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PeriodMkt-RFSMBHMLRF
01926072.96-2.56-2.430.22
11926082.64-1.173.820.25
21926090.36-1.400.130.23
3192610-3.24-0.090.700.32
41926112.53-0.10-0.510.31
..................
1149202204-9.46-1.416.190.01
1150202205-0.34-1.858.410.03
1151202206-8.432.09-5.970.06
11522022079.572.81-4.100.08
1153202208-3.781.390.310.19
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1154 rows × 5 columns

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" ], "text/plain": [ " Period Mkt-RF SMB HML RF\n", "0 192607 2.96 -2.56 -2.43 0.22\n", "1 192608 2.64 -1.17 3.82 0.25\n", "2 192609 0.36 -1.40 0.13 0.23\n", "3 192610 -3.24 -0.09 0.70 0.32\n", "4 192611 2.53 -0.10 -0.51 0.31\n", "... ... ... ... ... ...\n", "1149 202204 -9.46 -1.41 6.19 0.01\n", "1150 202205 -0.34 -1.85 8.41 0.03\n", "1151 202206 -8.43 2.09 -5.97 0.06\n", "1152 202207 9.57 2.81 -4.10 0.08\n", "1153 202208 -3.78 1.39 0.31 0.19\n", "\n", "[1154 rows x 5 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1" ] }, { "cell_type": "code", "execution_count": 4, "id": "46ed08c8", "metadata": {}, "outputs": [], "source": [ "df2 = pd.read_csv('30_Industry_Portfolios.csv')" ] }, { "cell_type": "code", "execution_count": 5, "id": "f854ee60", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PeriodFoodBeerSmokeGamesBooksHshldClthsHlthChems...TelcmServsBusEqPaperTransWhlslRtailMealsFinOther
01926070.56-5.191.292.9310.97-0.488.081.778.14...0.839.222.067.701.91-23.790.071.87-0.025.20
11926082.5927.036.500.5510.01-3.58-2.514.255.50...2.172.024.39-2.384.855.39-0.75-0.134.476.76
21926091.164.021.266.58-0.990.73-0.510.695.33...2.412.250.19-5.540.07-7.870.25-0.56-1.61-3.86
3192610-3.06-3.311.06-4.769.47-4.680.12-0.57-4.76...-0.11-2.00-1.09-5.08-2.61-15.38-2.20-4.11-5.51-8.49
41926116.357.294.551.66-5.80-0.541.875.425.20...1.633.773.643.841.614.676.524.332.344.00
..................................................................
11492022043.013.036.37-25.22-10.762.04-7.00-6.80-2.28...-10.70-12.59-12.26-0.74-10.93-2.14-11.41-5.47-7.99-7.65
1150202205-1.68-1.602.67-2.93-7.40-5.12-6.450.994.52...8.54-3.35-0.75-0.66-4.591.03-5.64-3.292.80-1.19
1151202206-1.64-0.02-11.63-11.33-12.53-2.56-12.00-2.05-15.65...-6.72-6.79-10.19-8.51-7.14-6.43-8.50-9.02-9.05-11.78
11522022073.675.490.5614.6212.100.7611.862.757.66...-0.408.6015.687.229.339.0816.3311.897.389.19
1153202208-1.61-1.87-0.12-2.95-4.97-2.16-6.01-5.07-1.39...-3.00-4.72-5.89-7.66-1.46-1.60-3.46-1.47-2.24-3.65
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1154 rows × 31 columns

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" ], "text/plain": [ " Period Food Beer Smoke Games Books Hshld Clths Hlth Chems \\\n", "0 192607 0.56 -5.19 1.29 2.93 10.97 -0.48 8.08 1.77 8.14 \n", "1 192608 2.59 27.03 6.50 0.55 10.01 -3.58 -2.51 4.25 5.50 \n", "2 192609 1.16 4.02 1.26 6.58 -0.99 0.73 -0.51 0.69 5.33 \n", "3 192610 -3.06 -3.31 1.06 -4.76 9.47 -4.68 0.12 -0.57 -4.76 \n", "4 192611 6.35 7.29 4.55 1.66 -5.80 -0.54 1.87 5.42 5.20 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "1149 202204 3.01 3.03 6.37 -25.22 -10.76 2.04 -7.00 -6.80 -2.28 \n", "1150 202205 -1.68 -1.60 2.67 -2.93 -7.40 -5.12 -6.45 0.99 4.52 \n", "1151 202206 -1.64 -0.02 -11.63 -11.33 -12.53 -2.56 -12.00 -2.05 -15.65 \n", "1152 202207 3.67 5.49 0.56 14.62 12.10 0.76 11.86 2.75 7.66 \n", "1153 202208 -1.61 -1.87 -0.12 -2.95 -4.97 -2.16 -6.01 -5.07 -1.39 \n", "\n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", "0 ... 0.83 9.22 2.06 7.70 1.91 -23.79 0.07 1.87 -0.02 \n", "1 ... 2.17 2.02 4.39 -2.38 4.85 5.39 -0.75 -0.13 4.47 \n", "2 ... 2.41 2.25 0.19 -5.54 0.07 -7.87 0.25 -0.56 -1.61 \n", "3 ... -0.11 -2.00 -1.09 -5.08 -2.61 -15.38 -2.20 -4.11 -5.51 \n", "4 ... 1.63 3.77 3.64 3.84 1.61 4.67 6.52 4.33 2.34 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "1149 ... -10.70 -12.59 -12.26 -0.74 -10.93 -2.14 -11.41 -5.47 -7.99 \n", "1150 ... 8.54 -3.35 -0.75 -0.66 -4.59 1.03 -5.64 -3.29 2.80 \n", "1151 ... -6.72 -6.79 -10.19 -8.51 -7.14 -6.43 -8.50 -9.02 -9.05 \n", "1152 ... -0.40 8.60 15.68 7.22 9.33 9.08 16.33 11.89 7.38 \n", "1153 ... -3.00 -4.72 -5.89 -7.66 -1.46 -1.60 -3.46 -1.47 -2.24 \n", "\n", " Other \n", "0 5.20 \n", "1 6.76 \n", "2 -3.86 \n", "3 -8.49 \n", "4 4.00 \n", "... ... \n", "1149 -7.65 \n", "1150 -1.19 \n", "1151 -11.78 \n", "1152 9.19 \n", "1153 -3.65 \n", "\n", "[1154 rows x 31 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2" ] }, { "cell_type": "code", "execution_count": 6, "id": "22674004", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Mkt-RFSMBHMLRF
Period
1926072.96-2.56-2.430.22
1926082.64-1.173.820.25
1926090.36-1.400.130.23
192610-3.24-0.090.700.32
1926112.53-0.10-0.510.31
...............
202204-9.46-1.416.190.01
202205-0.34-1.858.410.03
202206-8.432.09-5.970.06
2022079.572.81-4.100.08
202208-3.781.390.310.19
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1154 rows × 4 columns

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" ], "text/plain": [ " Mkt-RF SMB HML RF\n", "Period \n", "192607 2.96 -2.56 -2.43 0.22\n", "192608 2.64 -1.17 3.82 0.25\n", "192609 0.36 -1.40 0.13 0.23\n", "192610 -3.24 -0.09 0.70 0.32\n", "192611 2.53 -0.10 -0.51 0.31\n", "... ... ... ... ...\n", "202204 -9.46 -1.41 6.19 0.01\n", "202205 -0.34 -1.85 8.41 0.03\n", "202206 -8.43 2.09 -5.97 0.06\n", "202207 9.57 2.81 -4.10 0.08\n", "202208 -3.78 1.39 0.31 0.19\n", "\n", "[1154 rows x 4 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = df1.set_index('Period')\n", "df3" ] }, { "cell_type": "code", "execution_count": 7, "id": "47e3fdd6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FoodBeerSmokeGamesBooksHshldClthsHlthChemsTxtls...TelcmServsBusEqPaperTransWhlslRtailMealsFinOther
Period
1926070.56-5.191.292.9310.97-0.488.081.778.140.39...0.839.222.067.701.91-23.790.071.87-0.025.20
1926082.5927.036.500.5510.01-3.58-2.514.255.507.97...2.172.024.39-2.384.855.39-0.75-0.134.476.76
1926091.164.021.266.58-0.990.73-0.510.695.332.30...2.412.250.19-5.540.07-7.870.25-0.56-1.61-3.86
192610-3.06-3.311.06-4.769.47-4.680.12-0.57-4.761.00...-0.11-2.00-1.09-5.08-2.61-15.38-2.20-4.11-5.51-8.49
1926116.357.294.551.66-5.80-0.541.875.425.203.10...1.633.773.643.841.614.676.524.332.344.00
..................................................................
2022043.013.036.37-25.22-10.762.04-7.00-6.80-2.286.63...-10.70-12.59-12.26-0.74-10.93-2.14-11.41-5.47-7.99-7.65
202205-1.68-1.602.67-2.93-7.40-5.12-6.450.994.522.38...8.54-3.35-0.75-0.66-4.591.03-5.64-3.292.80-1.19
202206-1.64-0.02-11.63-11.33-12.53-2.56-12.00-2.05-15.65-11.17...-6.72-6.79-10.19-8.51-7.14-6.43-8.50-9.02-9.05-11.78
2022073.675.490.5614.6212.100.7611.862.757.666.86...-0.408.6015.687.229.339.0816.3311.897.389.19
202208-1.61-1.87-0.12-2.95-4.97-2.16-6.01-5.07-1.39-12.20...-3.00-4.72-5.89-7.66-1.46-1.60-3.46-1.47-2.24-3.65
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1154 rows × 30 columns

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" ], "text/plain": [ " Food Beer Smoke Games Books Hshld Clths Hlth Chems Txtls \\\n", "Period \n", "192607 0.56 -5.19 1.29 2.93 10.97 -0.48 8.08 1.77 8.14 0.39 \n", "192608 2.59 27.03 6.50 0.55 10.01 -3.58 -2.51 4.25 5.50 7.97 \n", "192609 1.16 4.02 1.26 6.58 -0.99 0.73 -0.51 0.69 5.33 2.30 \n", "192610 -3.06 -3.31 1.06 -4.76 9.47 -4.68 0.12 -0.57 -4.76 1.00 \n", "192611 6.35 7.29 4.55 1.66 -5.80 -0.54 1.87 5.42 5.20 3.10 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "202204 3.01 3.03 6.37 -25.22 -10.76 2.04 -7.00 -6.80 -2.28 6.63 \n", "202205 -1.68 -1.60 2.67 -2.93 -7.40 -5.12 -6.45 0.99 4.52 2.38 \n", "202206 -1.64 -0.02 -11.63 -11.33 -12.53 -2.56 -12.00 -2.05 -15.65 -11.17 \n", "202207 3.67 5.49 0.56 14.62 12.10 0.76 11.86 2.75 7.66 6.86 \n", "202208 -1.61 -1.87 -0.12 -2.95 -4.97 -2.16 -6.01 -5.07 -1.39 -12.20 \n", "\n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", "Period ... \n", "192607 ... 0.83 9.22 2.06 7.70 1.91 -23.79 0.07 1.87 -0.02 \n", "192608 ... 2.17 2.02 4.39 -2.38 4.85 5.39 -0.75 -0.13 4.47 \n", "192609 ... 2.41 2.25 0.19 -5.54 0.07 -7.87 0.25 -0.56 -1.61 \n", "192610 ... -0.11 -2.00 -1.09 -5.08 -2.61 -15.38 -2.20 -4.11 -5.51 \n", "192611 ... 1.63 3.77 3.64 3.84 1.61 4.67 6.52 4.33 2.34 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "202204 ... -10.70 -12.59 -12.26 -0.74 -10.93 -2.14 -11.41 -5.47 -7.99 \n", "202205 ... 8.54 -3.35 -0.75 -0.66 -4.59 1.03 -5.64 -3.29 2.80 \n", "202206 ... -6.72 -6.79 -10.19 -8.51 -7.14 -6.43 -8.50 -9.02 -9.05 \n", "202207 ... -0.40 8.60 15.68 7.22 9.33 9.08 16.33 11.89 7.38 \n", "202208 ... -3.00 -4.72 -5.89 -7.66 -1.46 -1.60 -3.46 -1.47 -2.24 \n", "\n", " Other \n", "Period \n", "192607 5.20 \n", "192608 6.76 \n", "192609 -3.86 \n", "192610 -8.49 \n", "192611 4.00 \n", "... ... \n", "202204 -7.65 \n", "202205 -1.19 \n", "202206 -11.78 \n", "202207 9.19 \n", "202208 -3.65 \n", "\n", "[1154 rows x 30 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df4 = df2.set_index('Period')\n", "df4" ] }, { "cell_type": "code", "execution_count": 8, "id": "f0af42fc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Food ', 'Beer ', 'Smoke', 'Games', 'Books', 'Hshld', 'Clths', 'Hlth ',\n", " 'Chems', 'Txtls', 'Cnstr', 'Steel', 'FabPr', 'ElcEq', 'Autos', 'Carry',\n", " 'Mines', 'Coal ', 'Oil ', 'Util ', 'Telcm', 'Servs', 'BusEq', 'Paper',\n", " 'Trans', 'Whlsl', 'Rtail', 'Meals', 'Fin ', 'Other'],\n", " dtype='object')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "indus = df4.columns\n", "indus" ] }, { "cell_type": "code", "execution_count": 9, "id": "b816c05a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Mkt-RFSMBHMLRFFoodBeerSmokeGamesBooksHshld...TelcmServsBusEqPaperTransWhlslRtailMealsFinOther
Period
1926072.96-2.56-2.430.220.56-5.191.292.9310.97-0.48...0.839.222.067.701.91-23.790.071.87-0.025.20
1926082.64-1.173.820.252.5927.036.500.5510.01-3.58...2.172.024.39-2.384.855.39-0.75-0.134.476.76
1926090.36-1.400.130.231.164.021.266.58-0.990.73...2.412.250.19-5.540.07-7.870.25-0.56-1.61-3.86
192610-3.24-0.090.700.32-3.06-3.311.06-4.769.47-4.68...-0.11-2.00-1.09-5.08-2.61-15.38-2.20-4.11-5.51-8.49
1926112.53-0.10-0.510.316.357.294.551.66-5.80-0.54...1.633.773.643.841.614.676.524.332.344.00
..................................................................
202204-9.46-1.416.190.013.013.036.37-25.22-10.762.04...-10.70-12.59-12.26-0.74-10.93-2.14-11.41-5.47-7.99-7.65
202205-0.34-1.858.410.03-1.68-1.602.67-2.93-7.40-5.12...8.54-3.35-0.75-0.66-4.591.03-5.64-3.292.80-1.19
202206-8.432.09-5.970.06-1.64-0.02-11.63-11.33-12.53-2.56...-6.72-6.79-10.19-8.51-7.14-6.43-8.50-9.02-9.05-11.78
2022079.572.81-4.100.083.675.490.5614.6212.100.76...-0.408.6015.687.229.339.0816.3311.897.389.19
202208-3.781.390.310.19-1.61-1.87-0.12-2.95-4.97-2.16...-3.00-4.72-5.89-7.66-1.46-1.60-3.46-1.47-2.24-3.65
\n", "

1154 rows × 34 columns

\n", "
" ], "text/plain": [ " Mkt-RF SMB HML RF Food Beer Smoke Games Books Hshld \\\n", "Period \n", "192607 2.96 -2.56 -2.43 0.22 0.56 -5.19 1.29 2.93 10.97 -0.48 \n", "192608 2.64 -1.17 3.82 0.25 2.59 27.03 6.50 0.55 10.01 -3.58 \n", "192609 0.36 -1.40 0.13 0.23 1.16 4.02 1.26 6.58 -0.99 0.73 \n", "192610 -3.24 -0.09 0.70 0.32 -3.06 -3.31 1.06 -4.76 9.47 -4.68 \n", "192611 2.53 -0.10 -0.51 0.31 6.35 7.29 4.55 1.66 -5.80 -0.54 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "202204 -9.46 -1.41 6.19 0.01 3.01 3.03 6.37 -25.22 -10.76 2.04 \n", "202205 -0.34 -1.85 8.41 0.03 -1.68 -1.60 2.67 -2.93 -7.40 -5.12 \n", "202206 -8.43 2.09 -5.97 0.06 -1.64 -0.02 -11.63 -11.33 -12.53 -2.56 \n", "202207 9.57 2.81 -4.10 0.08 3.67 5.49 0.56 14.62 12.10 0.76 \n", "202208 -3.78 1.39 0.31 0.19 -1.61 -1.87 -0.12 -2.95 -4.97 -2.16 \n", "\n", " ... Telcm Servs BusEq Paper Trans Whlsl Rtail Meals Fin \\\n", "Period ... \n", "192607 ... 0.83 9.22 2.06 7.70 1.91 -23.79 0.07 1.87 -0.02 \n", "192608 ... 2.17 2.02 4.39 -2.38 4.85 5.39 -0.75 -0.13 4.47 \n", "192609 ... 2.41 2.25 0.19 -5.54 0.07 -7.87 0.25 -0.56 -1.61 \n", "192610 ... -0.11 -2.00 -1.09 -5.08 -2.61 -15.38 -2.20 -4.11 -5.51 \n", "192611 ... 1.63 3.77 3.64 3.84 1.61 4.67 6.52 4.33 2.34 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "202204 ... -10.70 -12.59 -12.26 -0.74 -10.93 -2.14 -11.41 -5.47 -7.99 \n", "202205 ... 8.54 -3.35 -0.75 -0.66 -4.59 1.03 -5.64 -3.29 2.80 \n", "202206 ... -6.72 -6.79 -10.19 -8.51 -7.14 -6.43 -8.50 -9.02 -9.05 \n", "202207 ... -0.40 8.60 15.68 7.22 9.33 9.08 16.33 11.89 7.38 \n", "202208 ... -3.00 -4.72 -5.89 -7.66 -1.46 -1.60 -3.46 -1.47 -2.24 \n", "\n", " Other \n", "Period \n", "192607 5.20 \n", "192608 6.76 \n", "192609 -3.86 \n", "192610 -8.49 \n", "192611 4.00 \n", "... ... \n", "202204 -7.65 \n", "202205 -1.19 \n", "202206 -11.78 \n", "202207 9.19 \n", "202208 -3.65 \n", "\n", "[1154 rows x 34 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([df3, df4], axis =1, join = 'inner')\n", "df" ] }, { "cell_type": "code", "execution_count": 10, "id": "29c2a443", "metadata": {}, "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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "beta = pd.DataFrame(np.nan,index=indus,columns=[\"Betas\"])\n", "beta" ] }, { "cell_type": "code", "execution_count": 11, "id": "e497ebbe", "metadata": {}, "outputs": [], "source": [ "from sklearn import linear_model\n", "\n", "for i in range(len(indus)):\n", " model = linear_model.LinearRegression()\n", " reg = model.fit(df[['Mkt-RF']], pd.DataFrame(df[indus[i]]-df['RF']))\n", " beta.iloc[i] = reg.coef_" ] }, { "cell_type": "code", "execution_count": 12, "id": "c0399323", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Betas
Food0.729095
Beer0.924026
Smoke0.623424
Games1.385464
Books1.110920
Hshld0.884306
Clths0.831563
Hlth0.833813
Chems1.043679
Txtls1.145560
Cnstr1.181354
Steel1.361442
FabPr1.239619
ElcEq1.288733
Autos1.286918
Carry1.188716
Mines0.913507
Coal1.278337
Oil0.889536
Util0.763865
Telcm0.664651
Servs0.823919
BusEq1.080205
Paper0.948937
Trans1.138238
Whlsl1.086695
Rtail0.967677
Meals0.946614
Fin1.158976
Other1.055502
\n", "
" ], "text/plain": [ " Betas\n", "Food 0.729095\n", "Beer 0.924026\n", "Smoke 0.623424\n", "Games 1.385464\n", "Books 1.110920\n", "Hshld 0.884306\n", "Clths 0.831563\n", "Hlth 0.833813\n", "Chems 1.043679\n", "Txtls 1.145560\n", "Cnstr 1.181354\n", "Steel 1.361442\n", "FabPr 1.239619\n", "ElcEq 1.288733\n", "Autos 1.286918\n", "Carry 1.188716\n", "Mines 0.913507\n", "Coal 1.278337\n", "Oil 0.889536\n", "Util 0.763865\n", "Telcm 0.664651\n", "Servs 0.823919\n", "BusEq 1.080205\n", "Paper 0.948937\n", "Trans 1.138238\n", "Whlsl 1.086695\n", "Rtail 0.967677\n", "Meals 0.946614\n", "Fin 1.158976\n", "Other 1.055502" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta" ] }, { "cell_type": "markdown", "id": "e988f4e6", "metadata": {}, "source": [ "# 1.2" ] }, { "cell_type": "code", "execution_count": 13, "id": "1c424bb4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Excess Return
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": [ " Excess Return\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": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "excess = pd.DataFrame(data = np.nan, index = indus, columns = ['Excess Return'])\n", "excess" ] }, { "cell_type": "code", "execution_count": 14, "id": "648c6c22", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Excess Return
Food0.699220
Beer0.929593
Smoke0.866482
Games0.835659
Books0.631750
Hshld0.655269
Clths0.664896
Hlth0.807920
Chems0.777340
Txtls0.675537
Cnstr0.694168
Steel0.677374
FabPr0.800849
ElcEq0.896854
Autos0.923614
Carry0.850295
Mines0.640659
Coal0.828094
Oil0.773960
Util0.618354
Telcm0.566352
Servs0.953033
BusEq0.903787
Paper0.721256
Trans0.655191
Whlsl0.575069
Rtail0.777964
Meals0.800208
Fin0.742305
Other0.522088
\n", "
" ], "text/plain": [ " Excess Return\n", "Food 0.699220\n", "Beer 0.929593\n", "Smoke 0.866482\n", "Games 0.835659\n", "Books 0.631750\n", "Hshld 0.655269\n", "Clths 0.664896\n", "Hlth 0.807920\n", "Chems 0.777340\n", "Txtls 0.675537\n", "Cnstr 0.694168\n", "Steel 0.677374\n", "FabPr 0.800849\n", "ElcEq 0.896854\n", "Autos 0.923614\n", "Carry 0.850295\n", "Mines 0.640659\n", "Coal 0.828094\n", "Oil 0.773960\n", "Util 0.618354\n", "Telcm 0.566352\n", "Servs 0.953033\n", "BusEq 0.903787\n", "Paper 0.721256\n", "Trans 0.655191\n", "Whlsl 0.575069\n", "Rtail 0.777964\n", "Meals 0.800208\n", "Fin 0.742305\n", "Other 0.522088" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "for i in range(len(indus)):\n", " excess.iloc[i] = np.sum(df[indus[i]]-df['RF'])/len(df)\n", "excess" ] }, { "cell_type": "code", "execution_count": 15, "id": "7a0bb3a1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Excess Return')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.scatter(beta, excess)\n", "plt.xlabel(\"Beta\")\n", "plt.ylabel(\"Excess Return\")" ] }, { "cell_type": "markdown", "id": "4ffc1fc4", "metadata": {}, "source": [ "# 1.3" ] }, { "cell_type": "code", "execution_count": 16, "id": "04e75a03", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RMSEMAE
FoodNaNNaN
BeerNaNNaN
SmokeNaNNaN
GamesNaNNaN
BooksNaNNaN
HshldNaNNaN
ClthsNaNNaN
HlthNaNNaN
ChemsNaNNaN
TxtlsNaNNaN
CnstrNaNNaN
SteelNaNNaN
FabPrNaNNaN
ElcEqNaNNaN
AutosNaNNaN
CarryNaNNaN
MinesNaNNaN
CoalNaNNaN
OilNaNNaN
UtilNaNNaN
TelcmNaNNaN
ServsNaNNaN
BusEqNaNNaN
PaperNaNNaN
TransNaNNaN
WhlslNaNNaN
RtailNaNNaN
MealsNaNNaN
FinNaNNaN
OtherNaNNaN
\n", "
" ], "text/plain": [ " RMSE MAE\n", "Food NaN NaN\n", "Beer NaN NaN\n", "Smoke NaN NaN\n", "Games NaN NaN\n", "Books NaN NaN\n", "Hshld NaN NaN\n", "Clths NaN NaN\n", "Hlth NaN NaN\n", "Chems NaN NaN\n", "Txtls NaN NaN\n", "Cnstr NaN NaN\n", "Steel NaN NaN\n", "FabPr NaN NaN\n", "ElcEq NaN NaN\n", "Autos NaN NaN\n", "Carry NaN NaN\n", "Mines NaN NaN\n", "Coal NaN NaN\n", "Oil NaN NaN\n", "Util NaN NaN\n", "Telcm NaN NaN\n", "Servs NaN NaN\n", "BusEq NaN NaN\n", "Paper NaN NaN\n", "Trans NaN NaN\n", "Whlsl NaN NaN\n", "Rtail NaN NaN\n", "Meals NaN NaN\n", "Fin NaN NaN\n", "Other NaN NaN" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "p = pd.DataFrame(data = np.nan, index = indus, columns = ['RMSE', 'MAE'])\n", "p" ] }, { "cell_type": "code", "execution_count": 17, "id": "94320ebd", "metadata": {}, "outputs": [], "source": [ "from sklearn import linear_model\n", "from sklearn.model_selection import KFold\n", "kf = KFold(n_splits=10,shuffle=False)" ] }, { "cell_type": "code", "execution_count": 18, "id": "8e0f1e29", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE on 10-fold CV on the test data: 3.713825599096656\n", "MAE on 10-fold CV on the test data: 2.8338511655788796\n" ] } ], "source": [ "for i in range(len(indus)):\n", " err_rmse_test = 0\n", " err_mae_test = 0\n", " y = df['Mkt-RF'].to_numpy()\n", " x = (df[indus[i]]-df['RF']).to_numpy()\n", " for train,test in kf.split(y):\n", " lr=linear_model.LinearRegression()\n", " reg=lr.fit(y[train].reshape(-1,1),x[train])\n", " y_pred_test =reg.predict(x[test].reshape(-1,1))\n", " e_test = y[test]-y_pred_test\n", " err_rmse_test += np.sqrt(np.mean(e_test*e_test)) \n", " err_mae_test += np.mean(np.abs(e_test))\n", " rmse_test_10cv_model1 = err_rmse_test/10 \n", " mae_test_10cv_model1 = err_mae_test/10\n", " p.iloc[i][0] = rmse_test_10cv_model1\n", " p.iloc[i][1] = mae_test_10cv_model1\n", "\n", "print('RMSE on 10-fold CV on the test data: {}'.format(rmse_test_10cv_model1))\n", "print('MAE on 10-fold CV on the test data: {}'.format(mae_test_10cv_model1))" ] }, { "cell_type": "code", "execution_count": 19, "id": "eba1f6a3", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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RMSEMAE
Food3.0430702.289695
Beer4.2614173.160785
Smoke4.2549823.216801
Games7.7896055.777839
Books4.3896553.326886
Hshld3.0115332.282302
Clths3.6007812.630440
Hlth3.0888922.332975
Chems3.0306842.294720
Txtls5.5087093.962079
Cnstr3.9054792.933231
Steel7.2907075.301983
FabPr4.3408233.292714
ElcEq5.2183734.026759
Autos6.5295964.661341
Carry5.3932784.049622
Mines4.8508673.796924
Coal10.6855247.604276
Oil3.8090082.863135
Util3.4888362.659035
Telcm3.3786262.559508
Servs4.7811263.238643
BusEq3.6928462.825096
Paper2.6983962.043125
Trans4.2074793.120822
Whlsl4.0747433.021358
Rtail2.8655292.197308
Meals3.7706002.835271
Fin3.3119752.484423
Other3.7138262.833851
\n", "
" ], "text/plain": [ " RMSE MAE\n", "Food 3.043070 2.289695\n", "Beer 4.261417 3.160785\n", "Smoke 4.254982 3.216801\n", "Games 7.789605 5.777839\n", "Books 4.389655 3.326886\n", "Hshld 3.011533 2.282302\n", "Clths 3.600781 2.630440\n", "Hlth 3.088892 2.332975\n", "Chems 3.030684 2.294720\n", "Txtls 5.508709 3.962079\n", "Cnstr 3.905479 2.933231\n", "Steel 7.290707 5.301983\n", "FabPr 4.340823 3.292714\n", "ElcEq 5.218373 4.026759\n", "Autos 6.529596 4.661341\n", "Carry 5.393278 4.049622\n", "Mines 4.850867 3.796924\n", "Coal 10.685524 7.604276\n", "Oil 3.809008 2.863135\n", "Util 3.488836 2.659035\n", "Telcm 3.378626 2.559508\n", "Servs 4.781126 3.238643\n", "BusEq 3.692846 2.825096\n", "Paper 2.698396 2.043125\n", "Trans 4.207479 3.120822\n", "Whlsl 4.074743 3.021358\n", "Rtail 2.865529 2.197308\n", "Meals 3.770600 2.835271\n", "Fin 3.311975 2.484423\n", "Other 3.713826 2.833851" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p" ] }, { "cell_type": "code", "execution_count": 20, "id": "63a26249", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 4.4662\n", "MAE: 3.3208\n" ] } ], "source": [ "mean_RMSE = np.mean(p['RMSE'])\n", "mean_MAE = np.mean(p['MAE'])\n", "print(f\"RMSE: {mean_RMSE:.4f}\")\n", "print(f\"MAE: {mean_MAE:.4f}\")" ] }, { "cell_type": "markdown", "id": "a2202a7d", "metadata": {}, "source": [ "# 1.4" ] }, { "cell_type": "code", "execution_count": 21, "id": "2a49aa97", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BetasSMBHML
FoodNaNNaNNaN
BeerNaNNaNNaN
SmokeNaNNaNNaN
GamesNaNNaNNaN
BooksNaNNaNNaN
HshldNaNNaNNaN
ClthsNaNNaNNaN
HlthNaNNaNNaN
ChemsNaNNaNNaN
TxtlsNaNNaNNaN
CnstrNaNNaNNaN
SteelNaNNaNNaN
FabPrNaNNaNNaN
ElcEqNaNNaNNaN
AutosNaNNaNNaN
CarryNaNNaNNaN
MinesNaNNaNNaN
CoalNaNNaNNaN
OilNaNNaNNaN
UtilNaNNaNNaN
TelcmNaNNaNNaN
ServsNaNNaNNaN
BusEqNaNNaNNaN
PaperNaNNaNNaN
TransNaNNaNNaN
WhlslNaNNaNNaN
RtailNaNNaNNaN
MealsNaNNaNNaN
FinNaNNaNNaN
OtherNaNNaNNaN
\n", "
" ], "text/plain": [ " Betas SMB HML\n", "Food NaN NaN NaN\n", "Beer NaN NaN NaN\n", "Smoke NaN NaN NaN\n", "Games NaN NaN NaN\n", "Books NaN NaN NaN\n", "Hshld NaN NaN NaN\n", "Clths NaN NaN NaN\n", "Hlth NaN NaN NaN\n", "Chems NaN NaN NaN\n", "Txtls NaN NaN NaN\n", "Cnstr NaN NaN NaN\n", "Steel NaN NaN NaN\n", "FabPr NaN NaN NaN\n", "ElcEq NaN NaN NaN\n", "Autos NaN NaN NaN\n", "Carry NaN NaN NaN\n", "Mines NaN NaN NaN\n", "Coal NaN NaN NaN\n", "Oil NaN NaN NaN\n", "Util NaN NaN NaN\n", "Telcm NaN NaN NaN\n", "Servs NaN NaN NaN\n", "BusEq NaN NaN NaN\n", "Paper NaN NaN NaN\n", "Trans NaN NaN NaN\n", "Whlsl NaN NaN NaN\n", "Rtail NaN NaN NaN\n", "Meals NaN NaN NaN\n", "Fin NaN NaN NaN\n", "Other NaN NaN NaN" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "beta1 = pd.DataFrame(np.nan,index=indus,columns=[\"Betas\", \"SMB\", \"HML\"])\n", "beta1" ] }, { "cell_type": "code", "execution_count": 22, "id": "a65ab935", "metadata": {}, "outputs": [], "source": [ "from sklearn import linear_model\n", "\n", "for i in range(len(indus)):\n", " model = linear_model.LinearRegression()\n", " reg1 = model.fit(df[['Mkt-RF','SMB','HML']], pd.DataFrame(df[indus[i]]-df['RF']))\n", " beta1.iloc[i] = reg1.coef_" ] }, { "cell_type": "code", "execution_count": 23, "id": "c6946d7f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BetasSMBHML
Food0.747453-0.1403840.052259
Beer0.8667320.1964420.133388
Smoke0.649355-0.2168730.096605
Games1.2875350.4089230.138250
Books1.0124980.3724910.186158
Hshld0.906274-0.088170-0.035378
Clths0.7615060.422109-0.060045
Hlth0.878894-0.085659-0.189481
Chems1.068119-0.1515140.026175
Txtls0.9827310.5632680.372973
Cnstr1.1183410.2477420.107824
Steel1.2596590.2320490.380399
FabPr1.1733770.2615500.111985
ElcEq1.295485-0.035460-0.000616
Autos1.2477080.0550860.188627
Carry1.0975550.2181240.328079
Mines0.8426470.2587310.145621
Coal1.0841630.4853780.673339
Oil0.878785-0.1792070.290080
Util0.755451-0.1669030.259715
Telcm0.694991-0.131654-0.036739
Servs0.8321290.368626-0.505840
BusEq1.1211670.153322-0.455735
Paper0.952825-0.0576840.045355
Trans1.0380010.1634730.454423
Whlsl0.9729690.5501520.068217
Rtail0.9799820.041639-0.131479
Meals0.8969390.289515-0.030568
Fin1.121536-0.0575390.315218
Other0.9995470.2830970.018335
\n", "
" ], "text/plain": [ " Betas SMB HML\n", "Food 0.747453 -0.140384 0.052259\n", "Beer 0.866732 0.196442 0.133388\n", "Smoke 0.649355 -0.216873 0.096605\n", "Games 1.287535 0.408923 0.138250\n", "Books 1.012498 0.372491 0.186158\n", "Hshld 0.906274 -0.088170 -0.035378\n", "Clths 0.761506 0.422109 -0.060045\n", "Hlth 0.878894 -0.085659 -0.189481\n", "Chems 1.068119 -0.151514 0.026175\n", "Txtls 0.982731 0.563268 0.372973\n", "Cnstr 1.118341 0.247742 0.107824\n", "Steel 1.259659 0.232049 0.380399\n", "FabPr 1.173377 0.261550 0.111985\n", "ElcEq 1.295485 -0.035460 -0.000616\n", "Autos 1.247708 0.055086 0.188627\n", "Carry 1.097555 0.218124 0.328079\n", "Mines 0.842647 0.258731 0.145621\n", "Coal 1.084163 0.485378 0.673339\n", "Oil 0.878785 -0.179207 0.290080\n", "Util 0.755451 -0.166903 0.259715\n", "Telcm 0.694991 -0.131654 -0.036739\n", "Servs 0.832129 0.368626 -0.505840\n", "BusEq 1.121167 0.153322 -0.455735\n", "Paper 0.952825 -0.057684 0.045355\n", "Trans 1.038001 0.163473 0.454423\n", "Whlsl 0.972969 0.550152 0.068217\n", "Rtail 0.979982 0.041639 -0.131479\n", "Meals 0.896939 0.289515 -0.030568\n", "Fin 1.121536 -0.057539 0.315218\n", "Other 0.999547 0.283097 0.018335" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta1" ] }, { "cell_type": "markdown", "id": "ee3ac242", "metadata": {}, "source": [ "# 1.5" ] }, { "cell_type": "code", "execution_count": 24, "id": "2f34fa10", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Excess return')" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.scatter(beta1['Betas'], excess)\n", "plt.xlabel(\"Beta\")\n", "plt.ylabel(\"Excess return\")" ] }, { "cell_type": "code", "execution_count": 25, "id": "0b2a9e1d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Excess return')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEGCAYAAACQO2mwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAXt0lEQVR4nO3dfbRddXng8e9jgPGq4I0FlVygQSakpQWMvcUXLLXjtEFaS0g7FWlLS+miWVMcnVmmQp1OZ1anE0baWVIHh5Vl8WXVVbQaYlqx1MEXnFY73JjwLhppgdw4GtQYhCyB+MwfZ19zuLn7nn1O7j5nn3O+n7Xu4ux99tn34dyb+5z9/H6/Z0dmIknSQp416AAkSc1lkpAklTJJSJJKmSQkSaVMEpKkUkcNOoBuHX/88bly5cpBhyFJQ2X79u2PZuYJ3b5u6JLEypUrmZmZGXQYkjRUIuKhXl5nuUmSVMokIUkqZZKQJJUySUiSSpkkJEmlhm52k6Tht3XHLNfe+gB79h1gxeQEG9euZt2aqUGHpQWYJCT11dYds1y95W4OPHUQgNl9B7h6y90AJooGstwkqa+uvfWBHySIOQeeOsi1tz4woIi0GJOEpL7as+9AV/s1WCYJSX21YnKiq/0aLJOEpL7auHY1E0cve8a+iaOXsXHt6gFFpMU4cC2pr+YGp53dNBxMEpL6bt2aKZPCkLDcJEkqZZKQJJUySUiSSjkmIfWJrSg0jEwSUh/YikLDynKT1Ae2otCw8kpCjTKqJRlbUWhYeSWhxpgryczuO0ByqCSzdcfsoEM7Yrai0LAySagxRrkkYysKDSvLTWqMUS7J2IpCw8okocZYMTnB7AIJYVRKMrai0DCy3KTGsCQjNY9XEmoMSzJS85gk1CiWZKRmsdwkSSrllYRUwagu8pM6qfVKIiLOj4gHImJXRFy1wPPLI+LmiLgrIv5vRPx4nfFIvRjlRX5SJ7UliYhYBlwPvA44A3hjRJwx77DfB3Zm5lnApcB1dcUj9WqUF/lJndRZbjoH2JWZDwJExE3AhcB9bcecAWwCyMwvRcTKiHhRZn69xrjUxjJKZ6O8yE/qpM5y0xTwSNv27mJfuzuB9QARcQ7ww8BJNcakNpZRqrHvksZZnUkiFtiX87avAZZHxE7gTcAO4OnDThRxRUTMRMTM3r17lzzQcWUZpRoX+Wmc1Vlu2g2c3LZ9ErCn/YDM3A9cBhARAfxT8cW84zYDmwGmp6fnJxr1yDJKNS7y0zirM0ncAayKiFOBWeBi4JL2AyJiEngiM58Efhu4vUgc6oNR75W0lFzkp3FVW7kpM58GrgRuBe4HPpyZ90bEhojYUBz2o8C9EfElWrOg3lxXPDqcZRRJndS6mC4zbwFumbfvhrbHnwdW1RmDyllGkdSJK67HnGUUSYuxd5MkqZRJQpJUyiQhSSplkpAklTJJSJJKmSQkSaXGYgqsnU4lqTcjnyTmOp3ONbKb63QKmCgkqYORLzfZ6VSSejfyScJOp5LUu5FPEt4wRpJ6N/JJwk6nktS7kR+4ttOpJPVu5JME2OlUw8Gp2mqisUgSUtM5VVtNNfJjEtIwcKq2msokITWAU7XVVCYJqQGcqq2mMklIDeBUbTWVA9dSAzhVW01lkpAawqna1TlduH9MEpKGitOF+8sxCUlDxenC/WWSkDRUnC7cX5abJA2VFZMTzC6QEOqeLjyu4yBeSUgaKoOYLjw3DjK77wDJoXGQrTtma/ueTWGSkDRU1q2ZYtP6M5manCCAqckJNq0/s9ZP9eM8DmK5SdLQ6fd04XEeB/FKQpI6GOe2KSYJSepgnNumWG6SpA7GuW2KSUKSKhjXtimWmyRJpbySGELjuqhHUv+ZJIaMzc0k9ZPlpiEzzot6JPVfrVcSEXE+cB2wDHhPZl4z7/nnA38BnFLE8ieZ+d46Yxp247yoR511U4q0bKkqOl5JRMT6iPhKRHwnIvZHxGMRsb/C65YB1wOvA84A3hgRZ8w77HeB+zLzbOA1wJ9GxDFd/1+MkXFe1KPFddNfaJx7Eak7VcpN7wB+MTOfn5nHZeaxmXlchdedA+zKzAcz80ngJuDCecckcGxEBPA84FvA013EP3bGeVGPFtdNKdKypaqqUm76embe38O5p4BH2rZ3Ay+fd8z/BLYBe4BjgTdk5vfnnygirgCuADjllFN6CGV0jPOiHi2um1KkZUtVVSVJzETEh4CtwPfmdmbmlg6viwX25bzttcBO4F8BpwGfjIjPZeYzylmZuRnYDDA9PT3/HGNnXBf1aHHd3GdhUPdk0PCpUm46DngC+Dng9cXXL1R43W7g5Lbtk2hdMbS7DNiSLbuAfwJ+pMK5Jc3TTSnSsqWqWvRKohh8fjQzN/Zw7juAVRFxKjALXAxcMu+Yh4HXAp+LiBcBq4EHe/he0tjrphRp2VJVRebi1ZuIuC0zX9vTySMuAN5JawrsjZn5xxGxASAzb4iIFcD7gBNplaeuycy/WOyc09PTOTMz00s4kjS2ImJ7Zk53+7oqYxI7I2Ib8FfA43M7K4xJkJm3ALfM23dD2+M9tMpYkqQGqpIkXgB8k9bg8pwEOiYJSdJw65gkMvOyfgQiSWqejkkiIt7L4VNXyczfqiWiIWabg+p8r6ThUKXc9Ddtj58NXMThU1lHVtU/ZnZnrc73ShoeHddJZOZH274+CPwK8OP1hzZ43fS3sc1Bdb5X0vDopVX4KlpdW0deN3/MbHNQne+VNDyqdIF9rOj+ur/o/vrXwNvqD23wuvljZnfW6nyvpOFRpdx0bNH9de7r9Mz8aD+CG7Ru/pjZ5qA63ytpeFS5krityr5R1M0fs3Vrpti0/kymJicIYGpygk3rz3QgdgG+V9LwKG3LERHPBp4DfJrWDYHmuroeB3wiM3+0HwHO1++2HE7VlDQK6mjL8TvAW4AVwBfb9u+ndce5sWBbbknjrDRJZOZ1wHUR8abMfFcfY5IkNUSVKbA3RsR/jIjNABGxKiKq3E9CkjTkKiUJ4EngVcX2buC/1haRJKkxqiSJ0zLzHcBTAJl5gIVvTSpJGjFVksSTETFB0eQvIk6j7V7XkqTRVaXB3x8CfwucHBEfBM4FfrPOoCRJzdDpHtfPApYD64FX0CozvTkzH+1DbJKkAVs0SWTm9yPiysz8MPDxPsWkJeRiQElHokq56ZMR8VbgQzzzHtffqi0qLQnv2yDpSFVJEnN3oPvdtn0JvGTpw9FSWqzVuUlCUhVV7nF9aj8C0dLzvg2SjlQvNx3SkPC+DZKOlElihHnfBklHqsqYhIbU3LiDs5sk9apjkoiIc4Gdmfl4RPwa8DLgusx8qPbodMRsdS7pSFQpN/0v4ImIOBv4PeAh4AO1RiVJaoQqSeLpbN2+7kJaVxDXAcfWG5YkqQmqjEk8FhFXA78GnBcRy4Cj6w1LS8UV15KORJUriTfQ6vp6eWb+P2AKuLbWqLQk5lZcz+47QHJoxfXWHbODDk3SkKiSJB6jVWb6XEScDrwU+Mtao9KSWGzFtSRVUSVJ3A78i4iYAm4DLgPeV2dQWhquuJZ0pKokicjMJ2i1C39XZl4E/Fi9YWkpuOJa0pGqlCQi4pXAr3KoXfiyRY5XQ7jiWtKRqjK76S3A1cDNmXlvRLwE+HStUWlJuOJa0pGK1hKICgdGPDczH+98ZL2mp6dzZmZm0GFI0lCJiO2ZOd3t6zqWmyLilRFxH3B/sX12RLy7hxglSUOmypjEO4G1wDcBMvNO4LwqJ4+I8yPigYjYFRFXLfD8xojYWXzdExEHI+IFXcQvSapRpS6wmflIRLTvOlh27JxiZfb1wM8Cu4E7ImJbZt7Xdt5rKRbmRcTrgX8/SrdFdbWzpGFXJUk8EhGvAjIijgH+HUXpqYNzgF2Z+SBARNxEq//TfSXHv5ERWqTn/aUljYIq5aYNtO5vPUXriuClPPN+12WmgEfatncX+w4TEc8Bzgc+WvL8FRExExEze/furfCtB8/Vzqrb1h2znHvNpzj1qo9z7jWfst2KalHlHteP0loj0a1YYF/ZVKrXA39fVmrKzM3AZmjNbuohlr5ztbPq5JWq+qXK7Kb3R8Rk2/byiLixwrl3Aye3bZ8E7Ck59mJGqNQErnZWvbxSVb9UKTedlZn75jYy89vAmgqvuwNYFRGnFmMZFwPb5h8UEc8Hfhr4WKWIh4SrnVUnr1TVL1WSxLMiYvncRjFFtUqZ6mngSuBWWgPdHy5WbG+IiA1th14E/F0TFuotpXVrpti0/kymJicIYGpygk3rz7QUoCXhlar6peOK64i4lFZbjo/QGlP4FeC/ZeZAbmHqimvp8DEJaF2p+kFEZXpdcV3liuADEbEd+Blag9Hr29c6SOo/+3KpXzomiYi4PDP/HLi32F4WEX+Ymf+l9ugklVq3ZsqkoNpVWUz32oj4JeBy4IeA9wKfrTUqST/gyn0NUpVy0yUR8QbgbuAJ4I2Z+fe1RybJ9RACBvtBoco6iVXAm2mthv5n4NeLFdKSauZ6CM19UJjdd4Dk0AeFfq2wrzIF9q+BP8jM36G1nuErtNZASKqZ6yE06A8KVcYkzsnM/QDZmi/7pxFx2KI4SUtvxeQEswskBNdDjI9Bf1AovZKIiN8DyMz9EfFv5j19Wa1RSQJcud+NUW14OOiFk4uVmy5ue3z1vOfOryEWSfO4cr+aQdft6zToDwqLlZui5PFC25Jq4nqIzhar2w/7ezfohZOLJYksebzQtiQNzKDr9nUb5AeFxZLE2RGxn9ZVw0TxmGL72bVHJkkVOcBfn9IkkZnLyp7T4Lj6thn8OTTLxrWrF2x46AD/kasyBVYN4erbZvDn0DyDrtuPMpPEEBnlwblh4s+hmRzgr0eVFddqiFEfnBsW/hw0TkwSQ2TQi2rU4s9B48QkMUQGvahGLf4cNE4ckxgiDs41gz8HjZOO97huGu9xLS3MablaTG33uJbUfE7LVV0ck5BGwKDvOaDRZZKQRoDTclUXy03SCBjG3kWOoQwHrySkETBs03JH+f4Po8YkIY2AYbs5kWMow8NykzQihql3kWMow8MrCUl9Z2uT4WGSkNR3wzaGMs4sN/XAWRnSkbG1yfAwSXTJla3S0himMZRxZrmpS87KkDROTBJdclaGpHFikuiSszIkjROTRJeclSFpnDhw3SVnZUgaJyaJHjgrQ9K4qLXcFBHnR8QDEbErIq4qOeY1EbEzIu6NiM/WGY8kqTu1XUlExDLgeuBngd3AHRGxLTPvaztmEng3cH5mPhwRL6wrHklS9+q8kjgH2JWZD2bmk8BNwIXzjrkE2JKZDwNk5jdqjEeS1KU6k8QU8Ejb9u5iX7vTgeUR8ZmI2B4Rly50ooi4IiJmImJm7969NYUrSZqvziQRC+zLedtHAT8B/DywFviDiDj9sBdlbs7M6cycPuGEE5Y+UknSguqc3bQbOLlt+yRgzwLHPJqZjwOPR8TtwNnAl2uMS5JUUZ1XEncAqyLi1Ig4BrgY2DbvmI8BPxURR0XEc4CXA/fXGJMkqQu1XUlk5tMRcSVwK7AMuDEz742IDcXzN2Tm/RHxt8BdwPeB92TmPXXFJEnqTmTOHyZotunp6ZyZmRl0GJI0VCJie2ZOd/s6ezdJkkqZJCRJpUwSkqRSNviTpAbZumO2UV2mTRKS1BBbd8xy9Za7f3CL5Nl9B7h6y90AA0sUlpskqSGuvfWBHySIOQeeOsi1tz4woIhMEpLUGHv2Hehqfz9YbuqjptUaJTXLiskJZhdICCsmJwYQTYtXEn0yV2uc3XeA5FCtceuO2UGHJqkhNq5dzcTRy56xb+LoZWxcu3pAEZkk+qaJtUZJzbJuzRSb1p/J1OQEAUxNTrBp/ZnObhoHTaw1SmqedWumGlWG9kqiT8pqioOsNUpSJyaJPmlirVGSOrHc1Cdzl4/ObpI0TEwSfdS0WqMkdWK5SZJUyiQhSSplkpAklTJJSJJKmSQkSaVMEpKkUiYJSVIpk4QkqZRJQpJUyiQhSSplkpAklTJJSJJK2eBPGiPeZ13dMklIY2LuPutzt9Gdu886YKJQKctN0pjwPuvqhUlCGhPeZ129MElIY8L7rKsXJglpTHifdfXCgWtpTHifdfXCJCGNEe+zrm5ZbpIklTJJSJJKmSQkSaVMEpKkUiYJSVKpyMxBx9CViNgLPFTDqY8HHq3hvEvB2HpjbL0xtt40PbbnZuYJ3b5w6JJEXSJiJjOnBx3HQoytN8bWG2PrzajGZrlJklTKJCFJKmWSOGTzoANYhLH1xth6Y2y9GcnYHJOQJJXySkKSVMokIUkqNbZJIiJeEBGfjIivFP9dvsAxJ0fEpyPi/oi4NyLe3JTYiuNujIhvRMQ9fYjp/Ih4ICJ2RcRVCzwfEfFnxfN3RcTL6o6pi9h+JCI+HxHfi4i39iuuirH9avF+3RUR/xARZzcotguLuHZGxExEvLopsbUd95MRcTAifrkpsUXEayLiO8X7tjMi/lNTYmuLb2fxN+2zHU+amWP5BbwDuKp4fBXw3xc45kTgZcXjY4EvA2c0IbbiufOAlwH31BzPMuCrwEuAY4A7578PwAXAJ4AAXgH8Y59+jlVieyHwk8AfA2/t4+9YldheBSwvHr+uYe/b8zg0bnkW8KWmxNZ23KeAW4BfbkpswGuAv+nX71mXsU0C9wGnFNsv7HTesb2SAC4E3l88fj+wbv4Bmfm1zPxi8fgx4H6gH834O8ZWxHQ78K0+xHMOsCszH8zMJ4GbihjbXQh8IFu+AExGxIlNiC0zv5GZdwBP9SGebmP7h8z8drH5BeCkBsX23Sz+kgDPBfo1y6XK7xvAm4CPAt/oU1zdxDYIVWK7BNiSmQ9D699Gp5OOc5J4UWZ+DVrJgNanzVIRsRJYA/xj/aF1F1sfTAGPtG3v5vBkWeWYOgzq+1bRbWyX07oa64dKsUXERRHxJeDjwG81JbaImAIuAm7oU0xzqv5MXxkRd0bEJyLix/oTWqXYTgeWR8RnImJ7RFza6aQjfWe6iPjfwIsXeOrtXZ7nebQ+sbwlM/c3KbY+iQX2zf9UWeWYOgzq+1ZRObaI+BlaSaJfdf9KsWXmzcDNEXEe8EfAv647MKrF9k7gbZl5MGKhw2tTJbYvAj+cmd+NiAuArcCqugOjWmxHAT8BvBaYAD4fEV/IzC+XnXSkk0Rmlv5CR8TXI+LEzPxaURZZ8LIrIo6mlSA+mJlbmhRbH+0GTm7bPgnY08MxdRjU962iUmwRcRbwHuB1mfnNJsU2JzNvj4jTIuL4zKy7iV2V2KaBm4oEcTxwQUQ8nZlbBx1b+wfJzLwlIt7doPdtN/BoZj4OPB4RtwNn0xpvXdA4l5u2Ab9RPP4N4GPzD4jWb+CfA/dn5v9oUmx9dgewKiJOjYhjgItpxdhuG3BpMcvpFcB35kpmDYhtUDrGFhGnAFuAX1/s09yAYvuXxb8BitlqxwD9SGIdY8vMUzNzZWauBD4C/Ns+JIhKsUXEi9vet3No/Z1txPtG62/JT0XEURHxHODltMZay/V7BL4pX8APAbcBXyn++4Ji/wrgluLxq2ldrt0F7Cy+LmhCbMX2XwJfozUguxu4vMaYLqD1aeOrwNuLfRuADcXjAK4vnr8bmO7jz7JTbC8u3p/9wL7i8XENie09wLfbfr9mGvS+vQ24t4jr88CrmxLbvGPfR59mN1V8364s3rc7aU1GeFVTYiu2N9Ka4XQPrRL6oue0LYckqdQ4l5skSR2YJCRJpUwSkqRSJglJUimThCSplElCqiAi3l50zZzrivryorXBw3Nz4ovjtkbEd4vHKyPiQHH8nUWX19WD+7+QumeSkDqIiFcCv0CrI/BZtFpTzPXI2QecWxw3SatzcLuvZuZLM/NsWs0af78fMUtLxSQhdXYirVYG3wPIzEczc67dwU20VrYCrKe1errMcbQWzklDwyQhdfZ3wMkR8eWiD89Ptz13G3BeRCyjlSw+NO+1pxXlpq8C/wHoZ3sX6YiZJKQOMvO7tDpnXgHsBT4UEb9ZPH0Q+D/AG4CJzPzneS+fKzedBrwF2NyPmKWlMtJdYKWlkpkHgc8An4mIuznUgBFaJaebgf/c4TTbgPfWEZ9UF68kpA4iYnVEtN8P4KXAQ23bnwM20Wq4uJhX02q8Jg0NrySkzp4HvKuYvfQ0sItW6ekjANnqkvknJa89LSJ20uqS+yTw23UHKy0lu8BKkkpZbpIklTJJSJJKmSQkSaVMEpKkUiYJSVIpk4QkqZRJQpJU6v8Dyl9Y2GfflKAAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.scatter(beta1['SMB'], excess)\n", "plt.xlabel(\"SMB\")\n", "plt.ylabel(\"Excess return\")" ] }, { "cell_type": "code", "execution_count": 26, "id": "536c4b53", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Excess return')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.scatter(beta1['HML'], excess)\n", "plt.xlabel(\"HML\")\n", "plt.ylabel(\"Excess return\")" ] }, { "cell_type": "markdown", "id": "4a23f79b", "metadata": {}, "source": [ "# 1.6" ] }, { "cell_type": "code", "execution_count": 27, "id": "eec8f58f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RMSEMAE
FoodNaNNaN
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" ], "text/plain": [ " RMSE MAE\n", "Food NaN NaN\n", "Beer NaN NaN\n", "Smoke NaN NaN\n", "Games NaN NaN\n", "Books NaN NaN\n", "Hshld NaN NaN\n", "Clths NaN NaN\n", "Hlth NaN NaN\n", "Chems NaN NaN\n", "Txtls NaN NaN\n", "Cnstr NaN NaN\n", "Steel NaN NaN\n", "FabPr NaN NaN\n", "ElcEq NaN NaN\n", "Autos NaN NaN\n", "Carry NaN NaN\n", "Mines NaN NaN\n", "Coal NaN NaN\n", "Oil NaN NaN\n", "Util NaN NaN\n", "Telcm NaN NaN\n", "Servs NaN NaN\n", "BusEq NaN NaN\n", "Paper NaN NaN\n", "Trans NaN NaN\n", "Whlsl NaN NaN\n", "Rtail NaN NaN\n", "Meals NaN NaN\n", "Fin NaN NaN\n", "Other NaN NaN" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q = pd.DataFrame(data = np.nan, index = indus, columns = ['RMSE','MAE'])\n", "q" ] }, { "cell_type": "code", "execution_count": 28, "id": "784976bb", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import KFold\n", "kf = KFold(n_splits=10,shuffle=False)" ] }, { "cell_type": "code", "execution_count": 30, "id": "be0f605c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE on 10-fold CV on the test data: 3.015118435305405\n", "MAE on 10-fold CV on the test data: 2.297275482730651\n" ] } ], "source": [ "for i in range(len(indus)):\n", " err_rmse_test = 0\n", " err_mae_test = 0\n", " X = df[['Mkt-RF', 'SMB', 'HML']].to_numpy()\n", " ri_rf = (df[indus[i]]-df['RF']).to_numpy() \n", " for train,test in kf.split(X):\n", " lr=linear_model.LinearRegression()\n", " reg=lr.fit(X[test],ri_rf[test])\n", " fama_pred_test =reg.predict(X[test])\n", " e_test = ri_rf[test]-fama_pred_test\n", " err_rmse_test += np.sqrt(np.mean(e_test*e_test)) \n", " err_mae_test += np.mean(np.abs(e_test))\n", " rmse_test_10cv_model1 = err_rmse_test/10 \n", " mae_test_10cv_model1 = err_mae_test/10\n", " q.iloc[i][0] = rmse_test_10cv_model1\n", " q.iloc[i][1] = mae_test_10cv_model1\n", "\n", "print('RMSE on 10-fold CV on the test data: {}'.format(rmse_test_10cv_model1))\n", "print('MAE on 10-fold CV on the test data: {}'.format(mae_test_10cv_model1))" ] }, { "cell_type": "code", "execution_count": 31, "id": "498a0f32", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RMSEMAE
Food2.2288421.682840
Beer4.0721573.046692
Smoke4.2419963.282766
Games4.2919133.237562
Books3.3480712.567700
Hshld2.8962112.191722
Clths3.3548592.505754
Hlth2.8633662.197922
Chems2.5613301.962165
Txtls3.7301872.853315
Cnstr2.3886861.872385
Steel3.8653252.958378
FabPr2.5250071.952199
ElcEq3.0175872.359269
Autos4.1138673.022697
Carry3.7709222.879420
Mines4.9496693.856917
Coal7.2579435.436897
Oil3.6048402.773756
Util2.9633722.256085
Telcm2.6085881.993382
Servs4.4838452.979678
BusEq2.7965192.172683
Paper2.5983601.989367
Trans2.8316832.199756
Whlsl3.1004912.301708
Rtail2.6910282.098727
Meals3.4172452.607442
Fin2.1350341.624639
Other3.0151182.297275
\n", "
" ], "text/plain": [ " RMSE MAE\n", "Food 2.228842 1.682840\n", "Beer 4.072157 3.046692\n", "Smoke 4.241996 3.282766\n", "Games 4.291913 3.237562\n", "Books 3.348071 2.567700\n", "Hshld 2.896211 2.191722\n", "Clths 3.354859 2.505754\n", "Hlth 2.863366 2.197922\n", "Chems 2.561330 1.962165\n", "Txtls 3.730187 2.853315\n", "Cnstr 2.388686 1.872385\n", "Steel 3.865325 2.958378\n", "FabPr 2.525007 1.952199\n", "ElcEq 3.017587 2.359269\n", "Autos 4.113867 3.022697\n", "Carry 3.770922 2.879420\n", "Mines 4.949669 3.856917\n", "Coal 7.257943 5.436897\n", "Oil 3.604840 2.773756\n", "Util 2.963372 2.256085\n", "Telcm 2.608588 1.993382\n", "Servs 4.483845 2.979678\n", "BusEq 2.796519 2.172683\n", "Paper 2.598360 1.989367\n", "Trans 2.831683 2.199756\n", "Whlsl 3.100491 2.301708\n", "Rtail 2.691028 2.098727\n", "Meals 3.417245 2.607442\n", "Fin 2.135034 1.624639\n", "Other 3.015118 2.297275" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q" ] }, { "cell_type": "code", "execution_count": 32, "id": "947252d7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 3.3908\n", "MAE: 2.5720\n" ] } ], "source": [ "mean_RMSE = np.mean(q['RMSE'])\n", "mean_MAE = np.mean(q['MAE'])\n", "print(f\"RMSE: {mean_RMSE:.4f}\")\n", "print(f\"MAE: {mean_MAE:.4f}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "7663e866", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }