def calculate_marginal_tax_rate(self)->float:
"""
Calculates the marginal tax rate of the company or gives a default 30% if lower than 30%
Returns
-------
marginal_tax_rate (float) :
The tax rate is in percentage
"""
income_df = self.income_statement
income_before_tax = float(income_df.loc['Profit/Loss Before Tax', income_df.columns[0]])
tax_expense = float(income_df.loc['Total Tax Expenses', income_df.columns[0]])
# calculate marginal tax rate
marginal_tax_rate = tax_expense / income_before_tax * 100
default_marginal_tax_rate = resolve_config_value(['default', 'marginal_tax_rate'])
marginal_tax_rate = marginal_tax_rate if marginal_tax_rate > default_marginal_tax_rate else default_marginal_tax_rate
return marginal_tax_rate
def __init__(self):
self.__config_details = resolve_config_value(['nse_india'])
def calculate_roe(self, stock_obj: Stock) -> float:
"""
Calculates the minimum return on equity for the given stock
Parameters
----------
stock_obj (entities.Stock) : Contains all relevant information of the stock
Returns
-------
(float) : The expected return on equity
"""
dividend_df = stock_obj.dividend_history
current_share_price = stock_obj.stock_price
#usually the cod by Dividend Gordon method should be possible (as resolved by factory) - incase it is not
if not stock_obj.check_dividend_history():
logging.info(
"Returning default cost of equity as the stock failed dividend check"
)
return float(resolve_config_value(['default', 'rate_of_equity']))
# for the remaining part we will remove the initial years if the Dividend Amount is 0
start_index = dividend_df['Dividend Amount'].__ne__(0).idxmax()
dividend_df = dividend_df.loc[start_index:, :]
# substracting 1 as the first year of dividend payout is not calculated for geometric growth rate
total_years = len(dividend_df) - 1
# calculate percentage change of dividend and add it as an additional
# column in the dataframe
dividend_df['Percentage Change'] = dividend_df[
'Dividend Amount'].pct_change()
# This does not give us in % but only as Delta/original
# calculate the arithmetic growth rate of dividend
# for this we need to ignore the first column which is NaN
ap_growth = dividend_df['Percentage Change'].mean() * 100
# calculate the geometric growth rate of dividend
# Formula : {df['Dividend Amount'][-1]/df['Dividend Amount'][0]}^{1/total_years}
gp_growth = (
(dividend_df.loc[dividend_df.index[-1], 'Dividend Amount'] /
dividend_df.loc[dividend_df.index[0], 'Dividend Amount'])
**(1 / total_years)) * 100
# Select the more conservative of the growth
if ap_growth <= gp_growth:
growth_rate = ap_growth
else:
growth_rate = gp_growth
logging.info(
"Have selected the growth rate for dividends to be {}".format(
growth_rate))
# Project for the next 5 years.
last_year = dividend_df.index[-1]
year_of_concern = last_year + 1
for i in range(5):
projected_amount = dividend_df.loc[
last_year, 'Dividend Amount'] * (1 + growth_rate / 100)
logging.info("Projected dividend for {} is {}".format(
last_year, projected_amount))
last_year = last_year + 1
new_row = pd.Series({'Dividend Amount': projected_amount},
name=last_year)
dividend_df = dividend_df.append(new_row)
# calculate cost of equity
logging.info(
"Calculating the cost of equity with dividend amount: {}, current share price: {} and a conservative growth rate of {}"
.format(dividend_df.loc[year_of_concern, 'Dividend Amount'],
current_share_price, growth_rate))
roe = (dividend_df.loc[year_of_concern, 'Dividend Amount'] /
current_share_price) * 100 + growth_rate
return roe
def __init__(self):
self.__config_details = resolve_config_value(['moneycontrol'])
def __init__(self):
self.__config_details = resolve_config_value(['trendlyne'])
def __init__(self):
self.__config_details = resolve_config_value(['investing'])