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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Forecasting the P/Sales Ratio\n",
+ "\n",
+ "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
+ "/ [GitHub](https://github.com/Hvass-Labs/FinanceOps) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmlHaWuVxIA0pKL1yjryR0Z)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Introduction\n",
+ "\n",
+ "In a [previous paper](https://github.com/Hvass-Labs/FinanceOps/blob/master/01C_Theory_of_Long-Term_Stock_Forecasting.ipynb) we found a mathematical formula for doing long-term stock forecasting. The formula was derived from the definition of annualized return and separated the stock-return into 3 components: Dividends, change in the Sales Per Share, and change in the P/Sales ratio. If you can predict these 3 components, then you can predict the future stock-return.\n",
+ "\n",
+ "This makes intuitive sense, because if you buy a stock and hold it for some years, then you get dividends during those years, and the change in share-price can be decomposed into the change in Sales Per Share and the change in P/Sales ratios using this simple identity:\n",
+ "\n",
+ "$$\n",
+ "{Share\\ Price_t} = Sales\\ Per\\ Share_t \\cdot \\frac{Share\\ Price_t}{Sales\\ Per\\ Share_t} \\\\\n",
+ "= Sales\\ Per\\ Share_t \\cdot P/Sales_t\n",
+ "$$\n",
+ "\n",
+ "So the change in share-price is equal to the change in Sales Per Share multiplied by the change in P/Sales ratio:\n",
+ "\n",
+ "$$\n",
+ "\\frac{Share\\ Price_{t + Years}}{Share\\ Price_t} =\n",
+ "\\frac{Sales\\ Per\\ Share_{t + Years} \\cdot P/Sales_{t + Years}}{Sales\\ Per\\ Share_t \\cdot P/Sales_t} \\\\\n",
+ "= \\frac{Sales\\ Per\\ Share_{t + Years}}{Sales\\ Per\\ Share_t} \\cdot\n",
+ "\\frac{P/Sales_{t + Years}}{P/Sales_t}\n",
+ "$$\n",
+ "\n",
+ "Note that we could also use the change in Earnings Per Share and P/E ratios instead, but the Earnings (aka. Net Income) can be more unstable than Sales, because of temporary fluctuations in profit margins, as well as non-cash and/or non-recurring gains and losses. That is why we will use the P/Sales ratio here.\n",
+ "\n",
+ "This paper is a basic statistical study of how to predict the future P/Sales ratio."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from simfin.names import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "from IPython.display import display_jpeg\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import seaborn as sns\n",
+ "from scipy.stats import linregress\n",
+ "import statsmodels.api as sm\n",
+ "\n",
+ "# SimFin imports.\n",
+ "import simfin as sf\n",
+ "from simfin.names import *\n",
+ "from simfin.utils import BDAYS_PER_YEAR"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'0.5.0'"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Version of the SimFin Python API.\n",
+ "sf.__version__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Config\n",
+ "\n",
+ "Setup and configure the various Python packages we are using."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# SimFin data-directory.\n",
+ "sf.set_data_dir('~/simfin_data/')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# SimFin load API key or use free data.\n",
+ "sf.load_api_key(path='~/simfin_api_key.txt', default_key='free')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Seaborn set plotting style.\n",
+ "sns.set_style(\"whitegrid\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data-Hub\n",
+ "\n",
+ "We use [SimFin](https://github.com/SimFin/simfin) to easily load and process financial data with the following settings:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hub_args = \\\n",
+ "{\n",
+ " # We are interested in the US stock-market.\n",
+ " 'market': 'us',\n",
+ "\n",
+ " # Add this date-offset to the fundamental data such as\n",
+ " # Income Statements etc., because the REPORT_DATE is not\n",
+ " # when it was actually made available to the public,\n",
+ " # which can be 1, 2 or even 3 months after the Report Date.\n",
+ " 'offset': pd.DateOffset(days=60),\n",
+ " \n",
+ " # Use last-known values to fill in missing values.\n",
+ " 'fill_method': 'ffill',\n",
+ "\n",
+ " # Refresh the fundamental datasets (Income Statements etc.)\n",
+ " # every 30 days.\n",
+ " 'refresh_days': 30,\n",
+ "\n",
+ " # Refresh the dataset with shareprices every 10 days.\n",
+ " 'refresh_days_shareprices': 10\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can then create a `StockHub` object to handle all the data and signal processing:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 10 µs, sys: 3 µs, total: 13 µs\n",
+ "Wall time: 17.6 µs\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "hub = sf.StockHub(**hub_args)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Signals\n",
+ "\n",
+ "We can now use the stock-hub to calculate the signals that we will be using in our analysis, such as P/Sales, Net Profit Margin, Sales Growth, etc. We create a small helper-function for calculating and combining all the signals we need:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def all_signals(func=None, winsorize_quantile=0.03):\n",
+ " \"\"\"\n",
+ " Calculate Financial, Growth, and Valuation signals.\n",
+ " \n",
+ " :param func:\n",
+ " Function or callable object passed to signal-functions\n",
+ " e.g. to calculate 3-year averages: `func=sf.avg_ttm_3y`\n",
+ " \n",
+ " :param winsorize_quantile:\n",
+ " Float with the winsorization quantile.\n",
+ " If `None` then winsorization is not performed.\n",
+ " \n",
+ " :return:\n",
+ " Pandas DataFrame with signals.\n",
+ " \"\"\"\n",
+ " \n",
+ " # Financial Signals.\n",
+ " df_fin_signals = hub.fin_signals(variant='daily', func=func)\n",
+ " \n",
+ " # Growth Signals.\n",
+ " df_growth_signals = hub.growth_signals(variant='daily', func=func)\n",
+ " \n",
+ " # Valuation Signals.\n",
+ " df_val_signals = hub.val_signals(variant='daily', func=func)\n",
+ "\n",
+ " # Combine into a single DataFrame.\n",
+ " dfs = [df_fin_signals, df_growth_signals, df_val_signals]\n",
+ " df_signals = pd.concat(dfs, axis=1)\n",
+ "\n",
+ " # Winsorize the signals?\n",
+ " if winsorize_quantile is not None:\n",
+ " # Don't Winsorize these columns.\n",
+ " exclude_columns = [MARKET_CAP]\n",
+ " \n",
+ " # Remove outliers by setting them to NaN.\n",
+ " df_signals = sf.winsorize(df=df_signals, clip=False,\n",
+ " quantile=winsorize_quantile,\n",
+ " exclude_columns=exclude_columns)\n",
+ " \n",
+ " return df_signals"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now run the function to calculate all the signals. This may take several minutes the first time you run it. After that, it will automatically load the cached results from disk, until the underlying datasets are downloaded again from the SimFin server, at which point the signal-functions will be called again and new disk-cache files will be created."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset \"us-income-ttm\" on disk (14 days old).\n",
+ "- Loading from disk ... Done!\n",
+ "Dataset \"us-balance-ttm\" on disk (14 days old).\n",
+ "- Loading from disk ... Done!\n",
+ "Dataset \"us-shareprices-daily\" on disk (1 days old).\n",
+ "- Loading from disk ... Done!\n",
+ "Cache-file 'fin_signals-2a38bb7d.pickle' on disk (3 days old).\n",
+ "- Running function fin_signals() ... Done!\n",
+ "- Saving cache-file to disk ... Done!\n",
+ "Dataset \"us-income-quarterly\" on disk (14 days old).\n",
+ "- Loading from disk ... Done!\n",
+ "Dataset \"us-balance-quarterly\" on disk (12 days old).\n",
+ "- Loading from disk ... Done!\n",
+ "Dataset \"us-cashflow-ttm\" on disk (14 days old).\n",
+ "- Loading from disk ... Done!\n",
+ "Dataset \"us-cashflow-quarterly\" on disk (14 days old).\n",
+ "- Loading from disk ... Done!\n",
+ "Cache-file 'growth_signals-2a38bb7d.pickle' on disk (3 days old).\n",
+ "- Running function growth_signals() ... Done!\n",
+ "- Saving cache-file to disk ... Done!\n",
+ "Cache-file 'val_signals-739b68a6.pickle' on disk (3 days old).\n",
+ "- Running function val_signals() ... Done!\n",
+ "- Saving cache-file to disk ... Done!\n",
+ "CPU times: user 4min 7s, sys: 9.53 s, total: 4min 16s\n",
+ "Wall time: 4min 21s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "df_signals = all_signals()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is what the resulting DataFrames look like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " Asset Turnover Current Ratio Debt Ratio \\\n",
+ "Ticker Date \n",
+ "A 2011-06-29 0.711759 3.201005 0.24789 \n",
+ " 2011-06-30 0.711759 3.201005 0.24789 \n",
+ " 2011-07-01 0.711759 3.201005 0.24789 \n",
+ " 2011-07-05 0.711759 3.201005 0.24789 \n",
+ " 2011-07-06 0.711759 3.201005 0.24789 \n",
+ "\n",
+ " Gross Profit Margin Interest Coverage Net Profit Margin \\\n",
+ "Ticker Date \n",
+ "A 2011-06-29 0.532001 10.890411 0.144574 \n",
+ " 2011-06-30 0.532001 10.890411 0.144574 \n",
+ " 2011-07-01 0.532001 10.890411 0.144574 \n",
+ " 2011-07-05 0.532001 10.890411 0.144574 \n",
+ " 2011-07-06 0.532001 10.890411 0.144574 \n",
+ "\n",
+ " Return on Assets Return on Equity Assets Growth \\\n",
+ "Ticker Date \n",
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+ " 2011-06-30 0.102902 0.224691 NaN \n",
+ " 2011-07-01 0.102902 0.224691 NaN \n",
+ " 2011-07-05 0.102902 0.224691 NaN \n",
+ " 2011-07-06 0.102902 0.224691 NaN \n",
+ "\n",
+ " Assets Growth QOQ ... Dividend Yield Earnings Yield \\\n",
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+ " 2011-06-30 NaN ... NaN 0.049121 \n",
+ " 2011-07-01 NaN ... NaN 0.048280 \n",
+ " 2011-07-05 NaN ... NaN 0.048598 \n",
+ " 2011-07-06 NaN ... NaN 0.048448 \n",
+ "\n",
+ " FCF Yield Market-Cap P/Book P/E P/FCF \\\n",
+ "Ticker Date \n",
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+ " 2011-06-30 0.044761 1.811850e+10 4.574222 20.357860 22.340931 \n",
+ " 2011-07-01 0.043995 1.843400e+10 4.653875 20.712360 22.729963 \n",
+ " 2011-07-05 0.044284 1.831347e+10 4.623446 20.576933 22.581344 \n",
+ " 2011-07-06 0.044148 1.837019e+10 4.637766 20.640663 22.651282 \n",
+ "\n",
+ " P/NCAV P/NetNet P/Sales \n",
+ "Ticker Date \n",
+ "A 2011-06-29 43.956262 -29.990226 2.913281 \n",
+ " 2011-06-30 44.408076 -30.298487 2.943225 \n",
+ " 2011-07-01 45.181373 -30.826087 2.994477 \n",
+ " 2011-07-05 44.885956 -30.624532 2.974898 \n",
+ " 2011-07-06 45.024975 -30.719381 2.984111 \n",
+ "\n",
+ "[5 rows x 30 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_signals.dropna(how='all').head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also calculate 3-year averages for the signals, so instead of using e.g. just a single year's Net Profit Margin which may be quite volatile, we can smoothen it by using 3-year averages. Further below we will test if this is better at predicting future P/Sales ratios."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cache-file 'fin_signals-2186bbb1.pickle' on disk (3 days old).\n",
+ "- Running function fin_signals() ... Done!\n",
+ "- Saving cache-file to disk ... Done!\n",
+ "Cache-file 'growth_signals-2186bbb1.pickle' on disk (3 days old).\n",
+ "- Running function growth_signals() ... Done!\n",
+ "- Saving cache-file to disk ... Done!\n",
+ "Cache-file 'val_signals-65726cdc.pickle' on disk (3 days old).\n",
+ "- Running function val_signals() ... Done!\n",
+ "- Saving cache-file to disk ... Done!\n",
+ "CPU times: user 4min 9s, sys: 8.36 s, total: 4min 18s\n",
+ "Wall time: 4min 19s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "df_signals_3y = all_signals(func=sf.avg_ttm_3y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## P/Sales Signals\n",
+ "\n",
+ "Let us now create a new Pandas DataFrame just for the P/Sales signals that we want to predict. We first copy the basic P/Sales signal from the DataFrame we calculated above. Then we add a few more columns to the DataFrame:\n",
+ "\n",
+ "- `PSALES_3Y_PAST`: The average P/Sales ratio for the PAST 3 years.\n",
+ "- `PSALES_3Y_FUTURE`: The average P/Sales ratio for the FUTURE 3 years.\n",
+ "- `PSALES_REL_PAST`: The daily P/Sales ratio divided by its PAST 3-year average.\n",
+ "- `PSALES_REL_FUTURE`: The FUTURE 3-year average divided by the daily P/Sales."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 3.03 s, sys: 104 ms, total: 3.13 s\n",
+ "Wall time: 3.14 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "# Copy the P/Sales column to a new DataFrame.\n",
+ "# Note that we select a list of columns to create a DataFrame,\n",
+ "# so we can add more columns to it.\n",
+ "df_psales = df_signals[[PSALES]].copy()\n",
+ "\n",
+ "# Convert 3 years to the equivalent number of trading-days.\n",
+ "periods_3y, _ = sf.convert_to_periods(freq='bdays', years=3)\n",
+ "\n",
+ "# Calculate the PAST 3-year average P/Sales ratios.\n",
+ "# Note the use of groupby to ensure the different stock-tickers\n",
+ "# are processed individually.\n",
+ "PSALES_3Y_PAST = 'P/Sales 3Y Avg. PAST'\n",
+ "df_psales[PSALES_3Y_PAST] = df_psales[PSALES] \\\n",
+ " .groupby(TICKER, group_keys=False) \\\n",
+ " .rolling(window=periods_3y).mean()\n",
+ "\n",
+ "# Calculate the FUTURE 3-year average P/Sales ratios.\n",
+ "# Note that we again use groupby to ensure the different\n",
+ "# stock-tickers are processed individually. Also note\n",
+ "# the 1-periods_3y in the shifting, to properly align the data.\n",
+ "PSALES_3Y_FUTURE = 'P/Sales 3Y Avg. FUTURE'\n",
+ "df_psales[PSALES_3Y_FUTURE] = df_psales[PSALES_3Y_PAST] \\\n",
+ " .groupby(TICKER, group_keys=False).shift(1-periods_3y)\n",
+ "\n",
+ "# Calculate the current P/Sales divided by its PAST 3-year average.\n",
+ "# Note that we do not need to use groupby for this, because\n",
+ "# we only use data from within the same row of the DataFrame.\n",
+ "PSALES_REL_PAST = '(P/Sales) / (P/Sales 3Y Avg. PAST)'\n",
+ "df_psales[PSALES_REL_PAST] = df_psales[PSALES] / df_psales[PSALES_3Y_PAST]\n",
+ "\n",
+ "# Calculate the FUTURE 3-year average div. by current P/Sales.\n",
+ "PSALES_REL_FUTURE = '(P/Sales 3Y Avg. FUTURE) / (P/Sales)'\n",
+ "df_psales[PSALES_REL_FUTURE] = df_psales[PSALES_3Y_FUTURE] / df_psales[PSALES]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These are the resulting P/Sales ratios for ticker MSFT:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
P/Sales
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+ "
P/Sales 3Y Avg. PAST
\n",
+ "
P/Sales 3Y Avg. FUTURE
\n",
+ "
(P/Sales) / (P/Sales 3Y Avg. PAST)
\n",
+ "
(P/Sales 3Y Avg. FUTURE) / (P/Sales)
\n",
+ "
\n",
+ "
\n",
+ "
Date
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+ "
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+ "
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+ "
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+ "
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+ " \n",
+ " \n",
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+ "
2010-06-01
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+ "
3.890739
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NaN
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3.469545
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+ "
NaN
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0.891744
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+ "
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+ "
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2010-06-02
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+ "
3.976399
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NaN
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3.469552
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+ "
NaN
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+ "
0.872536
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+ "
\n",
+ "
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+ "
2010-06-03
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+ "
4.036511
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+ "
NaN
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+ "
3.469547
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+ "
NaN
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+ "
0.859541
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+ "
\n",
+ "
\n",
+ "
2010-06-04
\n",
+ "
3.875712
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+ "
NaN
\n",
+ "
3.469373
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+ "
NaN
\n",
+ "
0.895158
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+ "
\n",
+ "
\n",
+ "
2010-06-07
\n",
+ "
3.800572
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+ "
NaN
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+ "
3.469382
\n",
+ "
NaN
\n",
+ "
0.912858
\n",
+ "
\n",
+ "
\n",
+ "
...
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+ "
...
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+ "
...
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+ "
...
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+ "
...
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+ "
...
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+ "
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+ "
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+ "
2020-03-10
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+ "
9.253418
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+ "
7.500059
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+ "
NaN
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+ "
1.233779
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+ "
NaN
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+ "
\n",
+ "
\n",
+ "
2020-03-11
\n",
+ "
8.834220
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+ "
7.504583
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+ "
NaN
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+ "
1.177177
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+ "
NaN
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+ "
\n",
+ "
\n",
+ "
2020-03-12
\n",
+ "
7.996398
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+ "
7.508022
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+ "
NaN
\n",
+ "
1.065047
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+ "
NaN
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+ "
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+ "
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+ "
2020-03-13
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+ "
9.133237
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+ "
7.513000
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+ "
NaN
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+ "
1.215658
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+ "
NaN
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+ "
\n",
+ "
\n",
+ "
2020-03-16
\n",
+ "
7.787086
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+ "
7.516157
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+ "
NaN
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+ "
1.036046
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+ "
NaN
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+ "
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+ " \n",
+ "
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+ "
2465 rows × 5 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " P/Sales P/Sales 3Y Avg. PAST P/Sales 3Y Avg. FUTURE \\\n",
+ "Date \n",
+ "2010-06-01 3.890739 NaN 3.469545 \n",
+ "2010-06-02 3.976399 NaN 3.469552 \n",
+ "2010-06-03 4.036511 NaN 3.469547 \n",
+ "2010-06-04 3.875712 NaN 3.469373 \n",
+ "2010-06-07 3.800572 NaN 3.469382 \n",
+ "... ... ... ... \n",
+ "2020-03-10 9.253418 7.500059 NaN \n",
+ "2020-03-11 8.834220 7.504583 NaN \n",
+ "2020-03-12 7.996398 7.508022 NaN \n",
+ "2020-03-13 9.133237 7.513000 NaN \n",
+ "2020-03-16 7.787086 7.516157 NaN \n",
+ "\n",
+ " (P/Sales) / (P/Sales 3Y Avg. PAST) \\\n",
+ "Date \n",
+ "2010-06-01 NaN \n",
+ "2010-06-02 NaN \n",
+ "2010-06-03 NaN \n",
+ "2010-06-04 NaN \n",
+ "2010-06-07 NaN \n",
+ "... ... \n",
+ "2020-03-10 1.233779 \n",
+ "2020-03-11 1.177177 \n",
+ "2020-03-12 1.065047 \n",
+ "2020-03-13 1.215658 \n",
+ "2020-03-16 1.036046 \n",
+ "\n",
+ " (P/Sales 3Y Avg. FUTURE) / (P/Sales) \n",
+ "Date \n",
+ "2010-06-01 0.891744 \n",
+ "2010-06-02 0.872536 \n",
+ "2010-06-03 0.859541 \n",
+ "2010-06-04 0.895158 \n",
+ "2010-06-07 0.912858 \n",
+ "... ... \n",
+ "2020-03-10 NaN \n",
+ "2020-03-11 NaN \n",
+ "2020-03-12 NaN \n",
+ "2020-03-13 NaN \n",
+ "2020-03-16 NaN \n",
+ "\n",
+ "[2465 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_psales.loc['MSFT'].dropna(how='all')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For convenience, let us add these columns to the DataFrames with all the other signals."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 404 ms, sys: 196 ms, total: 600 ms\n",
+ "Wall time: 598 ms\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "# Add new P/Sales columns to DataFrame with normal signals.\n",
+ "dfs = [df_signals, df_psales.drop(columns=[PSALES])]\n",
+ "df_signals = pd.concat(dfs, axis=1)\n",
+ "\n",
+ "# Add new P/Sales columns to DataFrame with 3-year avg. signals.\n",
+ "dfs = [df_signals_3y, df_psales.drop(columns=[PSALES])]\n",
+ "df_signals_3y = pd.concat(dfs, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data Years\n",
+ "\n",
+ "Let us see how many years of P/Sales data we have for all the different stocks. First we need a small helper-function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def data_years(df):\n",
+ " \"\"\"\n",
+ " Calculate the number of years of data in DataFrame `df`.\n",
+ " \n",
+ " :param df:\n",
+ " Pandas DataFrame assumed to have daily data and be\n",
+ " grouped by TICKER, and not have any empty NaN rows.\n",
+ "\n",
+ " :return:\n",
+ " Pandas Series with number of years for each TICKER.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Count the number of data-points for each ticker.\n",
+ " df_data_days = df.groupby(TICKER).apply(lambda df_grp: len(df_grp))\n",
+ "\n",
+ " # Calculate the number of years of data for each ticker.\n",
+ " # This is the number of days divided by the average\n",
+ " # number of business/trading-days per year.\n",
+ " df_data_years = df_data_days / BDAYS_PER_YEAR\n",
+ " \n",
+ " return df_data_years"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "On average we have about 6 years of P/Sales data for all these stocks:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 1723.000000\n",
+ "mean 6.305951\n",
+ "std 2.895633\n",
+ "min 0.003973\n",
+ "25% 4.048953\n",
+ "50% 7.537649\n",
+ "75% 8.793261\n",
+ "max 11.288592\n",
+ "Name: P/Sales, dtype: float64"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Calculate the number of years of P/Sales data for all stocks.\n",
+ "df_data_years = data_years(df=df_psales[PSALES].dropna())\n",
+ "\n",
+ "# Show statistics.\n",
+ "df_data_years.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also plot a histogram, so we can see the distribution of how many years of P/Sales data we have for all the individual companies:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_data_years.plot(kind='hist', bins=50);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the scatter-plots further below, we will compare the current P/Sales ratio to the 3-year FUTURE average P/Sales. The summary statistics below shows that on average there is a bit more than 4 years of such data per company, for a total of nearly 1400 companies:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 1369.000000\n",
+ "mean 4.310333\n",
+ "std 1.995259\n",
+ "min 0.043708\n",
+ "25% 2.801287\n",
+ "50% 4.795963\n",
+ "75% 5.797274\n",
+ "max 8.292605\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = df_psales[[PSALES, PSALES_3Y_FUTURE]].dropna(how='any')\n",
+ "data_years(df=df).describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In another scatter-plot further below we will compare the PAST 3-year average P/Sales to the FUTURE 3-year average P/Sales. The statistics below show that there was only a bit more than 2 years of data-points per company, for a total of about 1000 companies:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 1013.000000\n",
+ "mean 2.336781\n",
+ "std 1.055751\n",
+ "min 0.019867\n",
+ "25% 1.796003\n",
+ "50% 2.296658\n",
+ "75% 2.876783\n",
+ "max 5.296619\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = df_psales[[PSALES_3Y_PAST, PSALES_3Y_FUTURE]].dropna(how='any')\n",
+ "data_years(df=df).describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is a fairly short data-period, because the SimFin database currently does not have any more data. What this means for our analysis, is that we should interpret the results with some caution, as the data may contain trends that are unique for that period in time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plotting Functions\n",
+ "\n",
+ "These are small helper-functions for making plots."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_with_mean(df):\n",
+ " \"\"\"\n",
+ " Make line-plot and a horizontal line for the mean.\n",
+ " \n",
+ " :param df:\n",
+ " Pandas DataFrame with a time-series.\n",
+ " \n",
+ " :return:\n",
+ " Matplotlib axes object.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Plot the time-series.\n",
+ " ax = df.dropna().plot()\n",
+ " \n",
+ " # Overlay the plot with a horizontal line for the mean.\n",
+ " ax.axhline(df.mean(), c='red')\n",
+ " \n",
+ " return ax"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_scatter(df, x, y, hue=None, num_samples=5000):\n",
+ " \"\"\"\n",
+ " Make a scatter-plot using a random sub-sample of the data.\n",
+ " \n",
+ " :param df:\n",
+ " Pandas DataFrame with columns named `x`, `y` and `hue`.\n",
+ "\n",
+ " :param x:\n",
+ " String with column-name for the x-axis.\n",
+ "\n",
+ " :param y:\n",
+ " String with column-name for the y-axis.\n",
+ "\n",
+ " :param hue:\n",
+ " Either None or string with column-name for the hue.\n",
+ "\n",
+ " :param num_samples:\n",
+ " Int with number of random samples for the scatter-plot.\n",
+ "\n",
+ " :return:\n",
+ " matplotlib Axes object\n",
+ " \"\"\"\n",
+ "\n",
+ " # Select the relevant columns from the DataFrame.\n",
+ " if hue is None:\n",
+ " df = df[[x, y]].dropna()\n",
+ " else:\n",
+ " df = df[[x, y, hue]].dropna()\n",
+ "\n",
+ " # Only plot a random sample of the data-points?\n",
+ " if num_samples is not None and len(df) > num_samples:\n",
+ " idx = np.random.randint(len(df), size=num_samples)\n",
+ " df = df.iloc[idx]\n",
+ "\n",
+ " # Ensure the plotting area is a square.\n",
+ " plt.figure(figsize=(5,5))\n",
+ "\n",
+ " # Make the scatter-plot.\n",
+ " ax = sns.scatterplot(x=x, y=y, hue=hue, s=20,\n",
+ " data=df.reset_index())\n",
+ "\n",
+ " # Move legend for the hue.\n",
+ " if hue is not None:\n",
+ " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)\n",
+ " \n",
+ " return ax"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_scatter_fit(df, x, y, num_samples=5000):\n",
+ " \"\"\"\n",
+ " Make a scatter-plot and fit a line through the points.\n",
+ " \n",
+ " If there are many data-points, you can use a random\n",
+ " sample for the scatter-plot, but the linear formula\n",
+ " is still found using all the data-points.\n",
+ " \n",
+ " :param df:\n",
+ " Pandas DataFrame with columns named `x` and `y`.\n",
+ "\n",
+ " :param x:\n",
+ " String with column-name for the x-axis.\n",
+ "\n",
+ " :param y:\n",
+ " String with column-name for the y-axis.\n",
+ "\n",
+ " :param num_samples:\n",
+ " Int with number of random samples for the scatter-plot.\n",
+ "\n",
+ " :return:\n",
+ " matplotlib Axes object\n",
+ " \"\"\"\n",
+ " \n",
+ " # Select the relevant columns from the DataFrame.\n",
+ " df = df[[x, y]].dropna(how='any').reset_index()\n",
+ "\n",
+ " # Fit a line through all the data-points and get stats.\n",
+ " slope, intercept, r_value, p_value, std_err = \\\n",
+ " linregress(x=df[x], y=df[y])\n",
+ "\n",
+ " # Show the fitted line and its stats.\n",
+ " msg = 'y = {0:.2f} * x + {1:.2f} (R^2={2:.2f}, p={3:.0e})'\n",
+ " msg = msg.format(slope, intercept, r_value**2, p_value)\n",
+ " print(msg)\n",
+ " \n",
+ " # Only plot a random sample of the data-points?\n",
+ " if num_samples is not None and len(df) > num_samples:\n",
+ " idx = np.random.randint(len(df), size=num_samples)\n",
+ " df = df.iloc[idx]\n",
+ "\n",
+ " # Make the scatter-plot with a fitted line.\n",
+ " # This uses the smaller sample of data-points.\n",
+ " ax = sns.jointplot(x=x, y=y, kind='reg', data=df,\n",
+ " line_kws={'color': 'red'},\n",
+ " scatter_kws={'s': 2})\n",
+ "\n",
+ " return ax"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Regression Function\n",
+ "\n",
+ "This is a small helper-function for doing multiple linear regression."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def regression(df, y, standardize=True, use_constant=True):\n",
+ " \"\"\"\n",
+ " Perform multiple linear-regression on the given DataFrame.\n",
+ " \n",
+ " :param df:\n",
+ " Pandas DataFrame with signals and returns.\n",
+ " \n",
+ " :param y:\n",
+ " String with column-name for the dependent variable.\n",
+ " This will be taken from the DataFrame `df`.\n",
+ " \n",
+ " :param standardize:\n",
+ " Boolean whether to standardize the predictor variables\n",
+ " so they have 0 mean and 1 standard deviation.\n",
+ "\n",
+ " :param use_constant:\n",
+ " Boolean whether to add a 'Constant' column to\n",
+ " find the bias.\n",
+ " \n",
+ " :return:\n",
+ " StatsModels Regression Results.\n",
+ " \"\"\"\n",
+ " \n",
+ " # Remove rows with missing values.\n",
+ " df = df.dropna(how='any').copy()\n",
+ "\n",
+ " # DataFrame for the x-signals.\n",
+ " df_x = df.drop(columns=[y])\n",
+ " \n",
+ " # DataFrame for the y-signal.\n",
+ " df_y = df[y]\n",
+ "\n",
+ " # Standardize the signals so they have mean 0 and std 1.\n",
+ " if standardize:\n",
+ " df_x = (df_x - df_x.mean()) / df_x.std()\n",
+ "\n",
+ " # Add a \"constant\" column so the regression can find the bias.\n",
+ " if use_constant:\n",
+ " df_x['Constant'] = 1.0\n",
+ "\n",
+ " # Perform the regression on this data.\n",
+ " model = sm.OLS(df_y, df_x).fit()\n",
+ " \n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## P/Sales Self-Prediction\n",
+ "\n",
+ "Let us now consider how the current P/Sales ratio is related to the future 3-year average P/Sales ratios. We use our helper-function to make a scatter-plot and fit a straight line through the data.\n",
+ "\n",
+ "Each dot in the scatter-plot has on the x-axis the P/Sales ratio for some company's stock at some date, and on the y-axis is the average P/Sales ratio from the same date and going 3 years into the future.\n",
+ "\n",
+ "Also note that the scatter-plot only uses a sample of 5000 randomly selected points from the dataset, because there is actually more than a million data-points, which would make the plotting very slow (or even crash the program), and even if it worked, it would make the plot very messy to look at. The red line on the plot is fitted to those 5000 randomly selected data-points, while the linear formula printed at the top, is fitted to all of the data-points.\n",
+ "\n",
+ "We see that the fitted line is roughly $y = 0.9 * x + 0.4$ with $R^2=0.82$ so it is a really good fit, that shows the current P/Sales ratio is a strong predictor for the future 3-year average P/Sales ratios."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "y = 0.88 * x + 0.43 (R^2=0.82, p=0e+00)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_scatter_fit(df=df_psales, x=PSALES, y=PSALES_3Y_FUTURE);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also compare the PAST 3-year average P/Sales to the FUTURE 3-year average P/Sales. The scatter-plot is shown below and it is very similar to the scatter-plot above. The fitted line has a slope of nearly 1, and the intercept is about 0.4 so the fitted line is roughly $y=x + 0.4$ with $R^2 = 0.78$, so the fit is nearly as good as above. The small bias suggests the stocks have generally seen a small upwards revaluation over the time-period being considered in this dataset, we will discuss this in more detail below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "y = 0.95 * x + 0.36 (R^2=0.78, p=0e+00)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_scatter_fit(df=df_psales,\n",
+ " x=PSALES_3Y_PAST, y=PSALES_3Y_FUTURE);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The scatter-plots above show us that **in aggregate**, the stocks tend to have the same average P/Sales ratios in the FUTURE 3-years as they did in the PAST 3-years. But the **individual** stocks can experience significant changes in their P/Sales ratios.\n",
+ "\n",
+ "The following scatter-plot shows the 3-year average PAST P/Sales ratio on the x-axis, and the 3-year average FUTURE P/Sales ratio on the y-axis. The hue of the dots is the current P/Sales ratio divided by its PAST 3-year average P/Sales, where a value below 1 shows that the current P/Sales ratio is below its PAST 3-year average, and vice versa for a value above 1.\n",
+ "\n",
+ "In the plot below, we can see that the dots with light hue tend to be below the diagonal, and the dots with dark hue tend to be above the diagonal. This indicates that stocks whose P/Sales has recently dropped below its PAST 3-year average, tend to stay down and have a lower average P/Sales in the FUTURE 3-years."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Limit some of the data-points to more clearly show the hue.\n",
+ "lower = {PSALES_REL_PAST: 0, PSALES_REL_FUTURE: 0}\n",
+ "upper = {PSALES_REL_PAST: 2, PSALES_REL_FUTURE: 2}\n",
+ "df_psales2 = sf.clip(df=df_psales, lower=lower, upper=upper)\n",
+ "\n",
+ "plot_scatter(x=PSALES_3Y_PAST, y=PSALES_3Y_FUTURE,\n",
+ " hue=PSALES_REL_PAST, df=df_psales2);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let us consider another scatter-plot, where the x-axis is the current P/Sales ratio divided by its PAST 3-year average P/Sales, and the y-axis is the FUTURE 3-year average P/Sales divided by its current P/Sales ratio.\n",
+ "\n",
+ "This is another way of showing if a recent change in P/Sales ratio compared to its PAST 3-year average, can serve as a predictor for the FUTURE change over the following 3 years.\n",
+ "\n",
+ "The plot shows a blob of seemingly random points without any tendency, and the fitted line has an $R^2$ of nearly zero, which means there is no linear relation between the x and y-values.\n",
+ "\n",
+ "So the plot shows that for stocks in general, their recent change in P/Sales ratio relative to their PAST 3-year average, does not predict their FUTURE change in P/Sales ratio. In other words, there does not seem to be any mean-reversion for P/Sales ratios in general."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "y = -0.16 * x + 1.24 (R^2=0.02, p=0e+00)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Remove some of the data-points by setting them to NaN,\n",
+ "# because clipped/limited values can distort this plot.\n",
+ "df_psales3 = sf.clip(df=df_psales, lower=lower, upper=upper,\n",
+ " clip=False)\n",
+ "\n",
+ "plot_scatter_fit(x=PSALES_REL_PAST, y=PSALES_REL_FUTURE,\n",
+ " df=df_psales3);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let us instead try and make the same scatter-plot but for individual stocks. The following plot shows it for ticker AAPL, which clearly shows strong tendecies for mean reversion, because a recent drop in the P/Sales ratio compared to its PAST 3-year average, is followed by a rise in P/Sales ratio for the FUTURE 3-years."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_scatter(x=PSALES_REL_PAST, y=PSALES_REL_FUTURE,\n",
+ " df=df_psales.loc['WMT']);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following plot shows it for ticker MSFT, which also has downwards-sloping curves, but even more spread out."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_scatter(x=PSALES_REL_PAST, y=PSALES_REL_FUTURE,\n",
+ " df=df_psales.loc['MSFT']);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also make a simple plot of the P/Sales for each stock where the mean is shown as a red line. The following plot is for ticker AAPL which seems to have a tendency for mean-reversion. The P/Sales in the beginning of year 2020 was historically high, so we might \"forecast\" that it will revert to its historical mean over the next year or so, which would cause the P/Sales ratio to drop maybe 40%, and unless this can be offset by a similar Sales Growth, we might expect AAPL stock to drop significantly over the coming year or so - unless the stock-market has \"decided\" that it wants to continue paying a historically high P/Sales ratio for AAPL stock.\n",
+ "\n",
+ "NOTE: This text was originally written before the March 2020 stock-market crash due to the Corona virus."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_with_mean(df=df_psales.loc['AAPL', PSALES]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Similarly, we can plot the P/Sales ratio for ticker WMT, which also shows a high P/Sales ratio about 25% above its historical mean in early 2020, so we can draw a similar conclusion, that it might experience a significantly drop over the coming year or two, unless the company either experiences a similar Sales Growth, or the stock-market has \"decided\" that it wants to permanently value WMT at a higher P/Sales ratio than its historical average."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_with_mean(df=df_psales.loc['WMT', PSALES]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following plot shows the P/Sales ratio for ticker MSFT, which has nearly tripled over the last 10 years, in a nearly continuous increase, without any indication of mean-reversion."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_with_mean(df=df_psales.loc['MSFT', PSALES]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is an example of a stock that dropped dramatically in March 2020 due to the Corono-virus panic. This is ticker CCL for the Carnival Corporation which is one of the world's largest cruise-ship operators, which had one of the early virus-incidents on one of their ships. At the time of this writing it is trading at a P/Sales ratio around 0.5. If it is to revert to its mean around 2.1 from the last 10 years, then it would give roughly a 400% return. The question is whether the company will go bankrupt or have to issue more shares at low valuation ratios, in order to survive the Corona-panic. Time will tell if this is a fantastic or terrible investment at these low prices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_with_mean(df=df_psales.loc['CCL', PSALES]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, let us try and fit a Multiple Linear Regression model to use the various P/Sales signals to predict the FUTURE 3-year average P/Sales ratio. This has $R^2 = 0.86$ which is only marginally better than if we only used either the current P/Sales ratio, or the PAST 3-year average P/Sales ratio. So there is little or no real advantage to combining these signals when predicting the FUTURE 3-year average P/Sales.\n",
+ "\n",
+ "Also note that because the data gets standardized to have 0 mean and 1 standard deviation before fitting the regression model, the fitted coefficients show the relative importance of the predictor signals. Clearly the most important predictive signal is the current P/Sales ratio. The second-most important signal is the PAST 3-year average P/Sales ratio. And of no real importance is the current P/Sales ratio divided by its PAST 3-year average."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "
OLS Regression Results
\n",
+ "
\n",
+ "
Dep. Variable:
P/Sales 3Y Avg. FUTURE
R-squared:
0.859
\n",
+ "
\n",
+ "
\n",
+ "
Model:
OLS
Adj. R-squared:
0.859
\n",
+ "
\n",
+ "
\n",
+ "
Method:
Least Squares
F-statistic:
1.214e+06
\n",
+ "
\n",
+ "
\n",
+ "
Date:
Wed, 18 Mar 2020
Prob (F-statistic):
0.00
\n",
+ "
\n",
+ "
\n",
+ "
Time:
11:14:13
Log-Likelihood:
-8.1478e+05
\n",
+ "
\n",
+ "
\n",
+ "
No. Observations:
595743
AIC:
1.630e+06
\n",
+ "
\n",
+ "
\n",
+ "
Df Residuals:
595739
BIC:
1.630e+06
\n",
+ "
\n",
+ "
\n",
+ "
Df Model:
3
\n",
+ "
\n",
+ "
\n",
+ "
Covariance Type:
nonrobust
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
coef
std err
t
P>|t|
[0.025
0.975]
\n",
+ "
\n",
+ "
\n",
+ "
P/Sales
1.9175
0.005
381.173
0.000
1.908
1.927
\n",
+ "
\n",
+ "
\n",
+ "
P/Sales 3Y Avg. PAST
0.4614
0.005
93.334
0.000
0.452
0.471
\n",
+ "
\n",
+ "
\n",
+ "
(P/Sales) / (P/Sales 3Y Avg. PAST)
-0.0127
0.002
-6.668
0.000
-0.016
-0.009
\n",
+ "
\n",
+ "
\n",
+ "
Constant
2.8828
0.001
2342.083
0.000
2.880
2.885
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Omnibus:
143750.752
Durbin-Watson:
0.008
\n",
+ "
\n",
+ "
\n",
+ "
Prob(Omnibus):
0.000
Jarque-Bera (JB):
3687217.475
\n",
+ "
\n",
+ "
\n",
+ "
Skew:
0.581
Prob(JB):
0.00
\n",
+ "
\n",
+ "
\n",
+ "
Kurtosis:
15.132
Cond. No.
8.08
\n",
+ "
\n",
+ "
Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
+ "text/plain": [
+ "\n",
+ "\"\"\"\n",
+ " OLS Regression Results \n",
+ "==================================================================================\n",
+ "Dep. Variable: P/Sales 3Y Avg. FUTURE R-squared: 0.859\n",
+ "Model: OLS Adj. R-squared: 0.859\n",
+ "Method: Least Squares F-statistic: 1.214e+06\n",
+ "Date: Wed, 18 Mar 2020 Prob (F-statistic): 0.00\n",
+ "Time: 11:14:13 Log-Likelihood: -8.1478e+05\n",
+ "No. Observations: 595743 AIC: 1.630e+06\n",
+ "Df Residuals: 595739 BIC: 1.630e+06\n",
+ "Df Model: 3 \n",
+ "Covariance Type: nonrobust \n",
+ "======================================================================================================\n",
+ " coef std err t P>|t| [0.025 0.975]\n",
+ "------------------------------------------------------------------------------------------------------\n",
+ "P/Sales 1.9175 0.005 381.173 0.000 1.908 1.927\n",
+ "P/Sales 3Y Avg. PAST 0.4614 0.005 93.334 0.000 0.452 0.471\n",
+ "(P/Sales) / (P/Sales 3Y Avg. PAST) -0.0127 0.002 -6.668 0.000 -0.016 -0.009\n",
+ "Constant 2.8828 0.001 2342.083 0.000 2.880 2.885\n",
+ "==============================================================================\n",
+ "Omnibus: 143750.752 Durbin-Watson: 0.008\n",
+ "Prob(Omnibus): 0.000 Jarque-Bera (JB): 3687217.475\n",
+ "Skew: 0.581 Prob(JB): 0.00\n",
+ "Kurtosis: 15.132 Cond. No. 8.08\n",
+ "==============================================================================\n",
+ "\n",
+ "Warnings:\n",
+ "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+ "\"\"\""
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Fit a multiple linear regression model and show the result.\n",
+ "df = df_psales[[PSALES, PSALES_3Y_PAST, PSALES_3Y_FUTURE,\n",
+ " PSALES_REL_PAST]]\n",
+ "model = regression(df=df, y=PSALES_3Y_FUTURE, standardize=True)\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## P/Sales vs. Other Signals\n",
+ "\n",
+ "Let us now investigate if any of the other signals can be used to predict the FUTURE 3-year average P/Sales ratio. First we calculate the linear correlations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 18.2 s, sys: 743 ms, total: 18.9 s\n",
+ "Wall time: 18.4 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "df_corr = df_signals.corr()\n",
+ "df_corr_3y = df_signals_3y.corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# New column names.\n",
+ "SIGNALS_NORMAL = 'Normal Signals'\n",
+ "SIGNALS_3Y = '3-Year Avg. Signals'\n",
+ "\n",
+ "# Create a new DataFrame with the correlations.\n",
+ "data = \\\n",
+ "{\n",
+ " SIGNALS_NORMAL: df_corr[PSALES_3Y_FUTURE],\n",
+ " SIGNALS_3Y: df_corr_3y[PSALES_3Y_FUTURE]\n",
+ "}\n",
+ "df = pd.DataFrame(data=data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The table below shows the absolute correlations between the FUTURE 3-year average P/Sales ratio and the various signals. A value of 1 means perfect correlation, while a value of 0 means there is no linear relation between the variables. Here we are only interested in comparing the correlation strength and not the direction, that is why we consider the absolute correlation.\n",
+ "\n",
+ "The first column shows the correlations for the normal signals which are calculated with the TTM financial data. The second column shows the correlations for the signals calculated with 3-year PAST averages for the financial data. The only real difference between these two columns seems to be for the P/E and Net Profit Margin, where the correlation is stronger when using the PAST 3-year averages.\n",
+ "\n",
+ "What we generally see is that the current P/Sales ratio has the strongest correlation with the FUTURE 3-year average P/Sales ratio. The next strongest correlation is for the PAST 3-year average P/Sales ratio. Then the Gross Profit Margin and Asset Turnover. The P/Book and P/E are also valuation ratios, so we will ignore those."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Normal Signals
\n",
+ "
3-Year Avg. Signals
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
P/Sales 3Y Avg. FUTURE
\n",
+ "
1.000000
\n",
+ "
1.000000
\n",
+ "
\n",
+ "
\n",
+ "
P/Sales
\n",
+ "
0.903879
\n",
+ "
0.895450
\n",
+ "
\n",
+ "
\n",
+ "
P/Sales 3Y Avg. PAST
\n",
+ "
0.882970
\n",
+ "
0.882970
\n",
+ "
\n",
+ "
\n",
+ "
Gross Profit Margin
\n",
+ "
0.547388
\n",
+ "
0.550507
\n",
+ "
\n",
+ "
\n",
+ "
Asset Turnover
\n",
+ "
0.437774
\n",
+ "
0.437289
\n",
+ "
\n",
+ "
\n",
+ "
P/Book
\n",
+ "
0.299908
\n",
+ "
0.313731
\n",
+ "
\n",
+ "
\n",
+ "
Sales Growth
\n",
+ "
0.217111
\n",
+ "
0.230814
\n",
+ "
\n",
+ "
\n",
+ "
Sales Growth YOY
\n",
+ "
0.196713
\n",
+ "
0.214804
\n",
+ "
\n",
+ "
\n",
+ "
P/E
\n",
+ "
0.195813
\n",
+ "
0.271600
\n",
+ "
\n",
+ "
\n",
+ "
Current Ratio
\n",
+ "
0.157852
\n",
+ "
0.169831
\n",
+ "
\n",
+ "
\n",
+ "
Assets Growth YOY
\n",
+ "
0.149265
\n",
+ "
0.210366
\n",
+ "
\n",
+ "
\n",
+ "
Assets Growth
\n",
+ "
0.143340
\n",
+ "
0.210357
\n",
+ "
\n",
+ "
\n",
+ "
FCF Yield
\n",
+ "
0.141977
\n",
+ "
0.161792
\n",
+ "
\n",
+ "
\n",
+ "
(P/Sales) / (P/Sales 3Y Avg. PAST)
\n",
+ "
0.133496
\n",
+ "
0.133496
\n",
+ "
\n",
+ "
\n",
+ "
Net Profit Margin
\n",
+ "
0.122203
\n",
+ "
0.261017
\n",
+ "
\n",
+ "
\n",
+ "
Market-Cap
\n",
+ "
0.093990
\n",
+ "
0.093990
\n",
+ "
\n",
+ "
\n",
+ "
P/FCF
\n",
+ "
0.090578
\n",
+ "
0.085985
\n",
+ "
\n",
+ "
\n",
+ "
Assets Growth QOQ
\n",
+ "
0.089949
\n",
+ "
0.118375
\n",
+ "
\n",
+ "
\n",
+ "
Earnings Growth
\n",
+ "
0.089451
\n",
+ "
0.093019
\n",
+ "
\n",
+ "
\n",
+ "
FCF Growth QOQ
\n",
+ "
0.079901
\n",
+ "
0.081530
\n",
+ "
\n",
+ "
\n",
+ "
Debt Ratio
\n",
+ "
0.070723
\n",
+ "
0.111618
\n",
+ "
\n",
+ "
\n",
+ "
Dividend Yield
\n",
+ "
0.064765
\n",
+ "
0.011235
\n",
+ "
\n",
+ "
\n",
+ "
P/NCAV
\n",
+ "
0.063899
\n",
+ "
0.083722
\n",
+ "
\n",
+ "
\n",
+ "
Earnings Growth YOY
\n",
+ "
0.059044
\n",
+ "
0.051050
\n",
+ "
\n",
+ "
\n",
+ "
Sales Growth QOQ
\n",
+ "
0.055037
\n",
+ "
0.042719
\n",
+ "
\n",
+ "
\n",
+ "
(P/Sales 3Y Avg. FUTURE) / (P/Sales)
\n",
+ "
0.054391
\n",
+ "
0.054391
\n",
+ "
\n",
+ "
\n",
+ "
FCF Growth YOY
\n",
+ "
0.050846
\n",
+ "
0.039591
\n",
+ "
\n",
+ "
\n",
+ "
Earnings Growth QOQ
\n",
+ "
0.039706
\n",
+ "
0.044029
\n",
+ "
\n",
+ "
\n",
+ "
P/NetNet
\n",
+ "
0.038984
\n",
+ "
0.012176
\n",
+ "
\n",
+ "
\n",
+ "
Return on Assets
\n",
+ "
0.036960
\n",
+ "
0.084172
\n",
+ "
\n",
+ "
\n",
+ "
FCF Growth
\n",
+ "
0.035851
\n",
+ "
0.044218
\n",
+ "
\n",
+ "
\n",
+ "
Earnings Yield
\n",
+ "
0.034488
\n",
+ "
0.033062
\n",
+ "
\n",
+ "
\n",
+ "
Interest Coverage
\n",
+ "
0.027020
\n",
+ "
0.000833
\n",
+ "
\n",
+ "
\n",
+ "
Return on Equity
\n",
+ "
0.015905
\n",
+ "
0.050056
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Normal Signals 3-Year Avg. Signals\n",
+ "P/Sales 3Y Avg. FUTURE 1.000000 1.000000\n",
+ "P/Sales 0.903879 0.895450\n",
+ "P/Sales 3Y Avg. PAST 0.882970 0.882970\n",
+ "Gross Profit Margin 0.547388 0.550507\n",
+ "Asset Turnover 0.437774 0.437289\n",
+ "P/Book 0.299908 0.313731\n",
+ "Sales Growth 0.217111 0.230814\n",
+ "Sales Growth YOY 0.196713 0.214804\n",
+ "P/E 0.195813 0.271600\n",
+ "Current Ratio 0.157852 0.169831\n",
+ "Assets Growth YOY 0.149265 0.210366\n",
+ "Assets Growth 0.143340 0.210357\n",
+ "FCF Yield 0.141977 0.161792\n",
+ "(P/Sales) / (P/Sales 3Y Avg. PAST) 0.133496 0.133496\n",
+ "Net Profit Margin 0.122203 0.261017\n",
+ "Market-Cap 0.093990 0.093990\n",
+ "P/FCF 0.090578 0.085985\n",
+ "Assets Growth QOQ 0.089949 0.118375\n",
+ "Earnings Growth 0.089451 0.093019\n",
+ "FCF Growth QOQ 0.079901 0.081530\n",
+ "Debt Ratio 0.070723 0.111618\n",
+ "Dividend Yield 0.064765 0.011235\n",
+ "P/NCAV 0.063899 0.083722\n",
+ "Earnings Growth YOY 0.059044 0.051050\n",
+ "Sales Growth QOQ 0.055037 0.042719\n",
+ "(P/Sales 3Y Avg. FUTURE) / (P/Sales) 0.054391 0.054391\n",
+ "FCF Growth YOY 0.050846 0.039591\n",
+ "Earnings Growth QOQ 0.039706 0.044029\n",
+ "P/NetNet 0.038984 0.012176\n",
+ "Return on Assets 0.036960 0.084172\n",
+ "FCF Growth 0.035851 0.044218\n",
+ "Earnings Yield 0.034488 0.033062\n",
+ "Interest Coverage 0.027020 0.000833\n",
+ "Return on Equity 0.015905 0.050056"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.abs().sort_values(by=SIGNALS_NORMAL, ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let us try and fit a Linear Regression Model to some of the signals with highest correlation. The reason we don't fit the regression model to all the signals, is that alot of them contain NaN (Not-a-Number), which results in the entire rows with NaN being removed, so the dataset becomes much smaller, and the correlation numbers already shows that many of the signals are not linearly related to the FUTURE 3-year average P/Sales anyway.\n",
+ "\n",
+ "The regression model has $R^2 = 0.86$ which is only marginally better than using only one signal, either the current P/Sales ratio or the PAST 3-year average P/Sales.\n",
+ "\n",
+ "Because the data is standardized (to having zero mean and one standard deviation) before the regression model is fitted, the coefficients show us which signals are most important in predicting the FUTURE 3-year average P/Sales ratio, and that is by far the current P/Sales ratio, followed by the PAST 3-year average P/Sales ratio. The Gross Profit Margin, Net Profit Margin, Asset Turnover, and Sales Growth only have a relatively minor effect on the linear regression model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "
OLS Regression Results
\n",
+ "
\n",
+ "
Dep. Variable:
P/Sales 3Y Avg. FUTURE
R-squared:
0.855
\n",
+ "
\n",
+ "
\n",
+ "
Model:
OLS
Adj. R-squared:
0.855
\n",
+ "
\n",
+ "
\n",
+ "
Method:
Least Squares
F-statistic:
5.318e+05
\n",
+ "
\n",
+ "
\n",
+ "
Date:
Wed, 18 Mar 2020
Prob (F-statistic):
0.00
\n",
+ "
\n",
+ "
\n",
+ "
Time:
11:14:32
Log-Likelihood:
-7.1183e+05
\n",
+ "
\n",
+ "
\n",
+ "
No. Observations:
541733
AIC:
1.424e+06
\n",
+ "
\n",
+ "
\n",
+ "
Df Residuals:
541726
BIC:
1.424e+06
\n",
+ "
\n",
+ "
\n",
+ "
Df Model:
6
\n",
+ "
\n",
+ "
\n",
+ "
Covariance Type:
nonrobust
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
coef
std err
t
P>|t|
[0.025
0.975]
\n",
+ "
\n",
+ "
\n",
+ "
P/Sales
1.7282
0.003
527.654
0.000
1.722
1.735
\n",
+ "
\n",
+ "
\n",
+ "
P/Sales 3Y Avg. PAST
0.4053
0.003
120.979
0.000
0.399
0.412
\n",
+ "
\n",
+ "
\n",
+ "
Gross Profit Margin
0.0750
0.002
48.056
0.000
0.072
0.078
\n",
+ "
\n",
+ "
\n",
+ "
Net Profit Margin
0.0489
0.001
37.744
0.000
0.046
0.051
\n",
+ "
\n",
+ "
\n",
+ "
Asset Turnover
-0.0655
0.001
-45.870
0.000
-0.068
-0.063
\n",
+ "
\n",
+ "
\n",
+ "
Sales Growth
-0.0562
0.001
-43.573
0.000
-0.059
-0.054
\n",
+ "
\n",
+ "
\n",
+ "
Constant
2.8131
0.001
2299.638
0.000
2.811
2.816
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Omnibus:
165847.566
Durbin-Watson:
0.009
\n",
+ "
\n",
+ "
\n",
+ "
Prob(Omnibus):
0.000
Jarque-Bera (JB):
4337850.336
\n",
+ "
\n",
+ "
\n",
+ "
Skew:
0.903
Prob(JB):
0.00
\n",
+ "
\n",
+ "
\n",
+ "
Kurtosis:
16.745
Cond. No.
6.35
\n",
+ "
\n",
+ "
Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
+ "text/plain": [
+ "\n",
+ "\"\"\"\n",
+ " OLS Regression Results \n",
+ "==================================================================================\n",
+ "Dep. Variable: P/Sales 3Y Avg. FUTURE R-squared: 0.855\n",
+ "Model: OLS Adj. R-squared: 0.855\n",
+ "Method: Least Squares F-statistic: 5.318e+05\n",
+ "Date: Wed, 18 Mar 2020 Prob (F-statistic): 0.00\n",
+ "Time: 11:14:32 Log-Likelihood: -7.1183e+05\n",
+ "No. Observations: 541733 AIC: 1.424e+06\n",
+ "Df Residuals: 541726 BIC: 1.424e+06\n",
+ "Df Model: 6 \n",
+ "Covariance Type: nonrobust \n",
+ "========================================================================================\n",
+ " coef std err t P>|t| [0.025 0.975]\n",
+ "----------------------------------------------------------------------------------------\n",
+ "P/Sales 1.7282 0.003 527.654 0.000 1.722 1.735\n",
+ "P/Sales 3Y Avg. PAST 0.4053 0.003 120.979 0.000 0.399 0.412\n",
+ "Gross Profit Margin 0.0750 0.002 48.056 0.000 0.072 0.078\n",
+ "Net Profit Margin 0.0489 0.001 37.744 0.000 0.046 0.051\n",
+ "Asset Turnover -0.0655 0.001 -45.870 0.000 -0.068 -0.063\n",
+ "Sales Growth -0.0562 0.001 -43.573 0.000 -0.059 -0.054\n",
+ "Constant 2.8131 0.001 2299.638 0.000 2.811 2.816\n",
+ "==============================================================================\n",
+ "Omnibus: 165847.566 Durbin-Watson: 0.009\n",
+ "Prob(Omnibus): 0.000 Jarque-Bera (JB): 4337850.336\n",
+ "Skew: 0.903 Prob(JB): 0.00\n",
+ "Kurtosis: 16.745 Cond. No. 6.35\n",
+ "==============================================================================\n",
+ "\n",
+ "Warnings:\n",
+ "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+ "\"\"\""
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Select signals with highest correlation.\n",
+ "df = df_signals[[PSALES_3Y_FUTURE, PSALES, PSALES_3Y_PAST,\n",
+ " GROSS_PROFIT_MARGIN, NET_PROFIT_MARGIN,\n",
+ " ASSET_TURNOVER, SALES_GROWTH]]\n",
+ "\n",
+ "# Fit a multiple linear regression model and show the result.\n",
+ "model = regression(df=df, y=PSALES_3Y_FUTURE, standardize=True)\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can then try and remove the PAST P/Sales signals and redo the regression. Now we get $R^2 = 0.37$ and the coefficients show us that the Gross Profit Margin is by far the most important of these signals in predicting the FUTURE 3-year average P/Sales ratio."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "
OLS Regression Results
\n",
+ "
\n",
+ "
Dep. Variable:
P/Sales 3Y Avg. FUTURE
R-squared:
0.373
\n",
+ "
\n",
+ "
\n",
+ "
Model:
OLS
Adj. R-squared:
0.373
\n",
+ "
\n",
+ "
\n",
+ "
Method:
Least Squares
F-statistic:
1.568e+05
\n",
+ "
\n",
+ "
\n",
+ "
Date:
Wed, 18 Mar 2020
Prob (F-statistic):
0.00
\n",
+ "
\n",
+ "
\n",
+ "
Time:
11:14:32
Log-Likelihood:
-2.1562e+06
\n",
+ "
\n",
+ "
\n",
+ "
No. Observations:
1054165
AIC:
4.312e+06
\n",
+ "
\n",
+ "
\n",
+ "
Df Residuals:
1054160
BIC:
4.312e+06
\n",
+ "
\n",
+ "
\n",
+ "
Df Model:
4
\n",
+ "
\n",
+ "
\n",
+ "
Covariance Type:
nonrobust
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
coef
std err
t
P>|t|
[0.025
0.975]
\n",
+ "
\n",
+ "
\n",
+ "
Gross Profit Margin
0.9288
0.002
444.548
0.000
0.925
0.933
\n",
+ "
\n",
+ "
\n",
+ "
Net Profit Margin
0.2327
0.002
125.331
0.000
0.229
0.236
\n",
+ "
\n",
+ "
\n",
+ "
Asset Turnover
-0.5591
0.002
-272.688
0.000
-0.563
-0.555
\n",
+ "
\n",
+ "
\n",
+ "
Sales Growth
0.3838
0.002
208.051
0.000
0.380
0.387
\n",
+ "
\n",
+ "
\n",
+ "
Constant
2.7000
0.002
1481.614
0.000
2.696
2.704
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Omnibus:
296969.460
Durbin-Watson:
0.003
\n",
+ "
\n",
+ "
\n",
+ "
Prob(Omnibus):
0.000
Jarque-Bera (JB):
1094935.742
\n",
+ "
\n",
+ "
\n",
+ "
Skew:
1.385
Prob(JB):
0.00
\n",
+ "
\n",
+ "
\n",
+ "
Kurtosis:
7.154
Cond. No.
1.73
\n",
+ "
\n",
+ "
Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
+ "text/plain": [
+ "\n",
+ "\"\"\"\n",
+ " OLS Regression Results \n",
+ "==================================================================================\n",
+ "Dep. Variable: P/Sales 3Y Avg. FUTURE R-squared: 0.373\n",
+ "Model: OLS Adj. R-squared: 0.373\n",
+ "Method: Least Squares F-statistic: 1.568e+05\n",
+ "Date: Wed, 18 Mar 2020 Prob (F-statistic): 0.00\n",
+ "Time: 11:14:32 Log-Likelihood: -2.1562e+06\n",
+ "No. Observations: 1054165 AIC: 4.312e+06\n",
+ "Df Residuals: 1054160 BIC: 4.312e+06\n",
+ "Df Model: 4 \n",
+ "Covariance Type: nonrobust \n",
+ "=======================================================================================\n",
+ " coef std err t P>|t| [0.025 0.975]\n",
+ "---------------------------------------------------------------------------------------\n",
+ "Gross Profit Margin 0.9288 0.002 444.548 0.000 0.925 0.933\n",
+ "Net Profit Margin 0.2327 0.002 125.331 0.000 0.229 0.236\n",
+ "Asset Turnover -0.5591 0.002 -272.688 0.000 -0.563 -0.555\n",
+ "Sales Growth 0.3838 0.002 208.051 0.000 0.380 0.387\n",
+ "Constant 2.7000 0.002 1481.614 0.000 2.696 2.704\n",
+ "==============================================================================\n",
+ "Omnibus: 296969.460 Durbin-Watson: 0.003\n",
+ "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1094935.742\n",
+ "Skew: 1.385 Prob(JB): 0.00\n",
+ "Kurtosis: 7.154 Cond. No. 1.73\n",
+ "==============================================================================\n",
+ "\n",
+ "Warnings:\n",
+ "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+ "\"\"\""
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Select signals with highest correlation except PAST P/Sales.\n",
+ "df = df_signals[[PSALES_3Y_FUTURE,\n",
+ " GROSS_PROFIT_MARGIN, NET_PROFIT_MARGIN,\n",
+ " ASSET_TURNOVER, SALES_GROWTH]]\n",
+ "\n",
+ "# Fit a multiple linear regression model and show the result.\n",
+ "model = regression(df=df, y=PSALES_3Y_FUTURE, standardize=True)\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we use the signals calculated from 3-year averages of the financial data, then we get a slightly higher $R^2 = 0.39$, probably because the 3-year average Net Profit Margin is a stronger predictor for FUTURE P/Sales ratios than the TTM Net Profit Margin:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "
OLS Regression Results
\n",
+ "
\n",
+ "
Dep. Variable:
P/Sales 3Y Avg. FUTURE
R-squared:
0.394
\n",
+ "
\n",
+ "
\n",
+ "
Model:
OLS
Adj. R-squared:
0.394
\n",
+ "
\n",
+ "
\n",
+ "
Method:
Least Squares
F-statistic:
8.840e+04
\n",
+ "
\n",
+ "
\n",
+ "
Date:
Wed, 18 Mar 2020
Prob (F-statistic):
0.00
\n",
+ "
\n",
+ "
\n",
+ "
Time:
11:14:33
Log-Likelihood:
-1.0928e+06
\n",
+ "
\n",
+ "
\n",
+ "
No. Observations:
544405
AIC:
2.186e+06
\n",
+ "
\n",
+ "
\n",
+ "
Df Residuals:
544400
BIC:
2.186e+06
\n",
+ "
\n",
+ "
\n",
+ "
Df Model:
4
\n",
+ "
\n",
+ "
\n",
+ "
Covariance Type:
nonrobust
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
coef
std err
t
P>|t|
[0.025
0.975]
\n",
+ "
\n",
+ "
\n",
+ "
Gross Profit Margin
0.8332
0.003
294.783
0.000
0.828
0.839
\n",
+ "
\n",
+ "
\n",
+ "
Net Profit Margin
0.4459
0.003
174.401
0.000
0.441
0.451
\n",
+ "
\n",
+ "
\n",
+ "
Asset Turnover
-0.5134
0.003
-186.497
0.000
-0.519
-0.508
\n",
+ "
\n",
+ "
\n",
+ "
Sales Growth
0.3496
0.002
141.262
0.000
0.345
0.354
\n",
+ "
\n",
+ "
\n",
+ "
Constant
2.7693
0.002
1134.388
0.000
2.765
2.774
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Omnibus:
153378.875
Durbin-Watson:
0.004
\n",
+ "
\n",
+ "
\n",
+ "
Prob(Omnibus):
0.000
Jarque-Bera (JB):
574252.393
\n",
+ "
\n",
+ "
\n",
+ "
Skew:
1.379
Prob(JB):
0.00
\n",
+ "
\n",
+ "
\n",
+ "
Kurtosis:
7.208
Cond. No.
1.78
\n",
+ "
\n",
+ "
Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+ ],
+ "text/plain": [
+ "\n",
+ "\"\"\"\n",
+ " OLS Regression Results \n",
+ "==================================================================================\n",
+ "Dep. Variable: P/Sales 3Y Avg. FUTURE R-squared: 0.394\n",
+ "Model: OLS Adj. R-squared: 0.394\n",
+ "Method: Least Squares F-statistic: 8.840e+04\n",
+ "Date: Wed, 18 Mar 2020 Prob (F-statistic): 0.00\n",
+ "Time: 11:14:33 Log-Likelihood: -1.0928e+06\n",
+ "No. Observations: 544405 AIC: 2.186e+06\n",
+ "Df Residuals: 544400 BIC: 2.186e+06\n",
+ "Df Model: 4 \n",
+ "Covariance Type: nonrobust \n",
+ "=======================================================================================\n",
+ " coef std err t P>|t| [0.025 0.975]\n",
+ "---------------------------------------------------------------------------------------\n",
+ "Gross Profit Margin 0.8332 0.003 294.783 0.000 0.828 0.839\n",
+ "Net Profit Margin 0.4459 0.003 174.401 0.000 0.441 0.451\n",
+ "Asset Turnover -0.5134 0.003 -186.497 0.000 -0.519 -0.508\n",
+ "Sales Growth 0.3496 0.002 141.262 0.000 0.345 0.354\n",
+ "Constant 2.7693 0.002 1134.388 0.000 2.765 2.774\n",
+ "==============================================================================\n",
+ "Omnibus: 153378.875 Durbin-Watson: 0.004\n",
+ "Prob(Omnibus): 0.000 Jarque-Bera (JB): 574252.393\n",
+ "Skew: 1.379 Prob(JB): 0.00\n",
+ "Kurtosis: 7.208 Cond. No. 1.78\n",
+ "==============================================================================\n",
+ "\n",
+ "Warnings:\n",
+ "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+ "\"\"\""
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Select signals with highest correlation except PAST P/Sales.\n",
+ "df = df_signals_3y[[PSALES_3Y_FUTURE,\n",
+ " GROSS_PROFIT_MARGIN, NET_PROFIT_MARGIN,\n",
+ " ASSET_TURNOVER, SALES_GROWTH]]\n",
+ "\n",
+ "# Fit a multiple linear regression model and show the result.\n",
+ "model = regression(df=df, y=PSALES_3Y_FUTURE, standardize=True)\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## P/Sales vs. Asset Turnover & Sales Growth\n",
+ "\n",
+ "We will now study how some of the individual signals might be used to predict the FUTURE 3-year average P/Sales ratio. First we make a scatter-plot of the Asset Turnover on the x-axis and the FUTURE 3-year average P/Sales on the y-axis. The hue is the PAST 3-year average P/Sales ratio.\n",
+ "\n",
+ "The scatter-plot is not completely random. There does seem to be a tendency for the FUTURE 3-year average P/Sales ratio to have an almost reciprocal relation with the Asset Turnover, but there is a very large spread of the data-points."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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