2558_ETF_vs_emaxis_slim.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MAXIS米株 S&P 500 (2558) ETF vs eMAXIS Slim 米国株式 (S&P 500) Mutual Fund\n",
    "\n",
    "MUFG S&P500 ETF vs eMAXIS Slim S&P500 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=182558' # link from https://maxis.mukam.jp/etf_fund/182558.html\n",
    "mutual_url = 'https://emaxis.jp/content/csv/fundCsv.php?fund_cd=253266' # link from https://emaxis.jp/fund/253266.html\n",
    "\n",
    "ind = 'S&P500'\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",
    "# Benchmark and benchmark total returns indices for comparison\n",
    "benchmark = yf.Ticker('^GSPC').history(start=str(etf.index.min()).split()[0])\n",
    "benchmark_tr = yf.Ticker('^SP500TR').history(start=str(etf.index.min()).split()[0])\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.index)] # cut down data to match dates in benchmark\n",
    "\n",
    "# Create columns with the JPY-based benchmark indices\n",
    "benchmark['Open_jpy'] = usdjpy['Open'] * benchmark['Open']\n",
    "benchmark_tr['Open_jpy'] = usdjpy['Open'] * benchmark_tr['Open']\n",
    "benchmark['Close_jpy'] = usdjpy['Close'] * benchmark['Close']\n",
    "benchmark_tr['Close_jpy'] = usdjpy['Close'] * benchmark_tr['Close']\n",
    "\n",
    "benchmark.to_csv('benchmark_' + ind + '.csv', index=True)\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>27.0</td>\n",
       "      <td>2020-07-17</td>\n",
       "      <td>0.082428</td>\n",
       "      <td>23.288391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-08</th>\n",
       "      <td>63.0</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>0.106306</td>\n",
       "      <td>55.538263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-08</th>\n",
       "      <td>47.0</td>\n",
       "      <td>2021-07-16</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>41.197145</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  27.0 2020-07-17          0.082428  23.288391\n",
       "2020-12-08  63.0 2021-01-15          0.106306  55.538263\n",
       "2021-06-08  47.0 2021-07-16          0.100000  41.197145"
      ]
     },
     "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",
    "# first dividend foreign tax rate source: https://starwaytohappylife.blog.fc2.com/blog-entry-1250.html\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'] = [0.0824283016, 0.1063057439, 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_2558_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['Open_jpy'], date).plot(ax=ax, label=ind + ' TR Gross')\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_2558_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-sp500-2558/:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3959149567621636"
      ]
     },
     "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": [
       "1.3986966824644549"
      ]
     },
     "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": [
       "1.3994055972921655"
      ]
     },
     "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": [
       "1.4063378561735982"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Benchmark total returns index (gross)\n",
    "benchmark_tr.loc[end_date, 'Open_jpy']/benchmark_tr.loc[start_date, 'Open_jpy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second and third 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 (S&P500 index growth) is a little off compared to Shintaro money, but still reasonable.\n",
    "\n",
    "Conclusion: the ETF lagged the mutual by about 0.35% for the one year period from the end of September 2020, i.e. slightly more than the 0.07% reported on the Shintaro website. "
   ]
  }
 ],
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