Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNPfgThvcmLjJ8XN7XvvJTz"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Developing market models using frequentist linear regression"
      ],
      "metadata": {
        "id": "fpDqf0ZG0066"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install Yahoo finance package\n",
        "!pip install yfinance\n",
        "\n",
        "# Import relevant Python packages\n",
        "import statsmodels.api as sm\n",
        "import pandas as pd\n",
        "import yfinance as yf -q\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn')\n",
        "from datetime import datetime"
      ],
      "metadata": {
        "id": "3-JvQeDEwuu1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AwDUrkSkwaD_",
        "outputId": "06e7ebc5-6dc8-441a-ff8d-7358ac294d3b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Descriptive Statistics of the Market's daily percentage returns:\n",
            "count    7554.000000\n",
            "mean        0.043484\n",
            "std         1.192538\n",
            "min       -10.942388\n",
            "25%        -0.448834\n",
            "50%         0.066914\n",
            "75%         0.595516\n",
            "max        14.519702\n",
            "Name: market, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Descriptive Statistics of the Apple's daily percentage returns:\n",
            "count    7554.000000\n",
            "mean        0.116955\n",
            "std         2.741895\n",
            "min       -51.869208\n",
            "25%        -1.185816\n",
            "50%         0.050958\n",
            "75%         1.391322\n",
            "max        33.228032\n",
            "Name: stock, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Descriptive Statistics of the 10 year note daily percentage returns:\n",
            "count    7533.000000\n",
            "mean        0.004218\n",
            "std         0.001073\n",
            "min         0.001125\n",
            "25%         0.003379\n",
            "50%         0.004390\n",
            "75%         0.005092\n",
            "max         0.006129\n",
            "Name: riskfree, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "According to AAPL's Market Model, the security had a realized Alpha of 0.07% and Beta of 1.11\n"
          ]
        }
      ],
      "source": [
        "# Import financial data\n",
        "start = datetime(1993, 3, 1)\n",
        "end = datetime(2023, 3, 1)\n",
        "\n",
        "# S&P 500 index is a proxy for the market\n",
        "market = yf.Ticker('SPY').history(start=start, end=end)\n",
        "# Ticker symbol for Apple, the most liquid stock in the world\n",
        "stock = yf.Ticker('AAPL').history(start=start, end=end)\n",
        "# 10 year US treasury note is the proxy for risk free rate\n",
        "riskfree_rate = yf.Ticker('^TNX').history(start=start, end=end)\n",
        "# Create dataframe to hold daily returns of securities\n",
        "daily_returns = pd.DataFrame()\n",
        "daily_returns['market'] = market['Close'].pct_change(1)*100\n",
        "daily_returns['stock'] = stock['Close'].pct_change(1)*100\n",
        "# Compounded daily rate based on 360 days for the calendar year used in the bond market\n",
        "daily_returns['riskfree'] = (1 + riskfree_rate['Close']) ** (1/360) - 1\n",
        "# Plot and summarize the distribution of daily returns\n",
        "plt.hist(daily_returns['market']), plt.title('Distribution of Market (SPY) Daily Returns'), plt.xlabel('Daily Percentage Returns'), plt.ylabel('Frequency'), plt.show()\n",
        "# Analyze descriptive statistics\n",
        "print(\"Descriptive Statistics of the Market's daily percentage returns:\\n{}\".format(daily_returns['market'].describe()))\n",
        "\n",
        "plt.hist(daily_returns['stock']), plt.title('Distribution of Apple Inc. (AAPL) Daily Returns'), plt.xlabel('Daily Percentage Returns'), plt.ylabel('Frequency'), plt.show()\n",
        "# Analyze descriptive statistics\n",
        "print(\"Descriptive Statistics of the Apple's daily percentage returns:\\n{}\".format(daily_returns['stock'].describe()))\n",
        "\n",
        "plt.hist(daily_returns['riskfree']), plt.title('Distribution of the riskfree rate (TNX) Daily Returns'), plt.xlabel('Daily Percentage Returns'), plt.ylabel('Frequency'), plt.show()\n",
        "# Analyze descriptive statistics\n",
        "print(\"Descriptive Statistics of the 10 year note daily percentage returns:\\n{}\".format(daily_returns['riskfree'].describe()))\n",
        "# Examine missing rows in the dataframe\n",
        "market.index.difference(riskfree_rate.index)\n",
        "# Fill rows with previous day's risk-free rate since daily rates are generally stable\n",
        "daily_returns = daily_returns.ffill()\n",
        "# Drop NaNs in first row because of percentage calculations\n",
        "daily_returns = daily_returns.dropna()\n",
        "# Check dataframe for null values\n",
        "daily_returns.isnull().sum()\n",
        "# Check first five rows of dataframe\n",
        "daily_returns.head()\n",
        "# AAPL's Market Model based on daily excess returns\n",
        "\n",
        "# Daily excess returns of AAPL\n",
        "y = daily_returns['stock'] - daily_returns['riskfree']\n",
        "# Daily excess returns of the market\n",
        "x = daily_returns['market'] - daily_returns['riskfree']\n",
        "\n",
        "# Plot the data\n",
        "plt.scatter(x,y)\n",
        "\n",
        "# Add the constant vector to obtain the intecept\n",
        "x = sm.add_constant(x)\n",
        "\n",
        "# Use Ordinary Least Squares algorithm to find the line of best fit\n",
        "market_model = sm.OLS(y, x).fit()\n",
        "\n",
        "# Plot the line of best fit\n",
        "plt.plot(x, x*market_model.params[0]+market_model.params['const'])\n",
        "plt.title('Market Model of AAPL'), plt.xlabel('SPY Daily Excess Returns'), plt.ylabel('AAPL Daily Excess Returns'), plt.show();\n",
        "\n",
        "# Display the values of alpha and beta of AAPL's market model\n",
        "print(\"According to AAPL's Market Model, the security had a realized Alpha of {0}% and Beta of {1}\".format(round(market_model.params['const'],2), round(market_model.params[0],2)))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(market_model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arP6quhu4ErC",
        "outputId": "2a9b4537-bcd1-47fb-d757-0196a0ca8261"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.232\n",
            "Model:                            OLS   Adj. R-squared:                  0.232\n",
            "Method:                 Least Squares   F-statistic:                     2276.\n",
            "Date:                Fri, 03 Mar 2023   Prob (F-statistic):               0.00\n",
            "Time:                        06:22:30   Log-Likelihood:                -17342.\n",
            "No. Observations:                7554   AIC:                         3.469e+04\n",
            "Df Residuals:                    7552   BIC:                         3.470e+04\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          0.0693      0.028      2.504      0.012       0.015       0.124\n",
            "0              1.1065      0.023     47.711      0.000       1.061       1.152\n",
            "==============================================================================\n",
            "Omnibus:                     2556.917   Durbin-Watson:                   1.993\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           475674.878\n",
            "Skew:                          -0.429   Prob(JB):                         0.00\n",
            "Kurtosis:                      41.866   Cond. No.                         1.20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    }
  ]
}