My take on Target 🎯
Quick Overview of TGT
Key Insights
- Current Total Assets of $53.34 which is steadily increasing year to year
- The current working capital stands at a deficit of $1.65, indicating operational challenges that have been shared by several companies within the peer group.
- The value of "Buildings and Improvements" currently stands at $34.75, reflecting their expansion of square footage in their stores, demonstrating notable growth.
- The current Debt-to-Equity (D/E) ratio stands at approximately 1.67, suggesting a relatively modest level of leverage that aligns with industry peers and remains comfortably below the threshold of 2.
- The Accounts Receivable Turnover for the specified period stands at an impressive 64.38, consistently surpassing the commendable benchmark of 60, demonstrating a strong financial performance.
Lets Plot the Stock 📈
Using Jupyter kernel and python's yfinance, pandas, and matplotlib.
import yfinance as yf
import pandas as pd
import datetime
import matplotlib.pyplot as plt
def get_daily_stock_info(symbol):
# Define the start and end dates for your desired one-year period
end_date = datetime.datetime.now().date()
start_date = end_date - datetime.timedelta(days=365)
# Fetch the historical data
target_data = yf.download(symbol, start=start_date, end=end_date)
return target_data
Collect the data 📊 ...
stock_df = get_daily_stock_info('TGT')
stock_df = stock_df.reset_index()
stock_df.columns.values[0] = 'Date'
stock_df.head()
colors = {
'red': '#ff207c',
'grey': '#d0d0d7',
'blue': '#207cff',
'orange': '#ffa320',
'green': '#00ec8b'
}
config_ticks = {
'size': 14,
'color': colors['grey'],
'labelcolor': colors['grey']
}
config_title = {
'size': 18,
'color': colors['grey'],
'ha': 'left',
'va': 'baseline'
}
dark_theme = {
'font.family': 'Consolas',
'axes.facecolor': 'black',
'axes.edgecolor': 'gray',
'axes.labelcolor': 'white',
'text.color': 'white',
'xtick.color': 'white',
'ytick.color': 'white',
'grid.color': 'gray',
'figure.facecolor': 'black',
'legend.edgecolor': 'gray',
}
plt.style.use(dark_theme)
Establish theming 🎨 ...
def format_borders(plot):
plot.spines['top'].set_visible(False)
plot.spines['left'].set_visible(False)
plot.spines['left'].set_color(colors['grey'])
plot.spines['bottom'].set_color(colors['grey'])
def format_legend(plot):
plot_legend = plot.legend(loc='upper left',
bbox_to_anchor=(-0.005, 0.95), fontsize=16)
for text in plot_legend.get_texts():
text.set_color(colors['grey'])
def plot_ma(plot, x, y):
mov_avg = {
'MA (50)': {'Range': 50, 'Color': colors['orange']},
'MA (100)': {'Range': 100, 'Color': colors['green']},
'MA (200)': {'Range': 200, 'Color': colors['red']}
}
for ma, ma_info in mov_avg.items():
plot.plot(
x, y.rolling(ma_info['Range']).mean(),
color=ma_info['Color'], label=ma, linewidth=2, ls='--'
)
def get_prev_day_info(plot):
previous_close = '$' + str("{:,}".format(stock_df['Close'][0]))
previous_volume = str("{:,}".format(stock_df['Volume'][0]))
previous_date = str(stock_df['Date'][0].date())
plot.set_title(
'Closing price on ' + previous_date + ': ' +
previous_close + '\nShares traded on ' + previous_date +
': ' + previous_volume, fontdict=config_title, loc='left'
)
def get_charts(stock_data):
plt.rc('figure', figsize=(15, 10))
fig, axes = plt.subplots(2, 1,
gridspec_kw={'height_ratios': [3, 1]})
fig.tight_layout(pad=3)
date = stock_data['Date']
close = stock_data['Close']
vol = stock_data['Volume']
plot_price = axes[0]
plot_price.plot(date, close, color=colors['blue'],
linewidth=2, label='Price')
plot_vol = axes[1]
plot_vol.bar(date, vol, width=15, color='darkgrey')
plot_price.yaxis.tick_right()
plot_price.tick_params(axis='both', **config_ticks)
plot_price.set_ylabel('Price (in USD)', fontsize=14)
plot_price.yaxis.set_label_position("right")
plot_price.yaxis.label.set_color(colors['grey'])
plot_price.grid(axis='y', color='gainsboro',
linestyle='-', linewidth=0.5)
plot_price.set_axisbelow(True)
format_borders(plot_price)
format_borders(plot_vol)
fig.suptitle('TGT Price and Volume', size=36, color=colors['grey'], x=0.24, y=1.10)
get_prev_day_info(plot_price)
plot_ma(plot_price, date, close)
format_legend(plot_price)
get_charts(stock_df)
It appears the stock is mean reverting towards a cheaper value 📉 ...
Observations
Based on the current moving average projections, buying this stock in the
next year appears to be a favorable opportunity. The company currently
boasts a price-to-earnings (P/E) ratio of approximately 22, making it an
attractive value stock. Additionally, with no intangible assets in the
picture, the Price to Tangible Book Value (P/TBV) stands at 6.86. A P/TBV
ratio exceeding 1 indicates that the market highly values the company's
tangible assets, reflecting investor confidence and a willingness to pay a
premium for these assets.