAhQ4bgp59+MnLkmulbHqVt27aha9euePrpp40U6ZPpW5acnBx8//33WLx4MQYPHgwPDw/06NED/fv3N3Lk6phYzNSNGzdQV1eH4OBg1bH27dujU6dOuHr1aqPXbt++He7u7ggLCzN0mDprTnkeVVdXB5lMBgcHB0OE2YBMJsONGzfU4gaA4OBgrXFfu3YN/v7+aNOmjdr5RUVFKCgoMGi8T9KU8mhSVVVltPegMU0tz5kzZ3DmzBm8+uqrhg5RZ00py6lTp+Dh4YFz585h1qxZmDlzJtauXYvi4mIjRKwdE4uZkkqlEIvFcHJyUjvu6uoKqVSq9brz588jIyMDr7/+uqFD1EtTy/O47du3w9bWFoMGDRI6RI1KS0shl8vh4uKidtzFxUXrL69UKtV4PgCT/8I3pTyPO336NC5evIjw8HDhA9RTU8pTVFSE9evXIyoqCnZ2doYPUkdNKUt+fj4KCwuRkZGBN998E1FRUcjJycEHH3wAuVxu+KC1YOe9kW3fvh3x8fGNnrNixQqtrykUCq0dwCUlJVi3bh0WLlxotG+ThizP45KSknDw4EEsW7bM6B8I+na6m0snvTZNje/KlStYvXo1Xn31VfTq1UvgqJpOn/L861//wvjx4+Hr62vAiJpOn7IoFArIZDJERUXBy8sLABAVFYXXXnsN169fR+/evQ0VZqOYWIxs0qRJGDlyZKPntG/fHnK5HHK5HKWlpXB2dla9VlxcrLWz8c6dOygqKsLixYtVx5Rt+s899xw+//xzdOrUqfmFeIQhy/OopKQkfP3114iJiTHqB5qTkxPEYnGDWlVxcXGDb5ZKmmphym+c2q4xlqaUR+mXX37Be++9hxdeeAETJkwwYJS6a0p5fv75Z1y+fBk7duxQHZPL5Xjuuecwd+5ck9XEmvqzJpFIVEkFADw9PSGRSHDv3j0mltbC2dlZ7YNVGx8fH1hZWeHChQuqD+779+8jJycHffr00XhNz549sX79erVj27dvR3l5OebOnWuQjnxDlkcpMTER33zzDWJiYow+gsfa2ho+Pj7IzMzEsGHDVMczMzMxdOhQjdf4+vpi27ZtqK2thY2Njer8p556yuSDKZpSHgC4fPky3n//fUyfPh3PPfecMULVSVPK8/jvyMmTJxEfH481a9bAzc3NoPE2pill6dOnD+rr65GXl4eOHTsCeNg8Vl9fD3d3d6PErQn7WMyUvb09xowZg61btyIzMxPZ2dlYs2YNunbtir59+6rOW7RoEWJjYwEAtra28Pb2Vvtjb2+Ptm3bwtvbG9bW1qYqTpPKAwAJCQmIjY3FG2+8AS8vL0ilUkilUlRUVBgt9oiICPzwww9ISUnB77//jo0bN6KoqAjjx48HAMTGxmLRokWq80eMGIE2bdrgk08+we3bt/HTTz9h165diIiIMIsmMn3Lc+nSJSxduhTh4eEYOXKk6j0oKSkxVRHU6Fuex39H3NzcIBaL4e3tbfIBCfqWJSgoCD169MC6deuQnZ2N7OxsrFu3Dr1794aPj4+pisEaizmbNWsWJBIJVq1ahZqaGvTt2xcLFiyARCJRnZOfn6/zhElTa0p59u/fj7q6OqxatUrtXqGhoViwYIFR4h4+fDhKS0sRHx+PoqIieHt7IyYmRvWNsKioCPn5+arz7e3t8cEHH2DDhg1YsGABHBwcMHnyZERERBgl3ifRtzypqamoqanBnj17sGfPHtVxd3d3bNmyxejxP07f8pgzfcsiFovx7rvvYuPGjYiOjoaNjQ2CgoIwc+ZMiMWmqzdwdWMiIhIUm8KIiEhQTCxERCQoJhYiIhIUEwsREQmKiYWIiATFxEJERILiPBYigRUXF+Pf//43fv75Z5SWlmL69OmYMWOGqcMiMhomFjJLqampWLdundoxJycndOrUCZMmTWrSHhqnT5/GjRs3DP4hv23bNpw+fRrTpk1Du3bt0LVrV4M+z5Cio6MhlUqxYcMGja8///zzGD58uGqyakFBAWbNmgUAmDZtGl544YUG12zduhUJCQkAgK+++gqurq4AgLVr1yIjI0P1GrVcTCxk1mbMmAEPDw8oFAqUlJTgyJEj+PDDD7Fw4UKMGDFCr3udOXMGBw8eNHhiuXTpEoKDg/GXv/zFoM8xZzY2Njh69GiDxKJQKHDs2DHY2NigtrbWRNGRobGPhcxacHAwRo0ahdDQUEyePBkffvgh2rRpg2PHjpk6NK2Ki4thb29v6jBMqn///sjLy8O1a9fUjl+6dAn379/HgAEDTBQZGQNrLNSi2NnZwdbWVm19MQA4duwY9u7di99++w1WVlbw9/dHZGQkunTpAuBhM0taWhoAYOLEiarrNm/ejA4dOiA1NRXp6em4ffs2ysvL4e7ujjFjxuD555/Xec2lR5vv0tLSVM/bvHkznnrqKXz33Xc4d+4c8vLyIJPJ0LVrV0ydOrXBpmUTJ05EeHg4QkJC8M033yAvLw/e3t6YN28efHx8kJaWhvj4eBQWFqJ79+6YP38+OnfurLr+1q1bSExMxJUrV/DgwQO0bdsWQUFBiIyMRPv27fX8H28aHx8f3L59G0ePHlXb9yQ9PR3dunVDly5dcPz4caPEQsbHxEJmrbKyUrWKbklJCZKTk1FaWorQ0FDVObt27UJsbCyGDBmC0NBQVFVV4cCBA3jnnXfwySefwMPDA+PHj8f9+/fx888/IyoqSnWtcsn//fv3w8vLCyEhIWjTpg0yMzMRGxuLyspKvPTSSzrFGhAQgKioKHz66afo3bs3xo0bp3pGZWUlDh48iOHDh2P06NGora1Feno6li9fjqVLlyIkJETtXteuXcPZs2cxYcIESCQS7Nq1C++99x5eeukl7Nq1C+PGjYNMJsPOnTvx8ccf49NPP1Vdm5mZibt372LkyJFo164d8vLykJycjOvXr+Ozzz5T2zJZF3K5vEkrGY8YMQL79+9XLT4qk8nw008/YerUqaiurtb7ftRyMLGQWYuJiVH7t5WVFebNm4fBgwcDAAoLC/H11183GHkVGhqKefPmIT4+Hm+88QZ8fX3h6emJn3/+GaNGjWrwnJUrV8LW1lb172eeeQaffvop9u3bh+nTp+u05YCHhwc8PDzw2WefoUOHDmrPsba2xtatW9Xu8+yzz2L+/PnYs2dPg8SSk5ODL774QrXHhouLC9auXYv//Oc/+Pe//63a4tnKygrbtm3DjRs3VMukjx8/HpMnT1a734ABA/CPf/wDJ06ceOLGbI/Ly8vDX//6V72uAYCRI0ciLi4OFy5cQP/+/XH69GlUVlbiz3/+Mw4dOqT3/ajlYGIhs/bqq6+qmnmKi4tx9OhRfPnll3B0dMTTTz+NEydOoL6+HsOHD1f7Vi2RSNCrVy9cvHhRp+cok0p9fT2qqqpQX1+PwMBAHD58GDk5OejWrVuzyiGRSFTNdzKZDNXV1ZDL5QgICEBGRkaD8wMDA1VJBYBq18yBAweqksqjx/Py8lSJ5dEEWVVVBZlMhk6dOsHe3h43btzQO7G0a9cO8+fP1/jae++9p/W6jh07onfv3khPT0f//v1x9OhRBAQEtJhtHqjpmFjIrPXs2VOtjf7Pf/4zFixYgH//+98YOHAg7t69CwCYN2+exut1bfb55ZdfsH37dvz666+oq6tTe02oTcVSUlKwd+9e5OTkqLaMBjTvcf54X4hyMIC24+Xl5apj5eXl2LZtG3766SeUlZWpnd+UsrRp0wZBQUEaX3vSxmUjRoxAbGws7t+/j7Nnz2LOnDl6P59aHiYWalHEYjECAgKQlJSE3Nxc1Qf00qVLG3ToK89/kvz8fLz77rvw9PTE7Nmz0b59e1hbWyM7Oxvbtm1TSwJNdfToUaxfvx6DBg3ClClT4OzsDIlEgtTUVBw9elTnuHUpz6pVq/DLL79g8uTJ6N69O9q2bQuRSIRVq1ZBLpc3uyz6GD58OLZs2YK1a9dCoVA0af4RtTxMLNTi1NfXAwCqq6tVzUXt27dXjQDTRtu361OnTqG2thZLlixR2ye8oKBAoIiBjIwMeHh4YNGiRWpxpKamCvYM4GFt5cKFC5gxYwamT5+uOl5bW2vU7ZyVXFxc0LdvX5w/fx5Dhgwx+da/ZBycx0ItSl1dHTIzM2FlZYXOnTtj6NChkEgkiIuL0/ht/NF+F2Wz2KPNRsAftYBHayYymQzff/+9YHFrekZ+fj5OnDgh2DO0PQcA9u7da/TaitL06dMxffp0TJkyxSTPJ+NjjYXM2oULF5CXlwfgYZI4duwY7t69iylTpsDOzg52dnaIjIzEli1bsHDhQgwZMgSOjo4oLCzEuXPn0KtXL7z++usAHvbXAMCGDRvQr18/SCQSDBw4ECEhIbCyssL777+P8PBwyGQyHDlyRNA9wwcNGoQTJ07ggw8+wKBBg/DgwQMcOHAAXl5euHXrlmDPsbOzQ2BgIHbv3o26ujq0b98eV65cweXLl+Ho6CjYc/Th6+ur1k/WGLlcju+++67BcWtrazz//PNCh0YGwsRCZi0uLk71dxsbG3Tq1Anz5s1DeHi46nhERAS8vLywZ88e7Ny5E/X19XBzc4Ofnx/GjBmjOu/pp5/GtWvXkJGRgWPHjkGhUGDz5s3w8vLCokWLsH37dmzbtg1OTk4YNWoUAgMD8e677wpSjrCwMJSUlODAgQO4ePEiOnbsiFmzZiEvL0/QxAIACxcuxMaNG3Hw4EHU1dXB398fy5cvx5IlSwR9jiHU19fj66+/bnDc3t6eiaUFEVVVVTW/Z5KIiOj/sY+FiIgExaYwIh3U1NSgsrKy0XPs7Oz0Xi7FVKRSaaOvW1lZmaxPhlo+NoUR6UDT/jCPmz9/PkaPHm2kiJrn0YU4NQkICMDKlSuNFA1ZGiYWIh0UFRXhzp07jZ7TpUsXPPXUU0aKqHkyMzMbfd3BwUG1RAyRvphYiIhIUOy8JyIiQTGxEBGRoJhYiIhIUEwsREQkKCYWIiIS1P8BXhsTMD9BvJAAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(beta_fama['HML'], Exp_return)\n", "plt.xlabel('Beta_fama_HML')\n", "plt.ylabel('Return')\n", "plt.title('Beta_fama_HML and Return')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f4431a70", "metadata": {}, "source": [ "From the relationship between expected excess returns and various risk prices, all of the factors are positively related. \n", "Mkt, SMB, and HML didn't show the trend.\n", "Using three-factor Fama-French model is more suitable compared to Q1. The SML reveals to be more linear than Q1.\n", "We can conclude that adding SMB and HML into the model get teh better beta where in one-factor model or CAPM has the omitted varaibles which caused teh result to be bias." ] }, { "cell_type": "markdown", "id": "2873d65d", "metadata": {}, "source": [ "# Q.6" ] }, { "cell_type": "code", "execution_count": 88, "id": "95a52869", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RMSEMAE
FoodNaNNaN
BeerNaNNaN
SmokeNaNNaN
GamesNaNNaN
BooksNaNNaN
HshldNaNNaN
ClthsNaNNaN
HlthNaNNaN
ChemsNaNNaN
TxtlsNaNNaN
CnstrNaNNaN
SteelNaNNaN
FabPrNaNNaN
ElcEqNaNNaN
AutosNaNNaN
CarryNaNNaN
MinesNaNNaN
CoalNaNNaN
OilNaNNaN
UtilNaNNaN
TelcmNaNNaN
ServsNaNNaN
BusEqNaNNaN
PaperNaNNaN
TransNaNNaN
WhlslNaNNaN
RtailNaNNaN
MealsNaNNaN
FinNaNNaN
OtherNaNNaN
\n", "
" ], "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": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val2 = pd.DataFrame(data = np.nan, index = name_indus, columns = ['RMSE','MAE'])\n", "cross_val2" ] }, { "cell_type": "code", "execution_count": 97, "id": "21b33818", "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
\n", "
" ], "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": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import KFold\n", "kf = KFold(n_splits=10,shuffle=False)\n", "\n", "for i in range(len(name_indus)):\n", " mae_err_test = 0\n", " rmse_err_test = 0\n", " ff = df[['Mkt-RF', 'SMB', 'HML']].to_numpy()\n", " y = (df[name_indus[i]]-df['RF']).to_numpy()\n", " for train,test in kf.split(ff):\n", " lr=linear_model.LinearRegression()\n", " reg=lr.fit(ff[train],y[train])\n", " y_pred_test =reg.predict(ff[test])\n", " e_test = y[test]-y_pred_test\n", " mae_err_test += np.mean(np.abs(e_test)) \n", " rmse_err_test += np.sqrt(np.mean(e_test*e_test))\n", " \n", " mae_test_10cv_model2 = mae_err_test/10 \n", " rmse_test_10cv_model2 = rmse_err_test/10 \n", " cross_val2.iloc[i][0] = rmse_test_10cv_model2\n", " cross_val2.iloc[i][1] = mae_test_10cv_model2\n", "\n", "cross_val2" ] }, { "cell_type": "code", "execution_count": 99, "id": "3424d7cd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE (Model2:Linear): 3.8058\n", "RMSE (Model2:Linear): 2.8489\n" ] } ], "source": [ "mean_rmse = np.mean(cross_val2['RMSE'])\n", "mean_mae = np.mean(cross_val2['MAE'])\n", "print(f'MAE (Model2:Linear): {mean_rmse:.4f}')\n", "print(f'RMSE (Model2:Linear): {mean_mae:.4f}')" ] }, { "cell_type": "code", "execution_count": null, "id": "6f3b5a07", "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }