{ "cells": [ { "cell_type": "markdown", "id": "9caf0baf", "metadata": {}, "source": [ "# 6304640391 Kanchai" ] }, { "cell_type": "markdown", "id": "f86d8d0f", "metadata": {}, "source": [ "### Import necessary module." ] }, { "cell_type": "code", "execution_count": 1, "id": "c61a1a4f", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\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) #when import sklearn, need to import metrics, model selection also\n", "\n", "import pandas_datareader.data as web\n", "from pandas_datareader.famafrench import get_available_datasets" ] }, { "cell_type": "markdown", "id": "9fcffd59", "metadata": {}, "source": [ "# 1.1" ] }, { "cell_type": "code", "execution_count": 2, "id": "50c8dbad", "metadata": {}, "outputs": [], "source": [ "start_date = dt.datetime(1926,7,1)\n", "end_date = dt.datetime(2022,8,1)" ] }, { "cell_type": "code", "execution_count": 3, "id": "8ad1cd10", "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
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2022-079.572.81-4.100.08
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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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ff_factor = web.DataReader(\"F-F_Research_Data_Factors\",'famafrench', start = start_date, end = end_date)[0]\n", "ff_factor" ] }, { "cell_type": "code", "execution_count": 4, "id": "385c7c6a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FoodBeerSmokeGamesBooksHshldClthsHlthChemsTxtls...TelcmServsBusEqPaperTransWhlslRtailMealsFinOther
Date
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1926-082.5927.036.500.5510.01-3.58-2.514.255.507.97...2.172.024.39-2.384.855.39-0.75-0.134.476.76
1926-091.164.021.266.58-0.990.73-0.510.695.332.30...2.412.250.19-5.540.07-7.870.25-0.56-1.61-3.86
1926-10-3.06-3.311.06-4.769.47-4.680.12-0.57-4.761.00...-0.11-2.00-1.09-5.08-2.61-15.38-2.20-4.11-5.51-8.49
1926-116.357.294.551.66-5.80-0.541.875.425.203.10...1.633.773.643.841.614.676.524.332.344.00
..................................................................
2022-043.013.036.37-25.22-10.762.04-7.00-6.80-2.286.63...-10.70-12.59-12.26-0.74-10.93-2.14-11.41-5.47-7.99-7.65
2022-05-1.68-1.602.67-2.93-7.40-5.12-6.450.994.522.38...8.54-3.35-0.75-0.66-4.591.03-5.64-3.292.80-1.19
2022-06-1.64-0.02-11.63-11.33-12.53-2.56-12.00-2.05-15.65-11.17...-6.72-6.79-10.19-8.51-7.14-6.43-8.50-9.02-9.05-11.78
2022-073.675.490.5614.6212.100.7611.862.757.666.86...-0.408.6015.687.229.339.0816.3311.897.389.19
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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FoodBeerSmokeGamesBooksHshldClthsHlthChemsTxtls...TelcmServsBusEqPaperTransWhlslRtailMealsFinOther
Date
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1926-082.3426.786.250.309.76-3.83-2.764.005.257.72...1.921.774.14-2.634.605.14-1.00-0.384.226.51
1926-090.933.791.036.35-1.220.50-0.740.465.102.07...2.182.02-0.04-5.77-0.16-8.100.02-0.79-1.84-4.09
1926-10-3.38-3.630.74-5.089.15-5.00-0.20-0.89-5.080.68...-0.43-2.32-1.41-5.40-2.93-15.70-2.52-4.43-5.83-8.81
1926-116.046.984.241.35-6.11-0.851.565.114.892.79...1.323.463.333.531.304.366.214.022.033.69
..................................................................
2022-043.003.026.36-25.23-10.772.03-7.01-6.81-2.296.62...-10.71-12.60-12.27-0.75-10.94-2.15-11.42-5.48-8.00-7.66
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1154 rows × 30 columns

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" ], "text/plain": [ " Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", "Date \n", "1926-07 0.34 -5.41 1.07 2.71 10.75 -0.70 7.86 1.55 7.92 0.17 \n", "1926-08 2.34 26.78 6.25 0.30 9.76 -3.83 -2.76 4.00 5.25 7.72 \n", "1926-09 0.93 3.79 1.03 6.35 -1.22 0.50 -0.74 0.46 5.10 2.07 \n", "1926-10 -3.38 -3.63 0.74 -5.08 9.15 -5.00 -0.20 -0.89 -5.08 0.68 \n", "1926-11 6.04 6.98 4.24 1.35 -6.11 -0.85 1.56 5.11 4.89 2.79 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "2022-04 3.00 3.02 6.36 -25.23 -10.77 2.03 -7.01 -6.81 -2.29 6.62 \n", "2022-05 -1.71 -1.63 2.64 -2.96 -7.43 -5.15 -6.48 0.96 4.49 2.35 \n", "2022-06 -1.70 -0.08 -11.69 -11.39 -12.59 -2.62 -12.06 -2.11 -15.71 -11.23 \n", "2022-07 3.59 5.41 0.48 14.54 12.02 0.68 11.78 2.67 7.58 6.78 \n", "2022-08 -1.80 -2.06 -0.31 -3.14 -5.16 -2.35 -6.20 -5.26 -1.58 -12.39 \n", "\n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", "Date ... \n", "1926-07 ... 0.61 9.00 1.84 7.48 1.69 -24.01 -0.15 1.65 -0.24 \n", "1926-08 ... 1.92 1.77 4.14 -2.63 4.60 5.14 -1.00 -0.38 4.22 \n", "1926-09 ... 2.18 2.02 -0.04 -5.77 -0.16 -8.10 0.02 -0.79 -1.84 \n", "1926-10 ... -0.43 -2.32 -1.41 -5.40 -2.93 -15.70 -2.52 -4.43 -5.83 \n", "1926-11 ... 1.32 3.46 3.33 3.53 1.30 4.36 6.21 4.02 2.03 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "2022-04 ... -10.71 -12.60 -12.27 -0.75 -10.94 -2.15 -11.42 -5.48 -8.00 \n", "2022-05 ... 8.51 -3.38 -0.78 -0.69 -4.62 1.00 -5.67 -3.32 2.77 \n", "2022-06 ... -6.78 -6.85 -10.25 -8.57 -7.20 -6.49 -8.56 -9.08 -9.11 \n", "2022-07 ... -0.48 8.52 15.60 7.14 9.25 9.00 16.25 11.81 7.30 \n", "2022-08 ... -3.19 -4.91 -6.08 -7.85 -1.65 -1.79 -3.65 -1.66 -2.43 \n", "\n", " Other \n", "Date \n", "1926-07 4.98 \n", "1926-08 6.51 \n", "1926-09 -4.09 \n", "1926-10 -8.81 \n", "1926-11 3.69 \n", "... ... \n", "2022-04 -7.66 \n", "2022-05 -1.22 \n", "2022-06 -11.84 \n", "2022-07 9.11 \n", "2022-08 -3.84 \n", "\n", "[1154 rows x 30 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "excess_return = indus_return - ff_factor[[\"RF\"]].values\n", "excess_return #E(RI-RF)" ] }, { "cell_type": "code", "execution_count": 7, "id": "052882ff", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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E(RI-RF)
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": [ " E(RI-RF)\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": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ex_return = pd.DataFrame(pd.array(excess_return.mean()),index=range(len(excess_return.mean())),columns=['E(RI-RF)'])\n", "ex_return2 = ex_return.set_index(indus_return.columns)\n", "ex_return2" ] }, { "cell_type": "code", "execution_count": 8, "id": "4170a27a", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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BETAs
FoodNaN
BeerNaN
SmokeNaN
GamesNaN
BooksNaN
HshldNaN
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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta = pd.DataFrame(np.nan,index=name,columns=['BETAs'])\n", "beta" ] }, { "cell_type": "code", "execution_count": 9, "id": "7475d9b3", "metadata": {}, "outputs": [], "source": [ "for i in range(len(name)):\n", " model = linear_model.LinearRegression()\n", " model = model.fit(ff_factor[['Mkt-RF']],excess_return[[excess_return.columns[i]]])\n", " beta.iloc[i,:] = model.coef_" ] }, { "cell_type": "code", "execution_count": 10, "id": "b394069c", "metadata": { "scrolled": true }, "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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta #assigning values to beta" ] }, { "cell_type": "code", "execution_count": 25, "id": "913d86f9", "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

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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": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ff_factor" ] }, { "cell_type": "code", "execution_count": 11, "id": "46238572", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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b*(Mkt-RF)
Food0.490636
Beer0.621812
Smoke0.419525
Games0.932331
Books0.747580
Hshld0.595083
Clths0.559590
Hlth0.561104
Chems0.702331
Txtls0.770890
Cnstr0.794978
Steel0.916165
FabPr0.834186
ElcEq0.867237
Autos0.866016
Carry0.799931
Mines0.614733
Coal0.860241
Oil0.598602
Util0.514033
Telcm0.447269
Servs0.554446
BusEq0.726910
Paper0.638575
Trans0.765963
Whlsl0.731278
Rtail0.651186
Meals0.637012
Fin0.779918
Other0.710287
\n", "
" ], "text/plain": [ " b*(Mkt-RF)\n", "Food 0.490636\n", "Beer 0.621812\n", "Smoke 0.419525\n", "Games 0.932331\n", "Books 0.747580\n", "Hshld 0.595083\n", "Clths 0.559590\n", "Hlth 0.561104\n", "Chems 0.702331\n", "Txtls 0.770890\n", "Cnstr 0.794978\n", "Steel 0.916165\n", "FabPr 0.834186\n", "ElcEq 0.867237\n", "Autos 0.866016\n", "Carry 0.799931\n", "Mines 0.614733\n", "Coal 0.860241\n", "Oil 0.598602\n", "Util 0.514033\n", "Telcm 0.447269\n", "Servs 0.554446\n", "BusEq 0.726910\n", "Paper 0.638575\n", "Trans 0.765963\n", "Whlsl 0.731278\n", "Rtail 0.651186\n", "Meals 0.637012\n", "Fin 0.779918\n", "Other 0.710287" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_mkt_rf = ff_factor[['Mkt-RF']].mean().values * beta\n", "beta_mkt_rf.columns = ['b*(Mkt-RF)']\n", "beta_mkt_rf " ] }, { "cell_type": "code", "execution_count": 12, "id": "9e5f3949", "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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E(RI-RF)b*(Mkt-RF)BETAsMkt-RF
Food0.6992200.4906360.7290950.672938
Beer0.9295930.6218120.9240260.672938
Smoke0.8664820.4195250.6234240.672938
Games0.8356590.9323311.3854640.672938
Books0.6317500.7475801.1109200.672938
Hshld0.6552690.5950830.8843060.672938
Clths0.6648960.5595900.8315630.672938
Hlth0.8079200.5611040.8338130.672938
Chems0.7773400.7023311.0436790.672938
Txtls0.6755370.7708901.1455600.672938
Cnstr0.6941680.7949781.1813540.672938
Steel0.6773740.9161651.3614420.672938
FabPr0.8008490.8341861.2396190.672938
ElcEq0.8968540.8672371.2887330.672938
Autos0.9236140.8660161.2869180.672938
Carry0.8502950.7999311.1887160.672938
Mines0.6406590.6147330.9135070.672938
Coal0.8280940.8602411.2783370.672938
Oil0.7739600.5986020.8895360.672938
Util0.6183540.5140330.7638650.672938
Telcm0.5663520.4472690.6646510.672938
Servs0.9530330.5544460.8239190.672938
BusEq0.9037870.7269101.0802050.672938
Paper0.7212560.6385750.9489370.672938
Trans0.6551910.7659631.1382380.672938
Whlsl0.5750690.7312781.0866950.672938
Rtail0.7779640.6511860.9676770.672938
Meals0.8002080.6370120.9466140.672938
Fin0.7423050.7799181.1589760.672938
Other0.5220880.7102871.0555020.672938
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" ], "text/plain": [ " E(RI-RF) b*(Mkt-RF) BETAs Mkt-RF\n", "Food 0.699220 0.490636 0.729095 0.672938\n", "Beer 0.929593 0.621812 0.924026 0.672938\n", "Smoke 0.866482 0.419525 0.623424 0.672938\n", "Games 0.835659 0.932331 1.385464 0.672938\n", "Books 0.631750 0.747580 1.110920 0.672938\n", "Hshld 0.655269 0.595083 0.884306 0.672938\n", "Clths 0.664896 0.559590 0.831563 0.672938\n", "Hlth 0.807920 0.561104 0.833813 0.672938\n", "Chems 0.777340 0.702331 1.043679 0.672938\n", "Txtls 0.675537 0.770890 1.145560 0.672938\n", "Cnstr 0.694168 0.794978 1.181354 0.672938\n", "Steel 0.677374 0.916165 1.361442 0.672938\n", "FabPr 0.800849 0.834186 1.239619 0.672938\n", "ElcEq 0.896854 0.867237 1.288733 0.672938\n", "Autos 0.923614 0.866016 1.286918 0.672938\n", "Carry 0.850295 0.799931 1.188716 0.672938\n", "Mines 0.640659 0.614733 0.913507 0.672938\n", "Coal 0.828094 0.860241 1.278337 0.672938\n", "Oil 0.773960 0.598602 0.889536 0.672938\n", "Util 0.618354 0.514033 0.763865 0.672938\n", "Telcm 0.566352 0.447269 0.664651 0.672938\n", "Servs 0.953033 0.554446 0.823919 0.672938\n", "BusEq 0.903787 0.726910 1.080205 0.672938\n", "Paper 0.721256 0.638575 0.948937 0.672938\n", "Trans 0.655191 0.765963 1.138238 0.672938\n", "Whlsl 0.575069 0.731278 1.086695 0.672938\n", "Rtail 0.777964 0.651186 0.967677 0.672938\n", "Meals 0.800208 0.637012 0.946614 0.672938\n", "Fin 0.742305 0.779918 1.158976 0.672938\n", "Other 0.522088 0.710287 1.055502 0.672938" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_mkt_rf = pd.DataFrame(pd.array(ff_factor[['Mkt-RF']].mean().values),index=range(len(ex_return2)),columns=['Mkt-RF'])\n", "avg_mkt_rf2 = avg_mkt_rf.set_index(indus_return.columns)\n", "avg_mkt_rf2\n", "\n", "reg_data =pd.concat([ex_return2, beta_mkt_rf,beta,avg_mkt_rf2] ,axis=1)\n", "reg_data #final regression data" ] }, { "cell_type": "markdown", "id": "67da5ead", "metadata": {}, "source": [ "# PLOTTING" ] }, { "cell_type": "code", "execution_count": 13, "id": "5152f955", "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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Mkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RF...Mkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RFMkt-RF
Date
1926-072.962.962.962.962.962.962.962.962.962.96...2.962.962.962.962.962.962.962.962.962.96
1926-082.642.642.642.642.642.642.642.642.642.64...2.642.642.642.642.642.642.642.642.642.64
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1926-10-3.24-3.24-3.24-3.24-3.24-3.24-3.24-3.24-3.24-3.24...-3.24-3.24-3.24-3.24-3.24-3.24-3.24-3.24-3.24-3.24
1926-112.532.532.532.532.532.532.532.532.532.53...2.532.532.532.532.532.532.532.532.532.53
..................................................................
2022-04-9.46-9.46-9.46-9.46-9.46-9.46-9.46-9.46-9.46-9.46...-9.46-9.46-9.46-9.46-9.46-9.46-9.46-9.46-9.46-9.46
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FoodBeerSmokeGamesBooksHshldClthsHlthChemsTxtls...TelcmServsBusEqPaperTransWhlslRtailMealsFinOther
BETAs0.7290950.9240260.6234241.3854641.110920.8843060.8315630.8338131.0436791.14556...0.6646510.8239191.0802050.9489371.1382381.0866950.9676770.9466141.1589761.055502
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FoodBeerSmokeGamesBooksHshldClthsHlthChemsTxtls...TelcmServsBusEqPaperTransWhlslRtailMealsFinOther
Date
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1926-090.2624740.3326500.2244330.4987670.3999310.3183500.2993630.3001730.3757250.412402...0.2392740.2966110.3888740.3416170.4097660.3912100.3483640.3407810.4172310.379981
1926-10-2.362268-2.993846-2.019893-4.488904-3.599379-2.865153-2.694265-2.701556-3.381521-3.711615...-2.153470-2.669498-3.499863-3.074555-3.687891-3.520892-3.135275-3.067030-3.755082-3.419826
1926-111.8446112.3377871.5772623.5052242.8106272.2372952.1038552.1095482.6405082.898267...1.6815682.0845152.7329182.4008102.8797422.7493392.4482242.3949342.9322092.670420
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1154 rows × 30 columns

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" ], "text/plain": [ " Food Beer Smoke Games Books Hshld \\\n", "Date \n", "1926-07 2.158122 2.735118 1.845334 4.100974 3.288322 2.617547 \n", "1926-08 1.924811 2.439430 1.645838 3.657625 2.932828 2.334569 \n", "1926-09 0.262474 0.332650 0.224433 0.498767 0.399931 0.318350 \n", "1926-10 -2.362268 -2.993846 -2.019893 -4.488904 -3.599379 -2.865153 \n", "1926-11 1.844611 2.337787 1.577262 3.505224 2.810627 2.237295 \n", "... ... ... ... ... ... ... \n", "2022-04 -6.897240 -8.741290 -5.897588 -13.106490 -10.509299 -8.365538 \n", "2022-05 -0.247892 -0.314169 -0.211964 -0.471058 -0.377713 -0.300664 \n", "2022-06 -6.146272 -7.789543 -5.255461 -11.679462 -9.365052 -7.454703 \n", "2022-07 6.977441 8.842933 5.966164 13.258891 10.631500 8.462812 \n", "2022-08 -2.755980 -3.492820 -2.356541 -5.237054 -4.199276 -3.342678 \n", "\n", " Clths Hlth Chems Txtls ... Telcm Servs \\\n", "Date ... \n", "1926-07 2.461427 2.468088 3.089290 3.390858 ... 1.967368 2.438800 \n", "1926-08 2.195327 2.201268 2.755313 3.024279 ... 1.754679 2.175146 \n", "1926-09 0.299363 0.300173 0.375725 0.412402 ... 0.239274 0.296611 \n", "1926-10 -2.694265 -2.701556 -3.381521 -3.711615 ... -2.153470 -2.669498 \n", "1926-11 2.103855 2.109548 2.640508 2.898267 ... 1.681568 2.084515 \n", "... ... ... ... ... ... ... ... \n", "2022-04 -7.866589 -7.887875 -9.873205 -10.836998 ... -6.287601 -7.794274 \n", "2022-05 -0.282732 -0.283497 -0.354851 -0.389490 ... -0.225981 -0.280132 \n", "2022-06 -7.010079 -7.029048 -8.798216 -9.657071 ... -5.603010 -6.945638 \n", "2022-07 7.958061 7.979595 9.988010 10.963010 ... 6.360712 7.884905 \n", "2022-08 -3.143309 -3.151815 -3.945107 -4.330217 ... -2.512382 -3.114414 \n", "\n", " BusEq Paper Trans Whlsl Rtail Meals \\\n", "Date \n", "1926-07 3.197406 2.808852 3.369184 3.216617 2.864325 2.801978 \n", "1926-08 2.851740 2.505193 3.004948 2.868875 2.554668 2.499061 \n", "1926-09 0.388874 0.341617 0.409766 0.391210 0.348364 0.340781 \n", "1926-10 -3.499863 -3.074555 -3.687891 -3.520892 -3.135275 -3.067030 \n", "1926-11 2.732918 2.400810 2.879742 2.749339 2.448224 2.394934 \n", "... ... ... ... ... ... ... \n", "2022-04 -10.218736 -8.976940 -10.767731 -10.280135 -9.154228 -8.954970 \n", "2022-05 -0.367270 -0.322638 -0.387001 -0.369476 -0.329010 -0.321849 \n", "2022-06 -9.106125 -7.999535 -9.595346 -9.160839 -8.157520 -7.979958 \n", "2022-07 10.337559 9.081323 10.892937 10.399672 9.260672 9.059098 \n", "2022-08 -4.083174 -3.586980 -4.302540 -4.107707 -3.657820 -3.578202 \n", "\n", " Fin Other \n", "Date \n", "1926-07 3.430568 3.124286 \n", "1926-08 3.059696 2.786525 \n", "1926-09 0.417231 0.379981 \n", "1926-10 -3.755082 -3.419826 \n", "1926-11 2.932209 2.670420 \n", "... ... ... \n", "2022-04 -10.963911 -9.985049 \n", "2022-05 -0.394052 -0.358871 \n", "2022-06 -9.770166 -8.897882 \n", "2022-07 11.091399 10.101154 \n", "2022-08 -4.380929 -3.989798 \n", "\n", "[1154 rows x 30 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_mkt_rf = beta2.values * df_mkt_rf\n", "beta_mkt_rf.columns = beta2.columns\n", "beta_mkt_rf" ] }, { "cell_type": "code", "execution_count": 20, "id": "983657cf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RI-RF')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x = beta_mkt_rf.iloc[:,0], y = excess_return.iloc[:,0]) \n", "ax.set_title(\"Food Industry\", fontsize=15)\n", "ax.set_xlabel(\"b*(MKT-RF)\", fontsize = 15)\n", "ax.set_ylabel(\"RI-RF\", fontsize = 15)" ] }, { "cell_type": "code", "execution_count": 21, "id": "10ca5ff5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RI-RF')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x = beta_mkt_rf.iloc[:,1], y = excess_return.iloc[:,1]) \n", "ax.set_title(\"Beer Industry\", fontsize=15)\n", "ax.set_xlabel(\"b*(MKT-RF)\", fontsize = 15)\n", "ax.set_ylabel(\"RI-RF\", fontsize = 15)" ] }, { "cell_type": "code", "execution_count": 22, "id": "0f4739e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RI-RF')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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twb28iFqHFw53fETGD0WjEQRBJAHbSyqxeMMBVNZ5YDGoUFnnweINB7C9pDLkeJ8gorLOg1PVrqiERpQk7Cytwic/VLT31AHQzoYgCCIpWL3tKNQcC72WAwD5b498PHB3wwsiah0+2N08pCiKkQmihC9KKrFhx0kcq3CiIEOPywZnQsW1716ExIYgCCIJKKt2wWIIvmXrNCzKql0AZNGoc/hQ7/JFJTJeXsRH+87izZ2ncLbWrRzvkW1ErcOHzBRtu86fxIYgCCIJKMjQo7LOo+xsAMDtFZGfrkON3Qub0wcxCpFxeHh88N8z2PT1KVgdPgAAA+CSQZn43WXdcfl52TGZP4kNQRBEEjBrQk8s3nAA8Mg7GpdHgIcXccPIPNQ1iEYkah1ebPr6NN7/72k4GiLSVCyDKy/IxpQxBSjIMFBSJ0EQRFdn3OAsFE8dhBc+PoKTVS5kW7T47ZgCDO+dHvF9Z2vd2LirDFu/K4eXlxM4dWoW1w7vhptG57e7uSwcJDYEQRBJgCRJuLBnKpb+bgh4oWV72bEKB97YWYbPfqxQzGtmvQo3jMzDpBF5SDGoYzzjYEhsCCIC55LXQBDtjd3Fw+rwRiUypWX1WL+jDLt+rlaOZZo1uOniAlwzLBd6DRfh3bGDxIYgwuDPa1BzbFBeQ/HUQSQ4bcAv4Iun9Mbcl/aQgEfA7uZR6/DCx0cWGUmS8N2RWqzfcRLfH6tTjuen6zFlbAHGD8mGRhXftEoSG4IIQ7R5DUT0BAo4w4AEPAySBJyqdik+lnDIiZjVWP/VSRw8Y1eO9841YtolhRg7IBMcy8R6ulFBYkMQYWgpr4FoPSTgkfFXYjZwUkSh8QkiPvuxAht2lAV9H4cUWTBtbCGG904Fw0QvMiqOgVmvglkfOz8OiQ1BhCFcXkNBhj6Os4o/bfFjkYCHxu0VYHV44fbKAmMI41ZxewVs+e4s3txVhqp6r3J8VL90TBtbiEGFKa36XJ2GhVmvhkkXeykgsSGIMDTNa3B7RfgEEbMm9Iz31OJGW/1YJODBeHwCah2+Fisx21w+vPvNGbyz+xTqXTwAgGWAy8/LxtSxBeiRbYz6MxkGMOlUSNGroYlhXk1TElps3njjDezcuROnTp2CWq1G//79MWPGDBQVFSljJEnCunXrsHXrVtjtdvTr1w933nln0BiCOBf8eQ0UjdZIW81ggQIOAC6P0CUFPNpy/9U2D97adQof7j2r9J9RcwyuHpqL315cgNw0XdSfGWgqi4cfJ6HF5scff8R1112Hvn37QpIkvPbaa1i4cCGeeeYZmM1mAMDGjRvx9ttvY968eSgoKMC6detQXFyMZ599FgaDIc5XQCQ74wZndWlxaUpbzWCBAi5JQJZF26UEPNpy/yerXXjxszPY9n05fA3hzgYth19f1A2/GZWPNJMm6s/UaziY9SoYO8BUFgnG5XJFUU0nMXC5XLj55pvx8MMPY+TIkZAkCTNmzMB1112HadOmAQA8Hg9+//vfY+bMmbjmmmta/RkVFXJ57ZSUFNhsNkXUiObQ+kSmM67P9BV7mpnBXB4BWRYt1swb2apzdcb1CYdPEFFrlysxR+LwWTs27DiJLw9UKYmYFoMavxmdj+sv6ha1YMTLVKbThd9pJfTOpikulwuiKMJolO2T5eXlsFqtGDp0qDJGq9Vi8ODBKC0tPSexIQgiPOTHah28IKLO6YPNFbnc//7jdVi/4yS+OWRVjvnL0Vx9YQ606ugSMVUcgxSD7PBPlJBnP0klNqtWrUKvXr0wYMAAAIDVKv9iUlNTg8alpqaiurq66dsJgmgj5MeKjmjK/UuShD0HrVi/4yQOnKxXjnfPMmDm5XkY0S8n6p4yiWIqi0TizqwJq1evxk8//YQnnngCHBes8k3jySVJChljvmXLFmzdujXi5yxYsACAvMWXJAk2m62NM++8dJb18fIinB4egghwLGDQqtol27qzrE9ThnbXYeVtA4OOnct1dtb1ESUJkiS3QU4NUeOSFyR8sr8Ka744hUNnncrxwQUmzLi8AJcOSAPHMpAgAYjs22EY+f7HQITo88HWcvHnmJL0ZrQXXngBX375JZYsWYLc3FzleFpaGgB5h5OV1fhkVVdX12y3AwATJ07ExIkTI36W32djNpu7lE35XOgM6xMYyhtoFmqPjPZ4rE8y1XLrDN8fP6Iood7lQ72ThxCmqYyXF/Hx9+V4c2cZzlgbm5UN65WKaWMLcX4PCxiGQZ0XSNMJsLpDm878pjKzTgU2wUxlkUh4sVm1ahW+/PJLLF26FIWFhUGv5eTkIC0tDfv27UO/fv0AAF6vFyUlJZg5c2Y8pkskGZ0po51quXU8kiSh3sWjzuELKzJOD48Pvj2Lt74ug9Xe2Kxs7MAMTB1biH550QmuXsMhxaCCQZvwt+2QJPSsn332WXz22Wd4+OGHYTKZFB+NTqeDXq8HwzCYNGkSNmzYgIKCAuTn52P9+vXQ6/W47LLL4jx7IhlIxIz2c92ddCbhTHQkSWookukLW4m51uHFO3tO471vzihRaCqWwfjz5WZlhZktp2awDGBqyI2JdyHNtpLQYvPhhx8CABYuXBh0/JZbbsGtt94KAJg8eTK8Xi+ee+45Jalz8eLFlGNDREWiZbS3ZXeSiMLZGWmp3H9FnRtv7jqFrXvPwtNQ30yrZnHtsFzcNLoAWZbompWlmzVJZyqLREKLzXvvvdfiGIZhcOuttyriQxCtIdFCeduyO0k04exstFTu/0SlExt2nsRnP1YqJjWTrqFZ2cg8WKJoVmbQylFlgtcFczs3N4u3Py+hxYYgYk2ihfKG2p34BAHfHanF+OIvIs4v0YSzs+D08LDafWGrMP98yob1O05iV2k1/DKUbtJg8sX5uGZYbos+llCmMps34ltaTSL480hsiC5PIpWkabo7sTl9OGP1QMUyLd4kEk04kx1/uX+Pr7nISJKEfUdrsX5HGfYdrVWOd0vTYerYAlx5fk6LPha1ikFKQ8XlWJvKEsGfR2JDEB1AtCaMpruTijoPJEjISZUDYlq6SSSScCYrHp8Aq92nFL4MRJQk7PpZblb2y+nGZmW9cuRmZZcMbLlZmd9U1pFRZYngzyOxIYgY0xoTRtPdiSgB+Wl6mPSN/1TJ6R8bIlVi5gURn+2vxBs7TuJEVePan9c9BdPGFuKiPmkRm5WxDOS+Mfr2SRhuLYngzyOxIYgY01oTRuDuxF/4MpDW3iTi7RhOdLy8CKs9tMi4fQK2fncWG3edQkXA72Fk33RMG1uAwd0tEc/dkaaySCSCP4/EhiBiTFtMGG29SSSCYzhR8fIi6hyhKzHbXD68980ZvLPnFOqcjc3KLhuchSljC9ErJ3KzMoOWQ4pBDb0mugKasSYR/HkkNgQRY9piwmjrTSIRHMOJRqRy/9U2LzZ9fQoffHsmqFnZVRfm4LdjCtAtLfzvzG8qMxtUUEdZQLMjibc/j8SGIGJMW3cnbblJJIJjOFHgBRG1DTuZppWYT9e48OauMny8L6BZmYbDdRd1w42j8pFhDt+sTKNikWJQwaRTRfTbdHVIbAgixsTThJEIjuF4E6mnzJFyBzbsOIntJZUBzcpUuHFUPq4fkQdThJL9iWYqS3RIbAiiA4iXCSMRHMPxIlJPmZITdVi/owx7DtYox7ItWky+OB9XD82FLkyzMo5lYNKrkKJXRd1rhpAhsSGITkwiOIY7GlGUUOv0web0IbAQsyRJ+O8huVnZ/hONzcoKM/WYOrYQV5yXFVZAtGoWKXo1jDqOTGXnCIkNQXRyWrurStZQ6XA9ZQRRwlc/VWHDjpM4fNahHO+XZ8K0Swpxcf8MsCEEhGEAo1aFFIMq6rbMRHhIbAiik9EWsUjGUOlwPWW8vIhPfijHGzvLcLqmsVnZhT1TMW1sAS7smRpyl6LiGJgbapW1VA2AiB4SG4LoRLRVLJIpVFqSJNhcPOqcwT1lnB4em/eexVtfn0J1QEXLMQMyMG1sIfrnh25WptfIZWSMEYICiHOHVpUgkpRQO5i2ikUyhEqHa1xW5/Thnd2n8G5AszKOZTB+iNysrHtW8x5Xnak5WaJDYkMQSUi4HYzTIyAnNbg5V2vEIjBU2u7iUVXvgdsnwKhVYXtJZdx3N6Eal1XWebDx6zJs3ntWqdCsVbGYOCwXky/OR7ZF1+w8iVJGpitBYkMQCUJrfC3hdjA+3ge3VzznvBp/qLSznkeVzQOAAcsw0Gu4Fs1xsQwsCNW47GSVE2/sLMOnP1SAD2hWdv2IbrhhZB5Sjc0TMSk3Jn6Q2BBEAtBaX0s4c5eKY1Dv8uF0jQu8IELFsTDpVVgweUBU8/CHSs9/+XtAYqBVs8gwa2A2qOHyCGHNcbEKLAjVuOzgablZ2Y6fGpuVpZnUuGl0Aa4dngtjk9L9/twYsz4xy8h0FUhsCKKdaPpkv3x6/6jfG42vJfD8NqcPPC9Cq2ZRbfPCy4vgWAbpZjW8DaYkJdKqaUZjC4wbnAWzQY2CTH1QtFYkc1x7Bxa4vAJqHV64vWLDJUj44Vgd1u84ib1HapVx3dJ0+O2YAvzqgubNyig3JrEgsSGIdiDUk73NxaPkdGVIsWhqZiqrdoFjgGPlbnh5ERqVvKPw39wDz88x8s243iU7wVUcwICBTxRRWeeFQcuBYxkIogSOZcCxbKtv+gUZehwrd8Du5pX5mHQq9AhT7bi9AguaNi4TJQm7f6nB+q9OovSUTRnXM8eIqWMLMG5QVlB4MuXGJC4kNgTRDoR8skfjk/3KDw/h+a1HwIsitCoWgiAGmZlMOhUOnbWBY1hwLAOfIOGU1YU+ueag8wuihDO17oYndXnHwguAVs0gL1WPk1VO1Dt5qFUMWFaucFxj98InNG9tHIkcixa7f5FLuTAAREmA2ydg2iWFIce3tQZb08ZlvCDi85JKvLGjDMcrncq4QYVys7KRfYOblZ1rbkyyJrAmIyQ2BNEOhHqyZxjg0Bk7Ji3dgZ8bnsrVKga8CFTbvcgwaRQxkiQJDGQB8Ru9GDCQGkxgZdUusIyEU9VuiBLAMo2mMY2KharBLyFJsgT5I6wYBuAFKSh6qyW2l1Ri675ycKxsgRMb/qQa1Nh9sAZzQ7znXGuweXkRtQ4vHG5ZZDw+AVv3lePNnWVBzcpG9EnDtLGFOK8ouFmZTuM3lbX+VpaMCazJDIkNQbQDoZ7sRVHOB3EE9E2Ro6kkMAxQY/eCa3BYOzwCuqVpUWPzKWardLMajoYnfaOWw5Fyh1LrK6jmFyQ4PSJ+OWVThMrtFcEyje4au5vH9BV7onpyX73tKARBgopjlN2DKMpCEM4s1toabLwgQpQknK5xQZLk+b3/zWls2n0adU4fADkH5tJBmZg6thC9c03KexlGjjpL0auhUZ+7wz+ZElg7AwkvNvv378emTZtw6NAh1NTUYN68eZgwYYLyuiRJWLduHbZu3Qq73Y5+/frhzjvvRFFRURxnTXQ1Qj3ZSw27gRq7F41GrwYkwMtLSgl7v1j1yGkM13V5BGRZtNheUoljFc6wuxN/OLCvyet+QWIA6NRs1E/uZdUuaNQseEGE31LFMoCHj2wWi6YGW2Al5lSt3Kzs7a9P4f1vzygmNDXHYMIFOZgypgB56Y2f195lZJIhgbUzkfBi43a7UVRUhPHjx2PZsmXNXt+4cSPefvttzJs3DwUFBVi3bh2Ki4vx7LPPwmBonjFMELEg1JM9wwAZKRrY3Tx8QnB/e78snKh0YntJZVgz1Ki+6Vjw6o9Bob9+mglYGCQAJr066if3ggw9eF729YiivAsTRIBlGFjtXowv/qLFnUtTX8htV/bA+T1SlUrMZ61urNpzAu/+t0IRSb2Gw7XDc3HT6HxkmBsTU2NVRoZ6/XQsjMvlal1cZByZMmUK5syZo+xsJEnCjBkzcN1112HatGkAAI/Hg9///veYOXMmrrnmmlZ/RkVFBQAgJSUFNpsNZnPoOkoEOt36RHIWby+pxN/e+QVHy+WqwT2yDXjgxv4Rb9onz1Zj9qoDEEQpyMkdCMsA+Rl6/GZUPrZ8dxZHyh1KFJm/z/3RckezXUtTWhIeo5ZDjxwjJElCnZPHp4svC3vNALB4wwF4fSJsbh5en7zD0akZZFn0QWIYapcU6AvRqhk4PSI8vIC51/RBVooW63ecxBf7G5uVpehVuGFUPiaN6AazXi1fTzuZyiIROM+WrqmjSdZ/Wzpd82oNfpI6w6m8vBxWqxVDhw5Vjmm1WgwePBilpaVxnBmRbPhvPJV1niBn8faSSmwvqcT9L+/Dz6ds8PIifLyIQ2fsWPCfH7C9pDLsOQ1aFXyCnP8SLs1DlAAfL+L5j46gzuEDAzSYiCTUO304dMbeotAAkNWm6aEAE5h/ZxT45B7umgGgeOog9MgxIs2kwfA+aeiZY0SWRQ+9Vs5Z0Ws5qDk5pLopq7cdhYploFax4AU5KEIQJDy56Wfc+dxefPajLDTZFg3uvLoX1swbif83rjvMejVUHIN0swaFmQZkpmhjJjRA4240y6JFnZNHlkWbEELTWUl4M1okrFYrACA1NTXoeGpqKqqrq+MwIyJZieQsttq9sLkazWASZMd7ncMX0SSlUbGKae1IuSPkGACorPeAAQO7mwfDyGLDC0B5rScqMxkQOm/Tf4xhZD+IyyMERYhFuuY180YGXdf44i+i8m9IkoQTlU4YdRx4QYDTK+fN+JMzAdl8NXVsAW66KAN2Xt7JtKeprDXhzPHqoNoVSWqx8dM0O1iSpJAZw1u2bMHWrVsjnmvBggUA5G2sJEmw2WwRx3dlOmJ9vLwIp4eHIAIcK+8WzrU6b6RzLZ7SO+TuQ5LkqLJwN32OZcKugSRJGNpdhydv7atElcUDRvmP/BfLMDh+phqPTO4l/9yw8wq81pNnq4PW599/PA+CiKA1kiR5Hf3XL0kSRAl4+a7z8GWpFet3nsGZmsbw5b7dDLjtikJcNihd3u0BSFcLYBgGDESIPh9svrZdq5cX0T2NVX6ffsGtttYlVVXnZL33RDKjJbXYpKWlAZB3OFlZjU8ndXV1zXY7ADBx4kRMnDgx4jn9Phuz2Zy0dtOOItbr05429ZbONfelPc2cxf5osO+O1MLLi403banRP5KiV+EP43tg98GaZk/S1dY6TF3+XVTRTSzTuGNqDRwrO++DzgVAapinWsXizqt7YdPuU1BzLHyCgDNWDyRIULFykijDAGlGDawOLyRJ3pHlpuqC1ue7E6HX7y9TBmKYRYdah5z1/8kPFfjP58eD+shoVSwMOg6/v7wHhvbJgEtkkKJVgxHc7f79mb4i/O9xzbyR7fpZsaQz3nuSR+pDkJOTg7S0NOzbt0855vV6UVJSggEDois8SCQugWaelvwEbT3XrAk94RNEuDwCJEkKMjn1zDGCCxCDQD2od/H4xweHsOeXGlhtHhyvcGDxhgNY+eEh1Dt9UYfRSlLrhQZoLjQAIEI2m7EsMLRXKnYfrFGuvaLWA0GUIAiAxydCkiRIklymX5JkLc00a5qtTyj/xgM39kevXBNOVrmwYUcZZv7jGzz93kFFaFL0KqSZ1OhfYMb9k/rh8vOykZumQ0GGASkGdesvNgrKql3QaYJvaxTOnBgk/M7G5XLhzJkzAABRFFFZWYkjR47AZDIhOzsbkyZNwoYNG1BQUID8/HysX78eer0el112WZxnTrSV9syDaOlcLSUlLnj1R9Q5fGGd9RLkPBR/ZYBXPj2Ga86/MKq5mXUsbO7WlZNpCS8vQatiMapvOp7begSSJIFlGXj44PlLkpy/wgsSNCoWmQ0VnoHma+33b3z03Vm8+MlRLN5wACzDwObilVpmLANc0dCsrEe2ESwDmPVqmA0dU3GZwpkTl4QXm0OHDuGhhx5Sfl67di3Wrl2L8ePH47777sPkyZPh9Xrx3HPPKUmdixcvphybTkBrbhwthS3bnD6UW91BJfObniucs3jc4Cw8/rshWL3tKP57yKrsBprKjt+nUe/0wcPLvg+WCc72bwoDtLvQ+DHqWPzrw0ONnx8glAzkuao42XeSolchM0Ubca0dbh4f7TuLv7/7C1weAQ6PoOzGVByDa4flYvLFBchJ1cWtOdm5ls0hYk+LeTaff/45hg8f3unsh+GgPJvoSRSfTaRxgJwz4uNFpRkYAyDdpIFGzbba/zN9xR58c7AmrICwjHwT16g4bJw/FNc+/t+Q42THfGgzWFthGH/xzMjjNCpGqX0295reil8ncA1/MyofO3+uxskqJ1KNapyqdistlwH5eo06FQoz9Vg288JWNSeL1fenMxTXTNZ7T5sCBJYvX46nnnpKuXBRFHH77bdj0aJFVBKGiCnR1tuKFMILAGqORYpBDa2aU9ocu7wC/vr/zmv1TWjWhJ7Yc7Am7OuiBDAS4PZGjj6TEBuhAZr7lcLh5SUwkJNK517bB0OKLEFrPaxXKt7YeRKCCDg8PMprGyPLOJZBqlGFFIMaHMOgxu5DfoY+ISK+KJw5MWlRbCSpqY1XQnV1NXy+NsYoEkQURHPjaMkf43/NpFc1VEaWs+jP5YY0bnAWjBoWdk+MlKIDYQBwHAO3l8f2kkplrd1eAZu/PYNF6w/A7Qt9nWoW0Kk5aNUcPF4BRVmGhBAaInGhbweR9BRk6IOSBoFGf0Ok186VwUWpsBhUoZL2YdSyctTaOZ+945CAoOZqHp+A09UuPLnpZ/xlXUkzoQm8Xp8oobzOjVq7F7wokU+EaJGEDxAgCCCyHb4lp3C4187Vtj9rQk8s+M8PULEAL/pv2vJrziTa8chJjxKqbR64vTxe/eIENuw4iWMVjXXc5N2P3KAtUEBFCZBEoLLei7nX9CazFdEiUYnNe++9h/T0dACNZrX33nuvWeIkwzD4wx/+0K4TJIiWmly15Nvxv3bojB0+XoSKY/DU2z+jxu5Fil59bo2zmpQbkKsSMAhfayDx4BgAkCBIQL1LwJObflZe02s46DUs6hy+sKVw1Jz896bdpzCkyEKCQ0SkRbHJysrCgQMHmh3bv39/s7EkNkQsiKbJVSTfjv/44g0HoDaoodOwOHLWAZ8owqhVKUme0ZTf315Sifkvfw+7m4ckyRFlouh3+Ceu0DAANGoWngDTmATAywePuWRQJs5Y3fD6BBh1Kph0qmZ5TX6Z5QXAoGWV5E8SGyISLYrNiy++2BHzIIiwtEdyZ1PBEkQJLBhU1Xtg0quiOuf2kkos+M8PqHc13qFjFVHWXnAsg0yzBpkWLURJwtGzdrh9sigGzj3dpMbf/3ABBhSkYN/RWizd+BM8PhEmvQoMIzt3/X1tAiU1w6yhDH0iKtrVZ+PxeKDValseSBCtoD2ywg+dscPl4eFryJRnWQaSKAU1JWvpnE+9/TNqnXzUTcsSAUGUUO/yoareAzAh6qgxgFmvwiPTBmF0/wywLIPx52dDxTGKWdKoVUGv4aBVy6IiSg25RBwDs0ENl0egDH2iRdolGq2urg6vvfYabrvttvY4HUEEEaluWTRsL6mE3c3DK8hNyXyCBJ8gQmhwRhw9a0dpWT1OVbswqm962PMcPmuHICSTV0bG5RUhSM2FhmtoPaDmGBi0wZn+4wZnYc28kfh08WX4+8wLoFGzUHEs8jN0UDXUXctJ1bX6d0F0XaLa2ZSUlODzzz9HVVUVcnNzcf311yMvLw9WqxWvv/46PvnkEwiCgCuuuCLW8yW6INEmd4Zj9bajSDWo5TbHklzlmGUYiIIEcA11xNQczDpVM2e3P2Kt5HhtwpvMWkP3TL1SA6263oP5L38Ps0Edcm2brn+vHCMYRu6/o1exUHEM/mfND+AFCWoViz7dTEmZtU/ElhbL1Xz99dd4/PHHYTQakZeXh6qqKng8Htx77714+umn4fF4cNVVV+Gmm25CdnZ2R807ZlC5muhJhvXZXlKJuau+a6hnJkdeSRKUXidcQ3JiZooWJr0KLo8AtYpFqlGNQ2fsqHN4IYE5J+f/hwsuCluuJt4M7p4CALC7eJyqcQISg375pla1cfBHCXp9Imrs3oYdn4RMsxZqVculgJLh+xMvknVt2lSu5s0338SwYcPwv//7v9BqtZAkCS+//DIef/xx5Ofn4y9/+Qtyc3PbdcIEES0tFeCUKxMDXkFSaoVxTGNNSgaAT5Bw2upCHvSQJAnHK53INGsCerIkm+GsOYGNxAKpqpfrxWnVbKui8oDGoIsam1fpMCqKchXonFQdRagRQbTosykrK8Ovf/1rxfHPMAwmT54MURTxu9/9joSGaHe2l1Ri+oo9GF/8Baav2IPtJZVhxy3ecACVdZ6gXBn/eP/NMCdVp8gFg6DixxAawpeZhsi0ijoPWEgNRTs7D4FC428RLUkS3D4BDOSoMpvTh2PlDpyscuK7I7Vh192Pv3eMlxeVtCOWkbtlUoQa0ZQWxcbpdDbbzplMJgAI6o5JEO1BSwISSEsN0cqqXeAFEVX1HuVm2/Th3t8KmYF84/UJIliOhdiJ/DNNuWZYLrIs2obCmgwEQUJ5nQdl1S74GhxTLIOw6+7HXwpIo2KV9RUbOn1SDxmiKVFFo5WXl+PEiRPKn5MnT4Y8fuLEiZhOluj8tKY7Z6SujNtLKlFj8+JElQtOr9A04T8ILy/Bx0swalXonWtKyoizaNCqGORYtCiv82DWhJ4waDlkpWjAcnLXTlFqSExl5Eizlrqi+qMETToVJAngBQmiJMKsV1GEGtGMqKLR/va3v4U8/sQTT4Bp+FcsSRIYhsE777zTfrMjuhytSeAMl39j0qmweMMB8Lxc5r+ldssMAIYFLj8vCz+ftoNP4EoAbaFPnhmSJKGs2qWIur/1wolKp9L2Oi9Nr1THjmQKC4xS8wmiEo1WlG2kaDSiGS2KzZIlSzpiHgQBoLmA2F08ymvdECXg+iVfKSG3BRl6jOqbjk27TzUrsqniGLkFMcNAxcqBAWJAQEAzUxrkiLSt+8qRn65HplmNKlvna6Fx6IycJ2TQcjjo4ZGbJkcOmfQqGLSckuDqr6gQjSmMescQ0dKi2AwZMqQj5kEQAIIrOPOCiFNWFxgwSDWocKTcAQkS8tNkQdq0+xR+Myofuw/WBEWjLVp/ABaDChoVK4sPywCSbCriOAZ8Q4SAX3gYBnB4BLCMXHdNr9V3SrHx+ESoOMCg5WC1+1Bd70WmRQ78MWg5ODzyTvBouQNmnQoaNUumMKLdOOdyNZIk4fXXX8fEiRORlpbWnnMiYkQytMsNNM18d6QWapZFtkWLapsXcoI7i2qbFz1yjIAH2H2wBmvmjQw6h393lGHW4IzVDVGUIEmAigXEAJua//9ULOALaKxpc3Y+oQHQ0A5bC5uLh08QUVHvAcMAahUDq8MHjpGbqXl8AgRBwpyre0X9/UiG7xYRX865XI0oinj99ddRXV3dnvMhYkRrorzijb9USpZFi165RpgNaiW81h9aC4T35fgd1yqORW6aVq4WIAG9upkx95o+UMm19cEwstAwDCPvciSgotaNE1WdM2RXAlBZ74GHF6Hm5Gu2Onw4Y/VAxTIoyDSgb54ZAwpSkJ+hx+4I7a8DSabvFhE/2lSIs2nLaCJxiaZMf6IR6L/xm8QkQGk/HMqn4H/Cdrh5xWF9Qc/UoCft3QdrcKzcIddL40V5XTQMnB4BlfXeptPoNPgFVWgo06PXcDDpVKio9wCS1JDgKftsWpMnk4zfLaLjobbQXYRIYcKJSmABznSzWg7NlURkmDUhC0AGPmHnpumQmaKFQcs1M+nMmtATGrWc7Nk3z4ScVB1ESYJP6NwPT1LA34IA6DWsXGZGkmvF+Ssp2F18q/JkkvG7RXQ85yw2LMvilltuQUZGRnvOh4gR/gS8QBI98c7vv8myaCFKDHrlGNEn1wxBArIs2ma1t6LN0Qk8b52TR63DC5tLaPrxnRaGkf+4vPJOUatilLpmkIDyWner8mSS8btFdDznbEZjGAa33HJLe86lTXzwwQd46623YLVa0b17d9xxxx0YPHhwvKeVMARGeQWGCSd6tFFrQmvD5egcPG3D9Uu+wrEKJ0RJajCbseibZ8bk0fn45weHYjH1hMMffecvROryCAADcCpWDqJoiA9nGCb69thI3u8W0bG0KDb//W/rqtZedNFF5zyZc+XLL7/ECy+8gLvuuguDBg3Chx9+iEWLFmHlypWdohJ1e9DWMv3JQKgkz9PVLtQ6edTYGyPMeEGAlxdQWlaPPb/UxKxawHMfJVZFjabXKUI2bUiSBBUHSJIcSNEj29Cq70VX+G4RbafFFgOTJk0CwzBRBQPEq4LA/Pnz0aNHD9xzzz3KsdmzZ2Ps2LGYMWNGq85FLQaiJ9HWx++zUXMsdBq5GvHZ2s5VULOtsAxQmGmASa/CwdM2ePmGzqUMFJ9Yn1wz3n1obMznkmjfn0QiWdemTS0GVq9e3a6TaW98Ph8OHTqE3/zmN0HHhw4dip9++ilOsyLiwbjBWfjNqHy88ukxJUExnqSb1EE7qnihJK9C3v35KwTIgQFyFWgvLxfUzDXrYXfz8Zwu0UlpMUAgOzs7qj9nz57FypUrO2LOQdTX10MURaSmpgYdT01NRW1tbYfPh4gf20sqsWn3KWSmaNE/3wQxzjXOXr4rvtU3OLah7htkH02KQaV055RfZ8AFtIIG5BwmcuwTsSCqAAG73Y69e/eiqqoKOTk5GDVqFFQq+a1fffUVNm7ciMOHDyMvLy+mk40EE6msbwNbtmzB1q1bI45ZsGABAHkbK0kSbDZbu8yvMxKL9fHyIpweHoIo3ywNWpWSV9MSPTNYrJ5zHgC5UkCs0sBEScLXv9Riw64z+OWMUzneK1uPKRd3wyUD0sCxDNJNany4oON9mIAsMGDkv/214tw+OWLM30gtcI0Ca8YZtVyHfO/p31d4knVt2mRGO3bsGIqLi4N2Cb1798aCBQvwt7/9DT///DMKCwsxf/58XHrppe0y4daQkpIClmVhtVqDjtfW1jbb7UycOBETJ06MeD6/z8ZsNiet3bSjaO/1aepziaZFcWCZlMo6D1L0HOpcPBgwSqWBjuJIhQtPvHMETzS4LePRFpppEBi1isWcq3ph7rV9lNealpSx2r2oc/iU5FaNioVJp0KPHGOzEkCxgP59haczrk2LYrNmzRoYDAY8/PDD6NmzJyoqKvD888/j/vvvh8/nw7333osrrriiI+YaErVajT59+mDfvn245JJLlOP79u3DmDFj4jYvovW0NhM9UJwsBhWq6z2osvnAsXK9r64Gy8i7GIaR+9I8v/UIhhRZlLVrGkY+vvgLZKRolGKcAFpsK0AQ50qLYnPo0CHccccd6N+/PwCgoKAAf/zjHzFnzhzMnTs3rkLj58Ybb8SyZcvQt29fDBo0CJs3b0ZNTQ2uueaaeE+NCKClYo2t6WUDNBennFQdjlc6IYoNJVm6GKIEsA0+GLm4qKgktIZa93D9gMhnQ8SCFsWmtrYWOTk5Qcf8uSs9eyZG0tall16K+vp6bNiwATU1NSgqKsIjjzxCOTYJRNNdiL9YY6CJLNLNL5RQNRUnk14FjYqRO28KUsjeNUDonjadDVECtCoWh87Yw647JWMSHUmbCnFyHNfyoA7iuuuuw3XXXRfvaRBhiMZEFu7mN6pvesgbpkmnQo3NC5ur0edg1KrgE3wNkVaAIELpXwN0PqHxGwuVa2poFCdBQopBA6dHgNqgDrnua+aNpGRMosOISmweeeSRkMKycOHCZsdfffXV9pkZ0amIxkQWLhPdL1S8IOJ4hRteXgTHMjBofahz8mAYBhwDeHgRbp8AjYoBx7Lw8SK0Kha8EJBz08nUpuml+AT5mlMMGqhVLFS8GLFIJnXaJDqKFsUmkeqfEclLtP6BUDe/ResPgGUknLXKzb5YFhBFCTV2HhaDCrwgwdsgLCadCi6vgMwULfRaDjanDw5Po6B1hq4Y/h2bHwaAScdi5pW9mnUtXb3taEL7ZajpWteBxIboEKL1D4S6+RRk6PH90Vo5L0SUzUT+tCqfIKFnjhF2F4+qeg+qbR65542LhcPNo8be+frTNBUaMADDsNh9sCbkzTpR/TJeXmzRj0d0HqifDdEhNC3rH6pFgD+I4HiFA1abB3t+qcHsZ7/FD8fq4PaJEBqEBmjcobg9AuwuHqetLvgECSwjJzBCklDr8HXaHjUsI7cGUHFyFQBeEEN2yIxm3eOF08NH1RKC6By0KUCAIFoikplke0klpq/Yo7xW6/DBx4uotnvlkvcAIAEub3CdMzmfhIUgihAb+q/Ixb/k92RbtPD4RHj4+NclixWiBHh5+XpZBvBI4fOSEtUvI4igpmtdCBIbImZECncG0Oy145XOhnpejVXG/aVV/DCM3Bbab0oza1VKGRZ/n5qqeg+c3o6tHhAP/MsiSgADCXYXD6OOS5qbNcfK/qNE9ScR7QuJDREzIoU7A2j2GsfKT+tBMVYNlYkDzWcenwiNmkWmSYuibCOsdi9OVDrh8goJUe25o2EgF9WsqveAY3VJc7M2aFXwCWJC+pOI9ofEhogZLYU7B75md/FB+TB+/GYiv/74C0vyvIgqmxc1Ng+8XU9fguA4gGMAt09Iqpu1RsVSnk8XgsSGiBkthTsHvlZV7wHLMmAlCYIYnD/irwANACqWAS8CggQwgoTO2nnFoGHh8ooRU4JYRi64qWIZeHwijFpVwjj/oyVR/UlE+0NiQ0SkLXkQLYU7B73mE8AyDLqly0JUbfPC5RWUpl9aNQe9moHLK8LTUM25s8WZMZBziARRNieqVUxDKwC5QGbT9jyiBPh4EfnZxharYxNEvKHQZyIsfgd/ZZ0nyMG/8sNDmL5iD6ptXkxfsSco1DaQSGG3TV8zalVIN2lgNqhhNqjRI8eIHtlGjOybjov6pMGo4VDr4BWh6Uz4c4YYRu7LpOYYaFQMBFEO5Z57TW+YdCqoOAY6jdx+wd/jRwISKpyZIMJBOxsiLKEc/M56Hs9/dAT56XowDFpMxItkJgl8zS9sLo+g7IK8vICpYwrg8PB4dP0BCE1iB6KBYYBMswaV9Ymb3CkFxUNIyE83KK2bq+s9eOXTY7C7eWVno/JXduYAi0HTIb1nCKKt0M6GCEtZtatZHkS90wdBkIIEKFIinj+XZnzxF1HvgqwOHywGNWb9qhfO1LqxetuxoKz51sAyQI0ttNAYtYn19WdZBvlpekVo7C4eVTYPnB4BOg3XUKZGrmit5hhkmrXo080U51kTRHTQzoYISygHv4cXoVUHF18Nl4gXKs9mwas/It2kgcMjBPmARFHChT1TsfR358Hu4rF1Xzme2XwYFXUe5XxqjoEoyaYlQIIvIAqNYxmo2MZER6AhZFpsvhnyd7PkBTmyTRSb+0M6Gr2Gw/k9LKgMuN6qeg8ABlo1iwyzBmesbjnPiGOQk6pTKmIHJsZSNBeRqCTWox2RUMya0BM+QYTLI0CSJLg8AlQsC7Mu+BklXCJecLVmJ45XOlFV78XRs3ZFfB5dX4L3vzmNk1VOnKh04j+fn8CMf3yjCA3LAJcNzsIfr+mllNNnWdm3wQV8e8WGLE+//wPwl9pvjiT5TVciWIaJq9DI1RDkOmFN19vtE8AAyDDLvqxuaTpoVCw8vIQsixa/GZWPTbtPNfOphds9EkQ8oZ0NEZZQJf9/fVE3bNp9Cq6G5EmXJ3xuR1m1K6has9hwV/eJsjnOoFXByzNYve0ohnS34P1vz8DZcF41x2DCBTmYMqYAffNMSDVq8PqXJ+H2ifDxIlhWdqD7kSAne7YEywAaNQuvT4SXBxgmvgEHogSIAqDXsM3W26hVQa/hYDaoAQBmgxoqjkWWRYs180Zi+oo9rWqjTRDxhMSGiEgoB/+QIgtWbzsKSZIjocKZbvzVmuW2AAykgH1Gdb0XXCqDeqcPZdUu/FRmAyCbk64dnoubRuejMNOANKMGGrW8hembZ1bMesfKHfAyrW8ZIEoNpjWpeSmceJJukgWlpaCJQGFvbRttgognJDZEq/HfEG02W8RIqFkTeuLO5/aCgYQA61ZDtruI45WNN8UUvQo3jMrHpBHdkGXRIs2ogU7DBeX5mHQq1Du9ADTw8uI5CwXDyDuhtgrNuYpV0/5tmWY13D4xpO8lUoZ9tD2CCCIRILEhYsZFfdJQlG1AWZUTgiiB4wBBkLP//bAMMHFoLmZf1QsWoxppJg30Gvnm2TTAwO0VAUYuq88wDFimZcd+0xs7g+jMbdHgF5rA2m3RoFYxYFkGoiibC1UcC6vD18z3EpiTFIpoewQRRCJAAQJEmwkMb/7907uxZe8ZlFU7UV7rwR+uKIJRpwLLshCa1DDTqBjcfEkh/nxjfxRlG5CXrleEBgjO8/H3O0nRy4K0cvZQGHXNW5WHQ8XKotCWzQzDICgogUHDOVt5UkGUwAsSREmEWa+C1e5DqkHd6r4uidyrhiCaQjsbok34dx8qloFRy+JMjRuPv1WKu67uDQ8vYv1XJ2G1N/aVYQAYdRyyLVr4BAlfHKjEFUOyQ94gW/JJaFUsbIhchVOrZpFl0cDHSzhb64k4tiVYyFFwmWYVbh1XhGe2HAYvSGAYQOX3S0ly91CNqtFwGLi7Yhm5HA3DSOBYBikGNby8iIwUTdjrjATVFiOSBRIbolUE+lD+/cfz8OSmn+US95wcQqxRM7DbRCx5sxTegNIyF/SwoN7lQ73DC6dXxIlKJ7RqDmqOwfyXv4fZoG6VT2L1tqNIMcg36Cpb8yZpDAPkp+uRn6FHWbULTg8PpuH4uYY6iwD65hjxwI39MW5wFl759BicXgEsA/CCBCGgmVlBhgFA485HbgMgixUjSeifb4bbK8LlFZCZoqW+LkSnh8xoRNT4dzEVtW6YdBwEETh81g6fIEIUJdTafThW4YLV4VOE5uL+GXj6tgvwxPTzYbX7UOuUWwmoOLn4Zq2Th93Nh8wTCZXn4/dJ+Ksb5KTpwTLN58oCMGg5rJk3Ep8uvgxmgxpqrnVC4xcno5bDoEIz8tL1eO/hSxQxVKtYSKIkJ5IGt+BBvcun9Jnx+2iEhl2PJAEOt6CYyxiGUa6z3unF4TN2HK90otbho5wZotOQ0DubLVu2YPv27Thy5AgcDgdWr16NnJycoDF2ux3PP/889uzZAwAYOXIk5syZA5OJyni0N6s+PgKWYcBxLARRUp7WK+o8SnViP2lGNf5v+hAUZRnBsQwsRjUEQZSTMTlZHYI7cDLN8kQiRWOF2vU0nku+4csZ+DJGLYezrdzRSABUDKPsPEw6FSYt3YGj5Q4AspCgoRoBJL9Ph4GKZZBu0ii7PY6RqzYrAQUsg9NWF/Kgh1HHoc7JY9G0QXjq7Z9xvNINNcciP10HHy9GrDtHEMlEQouNx+PB0KFDMWrUKKxevTrkmKeeegqVlZVYtGgRGIbBP/7xDyxbtgzFxcUdPNvOQaiWAhf1SYPNxeN4pbOheoAEXhDx3McnwAuN/ggGgEHHwaDh8Kdf90XPbCNSDGpYDGqwLAO1ioXbJ++CAs1ZTEDaf1NfRTifRGAklh8G8m6DZWWzVmAzNqZBgZpGp0VCzTHolqYDxzKod/ng8Qmy2QyMEtUmSnIFAK7BXyPnHsnleNJNGjx35zDMf/l7OEUBgASW9ZfIQVBnzXGDs7B621EUZRmDBdQDPPX2z9RgjEh6EtqMdsMNN2DKlCkYNGhQyNdPnjyJvXv34u6778bAgQMxYMAAzJ07F9988w3Kyso6eLbJT2BLgRQ9h7NWN4rX7cf7/5Uz+3NTdbC7eVTUenCswoW39pTL/WYYINuiRUaKBr1zTZj3676YcH4OCjINSDNpwDbYufrmmZFu0kDNsRBF2Z/BsYBO3fg1jNZXERiJ5YfjGKUeGiBBrWo8r7LLCSjn3xQVK5f2N2g5pJvUGNY7DUJD4qpWxcLuEuTQbVFWLVXDDk0U0RDGzKJbmg5qjlOuYdzgLJgNavTLN6EgQw8Gci02BlKzzpqhCp/6BAGHz9qpJA2R9CT0zqYlSktLodfrMXDgQOXYoEGDoNPpUFpaioKCgjjOLvlYve0oVA0+Bp8g54MIIos3dpYhzaiBIEooD4joSjOq4BMlzLuuLy4dlAWGAcx6FSwNZVWa4t+N5Oh00GlYVNd7UWXzwKxXybXAWpkn4t/1TF+xB8crHLC5eHh5ERoVCzWngo8XMb74CxRk6OF082BYBlquUWW8ys5ErrPm35mYdSr0yDEqCavbSypx53N7lUZukgT4eKlhRyOfqzDTEDbXxW/y85edqbZ5Q3bWDGUarKzznnNJmrY0viOI9oZxuVwJUrAjPAcPHsT999/fzGezYcMGfPTRR81MbLNmzcLVV1+NKVOmBB3fsmULtm7dGvGzFixYAEA2u0iSFGTi6cxIElBt88jRUsoxCT+csOH1nWfw7ZF6ZWyuRYPfjs7FlItzwbJyIy+GQUM15sh4eRFOD6+0elZzLHyCqPxs0KqUxmDR4uVF1Dt9slgEHGcDwpH9ddQCZygF/OzfoclBAQzM+sZ51Dq88PFSSPObPwjA32Ez8Br83x8vL8LmkhtYB1YdCPwM/3U0HSeIcoh006WVJLlAZ6Q1ieYz40lX+vfVWpJ1bbKywj/MdPjO5j//+Q82bNgQcczSpUsxZMiQqM4X6hcihcmymzhxIiZOnBjxfBUVFQAAs9kMm80Gs9kc1TySEVGUYHPzsLl88PESHlx7EDU2L7RqBk6PAKvdB3dAtn2PbAOmji3EZYOzwLEMdBoBPmhhMoXeyYQjo52vY3tJJRa8+iPsLh68IO9WGAAFmY1NyA6fsUMQJWhUrLL7MTXsYGZN6Nl8B5BnUc5/w1NfgGUknK7xBBX/BORSM4///vyQO4bA70/J6RC7jIDP8NN0XK3DBx8fHBbt8ghKMc5wTF+xp9kuKZr3dSSd/d9XW+iMa9PhYjNp0iRcfvnlEcdEUsdA0tLSUFdXF/QUIEkS6uvrkZaW1tapdlrkp14f7C4+KILsptH5WP7uLzhbKwQ51wsz9bj9yp4Y2S9d2b0YdRxYVkSWWdv09CE5F5NOtO9Zve0oUvRq5KTqAAC/nJKLelbVexSxybZocarGjZxUnWLuqnd6UevwYdH6AyjI0GPRtNBRX0YthxOVTgDBuwS9mg0rNOGuIfAzwl1f4Pn8frTWlqShIp1EotHhYmOxWGCxNH+iOxcGDBgAl8uF0tJSxW9TWloKt9uNAQMGtMtndCacHh42F6+U8ffj5UV8tO8s3tx5CnVOXjlu0qkwdWwBpowpUMTcoOWUSsy2EMmUoQjVRK2lkN6W3rO9pBKLXi/BaatbqeCcadYgO1Xu+eLjxaCkUhXHok83E1KN6oby/RzAMPDxYsQ5bS+pRI3dC58o977hGAYigFSDqkWh8TaELoe6BgBRrUlLxTjDQUU6iUQjoQMErFYrrFYrTp06BUCOPnM4HMjKyoLZbEZhYSGGDRuGlStX4u677wYArFy5EiNGjKDggAZEUYLdzaO+wVQWiMPN4/3/nsHbu0/B6pCFgwFwyaBMTB1biL4BLYf1Gg5pJnWzLp3REFjjDIjOyR3pPQBw/8v7YHM1iqYkAZX1cvvnDLMGp61uqFgmKPBgweQByudNX7EHfJP21qHm5N81GbUqVNV74OVFqFgGGSm6Fm/4Tg8f8RqiXZNzKUlDRTqJRCOhxWbz5s1Yt26d8vOjjz4KAJg3bx4mTJgAAPjzn/+MVatWKXk1o0aNwpw5czp+sjGmtWYonyA7zZuaygDZ4b3p69N477+nlV2Oim1sVpYf8PSr07BINWqCCmS2lnMx6UR6z+ptR+Fwy/Nu6BCtOO+rbV70yDYi1aBCRooOdU4+5HpFOyf/OIZhFJOcJElBO8BwCCKahTIHfkYszVznuiMiiFiR0GJz66234tZbb404xmw2Y/78+R00o/jQGjNUOFMZAJTXuvHmrjJs/a5cMTHp1CyuHd4NN43OR2ZKo/9Fq2aRalTDoG37V+RcTDqR3lNW7QoW0IBMTbEhLyZwF9OWObXFHMWxUGqe2Zw+JeTZoOWQm6YLWw+tvUKWqUgnkUgkRgwkEZFQpfYDS9CLotTQ8VIu699UaI5VOPDU2z9j5j+/wXvfnIGXl0vb/+6y7lgzbyRmX9VLERq1ikG2RYu8dH27CA0QucZZYHuC6Sv2RFUXrSCjeT00/48pehXWzBvZ4k020vnPZVwoDFoVfIKIqjoPzljd8PAiwEgwaDnU2L2od3qbnXdU33QlsZaSOInORELvbAiZcCafk1VOVNs8IU1lAFBaVo/1O8qw6+dq5ViGWYPJFxfgmmG5QaYxFccgzahRTEXtSTiTDhDZSR7JDHTgZB1sLiGovhrLAH8Y30P5eeWHh/DKp8fg8Agwajn8YXwPzL22j7JzcLjloqBqlRw8EGoH0RZzlEbFonjqIMx/+XuIkgSdmkNmihYmvQoujwC1ilUCFvznPRf/FkEkAyQ2SUBTU44oSnC4eWSmaFHfxHcgSRK+O1KL9TtO4vtjdcrx/HQ9powtwPgh2UFJfSqOQapRDZNOFdMkslAmnekr9kS8sYYzA40bnIVlMy8MikbTazjc8auemHttHwCy0KzcfAgMw0DFAk6vgJWbD+FYhQPfHa2FmmMVU5Z/pxLuZt4Wc5S/XE1Bpr5ZDbg6J493HxobNH7R+gMUskx0SkhskoBZE3ri0fUlEEQJahUDt1cEL4qYMqYx4k4QJewsrcKGHWU4eMauHO+da8S0SwoxdkCmXKW4AX8l5hR9bEUmEm3JBRk3OAufPnZ52Ndf+fSYLDQN5WlUAHgB+ODbsyjKMnTozqElv0+gj8bm9EEQRGQE+M8oZJnoDJDYJDheXsTg7im4a2JvrN9RhrO1buSm6jBlTAFG9EmHTxDx2Y8V2LCjLOgmPaTIgmljCzG8d2qQmLAMGkRGrRTI7AhCOb3b4nxvyYnu8AhoWpWFYwCPIEGnYRWHvZcXoVaxqHV42+1amxIpDLlp8AfPi6hsKBqabtZQyDLRaSCxSVCaRpUN752O4b3TldfdXgFv7z6FN3eVoaq+8UY5ql86po0txKDClKDzsQxgNqiRauhYkQHCR9P9ZlQ+Nu0+1epckGii84xaDk6vEPQFFyR5R1dd70WN3SvXc2NlQRcECdtLKtttd7O9pBI9M1jc8JRcCPQ3o/Kx+2BNM3FsakrMbKhi7fQI4LjQYdsEkYyQ2CQQkRIw/dhcPry75zTe2XMa9Q2FFlkGuPy8bEwZU4CeOcag8QwDpOjVsBjVQWa0jiSc03v3wZpzcr5H40T/w/geWLn5EHhB3tEIkuzPum54N2zdJ7dG4Bi5uRnDSEgzqdvNlOYXw9VzzlPEcNPuUyFD1UOZEjNSNKhz8vh08WVtngtBJAokNglAuFplgVTbPHhr1yl8uPcsXF55t6NRsbjqwhz89uIC5KbpgsYzjFxuJtXYuiKZsSCSb+ZcnO/R+Hr8gQKhotF2/O+ncHl4+AS5MGdmig5GHdduTni/GDJM6A6kgVBZGaKrQGITRyIlYPo5XePCGzvLsO37cvgaimMatBx+fVE3/GZUPtJMzcvMm3QqpJrUUMdZZPy09w012vPNvbaPIjqB9OlmClkRub1u8K0JfKCyMkRXITHuRl2IlhIw/Rw+a8fjG3/CrJX/xea9Z+ETJKQa1fjD+B5YM28kbruyZzOhMeo45GfokWXRJozQAG1LjIzF+dp7Pk0pyNDD7RWDjoUT18COo3VOHlkWbcQCpQSRrNDOpoOIxlQGAPuP12H9jpP45pBVOZZt0eK3Ywpw9YU5IQthBlZiTkTau05XW88X67ph/t2K1OAnamm3QmVliK5AUnTq7Ej8zdNSUlLapYFRNKYySZKw56AV63ecxIGTjR0xu2cZMHVsAS4fnBXS79KWSsztQUc2eIpHi+O2fKY/Gm3GM/spoiwMnbFBWHuRrGuj0+nCvkZi04T2EJumHTDDIYgSth+oxIYdZTha7lCOD8g3Y9olhRgV0KwsEK2aRZqpbZWY24OO+gcRGOoc6Ndob3NToLiYdCpU17uRYtCc82cm6w2jo6D1CU+yrk0ksSEzWjtjc/lQY/NGNJV5eREff1+ON3eW4YzVrRwf1isV08YW4vwelpBZ/RoVizRT+1RiTiY6ol5Y09ydI2cd4EUJBq0KDMNRjTKCaCNd667VAXh5MazQODw8Pvz2LN76ugxWe2OzsrEDMzB1bCH65YV+klGrGKQaNTDpuuavqyNaHDcVNEGUwELuj2M2qGPymQTRleiad68Optbhxdu7T+P9/56B3S0nYqpYBuPPlxMxCzMNId8Xy0rMyURH5KI0FTSNioW3SWtpyn8hiHOna9/FYkxFnRtv7jqFrXvPyr1MIPtbrh2Wi5tGFyDLog35PhXHwGJQwxzHIpmJREfkojQVtMwULcpqnFCzbFQRZQRBRIbEJgacqHRiw86T+OzHSggNNjWTToUbRuZh0sg8WBrMMk1payXmeERsdQQd0eK4qaBxrLyrTDdpwraWJggieigarQltiUb74Xgd/vXBQWwvqfJ3KUa6SYPJF+fjmmG5YR37LAOkGNSwtKFIZkdFbAWSrBEz4Whvse5s69Pe0PqEJ1nXhqLROgBBlHD/S/twskp2IOely20Arjw/J6hZWSDtWSSTOjy2HUquJIjYQWLTTnAsg9sn9MRrX5zAb8cU4JKBmWEFhGEAs14Fi6H9imR2RMQWQRDEuUJi045MHVuIX12QDZsrfLWAWBXJpOrBBEEkMolZTCtJ4VgmrGM/1kUyY11ckiAIoi0k7M7GZrNh7dq1+O6771BZWYmUlBSMGDECv/vd75CS0tiF0m634/nnn8eePXsAACNHjsScOXNgMpniNfUgOqpIZkdEbBEEQZwrCRuNdvz4cbz22mu48sorUVhYiOrqajz77LPIyMjAY489pox75JFHUFlZiXvuuQcMw+Af//gHcnNzUVxcfE6f29baaNU2D+qdfNyLZHYEyRox01EErk9nDUtvC/T9CU+yrk2kaLSENaMVFRXhoYcewqhRo5CXl4chQ4bgtttuw/fffw+n0wkAOHnyJPbu3Yu7774bAwcOxIABAzB37lx88803KCsri8u8NSoWuWk65KbpOrXQENHjD0uvrPMobaIXbziA7SWV8Z4aQXQYCSs2oXA6nVCr1dBq5cz70tJS6PV6DBw4UBkzaNAg6HQ6lJaWxmWOZr067tWYicQiMCzd3yZazbFYve1ovKdGEB1GwvpsmmK32/Hqq6/iqquuAsfJN3Or1YqUlJQgpzzDMLBYLLBarc3OsWXLFmzdujXi5yxYsACAvI2VJAk2m60dr6JzQesTGf/6LJ7SG6HiRiQJXXr96PsTnmRdm4RK6vzPf/6DDRs2RByzdOlSDBkyRPnZ7XbjscceQ0ZGBmbOnBk0NlT0lySFdkNNnDgREydOjPjZfp+N2WxOWrtpR0HrExn/+sx9aU+zsHSXR0CWRYs180bGcYbxhb4/4emMa9PhYjNp0iRcfvnlEcdkZTU6Tl0uFx599FEAQHFxMTQajfJaWloa6urqIEmSIjqSJKG+vh5paWntP/kkJVmd08k676Z0RCFRgkh0OlxsLBYLLBZLVGOdTicWLVoEAFi0aBH0+uAExQEDBsDlcqG0tFTx25SWlsLtdmPAgAHtOu+WSNQbY9OmYH7ndCxrprUHyTrvUFBYOkEksM/G6XSiuLgYTqcTCxcuhMfjgcfjAQCYTCao1WoUFhZi2LBhWLlyJe6++24AwMqVKzFixAgUFBR02FwT+caYrDXTknXe4aC6a0RXJ2HF5vDhw/j5558BAHPmzAl6LdCn8+c//xmrVq1S8mpGjRrVbHys6egbY2t2UclaM60t807UXSZBdGUSVmyGDBmC9957r8VxZrMZ8+fP74AZhacjb+it3UUla820c513Iu8yCaIrk1R5NolKQYYebq8YdCxWN/TW5mwka820c5035bQQRGJCYtMOdOQNvazaBZ0m+NcWaRfld05nWbSoc/LIsmiT4in/XOfd2vUhCKJjSFgzWjLRkdFG52JeSlbn9LnMO1nNhgTR2SGxaSc66oZOORuRofUhiMSEzGhJRrKaxToKWh+CSExoZ5OEJKtZrKOg9SGIxIN2NgRBEETMIbEhCIIgYg6Z0QgiiaFqCUSyQDsbgkhSqAMokUyQ2BBEkkLVEohkgsSGIJIUqpZAJBMkNgSRpHRkTT6CaCskNgSRpCRrkVWia0JiQxBJClVLIJIJCn0miCSGqiUQyQLtbAiCIIiYQ2JDEARBxBwSG4IgCCLmkNgQBEEQMYdxuVxSvCeRSFRUVMR7CgRBEElLdnZ2yOO0syEIgiBiDu1sInDfffdh+fLl8Z5GwkLrExlan8jQ+oSnM64N7WwIgiCImENiQxAEQcQcEhuCIAgi5pDYEARBEDGHxIYgCIKIOSQ2BEEQRMwhsSEIgiBiDokNQRAEEXNIbCJw9dVXx3sKCQ2tT2RofSJD6xOezrg2VEGAIAiCiDm0syEIgiBiDokNQRAEEXNIbAiCIIiYo4r3BBINm82GtWvX4rvvvkNlZSVSUlIwYsQI/O53v0NKSooyzm634/nnn8eePXsAACNHjsScOXNgMpniNfUOY8uWLdi+fTuOHDkCh8OB1atXIycnJ2hMV16fDz74AG+99RasViu6d++OO+64A4MHD473tDqc/fv3Y9OmTTh06BBqamowb948TJgwQXldkiSsW7cOW7duhd1uR79+/XDnnXeiqKgojrPuON544w3s3LkTp06dglqtRv/+/TFjxoyg6+9Ma0Q7mybU1NSguroaM2fOxD//+U/cf//92L9/P5566qmgcU899RQOHz6MRYsW4dFHH8Xhw4exbNmyOM26Y/F4PBg6dChuueWWsGO66vp8+eWXeOGFFzB16lSsWLECAwcOxKJFi7pkUz63242ioiLMnj0bGo2m2esbN27E22+/jdmzZ2PZsmWwWCwoLi6G0+mMw2w7nh9//BHXXXcdnnrqKSxZsgQcx2HhwoWw2WzKmM60RiQ2TSgqKsJDDz2EUaNGIS8vD0OGDMFtt92G77//XvkFnzx5Env37sXdd9+NgQMHYsCAAZg7dy6++eYblJWVxfkKYs8NN9yAKVOmYNCgQSFf78rr8/bbb+PKK6/E1VdfjcLCQsyZMwdpaWnYvHlzvKfW4Vx00UWYPn06xo4dC5YNvtVIkoR3330XkydPxtixY1FUVIT77rsPLpcLX3zxRZxm3LEsXrwYEyZMQFFREXr06IH7778f9fX1+OmnnwB0vjUisYkCp9MJtVoNrVYLACgtLYVer8fAgQOVMYMGDYJOp0NpaWm8ppkwdNX18fl8OHToEIYOHRp0fOjQocoNhJApLy+H1WoNWiutVovBgwd36u9IJFwuF0RRhNFoBND51ojEpgXsdjteffVVXHXVVeA4DgBgtVqRkpIChmGUcQzDwGKxwGq1xmuqCUNXXZ/6+nqIoojU1NSg46mpqaitrY3LnBIV//cg1Fp15u9IJFatWoVevXphwIABADrfGnWZAIH//Oc/2LBhQ8QxS5cuxZAhQ5Sf3W43HnvsMWRkZGDmzJlBYwNvpH4kKXnzY89lfSLR2danNYS6diI0TddKkqQuuX6rV6/GTz/9hCeeeEJ5qPXTWdaoy4jNpEmTcPnll0cck5WVpfy/y+XCo48+CgAoLi4OcnCmpaWhrq4u6JcuSRLq6+uRlpbW/pPvAFq7PpHojOsTDSkpKWBZttlTZ21tbbOn066O/3tgtVqDvld1dXVdbq1eeOEFfPnll1iyZAlyc3OV451tjbqM2FgsFlgslqjGOp1OLFq0CACwaNEi6PX6oNcHDBgAl8uF0tJSxS9RWloKt9utbIGTjdasT0t0xvWJBrVajT59+mDfvn245JJLlOP79u3DmDFj4jizxCMnJwdpaWnYt28f+vXrBwDwer0oKSlpZkXozKxatQpffvklli5disLCwqDXOtsadRmxiRan06mEFi5cuBAejwcejwcAYDKZoFarUVhYiGHDhmHlypW4++67AQArV67EiBEjUFBQEM/pdwhWqxVWqxWnTp0CIEefORwOZGVlwWw2d+n1ufHGG7Fs2TL07dsXgwYNwubNm1FTU4Nrrrkm3lPrcFwuF86cOQMAEEURlZWVOHLkCEwmE7KzszFp0iRs2LABBQUFyM/Px/r166HX63HZZZfFeeYdw7PPPovPPvsMDz/8MEwmk7Ij1ul00Ov1YBimU60RFeJswo8//oiHHnoo5GuBPgubzYZVq1Zh9+7dAIBRo0Z1maTFtWvXYt26dc2OBybtdeX18Sd11tTUoKioCLNmzcJ5550X72l1OOH+LY0fPx733XefkrC4ZcsWJWHxrrvuSsqExXPh+uuvD3n8lltuwa233goAnWqNSGwIgiCImEOhzwRBEETMIbEhCIIgYg6JDUEQBBFzSGwIgiCImENiQxAEQcQcEhuCIAgi5pDYEARBEDGHKggQRBOWL1+OEydOYPny5S2Ovf322/Hiiy+GfO3555+Hy+XCvffeC6AxGbZbt25YtWpVs/GzZ8/GmTNngpL61q5di/fffx9r165VxomiiGXLlmHnzp3w+XwR55ednR1yfk0Tc1NTU9GnTx9Mnz4dPXv2VI6Xl5dj1qxZIc/98ssvIzMzE88++yx8Ph/+9Kc/RZwL0bUhsSGIVrJlyxZccMEF6Natm3Ls8OHDOH36NC699FIAQGVlJT766CM8/fTTQe/VaDQoLy/HwYMH0bdvX+X4L7/8goqKipAdLQORJAn/+te/sGPHDtxzzz3Iy8tTXtuxYwfefvvtoK6yarU67LmMRqNSA7CiogKvvfYaiouL8cwzz8BsNgeNve2224L6EwFQaunddNNNuOuuu/Db3/42aD4EEQiJDUG0km7dumH58uUYPnw4eJ7H6tWrcfz4ccyYMUMZs3nzZvTu3btZcUWtVotBgwZh+/btQWLz5Zdf4vzzz8ehQ4cifvZzzz2HTz75BA8++CDGjh0b9Jr/vdEWO2VZVhk7YMAAZGdn44EHHsC3337brAJ4fn5+2PPm5OQodeBuv/32qD6b6HqQz4YgwrBr1y7ceeeduOmmm/Dggw/ixIkTAIALLrgAjz/+OA4ePIiamhoIgoDHHnsMffr0Ud772Wefha30PG7cOHz11VdKfx9JkvDVV19h3LhxEefz4osvYsuWLbjvvvuaCU174DefVVVVtfq9Y8aMweeffw5RFNt7WkQngcSGIEJQUVGBF198ETfffDP+/Oc/w+l04pFHHoHX61UKTPbt2xfp6elgWRaPPPIIjh49CgAoKytDVVVVM7OTn4svvhi1tbU4cOAAAKCkpAR1dXUYPXp02PmsWbMG77zzDu65554W+w6dK5WVlQDknUpTJEmCIAjKn6aiMnDgQNTW1uL48eMxmRuR/JAZjSBCUF9fj4ULFyqC0adPH9xxxx345JNPAMgVrvPy8vDRRx/hjjvuwOHDh3Hy5En07NlTMWeFq8xrMpkwfPhwbN++HYMHD8b27dsxbNiwsBWxbTYb3njjDdxwww1KVe32QhAEALK4Pv/88+jVq1dI0fvrX/8a9PPll1+O+fPnKz93794dLMvil19+CQowIAg/JDYEEYLU1NSgnUl2djb69OmDX375BfPmzWs2vnfv3ujduzcAuTOnRqOBTqcLe/5LL70Uq1evxqxZs7Bz507Mnj077FiDwYDCwkJ8/PHHGD9+PHr16hX1dfjFxA/Lskr3VJvNhhtvvFF5zWw2Y9myZSGDCmbNmoVBgwYFjQ2E4zgYjcZmXUoJwg+Z0QgiBKG6llosFtTU1AQdCxVW7PV6I0aBAXJ/H5fLhTVr1sDtdmPkyJFhx3Ich+LiYqSnp2PRokU4e/ZslFcBLFy4EDfeeKPyZ//+/cprRqMRy5Ytw9/+9jfMnTsXPM/jb3/7W0i/S7du3dC3b1/lT2D7Yj9qtRperzfquRFdC9rZEEQI6urqQh7r3r17i+81m81wOp0QRREsG/p5TqfTYcSIEXj33XcxduzYiLsgAEhJScHixYvx4IMPori4GE8++WRUfejnzp0Ll8ul/Jyfn6/8P8uySkRc//79odFosHz5cuzYsUMJ4W4NDoej2Y6HIPzQzoYgQlBbW4uffvpJ+bmiogKHDx9WesFHIj8/H5IkoaKiIuK4a6+9FiNGjIi6ZXRWVhYWLVoEm82GRYsWwel0tviegoKCoB2JwWAIO/aKK65A9+7d8eabb0Y1n0Dq6urg8XiCxIwgAiGxIYgQpKSkYNmyZfj888+xa9cuLF68GBaLBVdeeWWL7+3Xrx84jsPhw4cjjhsyZAgWLlyotBqPhqKiIhQXF+PkyZNYunRpixUEWgPDMJgyZQqOHDmC77//vlXvPXjwIBiGiTrHh+h6kNgQRAiys7Mxc+ZMrFu3Dk8++SQMBgMWL17cYoY/IJvIhg4dim+//TYmcxs4cCD+53/+B/v378eyZcvaNbfl0ksvRV5eHjZu3Niq9+3duxfnnXceUlJS2m0uROeCcblcUrwnQRCdjV27duGf//wn/v3vf7cYLJDsCIKA22+/HTNmzMAVV1wR7+kQCQrtbAgiBowePRoZGRn49NNP4z2VmLNjxw5oNJoWKyAQXRsSG4KIAQzDYO7cuVCpOn/ApyRJ+NOf/gSO4+I9FSKBITMaQRAEEXNoZ0MQBEHEHBIbgiAIIuaQ2BAEQRAxh8SGIAiCiDkkNgRBEETM+f9IX/ZkbT1MSAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x = beta_mkt_rf.iloc[:,2], y = excess_return.iloc[:,2]) \n", "ax.set_title(\"Smoke Industry\", fontsize=15)\n", "ax.set_xlabel(\"b*(MKT-RF)\", fontsize = 15)\n", "ax.set_ylabel(\"RI-RF\", fontsize = 15)" ] }, { "cell_type": "code", "execution_count": 23, "id": "059970dc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RI-RF')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x = beta_mkt_rf.iloc[:,3], y = excess_return.iloc[:,3]) \n", "ax.set_title(\"Games Industry\", fontsize=15)\n", "ax.set_xlabel(\"b*(MKT-RF)\", fontsize = 15)\n", "ax.set_ylabel(\"RI-RF\", fontsize = 15)" ] }, { "cell_type": "code", "execution_count": 24, "id": "5bffdd5c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RI-RF')" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x = beta_mkt_rf.iloc[:,4], y = excess_return.iloc[:,4]) \n", "ax.set_title(\"Books Industry\", fontsize=15)\n", "ax.set_xlabel(\"b*(MKT-RF)\", fontsize = 15)\n", "ax.set_ylabel(\"RI-RF\", fontsize = 15)" ] }, { "cell_type": "markdown", "id": "8a76873d", "metadata": {}, "source": [ "# 1.2" ] }, { "cell_type": "code", "execution_count": 26, "id": "9568ea57", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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E(RI-RF)b*(Mkt-RF)BETAsMkt-RF
Food0.6992200.4906360.7290950.672938
Beer0.9295930.6218120.9240260.672938
Smoke0.8664820.4195250.6234240.672938
Games0.8356590.9323311.3854640.672938
Books0.6317500.7475801.1109200.672938
Hshld0.6552690.5950830.8843060.672938
Clths0.6648960.5595900.8315630.672938
Hlth0.8079200.5611040.8338130.672938
Chems0.7773400.7023311.0436790.672938
Txtls0.6755370.7708901.1455600.672938
Cnstr0.6941680.7949781.1813540.672938
Steel0.6773740.9161651.3614420.672938
FabPr0.8008490.8341861.2396190.672938
ElcEq0.8968540.8672371.2887330.672938
Autos0.9236140.8660161.2869180.672938
Carry0.8502950.7999311.1887160.672938
Mines0.6406590.6147330.9135070.672938
Coal0.8280940.8602411.2783370.672938
Oil0.7739600.5986020.8895360.672938
Util0.6183540.5140330.7638650.672938
Telcm0.5663520.4472690.6646510.672938
Servs0.9530330.5544460.8239190.672938
BusEq0.9037870.7269101.0802050.672938
Paper0.7212560.6385750.9489370.672938
Trans0.6551910.7659631.1382380.672938
Whlsl0.5750690.7312781.0866950.672938
Rtail0.7779640.6511860.9676770.672938
Meals0.8002080.6370120.9466140.672938
Fin0.7423050.7799181.1589760.672938
Other0.5220880.7102871.0555020.672938
\n", "
" ], "text/plain": [ " E(RI-RF) b*(Mkt-RF) BETAs Mkt-RF\n", "Food 0.699220 0.490636 0.729095 0.672938\n", "Beer 0.929593 0.621812 0.924026 0.672938\n", "Smoke 0.866482 0.419525 0.623424 0.672938\n", "Games 0.835659 0.932331 1.385464 0.672938\n", "Books 0.631750 0.747580 1.110920 0.672938\n", "Hshld 0.655269 0.595083 0.884306 0.672938\n", "Clths 0.664896 0.559590 0.831563 0.672938\n", "Hlth 0.807920 0.561104 0.833813 0.672938\n", "Chems 0.777340 0.702331 1.043679 0.672938\n", "Txtls 0.675537 0.770890 1.145560 0.672938\n", "Cnstr 0.694168 0.794978 1.181354 0.672938\n", "Steel 0.677374 0.916165 1.361442 0.672938\n", "FabPr 0.800849 0.834186 1.239619 0.672938\n", "ElcEq 0.896854 0.867237 1.288733 0.672938\n", "Autos 0.923614 0.866016 1.286918 0.672938\n", "Carry 0.850295 0.799931 1.188716 0.672938\n", "Mines 0.640659 0.614733 0.913507 0.672938\n", "Coal 0.828094 0.860241 1.278337 0.672938\n", "Oil 0.773960 0.598602 0.889536 0.672938\n", "Util 0.618354 0.514033 0.763865 0.672938\n", "Telcm 0.566352 0.447269 0.664651 0.672938\n", "Servs 0.953033 0.554446 0.823919 0.672938\n", "BusEq 0.903787 0.726910 1.080205 0.672938\n", "Paper 0.721256 0.638575 0.948937 0.672938\n", "Trans 0.655191 0.765963 1.138238 0.672938\n", "Whlsl 0.575069 0.731278 1.086695 0.672938\n", "Rtail 0.777964 0.651186 0.967677 0.672938\n", "Meals 0.800208 0.637012 0.946614 0.672938\n", "Fin 0.742305 0.779918 1.158976 0.672938\n", "Other 0.522088 0.710287 1.055502 0.672938" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg_data" ] }, { "cell_type": "code", "execution_count": 27, "id": "f1d0e04d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reg_data.plot.scatter(x='BETAs',y = 'E(RI-RF)' ) \n", "plt.title(\"Each Industry Betas\",fontsize=15)\n", "plt.xlabel('BETAs', fontsize=10)\n", "plt.ylabel('RI-RF', fontsize=10)\n", "plt.xticks(fontsize=10)\n", "plt.yticks(fontsize=10)\n", "plt.show()\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 28, "id": "7bec259d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reg_data2 = reg_data[['BETAs','E(RI-RF)']]\n", "\n", "fig, ax = plt.subplots()\n", "reg_data2.plot('BETAs', 'E(RI-RF)', kind='scatter', ax=ax)\n", "\n", "for x, y in reg_data2.iterrows():\n", " ax.annotate(x, y, fontsize=10)\n", " \n", "plt.title(\"Each Industry Betas\",fontsize=15)\n", "plt.xlabel('BETAs', fontsize=10)\n", "plt.ylabel('RI-RF', fontsize=10)\n", "plt.xticks(fontsize=10)\n", "plt.yticks(fontsize=10)\n", "plt.show()\n", "\n", "plt.savefig(\"indystry beta 1.2.jpeg\")" ] }, { "cell_type": "code", "execution_count": 29, "id": "ea127e83", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RI-RF')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SpSgqKsLevXuxZs0abN68GUOGDPE7v6GhAY899hh++MMfYvny5ejs7MRLL72EtWvXYuvWrWo2dVAoCtw1rF2/FReTe/6Bxx1YBOi6Akv3IBNvwSVetyZl3kY9Lkn29io6nf5Fe2o5VGfhNNwIUrWQcPfu3bjxxhsxZ84cjBo1CkuWLEFWVhYqKysDnn/8+HFIkoSFCxdi+PDhGDt2LObPn48zZ86gtbVVzabGHElWYHfKON/pgvW8E02tdpy22PB1YwdOnevA2ZZOWNodaLc5u2aaxG53Ph63Ju2+cJ6a+ykkA1lWYHdKOG9zeXffO3XOhqY2O9ptgYv21ODZI8PS7kC6UQtLuwObK+twqM4yKM+fiFTrgTidTtTV1eH222/3OT5lyhTU1tYGvKawsBAajQb79+/HTTfdBLvdjoMHD2LcuHEwm81qNTXueHIxPXsuogDotCJ0GtE7PCaKAsSuCQCC4P7/YOdkPFuTxtO7eeZt+k9RFPfQk0uBS3bPgnL/jCpdw7uxMQ7VfY8MwD2M2ulwH2cvJDyCzWZT5bvb3NyMe+65B0888QQmTZrkPb5z5068++672LJlS8Drjhw5gt/85jdoa2uDoigYO3Ys1qxZg8zMTL9z33rrLezbty9oO1asWAEAAYd9+jNFN1Z++NUidP0luP+HUEbHEnmKc3O7I+C9UBQgJz0yawvF4/1Tuv5S3H8hWr8dAkJ/but5hzv/2O2YAvf3IUvF9aKiSeh6w9hTf3/28vKCv1lSPYkeyi+I1WrFc889h1mzZmHmzJmw2Wz405/+hCeffBIbNmyAKPqOuJWUlKCkpCTo52xsbAQApKen+z3W3t4e8Hh39c0dg9bFjhWe/ItnNplWFLzHRNH9CygKAi5cON/n/YtX/7rjQ7+8jc0uIc9swB8fuCoiz9Gfn7/BpCgKXLICWb7Yc3BKCiRJgVOKraR2llGCtVPT94nd/LLsK7/FEDsdMrLT9Xhq4T9EuokxwZyqgzlAcIzUz55qASQjIwOiKMJqtfocb2lpCdibANwztoxGIxYtWuQ99vDDD2PRokWora0NOnuLIsfz4mF3AoES/B5ZRgVfN16A2DWrTOjqyYii+1/Pux+N5uLssniZCFA6ewzWlR8F7J6hDjnm8zb9JcvuISe7S4bD6V4Q0DPklMjmXzMSmyvr0OlwL8Nud3IxxIFSLYDodDoUFhaipqYG1157rfd4TU0NrrnmmoDX2O12v16G52MlVt76kA9FASSf703/vk/unMzFLrYnwHh6OJ7jnqAkCIBe587vDIZ4zNv0pCjuiRieQOGUZG9eIhlxMcTIU3UIa968edi4cSPGjRuHoqIiVFZWwmKxYO7cuQCAl19+GceOHcOGDRsAAFOnTsUbb7yBnTt34rrrrkNHRwdeeeUV5ObmorCwUM2m0iDzfRHr/wua2BVIBFzc2Kd7/PIEIk8w6s4TmBQFcMnuoRnvNRr3BAPPVGtFUXDFmEy8sHiKTxCzOSSIwsVaHm9dD9CviQku6eK7fUVR0Nbh9D6fonT7Wro+tygK0HQ9FwDIigJZcfciunfiPNfKivtze4IF+eJiiJGlagCZMWMG2traUF5eDovFgoKCAqxevdpbA2KxWNDQ0OA9/4orrsAjjzyC119/HRUVFdDr9Rg/fjzWrl0LozE2N22hwSV37cMQizy9ph6daPcLu+x+4e8uywhY2x2D10BKKp1OCS1NDlzodGFUbooqz6HaLKxY4UmiZ2Rk+D3GJPrAhJPIpIt4/8KX7PfOJck41+7AuVY7mtrcfxq7/n+uzY6mVjvabC4AwI+uGYkNP5nkc31/k+h9vXHnWlhEAxCtymZWVCcuWVHQcsFdPNzUFQw8QcLzf0u7o98Dvw1W9fZsZwAhCpOnslkrij6Vzf86t1DVF/NoPS8NnKK4t9BtanOgqbWrt9Cj93CuzQ5nCPkrc4oWuRkGDDEbkZehR16GAXlmA/IyDLh0WBrGDUtT7ethAKG4Fs134tGqbGZFdeyyOyXfXkOrHY1d/7qDhQM2R/BFV7tL0WuQazZgSFdQyM1wB4a8DL03SBh0vQ/lmVN10Ko4c5EBhOJWtN+JN7R0It3o+ytk0IloaFFvyCCaz5vsXJKM5naHdzjpXLfg0NQj79AfOo1wMSCYe/ybYcAQswGpxth+iY7t1hEFEe134vmZRr/KZrtTRn6mujMGo/W8iUxWFLRecHqHk851Cwyejy3nHX4z6XojCkB2ut4bDLzBoVuAMKfqAi4zEk8YQChuRfudeLQqm1lRHRpFUXDBLgVMRjd1yz2Ek3fw9BTyMgw+Q0056QZokmATOQYQilvRficercpmVlT78s07OPxmLp1rtaMjhLyDSa/x6Slc/L/em4cwBsk7JBMGEIpbsfBOPFqVzclSUS3JCprbLyajPXUPLeftOG119xxaO0LPO+R26zn0zEGkGjQxv1ZbrGAAobjFd+LxTfHUO3QfVuo+zNRV7xBS3iFN7x5aCjRzyWxAZgLkHWIJAwjFtWR5Jx6PLnS6fGocPFNZG8PMO2SYtN6ewshsPTJSjT5DTTnpelWnrJI/BhAiCpndKeFcV76h5xIajWHkHYw6EUM8vYVeeg/d8w7JvpRJrAgaQN577z3v0uvnz59HWpp6FY1EFBskWYGl3YHG1s6uoSSH38yl1g5nvz+fVhR6FMEZfIMF8w5xK2gAefXVV70BZOXKldi0adOgNIqI1KEoClo7nD41Dp7eQ2Nr6HkHARfrHXwS02bmHZIBh7CIEsgFu8snGe0tiuuWqA4n79C99+AZYso1G5DLvENSCxpA7HY7jh8/DkVR4HA4vP/34CZPRIPH4ZJ9egqBVmvtsIeWd+i5hMaQIHkHop6CBpDs7Gxs374dAJCVleX9P+Dehc2zkyARDYwn7+C3t0O3nkQoeQeNKCA3Q+/tKeQFqHtIM2qZd6ABCRpAHn/88cFqB1HC8uYd2hxo8iSmWx1oOW/Dd1Z3HURzuz2kvENmmg5DMozIM3uW7+6+lLcRWWmxmXfgPiaJJawcyCeffIKKigqsX78+0u0hijuevIPP8JLPOksOOFz934Y3zaj1m8ravfeQk6GHLg7zDtFePZkiL2gA+fTTT/Ef//EfsFgsuPrqqzF//nw888wzUBQFCxYsGKw2EkWNJ+/QPc/QPTHd2Bpa3sGgE72BYES2HuZuxXCe/INJn5h5h2ivnkyRFzSA7NixA7/4xS8wYcIEfPTRR3jkkUdw991349Zbbx2s9hGpRpIVWM47Am8d2vVxy4XQ8g45XVNaA63QOqRH3iHZiuGivXoyRV6fQ1iXX345AGDatGnYsWMHgwfFBUVR0GZzdZvO2ommVodPcAgl7wAAWWk63+DQo2o6M1WfFEt4hyvaqydT5AUNIBcuXMB7773n/VhRFJ+PPUWGRIOtw+7yJqN7FsJ5Zi/Zw8g7eJft7p6Y7hpaise8QyyJhdWTKbKCBpBJkybhww8/DPixIAgMIKQKh0sOuIR3U5t7eY1zbQ6c7+z/Et4GregzlNRz69A8c+LmHWIJV09OPEEDyIMPPjhIzaBkIckKrOcdfjOVuucfrOfDyzt0r5DuPtSUbmK9Q6zg6smJJeRpvGvXrsXq1avVaAvFuZ55B89QUmO3ANHc7oAUQuIhK1Xn32vo1nvISmPegShaQg4gFotFjXZQHLA5JJ81ls532HDK4vSpmg4175Cb0fuspZx0A/Ra5h2IYlXIAWTs2LFqtIOizCl1r3fwLN/d6bOUdzh5B7/luzMM3urpFEPsr+XJymmi3gk2my2EiYzxp7GxEQCQkZHh91h7ezvS09ODXl/f3AGnK75vUaC8Q/dCOPce007096sUBSA3w4D8TD2y040B95XOSIC8Q/fK6e6zhiJVOZ1sdSCRxHvXP+ZUHbLT9H7H+/PaBwBGY/Ap1kHfAv7iF78I+CKgKAoEQcDzzz/fZwNIXYqioN3m8gYG31lL7gARTt6hZ++h+34PnrxDov8Ss3KaKLigAWTVqlWD1Q7qRadD8ttXuuesJbuz/3mHVIMmaFI6N4N5Bw9WThMFFzSADBkyJOBxSZJQXV3d6+PUP05JRnOQfaVDzTvotaK3EK63uod4yDvEClZOEwUX9NWko6MDe/bsQXNzM6666ipMmTIFb775Jnbt2oWxY8fi+uuvH6Rmxh9ZUWA97/Qu391zAb5w8w4995W+uFucHuYUXdznHWIJK6eJggsaQDZu3IjU1FRMmDAB+/fvx65du+B0OvHoo48m9WwsRVFwvtOFnvtKdx9aam5zwBVC3iEzVRcwGe35N5v1DoOOldNEwQUNIA0NDXjhhRcAADfddBN+8pOfYMeOHUhJSRmUxkWTS5LxwTELjta3ocHa6deDCDfvkNsjMe3pQTDvEJuSqXJaENybVXne9mg1AnQaEXqtCJ1GhCjC+yZGkhXIivvNlOdfSVbQ6ZDhlGQo8T1xkfopaADRaDQ+/x86dGhSBA+P0s0f9blaq04j9LqEhme11lTmHUgFguDOexl07hd4nVaEViNA6XpBVxR3MPD8XxQFiAIgQICCiy/8GlGAViNGrIerKIp36rvSFY5EUYBGECAIgEtWIEnugONuo7stctfHsqLAKclwuhSf2YOi4Pl6ItJMioCgr2xff/21z8ZRdrvd5+Py8vI+n2DPnj2oqKiA1WrF6NGjsXjxYhQXFwc8t6ysDDt37gz42CuvvILMzMw+ny9StBoRw7JNcLpk5KZ3H07SY4jZyLwDDQqtRoBRp4FJr3EHB7hfoEVBgEEnxuTPniAI0Ot6b5dOI0DXz9nfsqy4e0bdvk5FUXD+/HmMyDFB8gSjrh6QLLv/dfeQ3I+FsmQ/hUbVQsLq6mo8/fTTWLp0KYqKirB3714cOHAAmzdvDjiDy2azobPTd4rkU089BUEQwt6ffSCFhLKs4DurLaYLCaNZKZ3odSBq63n/NKIAvVaEXifC0NWz0HIJ+YD6WwgHuIfbXJIMV1evx9MDkhXFeyyUOql4EtVCwoHavXs3brzxRsyZMwcAsGTJEhw+fBiVlZX42c9+5ne+yWSCyWTyftzU1ISjR4/ioYceUrOZvRJjPGnNPabjk07r7lUIkJBnNkArCtBqBAYLlWhEARpRA4Ou93MURYFTcgcap6TA6XLnclySO8hQYKoFEKfTibq6Otx+++0+x6dMmYLa2tp+fY6//OUvSE1NxfTp09VoYtxjpXRs8+Qo3PkJd0K6e6+ivd2BNCPzYz1VHWnCtgMnUd9sw8gcE0pnj8HM4jxVn1MQBOi1QsDJLJ7g0r0n0z235MnbuM/tyunI7mOePE+iUu2nt62tDbIs++UtMjMz8emnn/Z5vSzL+Mtf/oIbbrgBOl3gtw5vvfUW9u3bF/TzrFixAoC7y9aToigBj3eXplVU7qeF76m7xkEQBHTvJ3l+sLOMkurPLwCD8jzxRuhKVLuH7btm68mALAO2blud9OfnL9k4XDJGZ4lYN/9SCMLFhHmztdXnxT2a987n5UDo+tMHz9ehwD2zYbBiiiBLaG+3B2hP/+5fVIewAISd5Pvoo49w7tw53HTTTb2eU1JSgpKSkqCfx5MDCTTeF++LKf6y7Cu/SulOh4zsdD2eWvgPqj8/cyBuBp0Ik16DVKM2pOnYoYzjJ4uFmz5EU6sdJsPFnyub3T3U98cHrvIeS4R755mt5pBk75CZ06VEdBq0OVUH8wByIH1RLYBkZGRAFEVYrVaf4y0tLf2aTbV//35MnDgRBQUFKrUw/rFSOjq0GgEmvQZGvXt2FAs8I6e+2QZziu/LklEvor7ZFqUWqcczW02v83/T4XDJ3nyMS5K9Q2KeITLPsJkn0MiyEpUpzqoFEJ1Oh8LCQtTU1ODaa6/1Hq+pqelzL/Xm5mYcOnQIy5YtU6t5CYGV0oPHoBORYtAgRa8N+AtPkTEyx+TXA+l0yBiZYwpyVeLRa8Wwi4u7F3eqPc1b1SGsefPmYePGjRg3bhyKiopQWVkJi8WCuXPnAgBefvllHDt2DBs2bPC57sCBAzAajT6BhwJLpkrpwSQKgKkrYJgM7GUMltLZY7Cu/Chg90wKcQ/tlM4eE+2mxQ1BEKARgH4lZwZI1QAyY8YMtLW1oby8HBaLBQUFBVi9erW3BsRisaChocHnGkVRsH//flx33XV9JnCIIsWgE2HUa7zv/HQaISaL9BLdzOI8rFpQNOizsCg83JEwjpPo0RbPSXRBAIw6jXtYyqCJSg1GIiSCo4X3bmDiopCQKJZoNQJSDO7Et0mvYQ+D4lI06mR6wwBCCU2rEZBq0CLF4J41RYMnll7oEkXVkSasKz8KnUaEOUWLplY71pUfxaoFRVG5t5xOQglHqxFgTtVheLYRo3JTkJ2uZ/AYZJ4XuqZWu88LXdWRpmg3La5tO3ASOo0Ik8HdgzYZNNBpRGw7cDIq7WEPhOKeRnSvTKvXikg1cJptLOj+Qge4Z7TB7j7OXkj4Yq1OhgGE4pJR767+TjGEVv1NgyPWXugSRazVyfA3j+KCViMgI0WLIWYDRuelYFiWCZmpegaPGDUyx4ROh++unclYEBhppbPHwCnJsNklKIoCm12Kap0MeyCkunD3LNFrRaQa2cuIRywIVEes1cmwDoR1IGHrTx1I9z1Luq/X1dueJaIApJm0SDfqEj6Xkei1DGrOwkr0e6c21oFQXOjPniV6rTufYTJoYIzRbVopdDOL85gwT3AMIKSqhpZOpBv9k6mNrXbkmQ1czZYojjGAkKryM42wtjtgMmogCgJEwZ1MLchLGdBufCxSi038viSXxB5kpqgx6ERkpemwdM5YQABcLsUbPAaaTGWRWmzi9yX5MIBQxOi0ArLSdBiZa8LwbPc021n/MBSrFhQhz2xAa4cLeWbDgJddiLVqXHLj9yX5cAiLBkSrEZBq1CItSAV4pJOpLFKLTfy+JB/2QCgkguAenjKn6CAKgnutqTT9oE65ZZFabOL3JfkwgFBQ7h6GBtnpeuRnGTE6NwXDs03ITtcjWrNtY60al9z4fUk+DCDkRxSANKMW+Vnu1WyHmI0wp+hg0msgxsCUW081biTzKjRw/L4kH+ZAyMuk1yDN6N47IxYCRTCh5lU4vXRwsHgwubAHkuRMeg1y0vUYnZeC/Cwj0kzamA8eoeL0UiJ1sAeSZETBvTdDil4LkyE5qsC5NwWROhhAkoBnW9dkXWuK00uJ1MEAkqA8ixOa9JqkXwo91jbhiTfMH1FvkvuVJYEIgjto5GZczGeYU3RJHzwATi8dCOaPKBi+usQxjSggzahFntmA0bnuoJFu0iVFXiMUnF4aPi5PQsFwCCuOeHoZRr0GJp0m4TdciiROLw0P80cUDANIjNNqBHc+o+tPok2xpdjG/BEFw7ewMUinFWBO1WF4trsSPDfDgFRj4tVnUOxj/oiCYQ8kRui1IlIMGqQGWdU2kjizhvrDkz/izwoFwgASJZ58hme6rU4zeJ1Bz8wanUb0mVnDxDIFwvwR9YYBZBDptIK3AjyaBX2szCaiSGAAUZleKyLVqEGqUTuovYxgOLOGiCKBASTCtBoBBp0Ik14Dgy42q8A5s4aIIiH2Xt3ikFEvIitNhxHZJu/+Gemm2K0C58waIooE9kDCZNJr3LOmjNq4q/zmzBoiigQGkBAYdCLSjNq4DBo9cWYNEQ2U6gFkz549qKiogNVqxejRo7F48WIUFxf3er6iKPjzn/+MyspKnD17Funp6Zg1axbuuecetZsakF4rIs0oxlQSnIgoFqgaQKqrq/Hiiy9i6dKlKCoqwt69e7FmzRps3rwZQ4YMCXjN9u3bcejQISxatAiXXHIJLly4AKvVqmYzgxpiNkbtuYmIYpmqAWT37t248cYbMWfOHADAkiVLcPjwYVRWVuJnP/uZ3/n19fV488038fzzz2PUqFFqNo2IiAZItQDidDpRV1eH22+/3ef4lClTUFtbG/CaDz74APn5+Th8+DDWrl0LRVEwadIkLFq0CJmZmWo1lYiIwqBaAGlra4Msy34v/JmZmfj0008DXtPQ0IDGxkZUV1fjwQcfhCAI2LFjB9avX4/f/va3EEXfHMRbb72Fffv2BW3HihUrAADt7e1+jymKEvA49Q/v38Dw/oWP925g+nv/jMbgQ/iqJ9FDWa5DURQ4nU4sX74cI0aMAAAsX74cP//5z/HVV19h/PjxPueXlJSgpKQk6OdsbGwEAKSnp/s91t7eHvA49Q/v38Dw/oWP925gInX/VJtWlJGRAVEU/RLgLS0tvQ5HZWVlQaPReIMHAAwfPhwajQZNTdxCk4golqgWQHQ6HQoLC1FTU+NzvKamBhMnTgx4zcSJEyFJEs6cOeM91tDQAEmSep21RURE0aFqYcO8efNw8OBB7Nu3D6dOncLWrVthsVgwd+5cAMDLL7+MlStXes+fPHkyLr30UmzatAnHjx/H8ePHsWnTJowfPx6FhYVqNpWIiEKkag5kxowZaGtrQ3l5OSwWCwoKCrB69Wpvb8JisaChocF7viiKWLVqFbZu3YoVK1ZAr9dj8uTJuPfee/0S6EREFF2CzWZTot0INXmS6BkZGX6PMRE3MLx/A8P7Fz7eu4Hp7/3raxYW39YTEVFYuJgiUZKqOtLEFZlpQNgDIUpCVUeasK78KJpa7TCnaNHUase68qOoOsLp8tR/DCBESWjbgZPQaUSYDBoIggCTQQOdRsS2Ayej3TSKIwwgREmovtkGo97319+oF1HfbItSiygeMQeiIo4xU6wamWNCU6sdJoPGe6zTIWNkjimKraJ4wx6ISjjGTLGsdPYYOCUZNrsERVFgs0twSjJKZ4+JdtMojjCAqIRjzBTLZhbnYdWCIuSZDWjtcCHPbMCqBUXsIVNIOISlkvpmG8wpvreXY8wUS2YW5zFg0ICwB6KSkTkmdDpkn2McYyaiRMIAohKOMRNRomMAUQnHmIko0TEHoiKOMRNRImMPhIiIwsIAQkREYWEAISKisDCAEBFRWBhAiIgoLAwgREQUFgYQIiIKCwMIERGFhQGEiIjCwgBCRERhYQAhIqKwMIAQEVFYuJgiUYKoOtKEbQdOor7ZhpE5JpTOHsPFPElV7IEQJYCqI01YV34UTa12mFO0aGq1Y135UVQdaYp20yiBMYAQJYBtB05CpxFhMmggCAJMBg10GhHbDpyMdtMogTGAECWA+mYbjHrfX2ejXkR9sy1KLaJkwABClABG5pjQ6ZB9jnU6ZIzMMUWpRZQMGECIEkDp7DFwSjJsdgmKosBml+CUZJTOHhPtplECYwAhSgAzi/OwakER8swGtHa4kGc2YNWCIs7CIlVxGi9RgphZnMeAQYOKPRAiIgoLAwgREYWFAYSIiMLCAEJERGERbDabEu1GqKmxsTHaTSAiiltDhgzp9TH2QIiIKCwJ3wMJ5qGHHsIzzzwT7WbELd6/geH9Cx/v3cBE6v6xB0JERGFhACEiorAwgBARUVgYQIiIKCwMIEREFBYGECIiCgsDCBERhYUBhIiIwpLUAWTOnDnRbkJc4/0bGN6/8PHeDUyk7l9SV6ITEVH4kroHQkRE4WMAISKisDCAEBFRWLTRboCa9uzZg4qKClitVowePRqLFy9GcXFxr+crioI///nPqKysxNmzZ5Geno5Zs2bhnnvuGbxGx4hQ793HH3+MsrIyfPvtt9BqtSgqKsKiRYswYsSIQWx1bPjiiy+wa9cu1NXVwWKx4IEHHsDs2bODXvP1119jy5Yt+Oqrr5CWloaSkhL8+Mc/hiAIg9Tq2BHq/fv888/xxhtv4NixY7hw4QKGDx+OW2+9Ff/4j/84iK2OHeH8/Hl89913ePDBB6EoCl577bU+z0/YHkh1dTVefPFFLFiwAJs2bcLEiROxZs2aoBtMbd++HXv37sU999yD//zP/8Tq1asxadKkQWx1bAj13jU0NOCxxx5DcXExnn32WTz22GOw2+1Yu3btILc8NnR2dqKgoAD33Xcf9Hp9n+d3dHT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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x = reg_data[['BETAs']], y = reg_data[['E(RI-RF)']]) \n", "ax.set_title(\"BETAs\", fontsize=15)\n", "ax.set_xlabel(\"b*(MKT-RF)\", fontsize = 10)\n", "ax.set_ylabel(\"RI-RF\", fontsize = 10)" ] }, { "cell_type": "code", "execution_count": null, "id": "7cf9661b", "metadata": {}, "outputs": [], "source": [ "#Beta in the SML measure volatility of the asset comparing to the market. \n", "#Since the the gaming has highest beta, at 1.385, its return will be positive/negative 1.385 times.\n", "#Smoke Industry with lowest beta of 0.623 so there are less volatility compare to the market." ] }, { "cell_type": "markdown", "id": "a2a7761b", "metadata": {}, "source": [ "# 1.3" ] }, { "cell_type": "code", "execution_count": 30, "id": "72578166", "metadata": {}, "outputs": [], "source": [ "X=ff_factor[['Mkt-RF']]\n", "y=excess_return\n", "X=X.to_numpy()\n", "y=y.to_numpy()" ] }, { "cell_type": "code", "execution_count": 31, "id": "a1bfa373", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import KFold\n", "kf = KFold(n_splits=10,shuffle=True)" ] }, { "cell_type": "code", "execution_count": 32, "id": "125d8e19", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = 0.2)" ] }, { "cell_type": "code", "execution_count": 33, "id": "3c3b6619", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE (Model1:Linear) on 10-fold CV on the train data: 4.287600980105208\n", "RMSE (Model1:Linear) on 10-fold CV on the test data: 4.294323681822247\n" ] } ], "source": [ "err_train = 0\n", "err_test =0\n", "for train,test in kf.split(X):\n", " lr=linear_model.LinearRegression()\n", " reg=lr.fit(X[train],y[train])\n", " y_pred_train =reg.predict(X[train])\n", " y_pred_test =reg.predict(X[test])\n", " e_train= y[train]-y_pred_train\n", " e_test = y[test]-y_pred_test\n", " err_train += np.sqrt(metrics.mean_squared_error(y[train], reg.predict(X[train]))) \n", " err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n", " \n", "rmse_train_10cv_model1 = err_train/10 #average the rmse\n", "rmse_test_10cv_model1 = err_test/10 #average the rmse\n", "print('RMSE (Model1:Linear) on 10-fold CV on the train data: {}'.format(rmse_train_10cv_model1))\n", "print('RMSE (Model1:Linear) on 10-fold CV on the test data: {}'.format(rmse_test_10cv_model1))" ] }, { "cell_type": "code", "execution_count": 34, "id": "72ac34fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE (Model2:Linear) on 10-fold CV on the train data: 2.8912344489413635\n", "MAE (Model2:Linear) on 10-fold CV on the test data: 2.898567292342405\n" ] } ], "source": [ "err_train = 0\n", "err_test =0\n", "for train,test in kf.split(X):\n", " lr=linear_model.LinearRegression()\n", " reg=lr.fit(X[train],y[train])\n", " y_pred_train =reg.predict(X[train])\n", " y_pred_test =reg.predict(X[test])\n", " e_train= y[train]-y_pred_train\n", " e_test = y[test]-y_pred_test\n", " err_train += metrics.mean_absolute_error(y[train], reg.predict(X[train])) \n", " err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n", "mae_train_10cv_model2 = err_train/10 #average the rmse\n", "mae_test_10cv_model2 = err_test/10 #average the rmse\n", "print('MAE (Model2:Linear) on 10-fold CV on the train data: {}'.format(mae_train_10cv_model2))\n", "print('MAE (Model2:Linear) on 10-fold CV on the test data: {}'.format(mae_test_10cv_model2))" ] }, { "cell_type": "markdown", "id": "a94a476e", "metadata": {}, "source": [ "# 1.4" ] }, { "cell_type": "code", "execution_count": 35, "id": "c1203dcb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BETAsBETAsmbBETAhml
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 BETAsmb BETAhml\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": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta3 = pd.DataFrame(np.nan,index=name,columns=['BETAs','BETAsmb','BETAhml'])\n", "beta3" ] }, { "cell_type": "code", "execution_count": 36, "id": "2c49ab1e", "metadata": {}, "outputs": [], "source": [ "for i in range(len(name)):\n", " model = linear_model.LinearRegression()\n", " model = model.fit(ff_factor[['Mkt-RF','SMB','HML']],excess_return[excess_return.columns[i]])\n", " beta3['BETAs'].iloc[i] = model.coef_[0]\n", " beta3['BETAsmb'].iloc[i] = model.coef_[1]\n", " beta3['BETAhml'].iloc[i] = model.coef_[2]" ] }, { "cell_type": "code", "execution_count": null, "id": "3d389f7a", "metadata": {}, "outputs": [], "source": [ "beta3" ] }, { "cell_type": "markdown", "id": "d5e7a0b7", "metadata": {}, "source": [ "# 1.5" ] }, { "cell_type": "code", "execution_count": 37, "id": "b644ab1a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BETAsBETAsmbBETAhmlE(RI-RF)
Food0.747453-0.1403840.0522590.699220
Beer0.8667320.1964420.1333880.929593
Smoke0.649355-0.2168730.0966050.866482
Games1.2875350.4089230.1382500.835659
Books1.0124980.3724910.1861580.631750
Hshld0.906274-0.088170-0.0353780.655269
Clths0.7615060.422109-0.0600450.664896
Hlth0.878894-0.085659-0.1894810.807920
Chems1.068119-0.1515140.0261750.777340
Txtls0.9827310.5632680.3729730.675537
Cnstr1.1183410.2477420.1078240.694168
Steel1.2596590.2320490.3803990.677374
FabPr1.1733770.2615500.1119850.800849
ElcEq1.295485-0.035460-0.0006160.896854
Autos1.2477080.0550860.1886270.923614
Carry1.0975550.2181240.3280790.850295
Mines0.8426470.2587310.1456210.640659
Coal1.0841630.4853780.6733390.828094
Oil0.878785-0.1792070.2900800.773960
Util0.755451-0.1669030.2597150.618354
Telcm0.694991-0.131654-0.0367390.566352
Servs0.8321290.368626-0.5058400.953033
BusEq1.1211670.153322-0.4557350.903787
Paper0.952825-0.0576840.0453550.721256
Trans1.0380010.1634730.4544230.655191
Whlsl0.9729690.5501520.0682170.575069
Rtail0.9799820.041639-0.1314790.777964
Meals0.8969390.289515-0.0305680.800208
Fin1.121536-0.0575390.3152180.742305
Other0.9995470.2830970.0183350.522088
\n", "
" ], "text/plain": [ " BETAs BETAsmb BETAhml E(RI-RF)\n", "Food 0.747453 -0.140384 0.052259 0.699220\n", "Beer 0.866732 0.196442 0.133388 0.929593\n", "Smoke 0.649355 -0.216873 0.096605 0.866482\n", "Games 1.287535 0.408923 0.138250 0.835659\n", "Books 1.012498 0.372491 0.186158 0.631750\n", "Hshld 0.906274 -0.088170 -0.035378 0.655269\n", "Clths 0.761506 0.422109 -0.060045 0.664896\n", "Hlth 0.878894 -0.085659 -0.189481 0.807920\n", "Chems 1.068119 -0.151514 0.026175 0.777340\n", "Txtls 0.982731 0.563268 0.372973 0.675537\n", "Cnstr 1.118341 0.247742 0.107824 0.694168\n", "Steel 1.259659 0.232049 0.380399 0.677374\n", "FabPr 1.173377 0.261550 0.111985 0.800849\n", "ElcEq 1.295485 -0.035460 -0.000616 0.896854\n", "Autos 1.247708 0.055086 0.188627 0.923614\n", "Carry 1.097555 0.218124 0.328079 0.850295\n", "Mines 0.842647 0.258731 0.145621 0.640659\n", "Coal 1.084163 0.485378 0.673339 0.828094\n", "Oil 0.878785 -0.179207 0.290080 0.773960\n", "Util 0.755451 -0.166903 0.259715 0.618354\n", "Telcm 0.694991 -0.131654 -0.036739 0.566352\n", "Servs 0.832129 0.368626 -0.505840 0.953033\n", "BusEq 1.121167 0.153322 -0.455735 0.903787\n", "Paper 0.952825 -0.057684 0.045355 0.721256\n", "Trans 1.038001 0.163473 0.454423 0.655191\n", "Whlsl 0.972969 0.550152 0.068217 0.575069\n", "Rtail 0.979982 0.041639 -0.131479 0.777964\n", "Meals 0.896939 0.289515 -0.030568 0.800208\n", "Fin 1.121536 -0.057539 0.315218 0.742305\n", "Other 0.999547 0.283097 0.018335 0.522088" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta3 = beta3.join(reg_data[['E(RI-RF)']])\n", "beta3" ] }, { "cell_type": "code", "execution_count": 38, "id": "612c38a9", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "beta3.plot.scatter(x='BETAs',y = 'E(RI-RF)' ) \n", "plt.title(\"BETAs\",fontsize=15)\n", "plt.xlabel('BETAs', fontsize=10)\n", "plt.ylabel('RI-RF', fontsize=10)\n", "plt.xticks(fontsize=10)\n", "plt.yticks(fontsize=10)\n", "plt.show()\n", "\n", "plt.savefig(\"indystry beta 1.5.jpeg\")" ] }, { "cell_type": "code", "execution_count": 39, "id": "fbbef01f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RI-RF')" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x = beta3[['BETAs']], y = beta3[['E(RI-RF)']]) \n", "ax.set_title(\"BETAs\", fontsize=15)\n", "ax.set_xlabel(\"b*(MKT-RF)\", fontsize = 10)\n", "ax.set_ylabel(\"RI-RF\", fontsize = 10)" ] }, { "cell_type": "code", "execution_count": 40, "id": "13f49f3c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "beta3.plot.scatter(x='BETAsmb',y = 'E(RI-RF)' ) \n", "plt.title(\"SMB Betas\",fontsize=15)\n", "plt.xlabel('BETAsmb', fontsize=10)\n", "plt.ylabel('RI-RF', fontsize=10)\n", "plt.xticks(fontsize=10)\n", "plt.yticks(fontsize=10)\n", "plt.show()\n", "\n", "plt.savefig(\"betasmb 1.5.jpeg\")" ] }, { "cell_type": "code", "execution_count": 41, "id": "5573b6b3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RI-RF')" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x = beta3[['BETAsmb']], y = beta3[['E(RI-RF)']]) \n", "ax.set_title(\"SMB BETAs\", fontsize=15)\n", "ax.set_xlabel(\"BETAsmb\", fontsize = 10)\n", "ax.set_ylabel(\"RI-RF\", fontsize = 10)" ] }, { "cell_type": "code", "execution_count": 42, "id": "96febd36", "metadata": {}, "outputs": [ { "data": { "image/png": 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atRJfLroJMa3+Yg9WLr7G01nh8Nn4Q075hEMuoo3nJCcno6qqCtXV1bDZbDAYDEhLS/Noc/nll+Obb74BANTW1qKyshJ9+vQRKySKYFI7Y0Fq8RAJUVit1rZjQQGyb98+92WumZmZuPfee7FlyxYAQHZ2NkwmE/7+97+jtrYWTqcTkydPxs0339yh93L1IGJjYwMWv1jC4S8Hf4RTPmZLk9d5kmDncql4OiucPhtfyCkfKeXS3hCTqAUimFggQkdO+cgpF4D5SJmUcmmvQPCSISIiEsQCQUREglggSBKkvqJY6vERiYG7uVLISX1FsdTjIxILexAUUq1XFEtxJ1ipxxcq7FFFBvYgKKQqzVZJ7wQr9fhCgT2qyMEeBIVUP203Se8EK/X4go09qsjCAkEhJfUVxVKPL9jcPapWXD0qkh8OMVHISX0nWKnHF0zsUUUW9iBIErQxXZCa1EOyX75Sjy9Y2KOKLOxBEJFf2KOKHCwQROQ3bUwXFoYIwCEmIiIS5LVAfPXVV+6f6+rqRA+GiIikw2uB2LBhg/vn+fPnix4MERFJB4eYiIhIkNdJ6sbGRhw7dgxOpxNNTU3un10GDBggeoBERBQaXguEVqvF6tWrAQC9evVy/wwACoUCS5YsETc6IiIKGa8F4q9//Wuw4iAimRD7nG0Kng7NQRw4cAALFy4MdCxEFOY2l1Xh5oU78fDyMty8cCc2l1WFOiTqBK89iG+++QZvvPEGzGYzRo8ejSlTpmDZsmVwOp245557ghUjEYWB1ju9urZHn7/uINJT9OxJhCmvBWLNmjV46qmnkJKSgn379uHPf/4z7r//fkycODFY8RFRmODZGfJzySGmYcOGQa1WY8yYMejRoweLAxEJ4k6v8uO1B3HhwgWP1dROp9Pjdnp6uniREVFYce30evFpc+w9hC+vBWLo0KEoLS0VvK1QKFggiMgDd3qVF68F4plnnglSGEQkF9zpVT78vsz1+eefFyMOIiKSGL8LhNlsFiMOIiKSGL8LxFVXXSVGHERhx2xpwre/nIPZ0hTqUIhE4feJcrNmzRIjDqKwsrmsqs3VOuNHXhHqsIgCymuBeOqpp6BQKNrc73Q6oVAoUFhYKFpgRFLFFcMUKbwWiOeeey5YcRCFDa4YpkjhtUD07t1b8H673Y7du3e3+ziRnHHFMEUKr5PU9fX1+Pe//42ioiLs378fTqcTn3zyCR577DHs2bMnWDESSYprxbBGrUR3TRQ0aiVXDJMsee1BLF26FJdddhlSUlKwfft2bNy4ETabDQsWLODVTBTRuGKYIoHXAlFdXY3XX38dADBu3DhMnz4da9asQXR0dFCCI5IyrhgmufM6xKRSqTx+7tOnD4sDEVGE8NqD+Pnnnz0OBmpsbPS4/f7773t98fLychQXF8PhcGDs2LGYMmWKx+MfffQRdu7cCaBl4ruyshJr165FTEyMv3kQEVGAeS0QmzZt6vAL2+12FBUVYdGiRdDpdJgzZw5GjRqFxMREd5tJkyZh0qRJAIDS0lJs2rSJxYHCDs9gJrnyeyW1r44ePYr4+HjExcUBADIyMlBSUuJRIFrbtWsXMjIyxAqHSBRcUU1y5vdeTL4ymUzQ6/Xu2zqdDiaTSbBtQ0MD9u/fz/MlKKy0XlFd19CMBpsD89cd9HlvJu7lRFInWg/C6XS2uU9o2w4AKCsrw6BBg9odXtq6dSu2bdvm9f1yc3MBABaLxc9Ig8/pdIZFnL6SUz5+5WJ3YOPc36H1r7pCAcBuhcXS6PWpDTY7LPXN6B4FnD1ng90WBY1a5fU5HSGnzwaQVz5SykWj0QjeL1qB0Ov1qKmpcd82mUzQarWCbQ0Gg9fhpaysLGRlZXl9P6PRCABhMYdhsVjCIk5fySkff3IxW5pw1yslLXsy/UajVuLLRTchxstchNnShMwXdgo+L9BzGHL6bAB55RMOuYg2xJScnIyqqipUV1fDZrPBYDAgLS2tTbsLFy7g4MGDGD16tFihEImioyuq3Xs5teLay4lISkTrQahUKuTk5CAvLw8OhwOZmZlISkrCli1bAADZ2dkAgL179+K6665rt4tDJGUdWVHNvZwoXCisVmvbyYIw5Bpiio2NDXEklxYOXUt/yCmfYOUSrKuf5PTZAPLKR0q5BH0Ogojax72cKBywQBCFCPdyIqkTbZKaiKijuEZEGtiDICLJMFuasH7PCby57SeouTo95FggiEgSNpdV4f+trUBjc8t1M4087zvkOMRERCHn2rbEVRxa4xqR0GGBIKKQE1o86MI1IqHDAkFEISe0eBAAuvK875BigSBZ4dUv4enibUu6Rikwa/wA7Fx0EyeoQ4iT1CQbPJshvHHxoPSwQJAstD6bAbz6JWxx8aC0cIiJZIE7pBIFHgsEycKldkjl3ETk4mffcRxiIllwTXJePAehjenCuYkIxs++c1ggSDaEJjk5NxG5+Nl3HgsEycrFk5zuuYlWx3u65ib4JSFv/Ow7j3MQJGs8vS1y8bPvPBYIkrWOnhtN4Y+ffedxiIlkjwuwIhc/+85hgaCIEM4LsMyWJn7BdUI4f/ahxgJBJGG8TJNCiXMQRBLV+jLNuoZmNNgcmL/uIBd8UdCwQBBJFLcPoVBjgSCSqI5cpsltJSiQOAdBJFHetg8RIjRfcWNKTJCjJjlhgSCSMF8v02xvWwnDC6ODGS7JDAsEkcT5cplme9tK2B1OscMjGeMcBJEMtDdfoVIqQhQRyQELBJEMtLethFLBAkEdxyEmIpkQmq+wWCwdfj2u4CYWCCIZCdS2ElzBTQCHmIjoIlzBTS4sEETkgSu4yYUFgog88KAdcmGBICIPPGiHXDhJTURt8KAdAlggiKgdPGiHRC0Q5eXlKC4uhsPhwNixYzFlypQ2bSoqKlBcXIzm5mbExsbixRdfFDMkIiLykWgFwm63o6ioCIsWLYJOp8OcOXMwatQoJCYmutvU1dVh5cqVyM/PR+/evXH27FmxwiEiIj+JNkl99OhRxMfHIy4uDmq1GhkZGSgpKfFos2vXLowZMwa9e/cGAPTs2VOscIiIyE+i9SBMJhP0er37tk6nw5EjRzzaVFVVobm5Gbm5ubBarZg4cSJuueUWsUIiIiI/iFYgnM622wwrLto4zG6349ixY1i8eDEaGxsxd+5cDBw4EH379vVot3XrVmzbts3r++Xm5gJAp/aeCRan0xkWcfpKTvnIKReA+UiZlHLRaDSC94tWIPR6PWpqaty3TSYTtFqtRxudTofY2FhoNBpoNBoMHToUx48fb1MgsrKykJWV5fX9jEYjACAmRvonaFkslrCI01dyykdOuQDMR8rCIRfR5iCSk5NRVVWF6upq2Gw2GAwGpKWlebQZPXo0vvvuO9jtdjQ0NODw4cNISEgQKyQiIvKDaD0IlUqFnJwc5OXlweFwIDMzE0lJSdiyZQsAIDs7GwkJCRg+fDiefvppKBQKjBs3DklJSWKFREREflBYrVZZnEnoGmKKjY0NcSSXFg5dS3/IKR855QIwHymTUi7tzUFwLyYiIhLEAkFERIJYIIiISBALBFGAmS1N+PaXczyBjcIed3MlCiCe5Uxywh4EUYDwLGeSGxYIogDhWc4kNywQFBGCMS/As5xJbjgHQbIXrHkB11nOF78XT2WjcMUCQbLWel4Atpa/7uevO4j0FL0oX9w8y5nkhAWCZM09L2D739CPa15ArC9vnuVMcsE5CJI1zgsQdRwLBMmaa15Ao1aiuyYKGrWS8wJEPuIQE8ke5wWIOoYFgiIC5wWI/MchJiIiEsQCQUREglggiIhIEAsEEREJYoEgIiJBLBBERCSIBYKIiASxQBARkSAWCCIiEsQCQUREglggiIhIEAsEEREJYoEg8iIYZ1kTSRV3cyVqR7DOsiaSKvYgiAS0Psu6rqEZDTYH5q87yJ4ERRQWCCIB7rOsW3GdZU0UKVggiATwLGsiFggiQTzLmoiT1ETt4lnWFOlYIIi84FnWFMk4xERERIJYIIiISBALBBERCWKBICIiQQqr1eoMdRCBYDQaQx0CEVHY6t27d5v72IMgIiJBsulBhJPZs2dj2bJloQ4jYOSUj5xyAZiPlIVDLuxBEBGRIBYIIiISxAJBRESCWCCIiEgQCwQREQligSAiIkEsEEREJIgFgoiIBLFAhMBtt90W6hACSk75yCkXgPlIWTjkwpXUREQkiD0IIiISxAJBRESCeCZ1EFgsFrz88ss4ffo0+vTpg3nz5qF79+6Cbe12O+bMmQOtVou8vLwgR+obX/I5c+YMli1bhtraWigUCmRlZWHixIkhirit8vJyFBcXw+FwYOzYsZgyZYrH406nE6tWrUJ5eTm6du2KWbNmYcCAASGK9tIulc/OnTvx4YcfAgA0Gg2eeOIJ9O/fPxShXtKlcnE5cuQI5s6di2effRbXX399kKP0nS/5VFRUoLi4GM3NzYiNjcWLL74YgkjbYg8iCD744AOkpqZi1apVSE1NxQcffNBu208++QT9+vULYnT+8yUflUqFRx55BCtXrsTf/vY3fPrppzhx4kQIom3LbrejqKgI+fn5WLFiBQwGQ5vYysvLUVVVhTfffBNPPvkkVq5cGaJoL82XfPr06YOCggIUFhbi3nvvxeuvvx6iaL3zJRdXu3feeQfXXXddCKL0nS/51NXVYeXKlViwYAHeeOMN/OUvfwlRtG2xQARBSUkJbr31VgDArbfeiq+//lqwXU1NDcrKyjBu3Lhghuc3X/LRarXuv7ijo6ORkJAAk8kU1Djbc/ToUcTHxyMuLg5qtRoZGRkoKSnxaPP111/jlltugUKhQEpKCi5cuACz2RyiiL3zJZ9Bgwa5e3kpKSmoqakJRaiX5EsuALB582akp6ejR48eIYjSd77ks2vXLowZM8Z9YE/Pnj1DEKkwFoggOHv2LLRaLYCWL86zZ88KtisuLsbDDz8MpVLaH4uv+bicPn0ax44dw8CBA4MQ3aWZTCbo9Xr3bZ1O16Z4+dJGKvyNdfv27Rg+fHgwQvObr5/N3r17kZWVFezw/OZLPlVVVairq0Nubi6eeeYZfPHFF8EOs12cgwiQBQsWoLa2ts39DzzwgE/PLy0tRY8ePTBgwABUVFQEOjy/dTYfF6vVioKCAjz22GOIjo4OVHid4nS2vbJboVBc8nm+tAkFf/L59ttv8dlnn+Gll14SO6wO8SWX4uJi/OEPf4BKpQpWWB3mSz52ux3Hjh3D4sWL0djYiLlz52LgwIHo27dvsMJsFwtEgCxevLjdx3r27Amz2QytVguz2SzYhfzhhx9QWlqK8vJyNDU1ob6+Hq+++ir+9Kc/iRh1+zqbDwA0NzejoKAAN910E9LT00WK1H96vd5jiMVkMrl7RC46ne6SbaTCl3wA4Pjx4ygsLER+fj5iY2ODGaLPfMnl6NGjeOWVVwAA58+fR3l5OZRKJcaMGRPUWH3h6+9abGwsNBoNNBoNhg4diuPHj0uiQEh7LEMm0tLS8PnnnwMAPv/8c4waNapNm4ceegj/+Mc/sHr1ajz77LNITU0NWXG4FF/ycTqdWL58ORISEnDnnXcGOULvkpOTUVVVherqathsNhgMBqSlpXm0GTVqFL744gs4nU4cOnQI0dHRki0QvuRjNBpRUFCAOXPmSOKLpz2+5LJ69Wr3f+np6Xj88cclWRwA3/IZPXo0vvvuO9jtdjQ0NODw4cNISEgIUcSe2IMIgsmTJ+Oll17CZ599hssvv9x9lYLJZHL/RRdOfMnn+++/x5dffokrr7wSM2fOBAA8+OCDGDFiRChDB9ByhVVOTg7y8vLgcDiQmZmJpKQkbNmyBQCQnZ2NESNGYN++ffjjH//ovsxVqnzJ51//+hfOnz/vvhpLpVJJ8jxkX3IJJ77kk5CQgOHDh+Ppp5+GQqHAuHHjkJSUFOLIW3CrDSIiEsQhJiIiEsQCQUREglggiIhIEAsEEREJYoEgIiJBvMyV6CJ33HEHkpKS4HQ6oVQqkZOTg0GDBuH06dN44oknPNYR3Hnnnfj0009hs9lgsVjQ1NQEnU4HAJg/fz70ej0eeOAB3HbbbXjooYfcz5sxYwaWLl3a4b2EcnNz8cgjjyA5OblzyRJ5wQJBdJEuXbpg+fLlAID9+/fjnXfecW+/HBcX537M5ZZbbgEA7NixAz/++CNycnLcj+3btw/9+vXDnj178OCDD0p2uw4iISwQRF7U19e3e3aHL3bt2oUJEyZgy5YtOHz4MFJSUtyPbd68GaWlpbDb7Zg3bx4SEhLw3nvv4fTp0zCbzaiqqsKMGTNw+PBhlJeXQ6fTYeHChYiK4v+2FBycgyC6SFNTE2bOnImcnBwUFhZi6tSp7seqq6sxc+ZM93/fffddu6/T2NiIb7/9FiNHjkRGRgZ27drl8XhsbCxee+01ZGdnY+PGje77T506hby8PCxYsACvvvoqhg0bhtdffx1dunRBWVlZ4BMmagf/FCG6SOshpkOHDmHp0qVYsWIFAOEhpvaUlZVh2LBh0Gg0SE9Px4YNG/Doo4+6dyF1bWA4YMAA7N271/284cOHIyoqCklJSXA4HO6tuZOSkmA0GgOWJ9GlsEAQeZGSkgKLxYJz5875/VyDwYAffvgBM2bMANCy82hFRQWuvfZaAHAPFSmVStjtdvfz1Gq1+/6oqCj3vMXF7YjExgJB5MXJkydht9sRExODxsZGn59XX1+P77//Hm+//bb7C3/Hjh0wGAzuAkEkdSwQRBdxzUEALduWz5492z0s5JqDcMnMzMTEiRPbvMZXX32F1NRUd3EAWrYQf/vtt2Gz2UTOgCgwuJsrEREJ4lVMREQkiAWCiIgEsUAQEZEgFggiIhLEAkFERIJYIIiISBALBBERCWKBICIiQf8f8UBEEPTswDAAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "beta3.plot.scatter(x='BETAhml',y = 'E(RI-RF)' ) \n", "plt.title(\"HML Betas\",fontsize=15)\n", "plt.xlabel('BETAhml', fontsize=10)\n", "plt.ylabel('RI-RF', fontsize=10)\n", "plt.xticks(fontsize=10)\n", "plt.yticks(fontsize=10)\n", "plt.show()\n", "\n", "plt.savefig(\"betahml 1.5.jpeg\")" ] }, { "cell_type": "code", "execution_count": 43, "id": "fa95ac69", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RI-RF')" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x = beta3[['BETAhml']], y = beta3[['E(RI-RF)']]) \n", "ax.set_title(\"BETAhml\", fontsize=15)\n", "ax.set_xlabel(\"BETAhml\", fontsize = 10)\n", "ax.set_ylabel(\"RI-RF\", fontsize = 10)" ] }, { "cell_type": "code", "execution_count": null, "id": "e10cdabb", "metadata": {}, "outputs": [], "source": [ "#For the BETAs, higher mean high volatility but also higher return. \n", "#The negative return in SMB and HBL may idicate that it is not the price factor. \n", "#The three-factor Fama_french is more suitable." ] }, { "cell_type": "markdown", "id": "749d42c9", "metadata": {}, "source": [ "# 1.6" ] }, { "cell_type": "code", "execution_count": 44, "id": "e331ffa6", "metadata": {}, "outputs": [], "source": [ "X=ff_factor[['Mkt-RF','SMB','HML']]\n", "y=excess_return\n", "X=X.to_numpy()\n", "y=y.to_numpy()" ] }, { "cell_type": "code", "execution_count": 45, "id": "c7cb56d5", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import KFold\n", "kf = KFold(n_splits=10,shuffle=True)" ] }, { "cell_type": "code", "execution_count": 46, "id": "55f06f3e", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = 0.2)" ] }, { "cell_type": "code", "execution_count": 47, "id": "e6242c39", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE (Model3:Linear) on 10-fold CV on the train data: 4.108872443695359\n", "RMSE (Model3:Linear) on 10-fold CV on the test data: 4.14659249960498\n" ] } ], "source": [ "err_train = 0\n", "err_test =0\n", "for train,test in kf.split(X):\n", " lr=linear_model.LinearRegression()\n", " reg=lr.fit(X[train],y[train])\n", " y_pred_train =reg.predict(X[train])\n", " y_pred_test =reg.predict(X[test])\n", " e_train= y[train]-y_pred_train\n", " e_test = y[test]-y_pred_test\n", " err_train += np.sqrt(metrics.mean_squared_error(y[train], reg.predict(X[train]))) \n", " err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n", " \n", "rmse_train_10cv_model3 = err_train/10 #average the rmse\n", "rmse_test_10cv_model3 = err_test/10 #average the rmse\n", "print('RMSE (Model3:Linear) on 10-fold CV on the train data: {}'.format(rmse_train_10cv_model3))\n", "print('RMSE (Model3:Linear) on 10-fold CV on the test data: {}'.format(rmse_test_10cv_model3))" ] }, { "cell_type": "code", "execution_count": 48, "id": "c1d7f3d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE (Model4:Linear) on 10-fold CV on the train data: 2.7770223405352477\n", "MAE (Model4:Linear) on 10-fold CV on the test data: 2.7916387393159345\n" ] } ], "source": [ "err_train = 0\n", "err_test =0\n", "for train,test in kf.split(X):\n", " lr=linear_model.LinearRegression()\n", " reg=lr.fit(X[train],y[train])\n", " y_pred_train =reg.predict(X[train])\n", " y_pred_test =reg.predict(X[test])\n", " e_train= y[train]-y_pred_train\n", " e_test = y[test]-y_pred_test\n", " err_train += metrics.mean_absolute_error(y[train], reg.predict(X[train])) \n", " err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n", "mae_train_10cv_model4 = err_train/10 #average the rmse\n", "mae_test_10cv_model4 = err_test/10 #average the rmse\n", "print('MAE (Model4:Linear) on 10-fold CV on the train data: {}'.format(mae_train_10cv_model4))\n", "print('MAE (Model4:Linear) on 10-fold CV on the test data: {}'.format(mae_test_10cv_model4))" ] }, { "cell_type": "code", "execution_count": null, "id": "f50e909c", "metadata": {}, "outputs": [], "source": [ "#The model with SMB and HML has lower RMSE and MAE." ] } ], "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 }