intrinsic valuation time series

[nexdep]

Hello,
I am trying to calculate the DCF evaluation over time but the intrinsic valuation method seems to return it only for the latest statement.
Is there a way to get the time series?
thanks for the help

[JerBouma]

Hi! The cash flow projection is based on the cashflow on the "Free Cash Flow", "Operating Cash Flow", "Change in Working Capital" or "Capital Expenditure" from the latest financial statements and then projects it forward given the growth rate, perpetual growth rate and WACC. So by setting periods to 1, you project one year forward given the growth rates and WACC.

You can obtain a timeseries by looping over these periods:

Do keep in mind that DCF models are highly sensitive to user input and can quickly become unreliable when market conditions change.

[nexdep]

Hi, thanks for taking the time to reply and for the library - I am just studying it now but it seems a great project.

I wasn't clear enough in my question, I'd like to calculate the how the DCF at five year evaluation changed depending on the previous financials. so not to change the projecting period parameter but just study its past evolution.

I tried to look at the source code but I am not sure it is natively implemented

tx

[nexdep]

bump :)

[JerBouma]

I'd recommend to look into the code itself. I've set it up in such a manner that you can use the actual model used to come to this conclusion as well:

FinanceToolkit/financetoolkit/models/intrinsic_model.py

Lines 9 to 105 in 833ed9b

	def get_intrinsic_value(
	cash_flow: float,
	growth_rate: float,
	perpetual_growth_rate: float,
	weighted_average_cost_of_capital: float,
	cash_and_cash_equivalents: float,
	total_debt: float,
	shares_outstanding: float,
	periods: int = 5,
	) -> pd.DataFrame:
	"""
	The Weighted Average Cost of Capital (WACC) is a financial metric used to estimate the
	cost of capital for a company. It represents the average rate of return a company must
	pay to its investors for using their capital. WACC takes into account the cost of both
	equity and debt, weighted by their respective proportions in the company's capital
	structure.
	The formula is as follows:
	WACC = (Market Value of Equity / Total Market Value) * Cost of Equity +
	(Market Value of Debt / Total Market Value) * Cost of Debt * (1 — Corporate Tax Rate)
	Args:
	share_price (float or pd.Series): The price of a single share of a company's stock.
	total_shares_outstanding (float or pd.Series): The total number of shares of a company's stock.
	interest_expense (float or pd.Series): Interest expense of the company.
	total_debt (float or pd.Series): Total debt of the company.
	risk_free_rate (float or pd.Series): The risk-free rate is the rate of return of a
	hypothetical investment with no risk of financial loss.
	beta (float or pd.Series): Beta is a measure of the volatility, or systematic risk,
	of a security or portfolio compared to the market as a whole.
	benchmark_returns (float or pd.Series): The benchmark return is the return of a
	chosen benchmark, such as the S&P 500 or the Russell 2000.
	income_tax_expense (float or pd.Series): Income tax expense of the company.
	income_before_tax (float or pd.Series): Income before taxes of the company.
	Returns:
	pd.DataFrame: A DataFrame containing the Dupont analysis components.
	Notes:
	- The market value of equity is calculated as the share price multiplied by the total shares outstanding.
	- The market value of debt is calculated as the total debt. This is a simplification, as the market value of debt
	should be calculated as the present value of all future cash flows of the debt.
	- The cost of equity is calculated using the Capital Asset Pricing Model (CAPM).
	- The cost of debt is calculated as the interest expense divided by the total debt.
	- The corporate tax rate is calculated as the income tax expense divided by the income before taxes.
	"""
	components = {}
	cash_flow_projection = [cash_flow]
	# Cash Flow to Use
	for period in range(1, periods + 1):
	if period == 1:
	cash_flow_projection.append(cash_flow_projection[0] * (1 + growth_rate))
	else:
	cash_flow_projection.append(
	cash_flow_projection[period - 1] * (1 + growth_rate)
	)
	# Calculate the Terminal Value
	terminal_value = (
	cash_flow_projection[-1]
	* (1 + perpetual_growth_rate)
	/ (weighted_average_cost_of_capital - perpetual_growth_rate)
	)
	# Add Terminal Value to the end of the cash flow projection
	cash_flow_projection[-1] = cash_flow_projection[-1] + terminal_value
	# Calculate the Present Value based on the Discounted Cash Flow Formula
	cash_flow_present_value = []
	for index, cash_flow_value in enumerate(cash_flow_projection):
	cash_flow_present_value.append(
	cash_flow_value / (1 + weighted_average_cost_of_capital) ** (index + 1)
	)
	# Calculate the Enterprise Value
	enterprise_value = sum(cash_flow_present_value)
	# Calculate the Equity Value
	equity_value = enterprise_value + cash_and_cash_equivalents - total_debt
	# Calculate the Intrinsic Value
	intrinsic_value = equity_value / shares_outstanding
	# Combine the components into a dictionary
	components = {
	"Terminal Value": terminal_value,
	"Cash Flow Projection": cash_flow_projection[-1],
	"Enterprise Value": enterprise_value,
	"Equity Value": equity_value,
	"Intrinsic Value": intrinsic_value,
	}
	return pd.DataFrame.from_dict(
	components, orient="index", columns=[f"Periods = {periods}"]

From there, and with all the data that is available in the Finance Toolkit you should be able to calculate what you are looking for. I can't create specialized functionality for each request so that is why I have seperated model from controller. E.g. you can directly access this function through from financetoolkit.models import intrinsic_model.

[nexdep]

Hi, thanks a lot for the answer - I'll try to go in that direction