fund_performance_attribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fund Performance Attribution\n",
    "\n",
    "In this notebook, the *'Washington Mutual Investors Fund' (CWMAX)* fund will be examined by its performance. Different performance metrics will be used to judge the fund returns.\n",
    "\n",
    "The Washington Mutual Investors Fund mutal fund is US based and searches for companies across a broad spectrum. Mainly, it seeks to find companies with strong balance sheets and consistent dividends. Due to the fund strategy, a natural benchmark index is the S&P 500 with dividends reinvested. \n",
    "\n",
    "Fund performance metric methodlogies are listed below.\n",
    "\n",
    "* Sharpe Ratio\n",
    "* Elton-Grubers Measure\n",
    "* Treynor Ratio\n",
    "* Jensen's Alpha with confidence interval\n",
    "* Tracking Error\n",
    "* Information Ratio\n",
    "* Lobosco's Relative RAP\n",
    "* Treynor-Mazuy\n",
    "* Henriksson-Merton\n",
    "* APT alpha from Fama-French 3 factor model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data Import and Plot Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from pandas_datareader import data as pdr\n",
    "\n",
    "fund_data = pdr.get_data_yahoo('CWMAX', start=\"2002-05-01\", end=\"2022-03-31\", interval=\"d\")['Adj Close']\n",
    "spx_data = pdr.get_data_yahoo('^GSPC', start=\"2002-05-01\", end=\"2022-03-31\", interval=\"d\")['Adj Close']\n",
    "\n",
    "fama_french_data = pd.read_csv('fama_french_3_factor_data.CSV')\n",
    "fama_french_data['date'] = pd.to_datetime(fama_french_data['date'], format='%Y%m%d', dayfirst=True)\n",
    "fama_french_data['date'] = fama_french_data['date']\n",
    "fama_french_data.set_index(fama_french_data['date'], inplace = True)\n",
    "fama_french_data.drop(columns=['date'], inplace = True)\n",
    "\n",
    "fund_returns = np.log(fund_data[1:]/fund_data.shift(1)[1:])\n",
    "spx_returns = np.log(spx_data[1:]/spx_data.shift(1)[1:])\n",
    "fama_french_returns = np.log(1 + fama_french_data / 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "sns.set_theme()\n",
    "sns.set_style(\"whitegrid\", {'grid.linestyle': '--'})\n",
    "seq_col_brew = sns.color_palette(\"flag_r\", 4)\n",
    "sns.set_palette(seq_col_brew)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 266,
       "width": 1056
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3)\n",
    "\n",
    "axs[0].set_title('CWMAX vs. SPX Returns')\n",
    "axs[0].plot(fund_returns, label = 'CWMAX')\n",
    "axs[0].plot(spx_returns, label = 'SPX')\n",
    "axs[0].set_frame_on(False)\n",
    "axs[0].legend()\n",
    "\n",
    "axs[1].set_title('CWMAX vs. SPX Prices')\n",
    "axs[1].plot(fund_data/fund_data[0], label = 'CWMAX')\n",
    "axs[1].plot(spx_data/spx_data[0], label = 'SPX')\n",
    "axs[1].set_frame_on(False)\n",
    "axs[1].legend()\n",
    "\n",
    "axs[2].set_title('Fama French Excess Return Factors')\n",
    "axs[2].plot(fama_french_returns)\n",
    "axs[2].set_frame_on(False)\n",
    "\n",
    "fig.set_size_inches(18, 4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. Sharpe Ratio\n",
    "\n",
    "The Sharpe ratio, defined below, is sometimes called the risk-adjusted excess returns. It measures the return above the risk-free rate (1M T-bill in this case), per unit volatility. For this ratio, volatility is a proxy for risk.\n",
    "\n",
    "$$\n",
    "S = \\frac{r_P-r_f}{\\sigma_P}\n",
    "$$\n",
    "\n",
    "For this we find $r_P$ and $r_f$ by average over the 20Y period. We use the sample standard deviation for $\\sigma_P$ for the same period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.024565897868257542\n"
     ]
    }
   ],
   "source": [
    "fund_mean = np.mean(fund_returns)\n",
    "fund_std = np.std(fund_returns)\n",
    "\n",
    "risk_free_mean = np.mean(fama_french_returns['RF'])\n",
    "\n",
    "fund_sharpe_ratio = (fund_mean - risk_free_mean)/fund_std\n",
    "print(fund_sharpe_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Elton-Grubers Measure\n",
    "\n",
    "The Elton-Grubers Measure is a measure of performance. It measures the difference between a portfolio's return on the CML (Capital Market Line) with the same volatility as the target portfolio. In theory, an efficient portfolio with the volatility of the assessed fund will have the return of\n",
    "$$\n",
    "\\mu_A = r_f + \\frac{\\mu_M-r_f}{\\sigma_M}\\sigma_P\n",
    "$$\n",
    "\n",
    "The measure then is the ex-post return versus this teoretical return. Namely\n",
    "\n",
    "$$\n",
    "\\textrm{EG} = \\mu_P - \\mu_A\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-2.4143623991421754e-05\n"
     ]
    }
   ],
   "source": [
    "market_excess_return = np.mean(fama_french_returns['Mkt-RF'])\n",
    "market_volatility = np.std(fama_french_returns['Mkt-RF'] + fama_french_returns['RF'])\n",
    "\n",
    "target_return = risk_free_mean + (market_excess_return) / market_volatility * fund_std\n",
    "acutal_return = fund_mean\n",
    "\n",
    "EG = acutal_return - target_return\n",
    "print(EG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. Treynor Ratio\n",
    "\n",
    "Similarly to the Sharpe ratio, the Treynor ratio measures excess return per unit of risk. It is displayed below. However, the Treynor index uses the funds beta as a measure of risk, instead of the volatility. \n",
    "$$\n",
    "T = \\frac{r_P-r_f}{\\beta_P}\n",
    "$$\n",
    "In our case, we use all available data to measure the beta. Since our benchmark index is the S&P 500 already, we use that as a proxy for the market portfolio. Recall, the beta can be estimated by\n",
    "$$\n",
    "\\hat{\\beta}_P = \\frac{\\textrm{Cov}[r_P, r_M]}{\\textrm{Var}[r_M]}\n",
    "$$\n",
    "However, it is estimated using linear regression below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.963\n",
      "Model:                            OLS   Adj. R-squared:                  0.963\n",
      "Method:                 Least Squares   F-statistic:                 1.309e+05\n",
      "Date:                Mon, 09 May 2022   Prob (F-statistic):               0.00\n",
      "Time:                        02:14:33   Log-Likelihood:                 23581.\n",
      "No. Observations:                5015   AIC:                        -4.716e+04\n",
      "Df Residuals:                    5013   BIC:                        -4.714e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       6.205e-05    3.1e-05      2.000      0.046    1.22e-06       0.000\n",
      "0              0.9135      0.003    361.763      0.000       0.909       0.918\n",
      "==============================================================================\n",
      "Omnibus:                     2826.620   Durbin-Watson:                   2.268\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          8463070.278\n",
      "Skew:                           0.988   Prob(JB):                         0.00\n",
      "Kurtosis:                     204.239   Cond. No.                         81.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 267,
       "width": 386
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets, linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "\n",
    "x = spx_returns - fama_french_returns['RF']\n",
    "y = fund_returns - fama_french_returns['RF']\n",
    "\n",
    "X = sm.add_constant(x)\n",
    "est = sm.OLS(y, X)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())\n",
    "\n",
    "\n",
    "plt.scatter(x, y, marker='+')\n",
    "plt.title('SPX versus CWMAX (excess $r_f$)')\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0003074615669699502\n"
     ]
    }
   ],
   "source": [
    "fund_beta = est2.params[0]\n",
    "fund_treynor_ratio = (fund_mean - risk_free_mean) / fund_beta\n",
    "print(fund_treynor_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. Jensen's Alpha\n",
    "\n",
    "The alpha, or Jensen's alpha, is the ex-post excess return over what the CAPM predicts. Thus we obtain the alpha as\n",
    "$$\n",
    "\\alpha = r_P - (r_f + \\beta_P(\\mu_M - r_f))\n",
    "$$\n",
    "The value is most easily found through linear regression. Doing so we also obtain a confidence interval for the value of the intercept, which is the alpha in a regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.20458285487068e-05 ± 3.1e-05\n"
     ]
    }
   ],
   "source": [
    "fund_alpha = est2.params['const']\n",
    "print(fund_alpha, '±', 3.1e-05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5. Tracking Error\n",
    "\n",
    "Tracking error is simply the variance between the spread of the portfolio returns and its benchmark index. If a portfolio aims to track an index this is supposed to be low. If a portfolio on the other hand aims at beating the index, it should not be close to zero. The tracking error is simply found by\n",
    "$$\n",
    "\\textrm{TE} = \\sqrt{\\textrm{Var}[r_P - r_A]} \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002439545034778632\n"
     ]
    }
   ],
   "source": [
    "tracking_error_fund = np.std(fund_returns - spx_returns)\n",
    "print(tracking_error_fund)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6. Information Ratio\n",
    "\n",
    "The information ratio can be thought of as a measure of risk-adjusted alpha. It measures the excess return, alpha, with the tracking error. It is calcuted as shown below\n",
    "$$\n",
    "\\textrm{IR} = \\frac{\\alpha_P}{\\textrm{TE}}\n",
    "$$\n",
    "Using the information ratio in relation to the alpha, we can also estimate how many years are required to tell whether or not the alpha is statistically significant. This is done with \n",
    "$$\n",
    "T_{\\textrm{req}} = (\\frac{t_{\\textrm{stat}}}{\\textrm{IR}})^2\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.025433360591491164 with time req. for significant alpha 5938.881302497973\n"
     ]
    }
   ],
   "source": [
    "information_ratio_fund = fund_alpha / tracking_error_fund\n",
    "t_req = (1.96 / information_ratio_fund) ** 2\n",
    "print(information_ratio_fund, 'with time req. for significant alpha', t_req)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 7. Lobosco Relative RAP\n",
    "\n",
    "Lobosco's (1999) relative RAP starts with solving mean market returns from the CAPM. That is, the fund returns imply some market return. This can be done for a benchmark portfolio as well. And then relative RAP can be found. Thus\n",
    "$$\n",
    "\\textrm{RAP}_i = \\frac{\\sigma_M}{\\sigma_i}(\\mu_i - r_f) + r_f\n",
    "$$\n",
    "and \n",
    "$$\n",
    "\\textrm{Relative RAP} = \\textrm{RAP}_P - \\textrm{RAP}_B\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-2.606809891512237e-05\n"
     ]
    }
   ],
   "source": [
    "rap_fund = market_volatility / fund_std * (fund_mean - risk_free_mean) + risk_free_mean\n",
    "rap_bench = market_excess_return + risk_free_mean\n",
    "\n",
    "relative_rap = rap_fund - rap_bench\n",
    "print(relative_rap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 8. Treynor-Mazuy\n",
    "\n",
    "Both Treynor-Mazuy and Henriksson-Merton, tries to estimate the market timing of the portfolio manager. The hypothesis is that if a portfolio manager has market timing, he/she will reduce the market beta during market downturns, and increase it during market upturns. This is estimated by adding a term in a linear regression model for large returns. Large returns explaining part of the return provides evidence in favour of this hypothesis.\n",
    "\n",
    "Treynor-Mazuy came up with the regression model given below\n",
    "$$\n",
    "(r_P - r_f) = \\alpha + \\beta_0 (r_M - r_f) + \\beta_1 (r_M - r_f)^2 + \\varepsilon_t\n",
    "$$\n",
    "if $\\beta_1 > 0$ significantly, the portfolio manager has good market timing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.963\n",
      "Model:                            OLS   Adj. R-squared:                  0.963\n",
      "Method:                 Least Squares   F-statistic:                 6.564e+04\n",
      "Date:                Mon, 09 May 2022   Prob (F-statistic):               0.00\n",
      "Time:                        02:22:02   Log-Likelihood:                 23589.\n",
      "No. Observations:                5015   AIC:                        -4.717e+04\n",
      "Df Residuals:                    5012   BIC:                        -4.715e+04\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       9.504e-05   3.21e-05      2.963      0.003    3.22e-05       0.000\n",
      "x              0.9125      0.003    359.754      0.000       0.907       0.917\n",
      "x squared     -0.2168      0.055     -3.977      0.000      -0.324      -0.110\n",
      "==============================================================================\n",
      "Omnibus:                     2917.438   Durbin-Watson:                   2.270\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          8538167.563\n",
      "Skew:                           1.087   Prob(JB):                         0.00\n",
      "Kurtosis:                     205.128   Cond. No.                     1.76e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.76e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "y = fund_returns - fama_french_returns['RF']\n",
    "x = spx_returns - fama_french_returns['RF']\n",
    "x_squared = x ** 2\n",
    "\n",
    "X = pd.DataFrame(x, columns = ['x'])\n",
    "X['x squared'] = x_squared\n",
    "X = sm.add_constant(X)\n",
    "est = sm.OLS(y, X)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.21683562441558799 statistically significant.\n"
     ]
    }
   ],
   "source": [
    "print(est2.params[2], 'statistically significant.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 9. Henriksson-Merton\n",
    "\n",
    "As mentioned in (8), Henriksson-Merton also tries to measure market timing, but for market downturns exclusivley, and without making use of quadratics (which are not realistic). Their regression model is displayed below\n",
    "$$\n",
    "(r_P - r_f) = \\alpha + \\beta_0 (r_M - r_f) + \\beta_1 D_t(r_M - r_f) + \\varepsilon_t\n",
    "$$\n",
    "in which $D(t)$ is defined as\n",
    "$$\n",
    "  D_t \\triangleq \\begin{cases}\n",
    "    0, & \\text{$(r_M-r_f) \\geq 0 $}\\\\\n",
    "    -1, & \\text{$(r_M-r_f) < 0$}\n",
    "  \\end{cases}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.963\n",
      "Model:                            OLS   Adj. R-squared:                  0.963\n",
      "Method:                 Least Squares   F-statistic:                 6.548e+04\n",
      "Date:                Mon, 09 May 2022   Prob (F-statistic):               0.00\n",
      "Time:                        03:03:10   Log-Likelihood:                 23583.\n",
      "No. Observations:                5015   AIC:                        -4.716e+04\n",
      "Df Residuals:                    5012   BIC:                        -4.714e+04\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0001   4.06e-05      2.905      0.004    3.83e-05       0.000\n",
      "x              0.9060      0.004    208.549      0.000       0.897       0.915\n",
      "d             -0.0141      0.007     -2.134      0.033      -0.027      -0.001\n",
      "==============================================================================\n",
      "Omnibus:                     2861.395   Durbin-Watson:                   2.269\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          8476225.031\n",
      "Skew:                           1.026   Prob(JB):                         0.00\n",
      "Kurtosis:                     204.395   Cond. No.                         245.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "y = fund_returns - fama_french_returns['RF']\n",
    "x = spx_returns - fama_french_returns['RF']\n",
    "D = [-1 if ret < 0 else 0 for ret in x]\n",
    "\n",
    "X = pd.DataFrame(x, columns = ['x'])\n",
    "X['d'] = x * D\n",
    "X = sm.add_constant(X)\n",
    "est = sm.OLS(y, X)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.014111443372309004 statistically significant\n"
     ]
    }
   ],
   "source": [
    "print(est2.params[2], 'statistically significant')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 10. Fama-French Alpha\n",
    "\n",
    "As evidenced by the APT result, regression models with high $R^2$, should have a low, or zero alpha. Fama-French used two different portfolios beyond the market portfolio to explain a stock's return. By running the regression we are able to see if an excess return beyond the APT is generated, and to which risk-factors we are exposed to (of the ones in the Fama-French 3 factor model).\n",
    "\n",
    "The Fama-French 3 factor model is simply an approximation of APT, estimated by the linear regression model below.\n",
    "$$\n",
    "(r_P - r_f) = \\alpha + \\beta_0 (r_M - r_f) + \\beta_1 SML + \\beta_2 HML + \\varepsilon_t\n",
    "$$\n",
    "\n",
    "The idea is that value stocks (in the days of Fama and French) outperformed growth stocks and small cap stocks outperform large cap stocks. This means investors get compensated by assuming the additional risk these kinds of stocks have. Returns are thus better explained by including the exposures to these kind of stocks. For this purpose wide portfolios of stocks of the aforementioned categories were created, and returns thereof calculated. The categories explain the abreviations above.\n",
    "\n",
    "* SML - Small cap. minus Large cap.\n",
    "* HML - High value minus Low value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.962\n",
      "Model:                            OLS   Adj. R-squared:                  0.962\n",
      "Method:                 Least Squares   F-statistic:                 4.186e+04\n",
      "Date:                Mon, 09 May 2022   Prob (F-statistic):               0.00\n",
      "Time:                        03:13:57   Log-Likelihood:                 23482.\n",
      "No. Observations:                5015   AIC:                        -4.696e+04\n",
      "Df Residuals:                    5011   BIC:                        -4.693e+04\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const      -7.258e-06   3.17e-05     -0.229      0.819   -6.93e-05    5.48e-05\n",
      "Mkt-RF         0.8997      0.003    339.891      0.000       0.894       0.905\n",
      "SMB           -0.1529      0.005    -28.985      0.000      -0.163      -0.143\n",
      "HML            0.1037      0.004     24.053      0.000       0.095       0.112\n",
      "==============================================================================\n",
      "Omnibus:                     2872.727   Durbin-Watson:                   2.293\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          7687751.499\n",
      "Skew:                           1.057   Prob(JB):                         0.00\n",
      "Kurtosis:                     194.798   Cond. No.                         168.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "y = fund_returns - fama_french_returns['RF']\n",
    "x = fama_french_returns[['Mkt-RF', 'SMB', 'HML']]\n",
    "X = sm.add_constant(x)\n",
    "est = sm.OLS(y, X)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-7.258203772667213e-06 statistically insignificant\n"
     ]
    }
   ],
   "source": [
    "print(est2.params[0], 'statistically insignificant')"
   ]
  }
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