2559_ETF_vs_emaxis_slim.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MAXIS全世界株式（オール・カントリー） (2559) ETF vs eMAXIS Slim 全世界株式（オール・カントリー） Mutual Fund\n",
    "\n",
    "MUFG All Country ETF vs eMAXIS Slim All Country mutual fund: which has performed best?\n",
    "\n",
    "Code to download price data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "\n",
    "def url_to_df(url):\n",
    "    return pd.read_csv(url, encoding='cp932', skiprows = 2, parse_dates = [0], index_col = [0],\n",
    "                      names=['date', 'price', 'price_div_reinv', 'div', 'assets'])\n",
    "\n",
    "etf_url = 'https://maxis.mukam.jp/content/csv/fundCsv.php?fund_cd=182559' # link from https://maxis.mukam.jp/etf_fund/182559.html\n",
    "mutual_url = 'https://emaxis.jp/content/csv/fundCsv.php?fund_cd=253425' # link from https://emaxis.jp/fund/253425.html\n",
    "\n",
    "ind = 'MSCI_ACWI'\n",
    "\n",
    "etf = url_to_df(etf_url)\n",
    "mutual = url_to_df(mutual_url)\n",
    "\n",
    "start_date = etf.index.min()\n",
    "mutual = mutual[mutual.index >= start_date] # match date range of mutual data to ETF data\n",
    "mutual.to_csv('mutual_' + ind + '.csv', index=True)\n",
    "\n",
    "# Spreadsheet data downloaded from https://www.msci.com/end-of-day-data-search then processed manually into TSV file\n",
    "file = 'MSCI_ACWI_Standard_Large_Mid_Cap.tsv'\n",
    "benchmark_tr = pd.read_csv(file, sep='\\t', index_col='Date', parse_dates=True)\n",
    "benchmark_tr = benchmark_tr[benchmark_tr.index >= start_date] \n",
    "\n",
    "# Daily USDJPY FX rates used to rebased the USD-based index to JPY\n",
    "usdjpy = yf.Ticker('JPY=X').history(start=str(start_date).split()[0])\n",
    "usdjpy = usdjpy[usdjpy.index.isin(benchmark_tr.index)] # cut down data to match dates in benchmark\n",
    "\n",
    "# Create columns with the JPY-based benchmark indices\n",
    "benchmark_tr['Gross_jpy'] = usdjpy['Close'] * benchmark_tr['Gross']\n",
    "benchmark_tr['Net_jpy'] = usdjpy['Close'] * benchmark_tr['Net']\n",
    "\n",
    "benchmark_tr.to_csv('benchmark_tr_' + ind + '.csv', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dividends table for the ETF. Add dividend pay dates, foreign tax data, and calculate net dividends (net of foreign tax and domestic tax, with foreign tax credit claimed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>div</th>\n",
       "      <th>pay_date</th>\n",
       "      <th>foreign_tax_rate</th>\n",
       "      <th>net_div</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-08</th>\n",
       "      <td>39.0</td>\n",
       "      <td>2020-07-17</td>\n",
       "      <td>0.100813</td>\n",
       "      <td>34.210123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-08</th>\n",
       "      <td>64.0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>0.100813</td>\n",
       "      <td>56.139689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-08</th>\n",
       "      <td>75.0</td>\n",
       "      <td>2021-07-16</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>65.740125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             div   pay_date  foreign_tax_rate    net_div\n",
       "date                                                    \n",
       "2020-06-08  39.0 2020-07-17          0.100813  34.210123\n",
       "2020-12-08  64.0 2021-01-15          0.100813  56.139689\n",
       "2021-06-08  75.0 2021-07-16          0.100000  65.740125"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jp_income_tax_rate = 0.15315\n",
    "jp_inhabitant_tax_rate = 0.05\n",
    "\n",
    "dividends = etf[etf['div'].notna()]['div'].copy().to_frame()\n",
    "dividends['pay_date'] = pd.to_datetime(['2020-07-17', '2021-01-15', '2021-07-16'])\n",
    "# couldn't find foreign tax rate for first dividend, so using the second dividend rate to approximate it\n",
    "# second dividend foreign tax rate source: https://mrmarket-japan.com/tousihoka/etf-toushin-double-taxation/\n",
    "# used reasonable approximation for third dividend tax rate\n",
    "dividends['foreign_tax_rate'] = [0.1008127489, 0.1008127489, 0.1]\n",
    "dividends['net_div'] = (dividends['div'] * (dividends['foreign_tax_rate']+1) * \n",
    "                        (1-jp_income_tax_rate-jp_inhabitant_tax_rate))\n",
    "dividends.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate growth of net dividends reinvested on pay date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, (_, row) in enumerate(dividends.iterrows()):\n",
    "    etf['div_'+str(i)] = 0.0\n",
    "    div = row['net_div']\n",
    "    pay_date = row['pay_date']\n",
    "    buy_price =  etf.loc[pay_date, 'price'] \n",
    "    etf.loc[etf.index >= pay_date, 'div_'+str(i)] = div*etf.loc[etf.index >= pay_date, 'price']/buy_price\n",
    "\n",
    "etf.to_csv('etf_' + ind + '.csv', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create function to calculate the return of the ETF based on lump sum investment on chosen date, subsequently reinvesting the net dividends on pay date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def etf_return(df, date):\n",
    "    date = pd.to_datetime(date)\n",
    "    investment = df['price'].copy()\n",
    "    for i, index in enumerate(dividends.index):\n",
    "        if date > index:\n",
    "            continue\n",
    "        investment += df['div_'+str(i)]\n",
    "        \n",
    "    return investment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to return a data series of prices normalized to a chosen date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normlize_to_date(series, date):\n",
    "    return (series/series.loc[date]).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the growths of the mutual and the ETF funds, and their difference, based on initial investment on date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grow(mutual, etf, date):\n",
    "    date = pd.to_datetime(date)\n",
    "\n",
    "    mutual_growth = normlize_to_date(mutual['price'], date)\n",
    "    etf_growth = normlize_to_date(etf_return(etf, date), date)\n",
    "\n",
    "    growth_difference = (mutual_growth - etf_growth)*100\n",
    "    \n",
    "    return mutual_growth, etf_growth, growth_difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results and save the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "date = '2020-01-08'\n",
    "mutual_growth, etf_growth, growth_difference = grow(mutual, etf, date)\n",
    "\n",
    "ax = growth_difference.plot()\n",
    "ax.set_ylabel('mutual growth minus ETF growth (%)')\n",
    "fig = plt.gcf()\n",
    "fig.suptitle('eMAXIS Slim ' + ind + ' mutual vs MAXIS ' + ind + ' ETF')\n",
    "fig.savefig('slim_vs_2559_growth.png', dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the performance of the funds against the benchmark index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = mutual_growth.plot(label='eMAXIS Slim')\n",
    "etf_growth.plot(ax=ax, label='MAXIS ETF')\n",
    "normlize_to_date(benchmark_tr['Gross_jpy'], date).plot(ax=ax, label=ind + ' TR Gross')\n",
    "normlize_to_date(benchmark_tr['Net_jpy'], date).plot(ax=ax, label=ind + ' TR Net')\n",
    "ax.legend()\n",
    "ax.set_ylabel('Relative performance')\n",
    "fig = plt.gcf()\n",
    "fig.suptitle('Lump sum invested on ' + date)\n",
    "fig.savefig('slim_vs_2559_benchmark.png', dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some checks of the annual growth figures for the year running from the end of September 2020 against numbers from last graph on https://shintaro-money.com/maxis-ac-2559/:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.356404140676715"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = '2020-09-30'\n",
    "end_date = '2021-09-30'\n",
    "\n",
    "# ETF paying Japan taxes on dividends (with foreign tax credit)\n",
    "((etf.loc[end_date, 'price']\n",
    "  +etf.loc[end_date, 'div_0']\n",
    "  +etf.loc[end_date, 'div_1'])\n",
    " /etf.loc[start_date, 'price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3608531482804367"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ETF assuming no Japan taxes on dividends\n",
    "etf.loc[end_date, 'price_div_reinv']/etf.loc[start_date, 'price_div_reinv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3633891763880361"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mutual fund\n",
    "mutual.loc[end_date, 'price']/mutual.loc[start_date, 'price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3557751792010904"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Benchmark total returns index (gross)\n",
    "benchmark_tr.loc[end_date, 'Gross_jpy']/benchmark_tr.loc[start_date, 'Gross_jpy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.349997367672186"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Benchmark total returns index (net)\n",
    "benchmark_tr.loc[end_date, 'Net_jpy']/benchmark_tr.loc[start_date, 'Net_jpy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These middle two numbers agree with the numbers from the final 騰落率 vs 実質コスト graph on the Shintaro website. The final number two numbers (ACWI TR Gross and Net) are a little off compared to Shintaro money, but still reasonable.\n",
    "\n",
    "Conclusion: the ETF lagged the mutual by about 0.70% for the one year period from the end of September 2020, i.e. slightly more than the 0.25% reported on the Shintaro website. "
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}