CAPM_analysis_.ipynb

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      "name": "python"
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/jidemaestri/Financial-Markets/blob/master/CAPM_analysis_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "<h2> Setup <h2>"
      ],
      "metadata": {
        "id": "Zsi1Djw9C6QW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance"
      ],
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        },
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        "outputId": "6dc7399d-d9ea-4c32-cc9c-1e20d21cbb4e"
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      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Successfully installed appdirs-1.4.4 cryptography-39.0.2 frozendict-2.3.5 yfinance-0.2.12\n"
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      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ICmkB_2N0Dr8"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import yfinance as yf\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MONTHS_PER_YEAR = 12\n",
        "RISK_FREE_RATE = 0.05"
      ],
      "metadata": {
        "id": "UFEYcWe2-Y8U"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2> Python Class  - only set for the first stock related to SP500 <h2>"
      ],
      "metadata": {
        "id": "jcDjF1g7C-Tt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CAPM: \n",
        "  def __init__(self, stocks, start_date, end_date):\n",
        "    self.stocks = stocks\n",
        "    self.data = None\n",
        "    self.start_date = start_date\n",
        "    self.end_date = end_date\n",
        "\n",
        "  def download_data(self):\n",
        "    data = {}\n",
        "    \n",
        "    for stock in self.stocks:\n",
        "      ticker = yf.download(stock, self.start_date, self.end_date)\n",
        "      data[stock] = ticker[\"Adj Close\"]\n",
        "    return pd.DataFrame(data)\n",
        "\n",
        "  def initialize(self):\n",
        "      stock_data = self.download_data()\n",
        "      stocks_data = stock_data.resample(\"M\").last() #using monthly returns\n",
        "      print(stocks_data)\n",
        "\n",
        "      self.data = pd.DataFrame({\"stock_adj_close\": stocks_data[self.stocks[0]],\n",
        "                                'market_adj_close': stocks_data[self.stocks[1]] })\n",
        "      \n",
        "      #calculating logarithmic monthly return\n",
        "      \n",
        "      self.data[[\"stock_returns\", \"market_returns\"]] = np.log(self.data[[\"stock_adj_close\", \"market_adj_close\"]]/\n",
        "                                                              self.data[[\"stock_adj_close\", \"market_adj_close\"]].shift(1))\n",
        "      \n",
        "      self.data = self.data[1:]\n",
        "      print(self.data)\n",
        "  \n",
        "  def calculate_beta(self):\n",
        "    #covariance matrix\n",
        "    covariance_matrix = np.cov(self.data[\"stock_returns\"], self.data[\"market_returns\"])\n",
        "    #calculating beta\n",
        "    beta = covariance_matrix[0,1] / covariance_matrix[1,1]\n",
        "    #\"1\" is the sp500, so [0,1] is equal to the covariance between the stock and the market, and [1,1] is the covariance of the market itself\n",
        "    print(\"Beta is: \", beta)\n",
        "  \n",
        "  def regression(self):\n",
        "  #using linear regression - beta is the slope\n",
        "    beta, alpha = np.polyfit(self.data['market_returns'], self.data[\"stock_returns\"], deg=1)\n",
        "    print(\"Beta for the regression: \", beta)\n",
        "    expected_return = RISK_FREE_RATE + beta * (self.data[\"market_returns\"].mean()*MONTHS_PER_YEAR - RISK_FREE_RATE)\n",
        "    print(\"Expected return: \", expected_return)\n",
        "    self.plot_regression(alpha, beta)\n",
        "\n",
        "  def plot_regression(self, alpha, beta):\n",
        "        fig, axis = plt.subplots(1, figsize=(20, 10))\n",
        "        axis.scatter(self.data[\"market_returns\"], self.data['stock_returns'],\n",
        "                     label=\"Data Points\")\n",
        "        axis.plot(self.data[\"market_returns\"], beta * self.data[\"market_returns\"] + alpha,\n",
        "                  color='red', label=\"CAPM Line\")\n",
        "        plt.title('Capital Asset Pricing Model, finding alpha and beta')\n",
        "        plt.xlabel('Market return $R_m$', fontsize=18)\n",
        "        plt.ylabel('Stock return $R_a$')\n",
        "        plt.text(0.08, 0.05, r'$R_a = \\beta * R_m + \\alpha$', fontsize=18)\n",
        "        plt.legend()\n",
        "        plt.grid(True)\n",
        "        plt.show()\n",
        "  "
      ],
      "metadata": {
        "id": "YUwvKVXW0OuX"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Python class is only set for the first stock related to SP500"
      ],
      "metadata": {
        "id": "nuzRUhx9Ecbd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For long-term models, monthly returns are favourable. The main benefit is that returns are at least approximately normally distributed. Daily returns are better for short-term tactical forecasting."
      ],
      "metadata": {
        "id": "_7SgE3y26U3j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "  if __name__ == \"__main__\":\n",
        "\n",
        "    capm = CAPM([\"AAPL\", \"^GSPC\"],\"1995-01-01\", \"2023-03-20\")\n",
        "    capm.initialize()\n",
        "    capm.calculate_beta()\n",
        "    capm.regression()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WHZebggs075F",
        "outputId": "bc86281f-11a0-4e41-da86-ebfcdd8d3b07"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "                  AAPL        ^GSPC\n",
            "Date                               \n",
            "1995-01-31    0.303357   470.420013\n",
            "1995-02-28    0.297600   487.390015\n",
            "1995-03-31    0.265579   500.709991\n",
            "1995-04-30    0.288182   514.710022\n",
            "1995-05-31    0.314007   533.400024\n",
            "...                ...          ...\n",
            "2022-11-30  147.804321  4080.110107\n",
            "2022-12-31  129.731918  3839.500000\n",
            "2023-01-31  144.070023  4076.600098\n",
            "2023-02-28  147.410004  3970.149902\n",
            "2023-03-31  155.000000  3916.639893\n",
            "\n",
            "[339 rows x 2 columns]\n",
            "            stock_adj_close  market_adj_close  stock_returns  market_returns\n",
            "Date                                                                        \n",
            "1995-02-28         0.297600        487.390015      -0.019163        0.035439\n",
            "1995-03-31         0.265579        500.709991      -0.113837        0.026962\n",
            "1995-04-30         0.288182        514.710022       0.081679        0.027577\n",
            "1995-05-31         0.314007        533.400024       0.085824        0.035668\n",
            "1995-06-30         0.350838        544.750000       0.110910        0.021055\n",
            "...                     ...               ...            ...             ...\n",
            "2022-11-30       147.804321       4080.110107      -0.033585        0.052358\n",
            "2022-12-31       129.731918       3839.500000      -0.130419       -0.060782\n",
            "2023-01-31       144.070023       4076.600098       0.104829        0.059921\n",
            "2023-02-28       147.410004       3970.149902       0.022918       -0.026459\n",
            "2023-03-31       155.000000       3916.639893       0.050207       -0.013570\n",
            "\n",
            "[338 rows x 4 columns]\n",
            "Beta is:  1.2306684574129603\n",
            "Beta for the regression:  1.2306684574129614\n",
            "Expected return:  0.08106659200308798\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "beta = Covariance(stock, market) / Variance(market)\n",
        "\n",
        "How risky is your portfolio related to the market.\n",
        "\n",
        "* β = 1 > stock moving exactly with the market <h5>\n",
        "* β > 1 > stock market risk is higher than that of an average stock <h5>\n",
        "* β < 1 > stock market risk is lower than that of an average stock <h5>"
      ],
      "metadata": {
        "id": "SullsHkUAFDS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "  if __name__ == \"__main__\":\n",
        "\n",
        "    capm = CAPM([\"JPM\", \"^GSPC\"],\"2010-01-01\", \"2023-03-20\")\n",
        "    capm.initialize()\n",
        "    capm.calculate_beta()\n",
        "    capm.regression()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K6_40LrhBTc_",
        "outputId": "33d53e07-7654-4529-9b99-43deba567f09"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "                   JPM        ^GSPC\n",
            "Date                               \n",
            "2010-01-31   27.732588  1073.869995\n",
            "2010-02-28   29.890528  1104.489990\n",
            "2010-03-31   31.870403  1169.430054\n",
            "2010-04-30   30.358879  1186.689941\n",
            "2010-05-31   28.219931  1089.410034\n",
            "...                ...          ...\n",
            "2022-11-30  137.166794  4080.110107\n",
            "2022-12-31  133.116730  3839.500000\n",
            "2023-01-31  139.960007  4076.600098\n",
            "2023-02-28  143.350006  3970.149902\n",
            "2023-03-31  125.809998  3916.639893\n",
            "\n",
            "[159 rows x 2 columns]\n",
            "            stock_adj_close  market_adj_close  stock_returns  market_returns\n",
            "Date                                                                        \n",
            "2010-02-28        29.890528       1104.489990       0.074933        0.028115\n",
            "2010-03-31        31.870403       1169.430054       0.064136        0.057133\n",
            "2010-04-30        30.358879       1186.689941      -0.048589        0.014651\n",
            "2010-05-31        28.219931       1089.410034      -0.073061       -0.085532\n",
            "2010-06-30        26.102367       1030.709961      -0.078002       -0.055388\n",
            "...                     ...               ...            ...             ...\n",
            "2022-11-30       137.166794       4080.110107       0.093228        0.052358\n",
            "2022-12-31       133.116730       3839.500000      -0.029971       -0.060782\n",
            "2023-01-31       139.960007       4076.600098       0.050130        0.059921\n",
            "2023-02-28       143.350006       3970.149902       0.023933       -0.026459\n",
            "2023-03-31       125.809998       3916.639893      -0.130516       -0.013570\n",
            "\n",
            "[158 rows x 4 columns]\n",
            "Beta is:  1.3002711473817683\n",
            "Beta for the regression:  1.3002711473817692\n",
            "Expected return:  0.11277167672364069\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2> Python class for multiple stocks <h2>\n",
        "<h5> not for portfolios - calculate beta for portfolios with weights <h5>"
      ],
      "metadata": {
        "id": "miZMOi32EMw2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CAPM_port:\n",
        "\n",
        "  def __init__(self, stocks, start_date, end_date):\n",
        "    self.stocks = stocks\n",
        "    self.data = None\n",
        "    self.start_date = start_date\n",
        "    self.end_date = end_date\n",
        "\n",
        "  def download_data(self):\n",
        "    data = {}\n",
        "    \n",
        "    for stock in self.stocks:\n",
        "      ticker = yf.download(stock, self.start_date, self.end_date)\n",
        "      data[stock] = ticker[\"Adj Close\"]\n",
        "      \n",
        "    return pd.DataFrame(data)\n",
        "\n",
        "  def initialize(self):\n",
        "    stock_data = self.download_data()\n",
        "    stocks_data = stock_data.resample(\"M\").last() #using monthly returns\n",
        "\n",
        "    #calculating logarithmic monthly return for each stock in the portfolio in relation to the market\n",
        "    stock_returns = pd.DataFrame()\n",
        "    for stock in self.stocks:\n",
        "      stock_returns[stock] = np.log(stocks_data[stock]/stocks_data[stock].shift(1))\n",
        "    market_returns = np.log(stocks_data[self.stocks[0]]/stocks_data[self.stocks[0]].shift(1))\n",
        "\n",
        "    #creating a dataframe with stock and market returns\n",
        "    self.data = pd.concat([stock_returns, market_returns], axis=1)\n",
        "    self.data.columns = self.stocks + [\"market\"]\n",
        "\n",
        "    self.data = self.data.dropna()\n",
        "  \n",
        "  def calculate_beta(self):\n",
        "    #covariance matrix\n",
        "    covariance_matrix = np.cov(self.data.T)\n",
        "    beta_values = {}\n",
        "\n",
        "    #calculating beta for each stock in the portfolio\n",
        "    for stock in self.stocks:\n",
        "      beta = covariance_matrix[self.stocks.index(stock),-1] / covariance_matrix[-1,-1]\n",
        "      beta_values[stock] = beta\n",
        "      \n",
        "    print(\"Beta values are: \", beta_values)"
      ],
      "metadata": {
        "id": "gOD60Z3aDWae"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gfKJ_vUuFBoG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "  if __name__ == \"__main__\":\n",
        "\n",
        "    capm = CAPM_port([\"^GSPC\", \"AAPL\", \"MSFT\", \"JPM\", \"PEP\", \"KO\", \"MCD\", \"WMT\", \"VALE\", \"PBR\", \"BMA\"],\"2000-01-01\", \"2023-03-20\")\n",
        "    capm.initialize()\n",
        "    capm.calculate_beta()\n",
        "   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "073568fb-3fff-454d-e385-44b7fd66b78b",
        "id": "QQW0ngNtFF0A"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Beta values are:  {'^GSPC': 1.0, 'AAPL': 1.2172273152308761, 'MSFT': 0.9745068261644186, 'JPM': 1.2465972587668679, 'PEP': 0.5320076821397155, 'KO': 0.5559651811709064, 'MCD': 0.5420167164813671, 'WMT': 0.39083980642145005, 'VALE': 1.2709159068161644, 'PBR': 1.615778211141982, 'BMA': 1.4945828538580441}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "boV3UXPsF_4K"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}