Evaluate_company.ipynb

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 "cells": [
  {
   "source": [
    "# This notebook is for inspecting a specific company"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from functions import monitor_stock,get_fundamentals\n",
    "import yfinance as yf\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example_ticker = \"TSLA\"\n",
    "example_ticker = \"MSFT\"\n",
    "ticker_obj = yf.Ticker(example_ticker)\n"
   ]
  },
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   "metadata": {},
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         "text": "MSFT STOCK MONITOR",
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   "source": [
    "monitor_stock(example_ticker)"
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  },
  {
   "source": [
    "## Get the key indicators:      \n",
    "\n",
    "__Earnings per share (EPS)__: Earnings per share  is a __company's net profit divided by the number of common shares__ it has outstanding.\n",
    "EPS indicates how much money a company makes for each share of its stock. A higher EPS indicates greater value because investors will pay more for a company's shares if they think the company has higher profits relative to its share price. EPS can be manipulated by the company by decreasing the number of shares outstanding.\n",
    "\n",
    "\n",
    "__Price-to-Book ratio (P/B)__: The book value of equity, is the __value of a company's assets expressed on the balance sheet__. The book value is defined as the difference between the book value of assets and the book value of liabilities. The price-to-book ratio is used to gauge whether a company's stock price is valued properly. A P/B ratio of one means that the stock price is trading in line with the book value of the company. In other words, the stock price would be considered fairly valued, strictly from a P/B standpoint. A company with a high P/B ratio could mean the stock price is overvalued, while a company with a lower P/B could be undervalued. However, the P/B ratio should be compared with companies within the same sector. The ratio is higher for some industries than others. So, it's important to compare it to companies with a similar makeup of assets and liabilities.      \n",
    "\n",
    "\n",
    "__Price-earnings ratio (P/E)__: To determine the P/E value, one simply must __divide the current stock price by the earnings per share (EPS)__. The forward (or leading) P/E uses future earnings guidance rather than trailing figures. However, there are inherent problems with the forward P/E metric – namely, companies could underestimate earnings in order to beat the estimate P/E when the next quarter's earnings are announced. Other companies may overstate the estimate and later adjust it going into their next earnings announcement. The trailing P/E relies on past performance by dividing the current share price by the total EPS earnings over the past 12 months. It's the most popular P/E metric because it's the most objective – assuming the company reported earnings accurately. Some investors prefer to look at the trailing P/E because they don't trust another individual’s earnings estimates. But the trailing P/E also has its share of shortcomings – namely, a company’s past performance doesn’t signal future behavior  \n",
    "\n",
    "\n",
    "__Price-sales ratio (P/S)__: The price-to-sales ratio is calculated by taking a __company's market capitalization (the number of outstanding shares multiplied by the share price) and divide it by the company's total sales or revenue over the past 12 months__. The lower the P/S ratio, the more attractive the investment. In a highly cyclical industry such as semiconductors, there are years when only a few companies produce any earnings. This does not mean semiconductor stocks are worthless. In this case, investors can use price-to-sales instead of the price-earnings ratio (P/E Ratio or PE) to determine how much they are paying for a dollar of the company's sales rather than a dollar of its earnings.  Comparing companies' sales on an apples-to-apples basis hardly ever works. Examination of sales must be coupled with a careful look at profit margins and then comparing the findings with other companies in the same industry. \n",
    "\n",
    " \n",
    "__Profit margin ratio__: Profit margin is one of the commonly used profitability ratios to gauge the degree to which a company or a business activity makes money. It represents what __percentage of sales has turned into profits__. Simply put, the percentage figure indicates how many cents of profit the business has generated for each dollar of sale. For instance, if a business reports that it achieved a 35% profit margin during the last quarter, it means that it had a net income of $0.35 for each dollar of sales generated. As typical profit margins vary by industry sector, care should be taken when comparing the figures for different businesses.  \n",
    "\n",
    " \n",
    "__Dividend payout ratio__: The dividend payout ratio is the __ratio of the total amount of dividends paid out to shareholders relative to the net income of the company__. It is the percentage of earnings paid to shareholders in dividends. A new, growth-oriented company that aims to expand, develop new products, and move into new markets would be expected to reinvest most or all of its earnings and could be forgiven for having a low or even zero payout ratio. The payout ratio is 0% for companies that do not pay dividends and is 100% for companies that pay out their entire net income as dividends. On the other hand, an older, established company that returns a pittance to shareholders would test investors' patience and could tempt activists to intervene. In 2012 and after nearly twenty years since its last paid dividend, Apple (AAPL) began to pay a dividend when the new CEO felt the company's enormous cash flow made a 0% payout ratio difficult to justify. Since it implies that a company has moved past its initial growth stage, a high payout ratio means share prices are unlikely to appreciate rapidly.   \n",
    "\n",
    " \n",
    "\n",
    "__Debt-equity ratio (D/E)__: The D/E ratio is an important metric used in corporate finance. It is a measure of the degree to which a company is financing its operations through debt versus wholly owned funds. More specifically, it reflects the ability of shareholder equity to cover all outstanding debts in the event of a business downturn. D/E can be used to evaluate how much leverage a company is using. Higher-leverage ratios tend to indicate a company or stock with higher risk to shareholders. However, the D/E ratio is difficult to compare across industry groups where ideal amounts of debt will vary. Investors will often modify the D/E ratio to focus on long-term debt only because the risks associated with long-term liabilities are different than short-term debt and payables. Because the ratio can be distorted by retained earnings/losses, intangible assets, and pension plan adjustments, further research is usually needed to understand a company’s true leverage. \n",
    "\n",
    " \n",
    "__EBITDA-to-sales ratio__: The EBITDA-to-sales ratio, also known as EBITDA margin, is a financial metric used to assess a company's profitability by comparing its gross revenue with its earnings. More specifically, since EBITDA itself is derived in part from revenue, this metric indicates the percentage of a company's earnings remaining after operating expenses. A low EBITDA-to-sales ratio suggests that a company may have problems with profitability as well as its cash flow, while a high result may indicate a solid business with stable earnings. Because the ratio excludes the impact of debt interest, highly leveraged companies should not be evaluated using this metric. Certain industries may experience more favorable taxation due to tax credits and deductions. These industries incur lower income tax figures and higher EBITDA-to-sales ratio calculations, and thus the EBITDA-to-sales ratio calculations won't tell much during comparison if used to compare against industries with different cost structures. \n",
    "\n",
    "\n",
    "__Free cash clow (FCF)__: Free cash flow (FCF) is the cash flow available for the company to repay creditors or pay dividends and interest to investors. Some investors prefer FCF or FCF per share over earnings or earnings per share as a measure of profitability because it removes non-cash items from the income statement. However, because FCF accounts for investments in property, plant, and equipment, it can be lumpy and uneven over time. Because FCF accounts for changes in working capital, it can provide important insights into the value of a company and the health of its fundamental trends. For example, a decrease in accounts payable (outflow) could mean that vendors are requiring faster payment. A decrease in accounts receivable (inflow) could mean the company is collecting cash from its customers quicker. An increase in inventory (outflow) could indicate a building stockpile of unsold products. Including working capital in a measure of profitability provides an insight that is missing from the income statement. A common approach is to use the stability of FCF trends as a measure of risk. If the trend of FCF is stable over the last four to five years, then bullish trends in the stock are less likely to be disrupted in the future. However, falling FCF trends, especially FCF trends that are very different compared to earnings and sales trends, indicate a higher likelihood of negative price performance in the future.  \n",
    "\n",
    " "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  Ticker  52WeekChange  SandP52WeekChange           address1 algorithm  \\\n",
       "0   MSFT      0.387652           0.382344  One Microsoft Way      None   \n",
       "\n",
       "  annualHoldingsTurnover annualReportExpenseRatio  ask  askSize  \\\n",
       "0                   None                     None    0      800   \n",
       "\n",
       "   averageDailyVolume10Day  ...  trailingPE  twoHundredDayAverage    volume  \\\n",
       "0                 21946500  ...    34.27228             233.26273  17771600   \n",
       "\n",
       "  volume24Hr  volumeAllCurrencies                   website  yield ytdReturn  \\\n",
       "0       None                 None  http://www.microsoft.com   None      None   \n",
       "\n",
       "          zip recommendation  \n",
       "0  98052-6399            Buy  \n",
       "\n",
       "[1 rows x 125 columns]"
      ],
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     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df = get_fundamentals([\"MSFT\"])\n",
    "df"
   ]
  },
  {
   "source": [
    "## Quarterly stats"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cashflows = ticker_obj.quarterly_cashflow\n",
    "financials = ticker_obj.quarterly_financials\n",
    "balance = ticker_obj.quarterly_balance_sheet\n",
    "earnings = ticker_obj.quarterly_earnings.transpose().astype(np.float64)\n",
    "earnings.columns = balance.columns\n",
    "df_quart = pd.concat([earnings,cashflows,financials,balance])\n",
    "# sorted(df_quart.index.tolist())\n",
    "# df_quart\n",
    "\n"
   ]
  },
  {
   "source": [
    "## Yearly stats"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cashflows = ticker_obj.cashflow\n",
    "financials = ticker_obj.financials\n",
    "balance = ticker_obj.balance_sheet\n",
    "earnings = ticker_obj.earnings.transpose().astype(np.float64)\n",
    "earnings.columns = balance.columns\n",
    "df_year = pd.concat([earnings,cashflows,financials,balance])\n",
    "# sorted(key_df.index.tolist())\n",
    "# df_year\n",
    "# sorted(df_year.index.tolist())\n",
    "# cashflows"
   ]
  },
  {
   "source": [
    "## Need to find out total tax rate -> tax shield = Interest Expense*Tax Rate. This can be a hard thing to estimate for an international corporation."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fundamentals_to_use = [\"Ticker\",\"forwardEps\",\"trailingEps\",\"priceToBook\",\"forwardPE\",\n",
    "\"trailingPE\",\"priceToSalesTrailing12Months\",\"profitMargins\",\n",
    "\"dividendRate\",\"trailingAnnualDividendRate\"] # ,\"enterpriseValue\",\"enterpriseToEbitda\"\n",
    "df_all = df.copy()\n",
    "df_all = df_all[fundamentals_to_use]\n",
    "\n",
    "debtToEquity = df_year.loc['Total Current Liabilities']/df_year.loc['Total Stockholder Equity']\n",
    "\n",
    "ebitdaToSales = (df_year.loc[\"Ebit\"]+df_year.loc[\"Depreciation\"]) / df_year.loc[\"Revenue\"]\n",
    "\n",
    "# df_year[\"Total Cash From Operating Activities\"]-df_year[\"Interest Expense\"]-df_year[\"\"]+df_year[\"Capital Expenditures\"]\n",
    "\n",
    "\n",
    "df_all[\"debtToEquity\"] = debtToEquity[0]\n",
    "df_all[\"ebitdaToSales\"] = ebitdaToSales[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  Ticker  forwardEps  trailingEps  priceToBook  forwardPE  trailingPE  \\\n",
       "0   MSFT        8.35        7.338    14.086708  30.118563    34.27228   \n",
       "\n",
       "   priceToSalesTrailing12Months  profitMargins  dividendRate  \\\n",
       "0                      11.84051        0.35016          2.24   \n",
       "\n",
       "   trailingAnnualDividendRate  debtToEquity  ebitdaToSales  \n",
       "0                        2.19      0.611222       0.675762  "
      ],
      "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>Ticker</th>\n      <th>forwardEps</th>\n      <th>trailingEps</th>\n      <th>priceToBook</th>\n      <th>forwardPE</th>\n      <th>trailingPE</th>\n      <th>priceToSalesTrailing12Months</th>\n      <th>profitMargins</th>\n      <th>dividendRate</th>\n      <th>trailingAnnualDividendRate</th>\n      <th>debtToEquity</th>\n      <th>ebitdaToSales</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>MSFT</td>\n      <td>8.35</td>\n      <td>7.338</td>\n      <td>14.086708</td>\n      <td>30.118563</td>\n      <td>34.27228</td>\n      <td>11.84051</td>\n      <td>0.35016</td>\n      <td>2.24</td>\n      <td>2.19</td>\n      <td>0.611222</td>\n      <td>0.675762</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "df_all"
   ]
  },
  {
   "source": [
    "## For MSFT\n",
    "\n",
    "The forward and trailing EPS are about 8\\$, which compared to the SP500 average of about 30\\$ seems quite low, though for their direct competitor AAPL the same numbers are about 4\\$. In addition, the trailing EPS is lower than the forward EPS and thus the earnings per share are expected to grow in the future. The P/B is about 14, which implies that the stock could be overvalued. Forward P/E and trailing P/E are about 30 which compared to AAPL are similar. The P/S ratio is about 12, which compared to AAPL (6\\$) is significantly larger, and this could sway an investor to choose AAPL instead of MSFT. The profit marging (net marging?) is about 35\\%, while for AAPL it is about 30\\%. There not that much of a difference and this could be the average of the sector. The D/E ratio of 0.6 versus the 1.7 of AAPL shows that MSFT is much less leveraged than AAPL. Ebitda to sales ratio of 0.68 shows that MSFT is keeping the majority of the earnings after operating expenses and thus shows that their operations are cost effective. Highly leveraged companies should not be evaluated with ebitda-to-sales ratio (it doesn't include the impact of debt interest) and thus comparison to AAPL could be pointless."
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}