TimeSeries_DuPont.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a14743d-27af-4f19-99c8-fed96bfa96e1",
   "metadata": {},
   "source": [
    "# DuPont Time-series analysis\n",
    "\n",
    "DuPont analysis is a framework that decomposes ROE into three component: Net profit margin, total asset turnover, and leverage. \n",
    "\n",
    "This analysis originated from DuPont, innovated by Donaldson Brown in 1914. Nowadays, this framework is widely used to determine the operational efficiency of two similar firms. Nevertheless, if we take one step back, DuPont analysis still provide us with a big picture of the performance of different companies.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4ba3d40-6428-4fbb-a53f-f577fff9fe2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/stock/lib/python3.10/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.9) or chardet (5.1.0)/charset_normalizer (2.0.4) doesn't match a supported version!\n",
      "  warnings.warn(\"urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported \"\n"
     ]
    }
   ],
   "source": [
    "from function.func import Dupont_analysis\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from tqdm import tqdm\n",
    "import seaborn as sns\n",
    "import time\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca7f5d1-e43d-4b0a-91c8-8925fe2430c9",
   "metadata": {},
   "source": [
    "## Dupont analysis for a individual stock "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f77dd2e-30d4-4a3f-a0d3-547385b01c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_id = '2330'\n",
    "Du_2330 = Dupont_analysis(stock_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8434d6c-3daf-405d-a025-570fb716fdc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stock id</th>\n",
       "      <th>quarter</th>\n",
       "      <th>profit_margin</th>\n",
       "      <th>total_asset_turnover</th>\n",
       "      <th>financial_leverage</th>\n",
       "      <th>ROE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2330</td>\n",
       "      <td>2013-03-31</td>\n",
       "      <td>0.297808</td>\n",
       "      <td>0.125048</td>\n",
       "      <td>1.381163</td>\n",
       "      <td>5.143510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2330</td>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>0.332046</td>\n",
       "      <td>0.133249</td>\n",
       "      <td>1.567715</td>\n",
       "      <td>6.936342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2330</td>\n",
       "      <td>2013-09-30</td>\n",
       "      <td>0.319447</td>\n",
       "      <td>0.138065</td>\n",
       "      <td>1.464284</td>\n",
       "      <td>6.458142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2330</td>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>0.307170</td>\n",
       "      <td>0.115439</td>\n",
       "      <td>1.489847</td>\n",
       "      <td>5.282903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2330</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>0.322813</td>\n",
       "      <td>0.114122</td>\n",
       "      <td>1.446261</td>\n",
       "      <td>5.328049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stock id     quarter  profit_margin  total_asset_turnover  \\\n",
       "0     2330  2013-03-31       0.297808              0.125048   \n",
       "0     2330  2013-06-30       0.332046              0.133249   \n",
       "0     2330  2013-09-30       0.319447              0.138065   \n",
       "0     2330  2013-12-31       0.307170              0.115439   \n",
       "0     2330  2014-03-31       0.322813              0.114122   \n",
       "\n",
       "   financial_leverage       ROE  \n",
       "0            1.381163  5.143510  \n",
       "0            1.567715  6.936342  \n",
       "0            1.464284  6.458142  \n",
       "0            1.489847  5.282903  \n",
       "0            1.446261  5.328049  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Du_2330.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34357a4-d5c4-4e77-b85d-db78d938b6f6",
   "metadata": {},
   "source": [
    "## Normal distribution test for ROE\n",
    "\n",
    "As being **normal distribution**, a **confidence interval** is able to be made telling the certainty of a company's performance. Therefore, we implement\n",
    "\n",
    "\n",
    "(1) normaltest from [scipy.stats.normaltest](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html), and \n",
    "\n",
    "(2) [qqplot](https://data.library.virginia.edu/understanding-q-q-plots/)\n",
    "\n",
    "at this step. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fdc357a-aef4-4959-9309-1f126a59e0d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s^2 + k^2:1.57743663656056,\n",
      "p-value:0.45442685278661166\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94180fb9d0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_roe = (Du_2330.ROE-min(Du_2330.ROE))/(max(Du_2330.ROE)-min(Du_2330.ROE))\n",
    "k2, p = stats.normaltest(norm_roe)\n",
    "print(f's^2 + k^2:{k2},\\np-value:{p}')\n",
    "lineStart, lineEnd = 0,1\n",
    "plt.figure()\n",
    "plt.scatter(sorted(norm_roe),np.linspace(0,1,len(norm_roe)))\n",
    "plt.title(label='qq-plot')\n",
    "plt.plot([lineStart, lineEnd], [lineStart, lineEnd], color = 'r')\n",
    "#plt.savefig(f'img/qqplot_ROE_2330.png',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "225990f9-5475-42b6-be3f-cfcc63e8b608",
   "metadata": {},
   "source": [
    "If so, we can further calculate `mean` and `upper & lower CI`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c58a4b64-88c8-4ac9-bc2b-6fc66944da50",
   "metadata": {},
   "outputs": [],
   "source": [
    "pm_mean = np.mean(Du_2330.ROE)\n",
    "upperCI,lowerCI = pm_mean+2*np.std(Du_2330.ROE), pm_mean-2*np.std(Du_2330.ROE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8541511a-b9dd-40fa-bc48-e299fec84eab",
   "metadata": {},
   "source": [
    "## Visualisation of  DuPont analysis\n",
    "\n",
    "We make a time-series plots combining a\n",
    "\n",
    "(1) line graph: trend of ROE's \n",
    "\n",
    "(2) bar graph: trend of Profit margin, Total asset turnover and Financial leverage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d00e50b-ad93-44e3-9c27-5c265b885026",
   "metadata": {},
   "outputs": [],
   "source": [
    "quarters = {'Q1':'03-31','Q2':'06-30','Q3':'09-30','Q4':'12-31'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3daab276-6e32-4255-9785-0010b0f028a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Dupont_plot = Du_2330.melt(id_vars='quarter')\n",
    "for i in range(len(Dupont_plot)):\n",
    "    for key,value in quarters.items():\n",
    "        if Dupont_plot['quarter'][i][-5:] == value:    \n",
    "            Dupont_plot['quarter'][i] = Dupont_plot['quarter'][i][:4] + '\\n' +  key\n",
    "bar = Dupont_plot[Dupont_plot['variable'] != 'ROE'][Dupont_plot['variable'] != 'stock id']\n",
    "line = Dupont_plot[Dupont_plot['variable'] == 'ROE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1d18218-d3ea-4c4f-ba99-cb2c4b7e596f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 4000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "# plot line graph on axis #1\n",
    "plt.figure(figsize=(40,16))\n",
    "plt.xticks(fontsize=8,rotation=30)\n",
    "ax1 = sns.barplot(\n",
    "    x='quarter',\n",
    "    y='value',\n",
    "    hue='variable',\n",
    "    data=bar, \n",
    ")\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_xlabel('')\n",
    "ax1.legend(loc=\"upper left\")\n",
    "ax1.set_ylim(round(min(bar.value)-0.5), round(max(bar.value)+3.5))\n",
    "# set up the 2nd axis\n",
    "ax2 = ax1.twinx()\n",
    "# plot bar graph on axis #2\n",
    "sns.lineplot(\n",
    "    x='quarter', \n",
    "    y='value',\n",
    "    data=line, \n",
    "    color='darkred', \n",
    "    ax = ax2       # Pre-existing axes for the plot\n",
    ")\n",
    "ax2.grid(b=False) # turn off grid #2\n",
    "ax2.set_ylabel('ROE (%)')\n",
    "ax2.set_ylim(min(line.value)-2, max(line.value)+2)\n",
    "ax2.legend(['ROE (%)'], loc=\"upper right\")\n",
    "#plt.savefig(f'img/Dupont_trend_2330.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3bb2df3-9f9d-4427-8e7c-178551944d7e",
   "metadata": {},
   "source": [
    "## Comparison of Dupont anaylsis result among companies in ETF0050 lists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8058a929-f97b-486a-a1d3-62fd02955b32",
   "metadata": {},
   "source": [
    "### Input\n",
    "\n",
    "A list of 50 top market capitalization companies: `0050-20220106.csv`. \n",
    "\n",
    "\n",
    "### Output\n",
    "\n",
    "1. A list of raw historical Dupont analyses throughout candidate stocks: `dupont_analysis_raw.csv`\n",
    "\n",
    "2. A list of statistics result of Dupont analyses thoughtout candidate stocks: `dupont_analysis_stat.csv`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8483e066-d3cd-45b2-ba3e-29c0accb9fcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|██████▏                                     | 7/50 [01:11<06:23,  8.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2881\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|███████▉                                    | 9/50 [01:27<05:44,  8.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2891\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|█████████▍                                 | 11/50 [02:29<12:11, 18.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2882\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24%|██████████▎                                | 12/50 [02:36<09:30, 15.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2886\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|████████████▉                              | 15/50 [04:11<13:00, 22.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2884\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███████████████▍                           | 18/50 [07:49<27:37, 51.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2892\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|████████████████▎                          | 19/50 [08:14<22:36, 43.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 5880\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|█████████████████▏                         | 20/50 [08:19<15:59, 31.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2885\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|██████████████████▉                        | 22/50 [08:36<09:16, 19.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2880\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████████████████████▌                     | 25/50 [10:00<10:01, 24.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2883\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|██████████████████████████▋                | 31/50 [14:22<13:27, 42.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2890\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|███████████████████████████▌               | 32/50 [14:31<09:47, 32.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2887\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|██████████████████████████████             | 35/50 [17:42<14:01, 56.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 5876\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|████████████████████████████████████▉      | 43/50 [20:48<02:30, 21.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sufficient data for 2801\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 50/50 [22:53<00:00, 27.48s/it]\n"
     ]
    }
   ],
   "source": [
    "today = datetime.today().strftime('%Y-%m-%d')\n",
    "\n",
    "# import ETF0050 list\n",
    "eft0050 = pd.read_csv('0050-20220106.csv',sep=';',header=1)\n",
    "candidate_stocks = eft0050['Stock ID'].apply(lambda x: str(x)).values\n",
    "\n",
    "# concatenate a list with historical Dupont analyses throughout candidate stocks\n",
    "df = pd.DataFrame()\n",
    "for stock in tqdm(candidate_stocks):\n",
    "    try:\n",
    "        df_dup = Dupont_analysis(stock)\n",
    "        df = pd.concat([df,df_dup],axis=0)\n",
    "    except IndexError or JSONDecodeError:\n",
    "        time.sleep(1)\n",
    "        print(f'not sufficient data for {stock}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "86be3475-73cc-4a25-92c0-59d37cc1b638",
   "metadata": {},
   "outputs": [],
   "source": [
    "# produce raw dupont analysis dataframe\n",
    "df_du = df.sort_values('ROE',ascending=False).reset_index(drop=True)\n",
    "df_du.to_csv(f'dupont_analysis_raw_{today}.csv',index=False)\n",
    "\n",
    "# aggregated dupont analysis dataframe\n",
    "df_aggr = df_du.groupby(by='stock id').mean()\n",
    "df_std = df_du.groupby(by='stock id').std()['ROE']\n",
    "df_aggr['UpperCI ROE'] = df_aggr['ROE'] + 2*df_std\n",
    "df_aggr['LowerCI ROE'] = df_aggr['ROE'] - 2*df_std\n",
    "df_aggr.sort_values('ROE',ascending=False).to_csv(f'dupont_analysis_stat_{today}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "401a98d6-eb02-41ff-91ea-f2ad0d13f94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stock id</th>\n",
       "      <th>quarter</th>\n",
       "      <th>profit_margin</th>\n",
       "      <th>total_asset_turnover</th>\n",
       "      <th>financial_leverage</th>\n",
       "      <th>ROE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2408</td>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>0.122282</td>\n",
       "      <td>0.140829</td>\n",
       "      <td>24.501145</td>\n",
       "      <td>42.192991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2609</td>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>0.394765</td>\n",
       "      <td>0.298210</td>\n",
       "      <td>3.344350</td>\n",
       "      <td>39.370712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2408</td>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>0.316485</td>\n",
       "      <td>0.133271</td>\n",
       "      <td>9.327890</td>\n",
       "      <td>39.343399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2912</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>0.408842</td>\n",
       "      <td>0.398007</td>\n",
       "      <td>2.350692</td>\n",
       "      <td>38.250953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2408</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>0.519174</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>5.941844</td>\n",
       "      <td>37.773755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stock id     quarter  profit_margin  total_asset_turnover  \\\n",
       "0     2408  2013-06-30       0.122282              0.140829   \n",
       "1     2609  2021-03-31       0.394765              0.298210   \n",
       "2     2408  2013-12-31       0.316485              0.133271   \n",
       "3     2912  2017-12-31       0.408842              0.398007   \n",
       "4     2408  2014-03-31       0.519174              0.122449   \n",
       "\n",
       "   financial_leverage        ROE  \n",
       "0           24.501145  42.192991  \n",
       "1            3.344350  39.370712  \n",
       "2            9.327890  39.343399  \n",
       "3            2.350692  38.250953  \n",
       "4            5.941844  37.773755  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_du.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a821d9b9-c1c4-4379-9bd4-ea48ef1c51fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profit_margin</th>\n",
       "      <th>total_asset_turnover</th>\n",
       "      <th>financial_leverage</th>\n",
       "      <th>ROE</th>\n",
       "      <th>UpperCI ROE</th>\n",
       "      <th>LowerCI ROE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stock id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>0.140403</td>\n",
       "      <td>0.085363</td>\n",
       "      <td>1.834248</td>\n",
       "      <td>2.239022</td>\n",
       "      <td>4.688957</td>\n",
       "      <td>-0.210912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>0.066518</td>\n",
       "      <td>0.256818</td>\n",
       "      <td>2.650230</td>\n",
       "      <td>4.361753</td>\n",
       "      <td>9.796316</td>\n",
       "      <td>-1.072811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>0.174675</td>\n",
       "      <td>0.115381</td>\n",
       "      <td>1.473116</td>\n",
       "      <td>2.947445</td>\n",
       "      <td>6.097652</td>\n",
       "      <td>-0.202761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>0.131395</td>\n",
       "      <td>0.141028</td>\n",
       "      <td>1.612789</td>\n",
       "      <td>3.020844</td>\n",
       "      <td>6.253721</td>\n",
       "      <td>-0.212032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>0.102191</td>\n",
       "      <td>0.160980</td>\n",
       "      <td>1.488651</td>\n",
       "      <td>2.375495</td>\n",
       "      <td>5.432475</td>\n",
       "      <td>-0.681484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          profit_margin  total_asset_turnover  financial_leverage       ROE  \\\n",
       "stock id                                                                      \n",
       "1101           0.140403              0.085363            1.834248  2.239022   \n",
       "1216           0.066518              0.256818            2.650230  4.361753   \n",
       "1301           0.174675              0.115381            1.473116  2.947445   \n",
       "1303           0.131395              0.141028            1.612789  3.020844   \n",
       "1326           0.102191              0.160980            1.488651  2.375495   \n",
       "\n",
       "          UpperCI ROE  LowerCI ROE  \n",
       "stock id                            \n",
       "1101         4.688957    -0.210912  \n",
       "1216         9.796316    -1.072811  \n",
       "1301         6.097652    -0.202761  \n",
       "1303         6.253721    -0.212032  \n",
       "1326         5.432475    -0.681484  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aggr.head(5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}