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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d7e90f45",
"metadata": {},
"outputs": [],
"source": [
"#### Pandas is for using data structures\n",
"import pandas as pd\n",
"# statsmodels contain modules for regression and time series analysis\n",
"import statsmodels.api as sm\n",
"# numpy is for numerical computing of array and mayatrix\n",
"import numpy as np\n",
"# Matplotlib, Seaborn: plotting package\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns \n",
"# matplotlib Showing the plot right after the current code \n",
"%matplotlib inline\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"# basic statistics package\n",
"import scipy.stats as stats\n",
"from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
"import datetime"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5159ee37",
"metadata": {},
"outputs": [],
"source": [
"# functions from last lab\n",
"def four_in_one(dataframe,model):\n",
" fitted_y = model.fittedvalues\n",
" studentized_residuals = model.get_influence().resid_studentized_internal\n",
" plt.figure(figsize=(10,10))\n",
" ax1 = plt.subplot(221)\n",
" stats.probplot(studentized_residuals, dist=\"norm\", plot=plt)\n",
" ax1.set_title('Normal Q-Q')\n",
" ax1.set_xlabel('Normal Quantiles')\n",
" ax1.set_ylabel('Studentized Residuals');\n",
"\n",
" ax2 = plt.subplot(222)\n",
" ax2.hist(studentized_residuals)\n",
" ax2.set_xlabel('Studentized Residuals')\n",
" ax2.set_ylabel('Count')\n",
" ax2.set_title('Histogram')\n",
"\n",
" ax3 = plt.subplot(223)\n",
" t = range(dataframe.shape[0])\n",
" ax3.scatter(t, studentized_residuals)\n",
" ax3.set_xlabel('Observation order')\n",
" ax3.set_ylabel('Residuals')\n",
" ax3.set_title('Time series plot of studentized residuals')\n",
"\n",
" ax4 = plt.subplot(224)\n",
" temp = pd.DataFrame({'fitted_y':fitted_y,'studentized_residuals':studentized_residuals})\n",
" ax4 = sns.residplot(data=temp,x=fitted_y, y=studentized_residuals,\n",
" lowess=True,\n",
" scatter_kws={'alpha': 0.5},\n",
" line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})\n",
" ax4.set_title('Internally Studentized Residuals vs Fitted values')\n",
" ax4.set_xlabel('Fitted values')\n",
" ax4.set_ylabel('Studentized Residuals');\n",
" \n",
"def getvif(X):\n",
" X = sm.add_constant(X)\n",
" vif = pd.DataFrame()\n",
" vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
" vif[\"Predictors\"] = X.columns\n",
" return(vif.drop(index = 0).round(2)) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "16326102",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rBH</th>\n",
" <th>rSP</th>\n",
" <th>SmB</th>\n",
" <th>HmL</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1/2/2009</th>\n",
" <td>-0.121807</td>\n",
" <td>-0.109931</td>\n",
" <td>0.0005</td>\n",
" <td>-0.0695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1/3/2009</th>\n",
" <td>0.103053</td>\n",
" <td>0.085404</td>\n",
" <td>0.0004</td>\n",
" <td>0.0348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1/4/2009</th>\n",
" <td>0.084198</td>\n",
" <td>0.093925</td>\n",
" <td>0.0539</td>\n",
" <td>0.0536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1/5/2009</th>\n",
" <td>-0.025532</td>\n",
" <td>0.053081</td>\n",
" <td>-0.0252</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1/6/2009</th>\n",
" <td>-0.017467</td>\n",
" <td>0.000196</td>\n",
" <td>0.0263</td>\n",
" <td>-0.0273</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rBH rSP SmB HmL\n",
"Date \n",
"1/2/2009 -0.121807 -0.109931 0.0005 -0.0695\n",
"1/3/2009 0.103053 0.085404 0.0004 0.0348\n",
"1/4/2009 0.084198 0.093925 0.0539 0.0536\n",
"1/5/2009 -0.025532 0.053081 -0.0252 0.0027\n",
"1/6/2009 -0.017467 0.000196 0.0263 -0.0273"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"BH2009-2022.csv\",index_col=0)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7fd1d118",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(167, 4)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "markdown",
"id": "09cc26c8",
"metadata": {},
"source": [
"# Part I: CAPM model"
]
},
{
"cell_type": "markdown",
"id": "ebaa4598-7164-4b6c-ac8d-7d674fa4ee4f",
"metadata": {},
"source": [
"### Task 1: Split the data into train (first 155 observations) and test (remaining 12 observations) set"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "095d1d13",
"metadata": {},
"outputs": [],
"source": [
"train = $$code here$$\n",
"test = $$code here$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0a06748",
"metadata": {},
"outputs": [],
"source": [
"train.shape, test.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abff4aee-07cb-4ddd-8cd2-7f909b217b41",
"metadata": {},
"outputs": [],
"source": [
"Y = train[\"rBH\"]\n",
"X = train[\"rSP\"]"
]
},
{
"cell_type": "markdown",
"id": "f84493d2-6484-4e62-a196-888c72c657f4",
"metadata": {},
"source": [
"### Task 2: Using training set, fit a simple regression model(SLR). Report the adjusted R-square of the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76e5007b-ec3f-4271-9c25-afd7cb2bd028",
"metadata": {},
"outputs": [],
"source": [
"SLR = $$code here$$\n",
"print(SLR.summary())"
]
},
{
"cell_type": "markdown",
"id": "b9fd7ede-dabc-47d9-918f-021ee1fae9b4",
"metadata": {},
"source": [
"### Report the adjusted R-square of the model.\n",
" "
]
},
{
"cell_type": "markdown",
"id": "3af44ff8",
"metadata": {},
"source": [
"# Part II: Multiple Regression Model"
]
},
{
"cell_type": "markdown",
"id": "3f8158d8-1c42-4226-99c3-0d04a026df5b",
"metadata": {},
"source": [
"### Task 3: Using training set, fit a multiple regression model with SmB and HmL explanatory variables in addition to rSP (MLR). Report the adjusted R-square of the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f2dfad6",
"metadata": {},
"outputs": [],
"source": [
"X = $$code here$$\n",
"MLR = $$code here$$\n",
"print(MLR.summary())"
]
},
{
"cell_type": "markdown",
"id": "18e59124-7f05-414a-9284-fde621aa94cc",
"metadata": {},
"source": [
"### Report the adjusted R-square of the model.\n",
" "
]
},
{
"cell_type": "markdown",
"id": "fcb66422-678b-4d10-9aea-e56ae1c0adfa",
"metadata": {},
"source": [
"### Task 4: Checking the multicollinearity problem among rSP, SmB and HmL by \n",
" i) Scatter plot matrix \n",
" ii) VIF. \n",
"#### Is the multicollinearity problem exist?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "284873a2",
"metadata": {},
"outputs": [],
"source": [
"$$code here$$ #<--code for scatter plot matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cad9bd49-5030-4a95-a5b5-ae8677092fbe",
"metadata": {},
"outputs": [],
"source": [
"$$code here$$ #<--code for VIF"
]
},
{
"cell_type": "markdown",
"id": "aea04a9e",
"metadata": {},
"source": [
"### Is the multicollinearity problem exist?"
]
},
{
"cell_type": "markdown",
"id": "88eb43de",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"id": "ece7bda1-23e4-47e5-84ba-d0ab6eff9f06",
"metadata": {
"tags": []
},
"source": [
"### Task 5: From the fitted multiple regression model in Task 3\n",
" i) Is the model as a whole useful at 5% significant level? \n",
" ii) Which of them is not an useful explanatory variable at 5% significant level?"
]
},
{
"cell_type": "markdown",
"id": "413b5d9a-44c6-4f4e-91c4-3f82317b2b00",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"id": "fe0773af-c6ee-4dce-97c6-fe33af6310b8",
"metadata": {},
"source": [
"### Task 6: Execute model diagnostic on the model fitted from Task3 using the “four_in_one” function. Comment on the normality, constant variance assumption.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b372cd7",
"metadata": {},
"outputs": [],
"source": [
"$$code here$$ #<--code for “four_in_one” function"
]
},
{
"cell_type": "markdown",
"id": "919fcacf",
"metadata": {},
"source": [
"### Comment on the normality, constant variance assumption."
]
},
{
"cell_type": "markdown",
"id": "52ff227e",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"id": "5b40b212",
"metadata": {},
"source": [
"# Part IV: Model Performance"
]
},
{
"cell_type": "markdown",
"id": "8cc559dd-6a77-4289-a451-ede8d00bbf90",
"metadata": {},
"source": [
"### Task 7: Compare the predictive power between SLR and MLR using the test set. Which one perform better in prediction?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e74fa4c",
"metadata": {},
"outputs": [],
"source": [
"Test_X_SLR = test['rSP']\n",
"Test_X_MLR = $$code here$$\n",
"\n",
"Test_Y_SLR = SLR.predict(sm.add_constant(Test_X_SLR))\n",
"Test_Y_MLR = $$code here$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5af214f",
"metadata": {},
"outputs": [],
"source": [
"Test_Y = test[\"rBH\"]\n",
"\n",
"from sklearn.metrics import mean_squared_error\n",
"rmse_SLR = np.sqrt(mean_squared_error(Test_Y, Test_Y_SLR))\n",
"rmse_MLR = $$code here$$\n",
"print(\"RMSE for test set (SLR): \", rmse_SLR)\n",
"print(\"RMSE for test set (MLR): \", rmse_MLR)"
]
},
{
"cell_type": "markdown",
"id": "bd0c8483",
"metadata": {},
"source": [
"### Which one perform better in prediction?"
]
},
{
"cell_type": "markdown",
"id": "36d05636",
"metadata": {},
"source": []
},
{
"cell_type": "code",
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"outputs": [],
"source": []
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bettyphan789/BH2009-2022.csv Normal file
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Date,rBH,rSP,SmB,HmL
1/2/2009,-0.121807334,-0.109931198,0.0005,-0.0695
1/3/2009,0.103053435,0.085404462,0.0004,0.0348
1/4/2009,0.084198385,0.093925079,0.0539,0.0536
1/5/2009,-0.025531915,0.053081446,-0.0252,0.0027
1/6/2009,-0.017467249,0.000195827,0.0263,-0.0273
1/7/2009,0.077777778,0.074141727,0.0187,0.0484
1/8/2009,0.039690722,0.033560189,-0.0108,0.0763
1/9/2009,0.001487357,0.035723346,0.0243,0.0104
1/10/2009,-0.01980198,-0.019762001,-0.0434,-0.042
1/11/2009,0.016161616,0.057364062,-0.0239,-0.0034
1/12/2009,-0.013916501,0.017770571,0.0604,-0.0017
1/1/2010,0.155241935,-0.036974246,0.004,0.0043
1/2/2010,0.045375218,0.028513689,0.0119,0.0323
1/3/2010,0.016694491,0.058796426,0.0148,0.0221
1/4/2010,-0.05316092,0.01475923,0.0487,0.0289
1/5/2010,-0.081638847,-0.081975842,0.0009,-0.0244
1/6/2010,0.133037485,-0.053882442,-0.0182,-0.047
1/7/2010,-0.025,0.06877785,0.0022,-0.0033
1/8/2010,0.014316239,-0.047449184,-0.0298,-0.0193
1/9/2010,0.049083632,0.087551103,0.0397,-0.0318
1/10/2010,-0.041767068,0.036855994,0.0119,-0.0251
1/11/2010,0.007544007,-0.00229025,0.0374,-0.0092
1/12/2010,0.002079867,0.06530004,0.0069,0.0376
1/1/2011,0.016396845,0.022645574,-0.0245,0.0075
1/2/2011,0.072493363,0.031956564,0.0153,0.0127
1/3/2011,-0.045696877,-0.001047313,0.0256,-0.0185
1/4/2011,-0.004389465,0.02849538,-0.0033,-0.0249
1/5/2011,-0.047895792,-0.013500953,-0.0067,-0.02
1/6/2011,-0.022479478,-0.018257461,-0.0015,-0.0039
1/7/2011,-0.039662375,-0.021474426,-0.0127,-0.009
1/8/2011,-0.015524664,-0.056791107,-0.0305,-0.0236
1/9/2011,-0.027047709,-0.071761988,-0.0331,-0.0172
1/10/2011,0.095037453,0.107723039,0.0328,0.001
1/11/2011,0.013253527,-0.005058715,-0.0016,-0.0045
1/12/2011,-0.031603376,0.008532764,-0.0059,0.0163
1/1/2012,0.027624069,0.043583062,0.0203,-0.0097
1/2/2012,7.63E-05,0.040589464,-0.0185,0.0043
1/3/2012,0.033628979,0.031332315,-0.0065,0.0114
1/4/2012,-0.00902379,-0.007497453,-0.0041,-0.0078
1/5/2012,-0.016142384,-0.062650726,0.0007,-0.0106
1/6/2012,0.05128313,0.039554982,0.0067,0.0062
1/7/2012,0.020008804,0.012597574,-0.0277,-0.0002
1/8/2012,-0.006944172,0.01976337,0.0048,0.013
1/9/2012,0.048514539,0.024236154,0.0051,0.016
1/10/2012,-0.024076865,-0.01978941,-0.0116,0.0359
1/11/2012,0.018617042,0.002846717,0.0064,-0.0084
1/12/2012,0.016252767,0.00706823,0.015,0.0351
1/1/2013,0.08813218,0.050428097,0.0033,0.0096
1/2/2013,0.046101114,0.011060649,-0.0028,0.0011
1/3/2013,0.024115334,0.035987724,0.0081,-0.0019
1/4/2013,0.017404658,0.018085768,-0.0236,0.0045
1/5/2013,0.077358491,0.020762812,0.0173,0.0263
1/6/2013,-0.015761821,-0.014999302,0.0133,0.0003
1/7/2013,0.03143535,0.04946208,0.0186,0.0057
1/8/2013,-0.039390454,-0.031298019,0.0028,-0.0269
1/9/2013,0.020113738,0.029749523,0.0291,-0.0122
1/10/2013,0.015163429,0.044595753,-0.0156,0.0125
1/11/2013,0.010150641,0.028049472,0.0129,0.0032
1/12/2013,0.018025751,0.023562792,-0.0045,-0.0002
1/1/2014,-0.047155705,-0.035582906,0.009,-0.0207
1/2/2014,0.024759455,0.04311703,0.0037,-0.0031
1/3/2014,0.078534092,0.006932166,-0.0185,0.0493
1/4/2014,0.0316253,0.006200789,-0.042,0.0117
1/5/2014,-0.006596818,0.02103028,-0.0188,-0.0013
1/6/2014,-0.0109375,0.019058332,0.0308,-0.007
1/7/2014,-0.009352291,-0.015079831,-0.0429,0.0003
1/8/2014,0.094384555,0.037655295,0.004,-0.0045
1/9/2014,0.004954342,-0.015513837,-0.0372,-0.0134
1/10/2014,0.014983084,0.023201461,0.042,-0.0181
1/11/2014,0.062214286,0.024533589,-0.0206,-0.0309
1/12/2014,0.0131576,-0.004188588,0.0249,0.0227
1/1/2015,-0.044845133,-0.031040806,-0.0055,-0.0358
1/2/2015,0.02462187,0.054892511,0.0061,-0.0186
1/3/2015,-0.016638032,-0.017396107,0.0304,-0.0037
1/4/2015,-0.018850575,0.00852082,-0.0303,0.0182
1/5/2015,0.00656045,0.010491382,0.0092,-0.0114
1/6/2015,-0.04632216,-0.021011672,0.029,-0.0079
1/7/2015,0.044666829,0.01974203,-0.0419,-0.0413
1/8/2015,-0.053593458,-0.062580818,0.0033,0.0277
1/9/2015,-0.035999427,-0.026442832,-0.0263,0.0056
1/10/2015,0.047920508,0.082983118,-0.0187,-0.0046
1/11/2015,-0.015816536,0.000504869,0.0359,-0.0042
1/12/2015,-0.017679778,-0.017530185,-0.0282,-0.0261
1/1/2016,-0.017391304,-0.050735322,-0.0343,0.0209
1/2/2016,0.042164026,-0.00412836,0.0071,-0.0057
1/3/2016,0.05378786,0.065991115,0.0082,0.0119
1/4/2016,0.026001405,0.002699398,0.0074,0.0328
1/5/2016,-0.033356164,0.015324602,-0.0018,-0.0166
1/6/2016,0.024941543,0.000910921,0.006,-0.0148
1/7/2016,-0.004493605,0.035609801,0.0251,-0.0127
1/8/2016,0.045185185,-0.001219243,0.0118,0.0313
1/9/2016,-0.042257264,-0.001234451,0.0213,-0.0123
1/10/2016,-0.002404958,-0.019425679,-0.0442,0.0412
1/11/2016,0.098748261,0.034174522,0.0567,0.0819
1/12/2016,0.030046414,0.018200762,0.0008,0.0356
1/1/2017,0.007615076,0.017884358,-0.0114,-0.0276
1/2/2017,0.045206927,0.03719816,-0.0202,-0.0168
1/3/2017,-0.028199144,-0.000389197,0.0114,-0.0332
1/4/2017,-0.008284971,0.009091209,0.0072,-0.021
1/5/2017,0.002663653,0.011576251,-0.0252,-0.0378
1/6/2017,0.025197231,0.004813775,0.0223,0.0148
1/7/2017,0.031747154,0.019348826,-0.0146,-0.0024
1/8/2017,0.032969793,0.000546433,-0.0167,-0.0209
1/9/2017,0.012120096,0.019302979,0.0446,0.0312
1/10/2017,0.020856082,0.022188135,-0.0193,0.0021
1/11/2017,0.039326844,0.028082628,-0.0058,-0.0008
1/12/2017,0.020926244,0.00983163,-0.0132,0.0005
1/1/2018,0.086609543,0.056178704,-0.0315,-0.0133
1/2/2018,-0.040587553,-0.038947372,0.0023,-0.0107
1/3/2018,-0.035938759,-0.026884499,0.0405,-0.0023
1/4/2018,-0.028251421,0.002718775,0.0114,0.0054
1/5/2018,-0.011869947,0.021608342,0.0526,-0.0318
1/6/2018,-0.017966574,0.004842436,0.0115,-0.0233
1/7/2018,0.069174585,0.036021556,-0.0222,0.0047
1/8/2018,0.047255845,0.030263211,0.0112,-0.0399
1/9/2018,0.013299557,0.004294287,-0.0228,-0.0169
1/10/2018,-0.038421875,-0.069403356,-0.0477,0.0344
1/11/2018,0.059456297,0.017859357,-0.0068,0.0027
1/12/2018,-0.061349693,-0.091776895,-0.0238,-0.0186
1/1/2019,0.017973856,0.078684402,0.029,-0.0045
1/2/2019,-0.029855538,0.029728889,0.0205,-0.0268
1/3/2019,-0.003259431,0.017924256,-0.0303,-0.041
1/4/2019,0.079229122,0.039313498,-0.0174,0.0214
1/5/2019,-0.086194168,-0.065777731,-0.0131,-0.0234
1/6/2019,0.071669023,0.068930164,0.0028,-0.0072
1/7/2019,-0.03041935,0.013128152,-0.0193,0.0047
1/8/2019,-0.018103711,-0.018091627,-0.0236,-0.0476
1/9/2019,0.028883654,0.017181178,-0.0097,0.0674
1/10/2019,0.022791118,0.020431771,0.0029,-0.0192
1/11/2019,0.036232634,0.034047037,0.0078,-0.0201
1/12/2019,0.027519327,0.028589819,0.0073,0.0176
1/1/2020,-0.010583351,-0.001628093,-0.031,-0.0622
1/2/2020,-0.080060477,-0.084110484,0.0107,-0.0379
1/3/2020,-0.120014494,-0.125119282,-0.0488,-0.1397
1/4/2020,0.035661765,0.126844038,0.0249,-0.0123
1/5/2020,-0.01086262,0.04528182,0.0248,-0.0489
1/6/2020,-0.040697674,0.018388396,0.027,-0.0217
1/7/2020,0.098507295,0.055101321,-0.0232,-0.0138
1/8/2020,0.115549789,0.070064667,-0.0022,-0.0295
1/9/2020,-0.023076688,-0.03922797,0.0004,-0.0268
1/10/2020,-0.054690454,-0.027665786,0.0436,0.0421
1/11/2020,0.136158678,0.107545635,0.0582,0.0214
1/12/2020,0.012007984,0.037121459,0.0489,-0.0151
1/1/2021,-0.010680965,-0.011136661,0.0734,0.0296
1/2/2021,0.059517582,0.026091451,0.0206,0.0718
1/3/2021,0.057935158,0.042438633,-0.0237,0.074
1/4/2021,0.069478509,0.052425321,-0.0319,-0.0094
1/5/2021,0.056969697,0.005486489,-0.0025,0.0708
1/6/2021,-0.039905963,0.02221401,0.017,-0.0782
1/7/2021,0.000714284,0.022748055,-0.0399,-0.0176
1/8/2021,0.02625925,0.028990416,-0.0043,-0.0016
1/9/2021,-0.043082112,-0.047569169,0.0072,0.0508
1/10/2021,0.052319151,0.069143836,-0.0235,-0.0048
1/11/2021,-0.037019926,-0.008333706,-0.0132,-0.0044
1/12/2021,0.081045683,0.043612914,-0.0166,0.0328
1/1/2022,0.042477511,-0.052585165,-0.0594,0.1275
1/2/2022,0.013622673,-0.031360492,0.0223,0.0304
1/3/2022,0.110700224,0.035773288,-0.016,-0.018
1/4/2022,-0.084286689,-0.087956712,-0.0141,0.0619
1/5/2022,-0.021245406,5.31776E-05,-0.0185,0.0841
1/6/2022,-0.137327286,-0.08392,0.0209,-0.0597
1/7/2022,0.104536007,0.091116392,0.0281,-0.041
1/8/2022,-0.067283595,-0.042440128,0.0139,0.0031
1/9/2022,-0.03521889,-0.093395672,-0.0082,0.0003
1/10/2022,0.094914754,0.079863414,0.001,0.0805
1/11/2022,0.079159645,0.053752893,-0.034,0.0139
1/12/2022,-0.024088032,-0.058971474,-0.0064,0.0136
1 Date rBH rSP SmB HmL
2 1/2/2009 -0.121807334 -0.109931198 0.0005 -0.0695
3 1/3/2009 0.103053435 0.085404462 0.0004 0.0348
4 1/4/2009 0.084198385 0.093925079 0.0539 0.0536
5 1/5/2009 -0.025531915 0.053081446 -0.0252 0.0027
6 1/6/2009 -0.017467249 0.000195827 0.0263 -0.0273
7 1/7/2009 0.077777778 0.074141727 0.0187 0.0484
8 1/8/2009 0.039690722 0.033560189 -0.0108 0.0763
9 1/9/2009 0.001487357 0.035723346 0.0243 0.0104
10 1/10/2009 -0.01980198 -0.019762001 -0.0434 -0.042
11 1/11/2009 0.016161616 0.057364062 -0.0239 -0.0034
12 1/12/2009 -0.013916501 0.017770571 0.0604 -0.0017
13 1/1/2010 0.155241935 -0.036974246 0.004 0.0043
14 1/2/2010 0.045375218 0.028513689 0.0119 0.0323
15 1/3/2010 0.016694491 0.058796426 0.0148 0.0221
16 1/4/2010 -0.05316092 0.01475923 0.0487 0.0289
17 1/5/2010 -0.081638847 -0.081975842 0.0009 -0.0244
18 1/6/2010 0.133037485 -0.053882442 -0.0182 -0.047
19 1/7/2010 -0.025 0.06877785 0.0022 -0.0033
20 1/8/2010 0.014316239 -0.047449184 -0.0298 -0.0193
21 1/9/2010 0.049083632 0.087551103 0.0397 -0.0318
22 1/10/2010 -0.041767068 0.036855994 0.0119 -0.0251
23 1/11/2010 0.007544007 -0.00229025 0.0374 -0.0092
24 1/12/2010 0.002079867 0.06530004 0.0069 0.0376
25 1/1/2011 0.016396845 0.022645574 -0.0245 0.0075
26 1/2/2011 0.072493363 0.031956564 0.0153 0.0127
27 1/3/2011 -0.045696877 -0.001047313 0.0256 -0.0185
28 1/4/2011 -0.004389465 0.02849538 -0.0033 -0.0249
29 1/5/2011 -0.047895792 -0.013500953 -0.0067 -0.02
30 1/6/2011 -0.022479478 -0.018257461 -0.0015 -0.0039
31 1/7/2011 -0.039662375 -0.021474426 -0.0127 -0.009
32 1/8/2011 -0.015524664 -0.056791107 -0.0305 -0.0236
33 1/9/2011 -0.027047709 -0.071761988 -0.0331 -0.0172
34 1/10/2011 0.095037453 0.107723039 0.0328 0.001
35 1/11/2011 0.013253527 -0.005058715 -0.0016 -0.0045
36 1/12/2011 -0.031603376 0.008532764 -0.0059 0.0163
37 1/1/2012 0.027624069 0.043583062 0.0203 -0.0097
38 1/2/2012 7.63E-05 0.040589464 -0.0185 0.0043
39 1/3/2012 0.033628979 0.031332315 -0.0065 0.0114
40 1/4/2012 -0.00902379 -0.007497453 -0.0041 -0.0078
41 1/5/2012 -0.016142384 -0.062650726 0.0007 -0.0106
42 1/6/2012 0.05128313 0.039554982 0.0067 0.0062
43 1/7/2012 0.020008804 0.012597574 -0.0277 -0.0002
44 1/8/2012 -0.006944172 0.01976337 0.0048 0.013
45 1/9/2012 0.048514539 0.024236154 0.0051 0.016
46 1/10/2012 -0.024076865 -0.01978941 -0.0116 0.0359
47 1/11/2012 0.018617042 0.002846717 0.0064 -0.0084
48 1/12/2012 0.016252767 0.00706823 0.015 0.0351
49 1/1/2013 0.08813218 0.050428097 0.0033 0.0096
50 1/2/2013 0.046101114 0.011060649 -0.0028 0.0011
51 1/3/2013 0.024115334 0.035987724 0.0081 -0.0019
52 1/4/2013 0.017404658 0.018085768 -0.0236 0.0045
53 1/5/2013 0.077358491 0.020762812 0.0173 0.0263
54 1/6/2013 -0.015761821 -0.014999302 0.0133 0.0003
55 1/7/2013 0.03143535 0.04946208 0.0186 0.0057
56 1/8/2013 -0.039390454 -0.031298019 0.0028 -0.0269
57 1/9/2013 0.020113738 0.029749523 0.0291 -0.0122
58 1/10/2013 0.015163429 0.044595753 -0.0156 0.0125
59 1/11/2013 0.010150641 0.028049472 0.0129 0.0032
60 1/12/2013 0.018025751 0.023562792 -0.0045 -0.0002
61 1/1/2014 -0.047155705 -0.035582906 0.009 -0.0207
62 1/2/2014 0.024759455 0.04311703 0.0037 -0.0031
63 1/3/2014 0.078534092 0.006932166 -0.0185 0.0493
64 1/4/2014 0.0316253 0.006200789 -0.042 0.0117
65 1/5/2014 -0.006596818 0.02103028 -0.0188 -0.0013
66 1/6/2014 -0.0109375 0.019058332 0.0308 -0.007
67 1/7/2014 -0.009352291 -0.015079831 -0.0429 0.0003
68 1/8/2014 0.094384555 0.037655295 0.004 -0.0045
69 1/9/2014 0.004954342 -0.015513837 -0.0372 -0.0134
70 1/10/2014 0.014983084 0.023201461 0.042 -0.0181
71 1/11/2014 0.062214286 0.024533589 -0.0206 -0.0309
72 1/12/2014 0.0131576 -0.004188588 0.0249 0.0227
73 1/1/2015 -0.044845133 -0.031040806 -0.0055 -0.0358
74 1/2/2015 0.02462187 0.054892511 0.0061 -0.0186
75 1/3/2015 -0.016638032 -0.017396107 0.0304 -0.0037
76 1/4/2015 -0.018850575 0.00852082 -0.0303 0.0182
77 1/5/2015 0.00656045 0.010491382 0.0092 -0.0114
78 1/6/2015 -0.04632216 -0.021011672 0.029 -0.0079
79 1/7/2015 0.044666829 0.01974203 -0.0419 -0.0413
80 1/8/2015 -0.053593458 -0.062580818 0.0033 0.0277
81 1/9/2015 -0.035999427 -0.026442832 -0.0263 0.0056
82 1/10/2015 0.047920508 0.082983118 -0.0187 -0.0046
83 1/11/2015 -0.015816536 0.000504869 0.0359 -0.0042
84 1/12/2015 -0.017679778 -0.017530185 -0.0282 -0.0261
85 1/1/2016 -0.017391304 -0.050735322 -0.0343 0.0209
86 1/2/2016 0.042164026 -0.00412836 0.0071 -0.0057
87 1/3/2016 0.05378786 0.065991115 0.0082 0.0119
88 1/4/2016 0.026001405 0.002699398 0.0074 0.0328
89 1/5/2016 -0.033356164 0.015324602 -0.0018 -0.0166
90 1/6/2016 0.024941543 0.000910921 0.006 -0.0148
91 1/7/2016 -0.004493605 0.035609801 0.0251 -0.0127
92 1/8/2016 0.045185185 -0.001219243 0.0118 0.0313
93 1/9/2016 -0.042257264 -0.001234451 0.0213 -0.0123
94 1/10/2016 -0.002404958 -0.019425679 -0.0442 0.0412
95 1/11/2016 0.098748261 0.034174522 0.0567 0.0819
96 1/12/2016 0.030046414 0.018200762 0.0008 0.0356
97 1/1/2017 0.007615076 0.017884358 -0.0114 -0.0276
98 1/2/2017 0.045206927 0.03719816 -0.0202 -0.0168
99 1/3/2017 -0.028199144 -0.000389197 0.0114 -0.0332
100 1/4/2017 -0.008284971 0.009091209 0.0072 -0.021
101 1/5/2017 0.002663653 0.011576251 -0.0252 -0.0378
102 1/6/2017 0.025197231 0.004813775 0.0223 0.0148
103 1/7/2017 0.031747154 0.019348826 -0.0146 -0.0024
104 1/8/2017 0.032969793 0.000546433 -0.0167 -0.0209
105 1/9/2017 0.012120096 0.019302979 0.0446 0.0312
106 1/10/2017 0.020856082 0.022188135 -0.0193 0.0021
107 1/11/2017 0.039326844 0.028082628 -0.0058 -0.0008
108 1/12/2017 0.020926244 0.00983163 -0.0132 0.0005
109 1/1/2018 0.086609543 0.056178704 -0.0315 -0.0133
110 1/2/2018 -0.040587553 -0.038947372 0.0023 -0.0107
111 1/3/2018 -0.035938759 -0.026884499 0.0405 -0.0023
112 1/4/2018 -0.028251421 0.002718775 0.0114 0.0054
113 1/5/2018 -0.011869947 0.021608342 0.0526 -0.0318
114 1/6/2018 -0.017966574 0.004842436 0.0115 -0.0233
115 1/7/2018 0.069174585 0.036021556 -0.0222 0.0047
116 1/8/2018 0.047255845 0.030263211 0.0112 -0.0399
117 1/9/2018 0.013299557 0.004294287 -0.0228 -0.0169
118 1/10/2018 -0.038421875 -0.069403356 -0.0477 0.0344
119 1/11/2018 0.059456297 0.017859357 -0.0068 0.0027
120 1/12/2018 -0.061349693 -0.091776895 -0.0238 -0.0186
121 1/1/2019 0.017973856 0.078684402 0.029 -0.0045
122 1/2/2019 -0.029855538 0.029728889 0.0205 -0.0268
123 1/3/2019 -0.003259431 0.017924256 -0.0303 -0.041
124 1/4/2019 0.079229122 0.039313498 -0.0174 0.0214
125 1/5/2019 -0.086194168 -0.065777731 -0.0131 -0.0234
126 1/6/2019 0.071669023 0.068930164 0.0028 -0.0072
127 1/7/2019 -0.03041935 0.013128152 -0.0193 0.0047
128 1/8/2019 -0.018103711 -0.018091627 -0.0236 -0.0476
129 1/9/2019 0.028883654 0.017181178 -0.0097 0.0674
130 1/10/2019 0.022791118 0.020431771 0.0029 -0.0192
131 1/11/2019 0.036232634 0.034047037 0.0078 -0.0201
132 1/12/2019 0.027519327 0.028589819 0.0073 0.0176
133 1/1/2020 -0.010583351 -0.001628093 -0.031 -0.0622
134 1/2/2020 -0.080060477 -0.084110484 0.0107 -0.0379
135 1/3/2020 -0.120014494 -0.125119282 -0.0488 -0.1397
136 1/4/2020 0.035661765 0.126844038 0.0249 -0.0123
137 1/5/2020 -0.01086262 0.04528182 0.0248 -0.0489
138 1/6/2020 -0.040697674 0.018388396 0.027 -0.0217
139 1/7/2020 0.098507295 0.055101321 -0.0232 -0.0138
140 1/8/2020 0.115549789 0.070064667 -0.0022 -0.0295
141 1/9/2020 -0.023076688 -0.03922797 0.0004 -0.0268
142 1/10/2020 -0.054690454 -0.027665786 0.0436 0.0421
143 1/11/2020 0.136158678 0.107545635 0.0582 0.0214
144 1/12/2020 0.012007984 0.037121459 0.0489 -0.0151
145 1/1/2021 -0.010680965 -0.011136661 0.0734 0.0296
146 1/2/2021 0.059517582 0.026091451 0.0206 0.0718
147 1/3/2021 0.057935158 0.042438633 -0.0237 0.074
148 1/4/2021 0.069478509 0.052425321 -0.0319 -0.0094
149 1/5/2021 0.056969697 0.005486489 -0.0025 0.0708
150 1/6/2021 -0.039905963 0.02221401 0.017 -0.0782
151 1/7/2021 0.000714284 0.022748055 -0.0399 -0.0176
152 1/8/2021 0.02625925 0.028990416 -0.0043 -0.0016
153 1/9/2021 -0.043082112 -0.047569169 0.0072 0.0508
154 1/10/2021 0.052319151 0.069143836 -0.0235 -0.0048
155 1/11/2021 -0.037019926 -0.008333706 -0.0132 -0.0044
156 1/12/2021 0.081045683 0.043612914 -0.0166 0.0328
157 1/1/2022 0.042477511 -0.052585165 -0.0594 0.1275
158 1/2/2022 0.013622673 -0.031360492 0.0223 0.0304
159 1/3/2022 0.110700224 0.035773288 -0.016 -0.018
160 1/4/2022 -0.084286689 -0.087956712 -0.0141 0.0619
161 1/5/2022 -0.021245406 5.31776E-05 -0.0185 0.0841
162 1/6/2022 -0.137327286 -0.08392 0.0209 -0.0597
163 1/7/2022 0.104536007 0.091116392 0.0281 -0.041
164 1/8/2022 -0.067283595 -0.042440128 0.0139 0.0031
165 1/9/2022 -0.03521889 -0.093395672 -0.0082 0.0003
166 1/10/2022 0.094914754 0.079863414 0.001 0.0805
167 1/11/2022 0.079159645 0.053752893 -0.034 0.0139
168 1/12/2022 -0.024088032 -0.058971474 -0.0064 0.0136

7
bettyphan789/gitUpdate.bat Normal file
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git status .
@pause
git add .
git commit -m"update bettyphan789,"
start git push

23
bettyphan789/package.json Normal file
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{
"name": "bettyphan789",
"version": "1.0.0",
"description": "",
"main": "index.js",
"directories": {
"doc": "docs"
},
"scripts": {
"test": "echo \"Error: no test specified\" && exit 1",
"gitUpdate": "git add . && git commit -m'update bettyphan789,'"
},
"keywords": [],
"author": "",
"license": "ISC",
"dependencies": {
"@fortawesome/free-solid-svg-icons": "^6.2.1",
"@fortawesome/react-fontawesome": "^0.2.0",
"bootstrap": "^5.2.3",
"react-bootstrap": "^2.6.0"
},
"devDependencies": {}
}