Frank_Howd_Financial_Adages.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Frank_Howd_DS23_Unit1_Project_Public.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7z_XRehJO7-T"
      },
      "source": [
        "#*An Examination of Some Financial Adages*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UtwFN59oPVkn"
      },
      "source": [
        "###*First Steps:*\n",
        "\n",
        "Import and wrangle data\n",
        "\n",
        "*All data has been sourced from the [Federal Reserve Bank of St. Louis](https://fred.stlouisfed.org/).  Three .csv files were utilized in the creation of this project. FRED only provides 10-years worth of data for the S&P 500 index, so I used this same 10-year period for all variables examined.  I worked with monthly data for everything but corporate profits, as these are released quarterly.  This forced me to wrangle, and decrease the size of my data set to 39 quarterly data points for all of the variables examined.  Enough to be statistically significant, but by no way encompassing.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgBXIgDxPX0F"
      },
      "source": [
        "# Grab and clean data.\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from statsmodels.formula.api import ols\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jV3zFifSPpXt",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "3cf9bfc0-ce11-4b7a-f7f2-0feb29eb7c12"
      },
      "source": [
        "from google.colab import files\n",
        "files = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ce02243c-4d14-4980-a846-1eb109befc17\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ce02243c-4d14-4980-a846-1eb109befc17\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving SP500_M2_monthly.csv to SP500_M2_monthly (3).csv\n",
            "Saving SP500_CorpProfits_Quarterly.csv to SP500_CorpProfits_Quarterly (3).csv\n",
            "Saving SP500_10year_monthly.csv to SP500_10year_monthly (3).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "jauBq_9HPu2f",
        "outputId": "6bd37b07-af7d-4618-8091-954649fc7930"
      },
      "source": [
        "# Read in montly data points from the S&P 500 Index, and for the 10-year Treasury.\n",
        "\n",
        "SP500_10year_df = pd.read_csv('SP500_10year_monthly.csv')\n",
        "\n",
        "# print out the shape and head of the data.\n",
        "\n",
        "print(SP500_10year_df.shape)\n",
        "display(SP500_10year_df.head(10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(121, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>DATE</th>\n",
              "      <th>SP500</th>\n",
              "      <th>DGS10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-11-01</td>\n",
              "      <td>.</td>\n",
              "      <td>2.763000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-12-01</td>\n",
              "      <td>.</td>\n",
              "      <td>3.290909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.6185000000000000</td>\n",
              "      <td>3.394000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>1321.1178947368421053</td>\n",
              "      <td>3.576316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>1304.4860869565217391</td>\n",
              "      <td>3.414348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1331.5050000000000000</td>\n",
              "      <td>3.455000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>1338.3104761904761905</td>\n",
              "      <td>3.168571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2011-06-01</td>\n",
              "      <td>1287.2886363636363636</td>\n",
              "      <td>3.002273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2011-07-01</td>\n",
              "      <td>1325.1845000000000000</td>\n",
              "      <td>3.003000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2011-08-01</td>\n",
              "      <td>1185.3056521739130435</td>\n",
              "      <td>2.303043</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE                  SP500     DGS10\n",
              "0  2010-11-01                      .  2.763000\n",
              "1  2010-12-01                      .  3.290909\n",
              "2  2011-01-01  1282.6185000000000000  3.394000\n",
              "3  2011-02-01  1321.1178947368421053  3.576316\n",
              "4  2011-03-01  1304.4860869565217391  3.414348\n",
              "5  2011-04-01  1331.5050000000000000  3.455000\n",
              "6  2011-05-01  1338.3104761904761905  3.168571\n",
              "7  2011-06-01  1287.2886363636363636  3.002273\n",
              "8  2011-07-01  1325.1845000000000000  3.003000\n",
              "9  2011-08-01  1185.3056521739130435  2.303043"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcoSSPs6Ql6O",
        "outputId": "6dd6f1e2-b96a-41b0-8982-c530172a1b98"
      },
      "source": [
        "# Examine the data for any NaN / Na values.\n",
        "\n",
        "SP500_10year_df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DATE     0\n",
              "SP500    0\n",
              "DGS10    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALoHA_GXQztv",
        "outputId": "ca83b591-ab3a-47b5-90c8-38da9560e5cd"
      },
      "source": [
        "# Ensure there are no spaces on either side of the feature names to be stripped.\n",
        "\n",
        "cols = list(SP500_10year_df.columns)\n",
        "print(cols, \"\\n\")\n",
        "\n",
        "# Probe data for types of objects stored.\n",
        "\n",
        "print(SP500_10year_df.dtypes, \"\\n\")\n",
        "\n",
        "# Closer examination of an entry in the 'DATE' feature/variable.\n",
        "\n",
        "print(type(SP500_10year_df.loc[6, 'DATE']), end=' ')\n",
        "\n",
        "# Closer examination of an entry in the 'SP500' feature/variable.\n",
        "\n",
        "print(type(SP500_10year_df.loc[6, 'SP500']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['DATE', 'SP500', 'DGS10'] \n",
            "\n",
            "DATE      object\n",
            "SP500     object\n",
            "DGS10    float64\n",
            "dtype: object \n",
            "\n",
            "<class 'str'> <class 'str'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "nLhPoOJYSb-4",
        "outputId": "a218b528-efa6-4ece-fefa-3129b0197415"
      },
      "source": [
        "# Slice off the first two entries; they have no values for the 'SP500' feature.\n",
        "\n",
        "SP500_10year_df = SP500_10year_df[2:]\n",
        "\n",
        "print(SP500_10year_df.shape)\n",
        "display(SP500_10year_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(119, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>DATE</th>\n",
              "      <th>SP500</th>\n",
              "      <th>DGS10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.6185000000000000</td>\n",
              "      <td>3.394000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>1321.1178947368421053</td>\n",
              "      <td>3.576316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>1304.4860869565217391</td>\n",
              "      <td>3.414348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1331.5050000000000000</td>\n",
              "      <td>3.455000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>1338.3104761904761905</td>\n",
              "      <td>3.168571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE                  SP500     DGS10\n",
              "2  2011-01-01  1282.6185000000000000  3.394000\n",
              "3  2011-02-01  1321.1178947368421053  3.576316\n",
              "4  2011-03-01  1304.4860869565217391  3.414348\n",
              "5  2011-04-01  1331.5050000000000000  3.455000\n",
              "6  2011-05-01  1338.3104761904761905  3.168571"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "YTLvlwgrTbb1",
        "outputId": "92dcb46c-4283-451d-e9c7-ef7dbcc8dd07"
      },
      "source": [
        "# Reset the index.\n",
        "\n",
        "SP500_10year_df.reset_index(drop=True, inplace=True)\n",
        "\n",
        "print(SP500_10year_df.shape)\n",
        "display(SP500_10year_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(119, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>DATE</th>\n",
              "      <th>SP500</th>\n",
              "      <th>DGS10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.6185000000000000</td>\n",
              "      <td>3.394000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>1321.1178947368421053</td>\n",
              "      <td>3.576316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>1304.4860869565217391</td>\n",
              "      <td>3.414348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1331.5050000000000000</td>\n",
              "      <td>3.455000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>1338.3104761904761905</td>\n",
              "      <td>3.168571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE                  SP500     DGS10\n",
              "0  2011-01-01  1282.6185000000000000  3.394000\n",
              "1  2011-02-01  1321.1178947368421053  3.576316\n",
              "2  2011-03-01  1304.4860869565217391  3.414348\n",
              "3  2011-04-01  1331.5050000000000000  3.455000\n",
              "4  2011-05-01  1338.3104761904761905  3.168571"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "hFFCUEVfTpqe",
        "outputId": "5c326833-eda9-4ef4-cb68-849279d9c9ba"
      },
      "source": [
        "display(SP500_10year_df.tail())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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>DATE</th>\n",
              "      <th>SP500</th>\n",
              "      <th>DGS10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>3207.6190909090909091</td>\n",
              "      <td>0.623636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>2020-08-01</td>\n",
              "      <td>3391.7100000000000000</td>\n",
              "      <td>0.650000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>3365.5166666666666667</td>\n",
              "      <td>0.679524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>3418.7013636363636364</td>\n",
              "      <td>0.787143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>2020-11-01</td>\n",
              "      <td>3548.9925000000000000</td>\n",
              "      <td>0.870000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           DATE                  SP500     DGS10\n",
              "114  2020-07-01  3207.6190909090909091  0.623636\n",
              "115  2020-08-01  3391.7100000000000000  0.650000\n",
              "116  2020-09-01  3365.5166666666666667  0.679524\n",
              "117  2020-10-01  3418.7013636363636364  0.787143\n",
              "118  2020-11-01  3548.9925000000000000  0.870000"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "no-FKCZyTzOc",
        "outputId": "9317a110-983a-4bb9-b909-727b1410776d"
      },
      "source": [
        "# Read in the montly data points for the M2 money supply.\n",
        "\n",
        "M2_df = pd.read_csv('SP500_M2_monthly.csv')\n",
        "\n",
        "# print out the shape and head of the data.\n",
        "\n",
        "print(M2_df.shape)\n",
        "display(M2_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(121, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>DATE</th>\n",
              "      <th>SP500</th>\n",
              "      <th>M2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-11-01</td>\n",
              "      <td>.</td>\n",
              "      <td>8755.120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-12-01</td>\n",
              "      <td>.</td>\n",
              "      <td>8788.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.6185000000000000</td>\n",
              "      <td>8821.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>1321.1178947368421053</td>\n",
              "      <td>8871.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>1304.4860869565217391</td>\n",
              "      <td>8913.025</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE                  SP500        M2\n",
              "0  2010-11-01                      .  8755.120\n",
              "1  2010-12-01                      .  8788.950\n",
              "2  2011-01-01  1282.6185000000000000  8821.480\n",
              "3  2011-02-01  1321.1178947368421053  8871.375\n",
              "4  2011-03-01  1304.4860869565217391  8913.025"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Za7dCdRuUFO3",
        "outputId": "0255b1de-cff5-439a-8252-a715546e3b6e"
      },
      "source": [
        "# Examine for data types and NaN values.\n",
        "\n",
        "print(M2_df.info())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 121 entries, 0 to 120\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   DATE    121 non-null    object \n",
            " 1   SP500   121 non-null    object \n",
            " 2   M2      121 non-null    float64\n",
            "dtypes: float64(1), object(2)\n",
            "memory usage: 3.0+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0IvGW14UNmM",
        "outputId": "a0fbecde-c58d-45c2-8d7a-bd8fd06f7908"
      },
      "source": [
        "# Examine column names for spaces to strip on either side of name.\n",
        "\n",
        "cols = list(M2_df.columns)\n",
        "print(cols, \"\\n\")\n",
        "\n",
        "# Closer examination of the 'DATE', 'SP500', and 'M2' features/variables.\n",
        "\n",
        "print(type(M2_df.loc[2, 'DATE']), end=' ')\n",
        "print(type(M2_df.loc[2, 'SP500']), end=' ')\n",
        "print(type(M2_df.loc[2, 'M2']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['DATE', 'SP500', 'M2'] \n",
            "\n",
            "<class 'str'> <class 'str'> <class 'numpy.float64'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "wZdJVNVVUoNv",
        "outputId": "4bbdeb12-f591-4971-8554-33fd53fe281d"
      },
      "source": [
        "# Exclude the first two entries; they have no values for the 'SP500' feature.\n",
        "\n",
        "M2_df = M2_df[2:]\n",
        "\n",
        "print(M2_df.shape)\n",
        "display(M2_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(119, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>DATE</th>\n",
              "      <th>SP500</th>\n",
              "      <th>M2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.6185000000000000</td>\n",
              "      <td>8821.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>1321.1178947368421053</td>\n",
              "      <td>8871.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>1304.4860869565217391</td>\n",
              "      <td>8913.025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1331.5050000000000000</td>\n",
              "      <td>8966.725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>1338.3104761904761905</td>\n",
              "      <td>9027.200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE                  SP500        M2\n",
              "2  2011-01-01  1282.6185000000000000  8821.480\n",
              "3  2011-02-01  1321.1178947368421053  8871.375\n",
              "4  2011-03-01  1304.4860869565217391  8913.025\n",
              "5  2011-04-01  1331.5050000000000000  8966.725\n",
              "6  2011-05-01  1338.3104761904761905  9027.200"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "X5gVY7IdVarW",
        "outputId": "b2444403-8212-4e74-eb50-b7ca278e7667"
      },
      "source": [
        "# Merge SP500_10year_df with M2_df.\n",
        "\n",
        "SP500_10yr_M2 = pd.merge(SP500_10year_df, M2_df, how='left', on='SP500')\n",
        "\n",
        "print(SP500_10yr_M2.shape)\n",
        "display(SP500_10yr_M2.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(119, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>DATE_x</th>\n",
              "      <th>SP500</th>\n",
              "      <th>DGS10</th>\n",
              "      <th>DATE_y</th>\n",
              "      <th>M2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.6185000000000000</td>\n",
              "      <td>3.394000</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>8821.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>1321.1178947368421053</td>\n",
              "      <td>3.576316</td>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>8871.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>1304.4860869565217391</td>\n",
              "      <td>3.414348</td>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>8913.025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1331.5050000000000000</td>\n",
              "      <td>3.455000</td>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>8966.725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>1338.3104761904761905</td>\n",
              "      <td>3.168571</td>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>9027.200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       DATE_x                  SP500     DGS10      DATE_y        M2\n",
              "0  2011-01-01  1282.6185000000000000  3.394000  2011-01-01  8821.480\n",
              "1  2011-02-01  1321.1178947368421053  3.576316  2011-02-01  8871.375\n",
              "2  2011-03-01  1304.4860869565217391  3.414348  2011-03-01  8913.025\n",
              "3  2011-04-01  1331.5050000000000000  3.455000  2011-04-01  8966.725\n",
              "4  2011-05-01  1338.3104761904761905  3.168571  2011-05-01  9027.200"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dW7IFXgaVz5v"
      },
      "source": [
        "# Drop the duplicate 'DATE' column.\n",
        "\n",
        "SP500_10yr_M2.drop(['DATE_y'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "cH1BTELfWE3P",
        "outputId": "556901ef-aaeb-4eda-ffa2-be7ccf0c678e"
      },
      "source": [
        "SP500_10yr_M2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>DATE_x</th>\n",
              "      <th>SP500</th>\n",
              "      <th>DGS10</th>\n",
              "      <th>M2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.6185000000000000</td>\n",
              "      <td>3.394000</td>\n",
              "      <td>8821.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>1321.1178947368421053</td>\n",
              "      <td>3.576316</td>\n",
              "      <td>8871.375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>1304.4860869565217391</td>\n",
              "      <td>3.414348</td>\n",
              "      <td>8913.025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1331.5050000000000000</td>\n",
              "      <td>3.455000</td>\n",
              "      <td>8966.725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>1338.3104761904761905</td>\n",
              "      <td>3.168571</td>\n",
              "      <td>9027.200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       DATE_x                  SP500     DGS10        M2\n",
              "0  2011-01-01  1282.6185000000000000  3.394000  8821.480\n",
              "1  2011-02-01  1321.1178947368421053  3.576316  8871.375\n",
              "2  2011-03-01  1304.4860869565217391  3.414348  8913.025\n",
              "3  2011-04-01  1331.5050000000000000  3.455000  8966.725\n",
              "4  2011-05-01  1338.3104761904761905  3.168571  9027.200"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lddw5RGrWIQ-",
        "outputId": "e19f3d34-497a-4fe7-a631-57776804e205"
      },
      "source": [
        "# Rename 'DATE_x' column.\n",
        "\n",
        "SP500_10yr_M2.rename(columns = {'DATE_x': 'DATE'}, inplace=True)\n",
        "\n",
        "print(SP500_10yr_M2.columns)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['DATE', 'SP500', 'DGS10', 'M2'], dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "MI7YmdvCWbu2",
        "outputId": "20f14dfa-2a62-4bae-87b9-64e940d9cf34"
      },
      "source": [
        "# Read in quarterly corporate profit data.\n",
        "\n",
        "corp_profits_df = pd.read_csv('SP500_CorpProfits_Quarterly.csv')\n",
        "\n",
        "print(corp_profits_df.shape, \"\\n\")\n",
        "display(corp_profits_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(40, 3) \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>DATE</th>\n",
              "      <th>CP</th>\n",
              "      <th>CPATAX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-10-01</td>\n",
              "      <td>1599.159</td>\n",
              "      <td>1537.504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1464.223</td>\n",
              "      <td>1385.233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1529.084</td>\n",
              "      <td>1506.637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-07-01</td>\n",
              "      <td>1539.465</td>\n",
              "      <td>1562.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-10-01</td>\n",
              "      <td>1617.095</td>\n",
              "      <td>1662.068</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE        CP    CPATAX\n",
              "0  2010-10-01  1599.159  1537.504\n",
              "1  2011-01-01  1464.223  1385.233\n",
              "2  2011-04-01  1529.084  1506.637\n",
              "3  2011-07-01  1539.465  1562.105\n",
              "4  2011-10-01  1617.095  1662.068"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VkhAaVuCXBGq"
      },
      "source": [
        "# Drop pre-tax corporate profit column.  We are interested in after-tax profits.\n",
        "\n",
        "corp_profits_df.drop(['CP'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "7_wHK5_eXc5V",
        "outputId": "1910c22d-7ced-4359-a5eb-375ac522572f"
      },
      "source": [
        "corp_profits_df.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>DATE</th>\n",
              "      <th>CPATAX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-10-01</td>\n",
              "      <td>1537.504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1385.233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1506.637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-07-01</td>\n",
              "      <td>1562.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-10-01</td>\n",
              "      <td>1662.068</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE    CPATAX\n",
              "0  2010-10-01  1537.504\n",
              "1  2011-01-01  1385.233\n",
              "2  2011-04-01  1506.637\n",
              "3  2011-07-01  1562.105\n",
              "4  2011-10-01  1662.068"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvrMLIREXe7h"
      },
      "source": [
        "# Merge SP500_10year_M2 with corp_profits_df.  Corporate profits are generated \n",
        "# quarterly, and are indicated less frequently than the data for the index,\n",
        "# for interest rates, or for the money supply.  The merge is completed with \n",
        "# how='inner' making quarterly data points what's available for all features. \n",
        "\n",
        "SP_10yr_M2_CP = pd.merge(SP500_10yr_M2, corp_profits_df, how='inner', \n",
        "                            on='DATE')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "Y5jW9ajaYJ7W",
        "outputId": "15394ada-39dd-421b-b48b-2eedaf1e46af"
      },
      "source": [
        "SP_10yr_M2_CP.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>DATE</th>\n",
              "      <th>SP500</th>\n",
              "      <th>DGS10</th>\n",
              "      <th>M2</th>\n",
              "      <th>CPATAX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.6185000000000000</td>\n",
              "      <td>3.3940</td>\n",
              "      <td>8821.480</td>\n",
              "      <td>1385.233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1331.5050000000000000</td>\n",
              "      <td>3.4550</td>\n",
              "      <td>8966.725</td>\n",
              "      <td>1506.637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-07-01</td>\n",
              "      <td>1325.1845000000000000</td>\n",
              "      <td>3.0030</td>\n",
              "      <td>9261.100</td>\n",
              "      <td>1562.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-10-01</td>\n",
              "      <td>1207.2161904761904762</td>\n",
              "      <td>2.1520</td>\n",
              "      <td>9570.400</td>\n",
              "      <td>1662.068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-01</td>\n",
              "      <td>1300.5775000000000000</td>\n",
              "      <td>1.9665</td>\n",
              "      <td>9727.720</td>\n",
              "      <td>1705.692</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE                  SP500   DGS10        M2    CPATAX\n",
              "0  2011-01-01  1282.6185000000000000  3.3940  8821.480  1385.233\n",
              "1  2011-04-01  1331.5050000000000000  3.4550  8966.725  1506.637\n",
              "2  2011-07-01  1325.1845000000000000  3.0030  9261.100  1562.105\n",
              "3  2011-10-01  1207.2161904761904762  2.1520  9570.400  1662.068\n",
              "4  2012-01-01  1300.5775000000000000  1.9665  9727.720  1705.692"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEM_MoD_YN_2",
        "outputId": "27d75645-beec-4ddd-d44f-cfa484b5bfd5"
      },
      "source": [
        "# Examine the data types for the new dataframe.\n",
        "\n",
        "print(SP_10yr_M2_CP.dtypes, \"\\n\")\n",
        "print(type(SP_10yr_M2_CP.loc[0, 'DATE']), end=' ')\n",
        "print(type(SP_10yr_M2_CP.loc[0, 'SP500']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DATE       object\n",
            "SP500      object\n",
            "DGS10     float64\n",
            "M2        float64\n",
            "CPATAX    float64\n",
            "dtype: object \n",
            "\n",
            "<class 'str'> <class 'str'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9xVrMTkPYQ6O"
      },
      "source": [
        "# Transform 'DATE' (string object) to a datetime object.\n",
        "\n",
        "SP_10yr_M2_CP['DATE'] = pd.to_datetime(SP_10yr_M2_CP['DATE'], infer_datetime_format=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKhcm3lXYo6Y",
        "outputId": "426fc02f-318d-451c-f5e8-4951147d7a92"
      },
      "source": [
        "# Verify 'DATE' is nw a datetime object.\n",
        "\n",
        "print(SP_10yr_M2_CP.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DATE      datetime64[ns]\n",
            "SP500             object\n",
            "DGS10            float64\n",
            "M2               float64\n",
            "CPATAX           float64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2DrhesMYu61"
      },
      "source": [
        "\n",
        "# Change 'SP500' instances from strings to floats.\n",
        "\n",
        "SP_10yr_M2_CP['SP500'] = SP_10yr_M2_CP['SP500'].apply(lambda x: float(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "FkhIDDbTY8Zw",
        "outputId": "0352fec8-7170-4695-ddbd-7179e6e0fb6f"
      },
      "source": [
        "# Verify changes.\n",
        "\n",
        "print(SP_10yr_M2_CP.dtypes, \"\\n\")\n",
        "print(SP_10yr_M2_CP.shape, \"\\n\")\n",
        "display(SP_10yr_M2_CP.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DATE      datetime64[ns]\n",
            "SP500            float64\n",
            "DGS10            float64\n",
            "M2               float64\n",
            "CPATAX           float64\n",
            "dtype: object \n",
            "\n",
            "(39, 5) \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>DATE</th>\n",
              "      <th>SP500</th>\n",
              "      <th>DGS10</th>\n",
              "      <th>M2</th>\n",
              "      <th>CPATAX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.61850</td>\n",
              "      <td>3.3940</td>\n",
              "      <td>8821.480</td>\n",
              "      <td>1385.233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1331.50500</td>\n",
              "      <td>3.4550</td>\n",
              "      <td>8966.725</td>\n",
              "      <td>1506.637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-07-01</td>\n",
              "      <td>1325.18450</td>\n",
              "      <td>3.0030</td>\n",
              "      <td>9261.100</td>\n",
              "      <td>1562.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-10-01</td>\n",
              "      <td>1207.21619</td>\n",
              "      <td>2.1520</td>\n",
              "      <td>9570.400</td>\n",
              "      <td>1662.068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-01</td>\n",
              "      <td>1300.57750</td>\n",
              "      <td>1.9665</td>\n",
              "      <td>9727.720</td>\n",
              "      <td>1705.692</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        DATE       SP500   DGS10        M2    CPATAX\n",
              "0 2011-01-01  1282.61850  3.3940  8821.480  1385.233\n",
              "1 2011-04-01  1331.50500  3.4550  8966.725  1506.637\n",
              "2 2011-07-01  1325.18450  3.0030  9261.100  1562.105\n",
              "3 2011-10-01  1207.21619  2.1520  9570.400  1662.068\n",
              "4 2012-01-01  1300.57750  1.9665  9727.720  1705.692"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_3JVlDJZOmI"
      },
      "source": [
        "# Truncate decimals to 1000ths.\n",
        "\n",
        "cols = ['SP500', 'DGS10']\n",
        "\n",
        "for col in cols:\n",
        "    SP_10yr_M2_CP[col] = SP_10yr_M2_CP[col].apply(lambda x: round(x, 3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "vH3joUeMZkhJ",
        "outputId": "56d29444-5328-44d0-93eb-613754309a41"
      },
      "source": [
        "# Verify changes.\n",
        "\n",
        "SP_10yr_M2_CP.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>DATE</th>\n",
              "      <th>SP500</th>\n",
              "      <th>DGS10</th>\n",
              "      <th>M2</th>\n",
              "      <th>CPATAX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1282.619</td>\n",
              "      <td>3.394</td>\n",
              "      <td>8821.480</td>\n",
              "      <td>1385.233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1331.505</td>\n",
              "      <td>3.455</td>\n",
              "      <td>8966.725</td>\n",
              "      <td>1506.637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-07-01</td>\n",
              "      <td>1325.185</td>\n",
              "      <td>3.003</td>\n",
              "      <td>9261.100</td>\n",
              "      <td>1562.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-10-01</td>\n",
              "      <td>1207.216</td>\n",
              "      <td>2.152</td>\n",
              "      <td>9570.400</td>\n",
              "      <td>1662.068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-01</td>\n",
              "      <td>1300.578</td>\n",
              "      <td>1.966</td>\n",
              "      <td>9727.720</td>\n",
              "      <td>1705.692</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        DATE     SP500  DGS10        M2    CPATAX\n",
              "0 2011-01-01  1282.619  3.394  8821.480  1385.233\n",
              "1 2011-04-01  1331.505  3.455  8966.725  1506.637\n",
              "2 2011-07-01  1325.185  3.003  9261.100  1562.105\n",
              "3 2011-10-01  1207.216  2.152  9570.400  1662.068\n",
              "4 2012-01-01  1300.578  1.966  9727.720  1705.692"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-fkkayr7ZvCb"
      },
      "source": [
        "###*Next steps:*\n",
        "\n",
        "Visualize the data in relation to the S&P 500 index."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "id": "sM5AfdZ14APr",
        "outputId": "4e73cff7-fbfc-42d5-cec7-dfcecf1c0b8a"
      },
      "source": [
        "# Quick graph of the 4 variables (1 dependent, 3 independent)\n",
        "\n",
        "fig, ax = plt.subplots(2, 2, figsize=(12, 12))\n",
        "\n",
        "ax[0][0].plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['SP500'], 'black')\n",
        "ax[0][1].plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['CPATAX'], 'purple')\n",
        "ax[1][0].plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['M2'], 'maroon')\n",
        "ax[1][1].plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['DGS10'], 'blue')\n",
        "\n",
        "ax[0][0].set_xlabel('Reporting Period')\n",
        "ax[0][0].set_ylabel('S&P 500')\n",
        "\n",
        "ax[0][1].set_xlabel('Reporting Period')\n",
        "ax[0][1].set_ylabel('Corporate Profits', color='purple')\n",
        "ax[0][1].tick_params(axis='y', labelcolor='purple')\n",
        "\n",
        "ax[1][0].set_xlabel('Reporting Period')\n",
        "ax[1][0].set_ylabel('Money Supply', color='maroon')\n",
        "ax[1][0].tick_params(axis='y', labelcolor='maroon')\n",
        "\n",
        "ax[1][1].set_xlabel('Reporting Period')\n",
        "ax[1][1].set_ylabel('Risk-Free Rate', color='blue')\n",
        "ax[1][1].tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x864 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "rzqLGhq_ZxES",
        "outputId": "50430f12-12ce-4d76-d770-6dbe204f6a81"
      },
      "source": [
        "# SP500 versus After-tax Corporate Profits.\n",
        "\n",
        "import matplotlib.patheffects as path_effects\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(9, 9))\n",
        "\n",
        "ax.set_facecolor('xkcd:pale grey')\n",
        "ax.set_title('S&P 500 Index Level | After-Tax Corporate Profits',\n",
        "             fontsize=22, pad=24)\n",
        "\n",
        "ax.set_xlabel('Reporting Period', fontsize=12, labelpad=25)\n",
        "\n",
        "ax.plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['SP500'], color='black',\n",
        "        path_effects=[path_effects.SimpleLineShadow(), path_effects.Normal()])\n",
        "ax.set_ylabel('S&P 500', verticalalignment='bottom', fontsize=16)\n",
        "ax.tick_params(axis='y')\n",
        "\n",
        "ax2 = ax.twinx()\n",
        "\n",
        "ax2.plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['CPATAX'], color='purple',\n",
        "         path_effects=[path_effects.SimpleLineShadow(), path_effects.Normal()])\n",
        "ax2.set_ylabel('Corporate Profits', color='purple', verticalalignment='top', \n",
        "               fontsize=16, labelpad=15)\n",
        "ax2.tick_params(axis='y', labelcolor='purple')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "BC0j4u98cBXy",
        "outputId": "7eb1c6fd-e6ab-4f93-8ff2-20ca7351f8fa"
      },
      "source": [
        "# SP500 versus M2 money supply.\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(9, 9))\n",
        "\n",
        "ax.set_title('S&P 500 Index Level | M2 Money Supply', fontsize=22, pad=24)\n",
        "ax.set_facecolor('xkcd:pale grey')\n",
        "\n",
        "ax.set_xlabel('Reporting Period', fontsize=12, labelpad=25)\n",
        "\n",
        "ax.plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['SP500'], color='black',  \n",
        "        path_effects=[path_effects.SimpleLineShadow(), path_effects.Normal()])\n",
        "ax.set_ylabel('S&P 500', verticalalignment='bottom', fontsize=16)\n",
        "ax.tick_params(axis='y')\n",
        "\n",
        "ax2 = ax.twinx()\n",
        "\n",
        "ax2.plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['M2'], color='maroon', \n",
        "         path_effects=[path_effects.SimpleLineShadow(), path_effects.Normal()])\n",
        "ax2.set_ylabel('M2 Money Supply', color='maroon', verticalalignment='top', \n",
        "               fontsize=16, labelpad=15)\n",
        "ax2.tick_params(axis='y', labelcolor='maroon')\n",
        "\n",
        "ax.axvline(x=SP_10yr_M2_CP.loc[37, 'DATE'], linewidth=22, color=(0,0,0,0.1))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "jw-v4FANc2W0",
        "outputId": "d17ac93b-4fa7-466e-a359-17e741c2ab28"
      },
      "source": [
        "# SP500 versus 10-year Treasury Yields.\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(9, 9))\n",
        "\n",
        "# fig.set_facecolor('xkcd:baby blue')\n",
        "\n",
        "ax.set_title('S&P500 Index Level | 10-year Treasury Yields', fontsize=22, pad=24)\n",
        "ax.set_facecolor('xkcd:pale grey')\n",
        "ax.set_xlabel('Reporting Period', fontsize=12, labelpad=25)\n",
        "\n",
        "ax.plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['SP500'], color='black', \n",
        "        path_effects=[path_effects.SimpleLineShadow(), path_effects.Normal()])\n",
        "ax.set_ylabel('S&P 500', verticalalignment='bottom', fontsize=16)\n",
        "ax.tick_params(axis='y')\n",
        "\n",
        "ax2 = ax.twinx()\n",
        "\n",
        "ax2.plot(SP_10yr_M2_CP['DATE'], SP_10yr_M2_CP['DGS10'], color='blue', \n",
        "         path_effects=[path_effects.SimpleLineShadow(), path_effects.Normal()])\n",
        "ax2.set_ylabel('10-year Treasury', color='blue', verticalalignment='top',\n",
        "               fontsize=16, labelpad=15)\n",
        "ax2.tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "ax.axvline(x=SP_10yr_M2_CP.loc[9, 'DATE'], linewidth=80, color=(0,0,0,0.05))\n",
        "ax.axvline(x=SP_10yr_M2_CP.loc[26, 'DATE'], linewidth=50, color=(0,0,0,0.05))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "id": "yH_F_KOpc9be",
        "outputId": "983833f0-5cff-4000-d636-8822b6980493"
      },
      "source": [
        "# Pair plotting the features to further visually explore the data.\n",
        "\n",
        "# SP500 levels appear related to Corporate Profits and the Money Supply.  There\n",
        "# does not appear to be a strong relationship between the SP500 index level and\n",
        "# the risk-free rate. \n",
        "# There also appears to possibly be a relationship betwen corporate profits and M2. \n",
        "\n",
        "to_plot = SP_10yr_M2_CP[['SP500', 'CPATAX', 'M2', 'DGS10']]\n",
        "\n",
        "sns.pairplot(to_plot)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NM9Jb-bBdn6Y"
      },
      "source": [
        "###*Next, let's look at the correlation coefficients and examine the strength between the S&P 500 with:*\n",
        "- Corporate Profits\n",
        "- Money Supply\n",
        "- Risk-Free Rate\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwQ5FACndrTd",
        "outputId": "61645bd2-74c4-4753-d6a5-0f51390aa301"
      },
      "source": [
        "# SP500 levels and corporate profits appear moderately to strongly correlated.\n",
        "\n",
        "np.corrcoef(SP_10yr_M2_CP['CPATAX'], SP_10yr_M2_CP['SP500'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.79121386],\n",
              "       [0.79121386, 1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqrieZEPdugE",
        "outputId": "ae917c26-1c36-4ed9-9e58-f9f18471f440"
      },
      "source": [
        "# SP500 levels and the money supply appear very strongly correlated.\n",
        "\n",
        "np.corrcoef(SP_10yr_M2_CP['M2'], SP_10yr_M2_CP['SP500'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.95396111],\n",
              "       [0.95396111, 1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1v9FRZy-d4sw",
        "outputId": "d81eb4c8-4b41-4fc3-971a-6a3c9f8cc037"
      },
      "source": [
        "# SP500 levels and the risk-free rate do not appear strongly negatively correlated\n",
        "\n",
        "np.corrcoef(SP_10yr_M2_CP['DGS10'],SP_10yr_M2_CP['SP500'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.        , -0.21826718],\n",
              "       [-0.21826718,  1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBA-UdEkeOce",
        "outputId": "e9378e78-78de-4c4e-ae30-e0039115d75c"
      },
      "source": [
        "# Lets start our modeling looking at the features versus the SP500.\n",
        "\n",
        "# We are able to reject the null hypothesis at the 0.05 alpha level, and \n",
        "# indicate that there is a relationship between after-tax corporate earnings \n",
        "# and the S&P 500 index level. \n",
        "\n",
        "model_earnings = ols('SP500 ~ CPATAX', data=SP_10yr_M2_CP).fit()\n",
        "\n",
        "print(model_earnings.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  SP500   R-squared:                       0.626\n",
            "Model:                            OLS   Adj. R-squared:                  0.616\n",
            "Method:                 Least Squares   F-statistic:                     61.94\n",
            "Date:                Fri, 08 Jan 2021   Prob (F-statistic):           2.03e-09\n",
            "Time:                        01:34:01   Log-Likelihood:                -285.15\n",
            "No. Observations:                  39   AIC:                             574.3\n",
            "Df Residuals:                      37   BIC:                             577.6\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept  -3256.0356    685.434     -4.750      0.000   -4644.858   -1867.213\n",
            "CPATAX         3.0950      0.393      7.870      0.000       2.298       3.892\n",
            "==============================================================================\n",
            "Omnibus:                        9.491   Durbin-Watson:                   0.596\n",
            "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.936\n",
            "Skew:                           0.785   Prob(JB):                      0.00696\n",
            "Kurtosis:                       4.911   Cond. No.                     2.01e+04\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 2.01e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SV49nFpRefbm",
        "outputId": "b87716b2-66b0-48c4-b967-0e7ccf6f5148"
      },
      "source": [
        "# We are able to reject the null hypothesis at the 0.05 alpha level, refute\n",
        "# the null hypothesis, and indicate that there is a relationship between \n",
        "# the M2 money supply and the S&P 500 index level.\n",
        "\n",
        "model_M2 = ols('SP500 ~ M2', data=SP_10yr_M2_CP).fit()\n",
        "\n",
        "print(model_M2.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  SP500   R-squared:                       0.910\n",
            "Model:                            OLS   Adj. R-squared:                  0.908\n",
            "Method:                 Least Squares   F-statistic:                     374.3\n",
            "Date:                Fri, 08 Jan 2021   Prob (F-statistic):           6.08e-21\n",
            "Time:                        01:34:01   Log-Likelihood:                -257.37\n",
            "No. Observations:                  39   AIC:                             518.7\n",
            "Df Residuals:                      37   BIC:                             522.1\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept  -1073.3320    167.514     -6.407      0.000   -1412.747    -733.917\n",
            "M2             0.2575      0.013     19.347      0.000       0.230       0.284\n",
            "==============================================================================\n",
            "Omnibus:                        5.435   Durbin-Watson:                   1.032\n",
            "Prob(Omnibus):                  0.066   Jarque-Bera (JB):                4.246\n",
            "Skew:                          -0.564   Prob(JB):                        0.120\n",
            "Kurtosis:                       4.157   Cond. No.                     7.22e+04\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 7.22e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTt8Mk1BelK0",
        "outputId": "9e00eab4-9e03-43fe-de4d-dcefe668f5eb"
      },
      "source": [
        "# The risk-free rate, by itself, does not appear statistically significant in \n",
        "# explaining variations in the SP500 index levels.\n",
        "\n",
        "model_10year = ols('SP500 ~ DGS10', data=SP_10yr_M2_CP).fit()\n",
        "\n",
        "print(model_10year.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  SP500   R-squared:                       0.048\n",
            "Model:                            OLS   Adj. R-squared:                  0.022\n",
            "Method:                 Least Squares   F-statistic:                     1.851\n",
            "Date:                Fri, 08 Jan 2021   Prob (F-statistic):              0.182\n",
            "Time:                        01:34:01   Log-Likelihood:                -303.38\n",
            "No. Observations:                  39   AIC:                             610.8\n",
            "Df Residuals:                      37   BIC:                             614.1\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept   2589.1003    359.183      7.208      0.000    1861.326    3316.875\n",
            "DGS10       -211.4857    155.450     -1.360      0.182    -526.458     103.487\n",
            "==============================================================================\n",
            "Omnibus:                        6.264   Durbin-Watson:                   0.117\n",
            "Prob(Omnibus):                  0.044   Jarque-Bera (JB):                2.201\n",
            "Skew:                           0.144   Prob(JB):                        0.333\n",
            "Kurtosis:                       1.873   Cond. No.                         10.3\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKUAjq1ti9up"
      },
      "source": [
        "###*Let's use the model_M2 and add corporate earnings to see it's effects.*\n",
        "\n",
        "Ho: $\\beta_2$ = 0\n",
        "\n",
        "Ha: $\\beta_2 \\neq$ 0\n",
        "\n",
        "The Null Hypthothesis states that after taking into account the effects of the M2 money supply, there is no relationship betwteen after-tax coroprate earnings and the SP500 Index level. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkG3Yn3-es5Y",
        "outputId": "70dc07a1-6135-4d11-f79b-4e5b883fed0e"
      },
      "source": [
        "# Let's use the model_M2 and add corporate earnings to see it's effects.\n",
        "\n",
        "# Adjusted R-squared improves to about 93%.\n",
        "# After accounting for differences in the money supply, corporate profits appear \n",
        "# to be significantly associated with SP500 Index levels. \n",
        "\n",
        "model_M2_CP = ols('SP500 ~ M2 + CPATAX', data=SP_10yr_M2_CP).fit()\n",
        "\n",
        "print(model_M2_CP.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  SP500   R-squared:                       0.933\n",
            "Model:                            OLS   Adj. R-squared:                  0.929\n",
            "Method:                 Least Squares   F-statistic:                     251.2\n",
            "Date:                Fri, 08 Jan 2021   Prob (F-statistic):           7.16e-22\n",
            "Time:                        01:34:01   Log-Likelihood:                -251.58\n",
            "No. Observations:                  39   AIC:                             509.2\n",
            "Df Residuals:                      36   BIC:                             514.2\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept  -2032.0111    308.889     -6.578      0.000   -2658.467   -1405.555\n",
            "M2             0.2151      0.017     12.857      0.000       0.181       0.249\n",
            "CPATAX         0.8546      0.242      3.525      0.001       0.363       1.346\n",
            "==============================================================================\n",
            "Omnibus:                        4.901   Durbin-Watson:                   1.209\n",
            "Prob(Omnibus):                  0.086   Jarque-Bera (JB):                5.487\n",
            "Skew:                           0.097   Prob(JB):                       0.0643\n",
            "Kurtosis:                       4.827   Cond. No.                     1.54e+05\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.54e+05. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nV-b9CokfCn"
      },
      "source": [
        "Based on the test outputs, with a t-stat of 3.525 and a P value of < 0.05, we can reject the null hypthesis and state that after accounting for changes in the money supply, there is a statistically significant relationship between corporate earnings and the SP500 Index. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3esi3l4kB-Z"
      },
      "source": [
        "###*Let's use the model_M2_CP and add the risk-free rate to see it's effects.*\n",
        "\n",
        "Ho: $\\beta_3$ = 0\n",
        "\n",
        "Ha: $\\beta_3 \\neq$ 0\n",
        "\n",
        "The Null Hypthothesis states that after accounting for the effects of the M2 money supply, and after-tax coroprate earnings, there is no significant relationship between the risk-free rate and the SP500 Index level. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0vh4gZ-kexRf",
        "outputId": "d0cfcd37-4dab-4df2-9a85-deb9659e5637"
      },
      "source": [
        "# Let's use the model_M2_CP and add the risk-free rate to see it's effects.\n",
        "\n",
        "# Adjusted R-squared improves to about 95%\n",
        "# After accounting for changes in the money supply, and corporate profits, the  \n",
        "# risk-free rate appears to be significantly associated SP500 Index levels. \n",
        "\n",
        "model_M2_CP_10year = ols('SP500 ~ M2 + CPATAX + DGS10', data=SP_10yr_M2_CP).fit()\n",
        "print(model_M2_CP_10year.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  SP500   R-squared:                       0.957\n",
            "Model:                            OLS   Adj. R-squared:                  0.953\n",
            "Method:                 Least Squares   F-statistic:                     259.6\n",
            "Date:                Fri, 08 Jan 2021   Prob (F-statistic):           5.78e-24\n",
            "Time:                        01:34:01   Log-Likelihood:                -242.98\n",
            "No. Observations:                  39   AIC:                             494.0\n",
            "Df Residuals:                      35   BIC:                             500.6\n",
            "Df Model:                           3                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept  -2290.2451    257.980     -8.878      0.000   -2813.973   -1766.517\n",
            "M2             0.2534      0.016     15.694      0.000       0.221       0.286\n",
            "CPATAX         0.5016      0.213      2.356      0.024       0.069       0.934\n",
            "DGS10        177.9690     40.387      4.407      0.000      95.979     259.959\n",
            "==============================================================================\n",
            "Omnibus:                       16.537   Durbin-Watson:                   1.480\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               27.116\n",
            "Skew:                           1.084   Prob(JB):                     1.29e-06\n",
            "Kurtosis:                       6.462   Cond. No.                     1.58e+05\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.58e+05. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}