Models

The Models module executes well-known models such as DuPont analysis and the Discounted Cash Flow (DCF) model, using data retrieved from the Toolkit module.

To install the FinanceToolkit it simply requires the following:

pip install financetoolkit -U

get_dupont_analysis

Perform a Dupont analysis to breakdown the return on equity (ROE) into its components.

The Dupont analysis is a method used to dissect and understand the factors that drive a company’s return on equity (ROE). It breaks down the ROE into three key components: Profit Margin, Asset Turnover, and Financial Leverage.

The formula is as follows:

Also known as: DuPont, ROE decomposition, three-factor DuPont.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| str, optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing Dupont analysis results, including Profit Margin, Asset Turnover, Financial Leverage, and the calculated ROE values.

Notes:

• The Profit Margin is the ratio of Net Income to Total Revenue, indicating the percentage of revenue that translates into profit.
• Asset Turnover measures the efficiency of a company’s use of its assets to generate sales revenue.
• Financial Leverage represents the use of debt to finance a company’s operations, which can amplify returns as well as risks.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

dupont_analysis = toolkit.models.get_dupont_analysis()

dupont_analysis.loc["AAPL"]

Which returns:

get_extended_dupont_analysis

Perform an Extended Dupont analysis to breakdown the return on equity (ROE) into its components, while considering additional financial metrics.

The Extended Dupont analysis splits the three-factor decomposition’s Net Profit Margin into three separate drivers - the Tax Burden, the Interest Burden and the Operating Profit Margin - so that the effect of taxation, of financing costs and of operating performance on the return on equity (ROE) can be read separately.

The formula is as follows:

Also known as: extended DuPont, five-factor DuPont, ROE breakdown.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| str, optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing Extended Dupont analysis results, including the Interest Burden Ratio, Tax Burden Ratio, Operating Profit Margin, Asset Turnover, Equity Multiplier, and the calculated ROE values.

Notes:

• The Interest Burden Ratio (Income Before Tax / Operating Income) measures how much of operating profit survives the cost of debt financing. It equals 1 for a company with no net interest expense and falls as interest costs rise.
• The Tax Burden Ratio (Net Income / Income Before Tax) measures how much of pre-tax profit survives taxation, i.e. it equals (1 - Effective Tax Rate).
• The Operating Profit Margin measures operating performance before financing and tax effects.
• Asset Turnover measures the efficiency of a company’s use of its assets to generate sales revenue.
• The Equity Multiplier represents the use of debt to finance a company’s operations, which can amplify returns as well as risks.
• Multiplying the first three components back together reproduces the Net Profit Margin of the three-factor get_dupont_analysis, so both decompositions resolve to the same ROE.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

extended_dupont_analysis = toolkit.models.get_extended_dupont_analysis()

extended_dupont_analysis.loc["AAPL"]

Which returns:

get_enterprise_value_breakdown

Calculate the Enterprise Value (EV) breakdown, providing a detailed view of its components.

The Enterprise Value breakdown includes the following components for each quarter or year:

• Share Price: The market price per share of the company’s stock.
• Market Capitalization (Market Cap): The total value of a company’s outstanding common shares, i.e. the share price multiplied by the shares outstanding. Preferred shares are excluded here and enter as their own component below, so that they are counted once rather than twice.
• Debt: The sum of long-term and short-term debt on the company’s balance sheet.
• Preferred Equity: The value of preferred shares, if applicable.
• Minority Interest: The equity value of a subsidiary with less than 50% ownership.
• Cash and Cash Equivalents: The total amount of liquid assets including cash, marketable securities, and short-term investments.

The Enterprise Value is calculated as the sum of Market Cap, Debt, Preferred Equity, Minority Interest, minus Cash and Cash Equivalents.

This breakdown is displayed in a DataFrame for each company and includes the option to show growth values as well.

Also known as: EV breakdown, enterprise value components, EV bridge.

Args:

• diluted (bool, optional): Whether to use diluted shares in the calculation. Defaults to True.
• rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| str, optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.

Returns:

pd.DataFrame: DataFrame containing the Enterprise Value breakdown, including the calculated components.

Notes:

• All the inputs must be in the same currency and unit for accurate calculations.
• The Enterprise Value is an important metric used for valuation and investment analysis. It represents the cost of acquiring the entire business: the equity is bought at its market value, the debt (and any preferred equity and minority interest) is assumed, and the acquired cash reduces the effective price, which is why cash is subtracted rather than added.
• Enterprise Value is positive for essentially every going concern. It only turns negative when a company’s cash exceeds its market capitalization plus its debt, which is a sign of a cash-rich balance sheet priced below its net cash — not of financial distress. A distressed, heavily indebted company shows the opposite: an Enterprise Value far above its market capitalization.
• Understanding the Enterprise Value breakdown can provide insights into the sources of a company’s value and potential risks.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

enterprise_value_breakdown = toolkit.models.get_enterprise_value_breakdown()

enterprise_value_breakdown.loc["AAPL"]

Which returns:

get_tobins_q_ratio

Calculate Tobin’s Q Ratio, a valuation metric developed by economist James Tobin that compares the market value of a company to the cost of replacing its assets.

The formula is as follows:

Tobin’s Q Ratio can be interpreted as follows:

• A Q ratio greater than 1 indicates that the market values the company above the cost of replacing its assets, which can reflect growth expectations, unrecognized intangible value, or overvaluation.
• A Q ratio less than 1 indicates that the market values the company below the cost of replacing its assets, which can reflect undervaluation or declining growth prospects.

Also known as: Tobin’s Q, Q ratio.

Args:

• diluted (bool, optional): Whether to use diluted shares in the calculation. Defaults to True.
• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Market Value of Equity, Total Liabilities, Total Assets and Tobin’s Q Ratio.

Notes:

• This implementation approximates the market value of debt with the book value of Total Liabilities, and the replacement cost of assets with the book value of Total Assets, consistent with the simplifications used in the Weighted Average Cost of Capital calculation elsewhere in this module.

References:

• Tobin, James. “A General Equilibrium Approach to Monetary Theory.” Journal of Money, Credit and Banking, Vol. 1, No. 1, 1969, pp. 15-29.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_tobins_q_ratio().loc["AAPL"]

Which returns:

get_weighted_average_cost_of_capital

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:

Cost of Equity (Re): The cost of equity represents the return required by the company’s shareholders or equity investors. It is the cost of raising funds by selling equity (such as common stock). The cost of equity is often estimated using methods like the Capital Asset Pricing Model (CAPM) or the Dividend Discount Model (DDM).

Cost of Debt (Rd): The cost of debt is the interest rate the company pays on its outstanding debt. It is the cost of raising funds through borrowing, such as issuing bonds or taking loans. The cost of debt is typically based on the prevailing interest rates in the market and the company’s creditworthiness.

Corporate Tax Rate (Tc): The corporate tax rate is the percentage of a company’s profits that is paid in taxes. It is used to calculate the tax shield on interest payments. Interest expenses on debt reduce taxable income, and the tax shield represents the tax savings resulting from these deductions.

Market Value of Equity (E): The market value of equity is the total value of the company’s outstanding shares of common stock. It is calculated by multiplying the current stock price by the number of shares outstanding.

Market Value of Debt (D): The market value of debt is the total value of the company’s outstanding debt obligations, such as bonds and loans. It represents the current market price of the debt instruments.

Total Market Value of Capital (V): The total market value of capital is the sum of the market value of equity and the market value of debt (V = E + D). It represents the total value of the company’s financing, both through equity and debt.

Also known as: WACC, blended cost of capital, discount rate.

Args:

• show_full_results (bool, optional): Whether to show the full results or just the WACC values. Defaults to True.
• diluted (bool, optional): Whether to use diluted shares in the calculation. Defaults to True.
• rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| str, optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the WACC values.

Notes:

• The Cost of Equity is approximated with the Capital Asset Pricing Model (CAPM).
• The Market Value of Debt is approximated as the Total Debt.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_weighted_average_cost_of_capital().loc["AAPL"]

Which returns:

get_economic_value_added

Economic Value Added (EVA) is a measure of a company’s financial performance that represents the value created in excess of the required return of the company’s capital providers. It captures whether a company is generating returns above its true cost of capital, which distinguishes it from purely accounting-based measures such as Net Income.

The formula is as follows:

Also known as: EVA, economic profit.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the EVA and its components.

Notes:

• A positive EVA indicates that the company is generating returns in excess of its cost of capital, i.e. it is creating value for its capital providers. A negative EVA indicates the company is destroying value.
• EBIT is approximated as Net Income + Income Tax Expense + Interest Expense, consistent with the Altman Z-Score calculation elsewhere in this module.
• Invested Capital is the sum of the two-period average of Total Equity and the two-period average of Total Debt, i.e. the capital employed over the course of the period rather than its closing balance, consistent with the Return on Invested Capital calculation in the Ratios module.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_economic_value_added().loc["AAPL"]

Which returns:

get_market_value_added

Market Value Added (MVA) is a measure of a company’s financial performance that represents the difference between the current market value of a company (both its equity and its debt) and the total capital that has historically been invested in it. It is the market-priced counterpart to Economic Value Added (EVA): where EVA measures a single period’s excess return over the cost of capital, MVA reflects the market’s cumulative, forward-looking verdict on all of a company’s expected future EVA.

The formula is as follows:

Also known as: MVA, market value added.

Args:

• diluted (bool, optional): Whether to use diluted shares in the calculation. Defaults to True.
• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the MVA and its components.

Notes:

• A positive MVA indicates that the market believes management has created value in excess of the capital invested in the company. A negative MVA indicates the market values the company below the capital that has historically been invested in it.
• The Market Value of Debt is approximated as the closing book value of Total Debt, the same simplification used in the Weighted Average Cost of Capital calculation elsewhere in this module.
• Invested Capital is the sum of the two-period average of Total Equity and the two-period average of Total Debt, i.e. the capital that was employed during the period, matching the Economic Value Added calculation elsewhere in this module. Note that this leaves the two sides of the MVA measured on slightly different bases: the market value is a closing (point-in-time) figure while the invested capital is an average over the period.

References:

• Stern, Joel M., G. Bennett Stewart, and Donald H. Chew. “The EVA Financial Management System.” Journal of Applied Corporate Finance, Vol. 8, No. 2, 1995, pp. 32-46.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_market_value_added().loc["AAPL"]

Which returns:

get_intrinsic_valuation

Intrinsic value is a fundamental concept in finance and investing that represents the true worth or value of an asset, security, or investment, independent of its current market price or prevailing market sentiment. It is a concept often associated with the value investing philosophy, made famous by legendary investors like Benjamin Graham and Warren Buffett. Understanding intrinsic value is crucial for investors looking to make informed decisions about where to allocate their capital.

This functionality uses DCF, or Discounted Cash Flow which is a widely used financial valuation method that allows investors and analysts to estimate the intrinsic value of an investment or business based on its expected future cash flows. It is a fundamental tool in finance and investment analysis, providing a systematic way to assess the present value of future cash flows while considering the time value of money.

The formula is as follows:

Also known as: DCF, discounted cash flow, fair value, intrinsic value.

Args:

• growth_rate (float, list or dict): The growth rate to use for the cash flow projections. Can be one number to use for all tickers, or a list or dict that contains a growth rate for each ticker.
• perpetual_growth_rate (float, list or dict): The perpetual growth rate to use for the terminal value. Can be one number to use for all tickers, or a list or dict that contains a perpetual growth rate for each ticker.
• weighted_average_cost_of_capital (float, list or dict): The weighted average cost of capital to use for the terminal value. Can be one number to use for all tickers, or a list or dict that contains a weighted average cost of capital for each ticker.
• periods (int, optional): The number of periods to use for the cash flow projections. Defaults to 5.
• cash_flow_type (str, optional): The type of cash flow to use for the cash flow projections. Defaults to “Free Cash Flow”. Other options are “Operating Cash Flow”, “Change in Working Capital”, and “Capital Expenditure”.
• trailing (int \| None, optional): The number of trailing periods to sum for the base cash flow. When set, uses the sum of the last N periods instead of only the most recent period. Defaults to None.
• rounding (int, optional): The number of decimals to round the results to. Defaults to 4.

Returns:

pd.DataFrame: DataFrame containing the intrinsic value for each ticker.

Notes:

• The results are highly dependent on the input. Therefore, think carefully about each input parameter to ensure the results are accurate (given your beliefs)

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_intrinsic_valuation(0.05, 0.025, 0.094).loc["AAPL"]

Which returns:

get_free_cash_flow_to_firm

Free Cash Flow to the Firm (FCFF) is the cash flow available to all providers of capital, both debt and equity holders, after the company has paid all of its operating expenses and invested in the assets needed to sustain its operations. Because it is measured before any financing cash flows, FCFF is capital-structure neutral, making it the cash flow base typically discounted at the Weighted Average Cost of Capital (WACC) when valuing the Enterprise Value of a company directly.

The formula is as follows:

Also known as: FCFF, unlevered free cash flow.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the FCFF and its components.

Notes:

• EBIT is approximated as Net Income + Income Tax Expense + Interest Expense, consistent with the Altman Z-Score and Economic Value Added calculations elsewhere in this module.
• The Capital Expenditure and Change in Working Capital line items from the cash flow statement are stored using a cash-flow-impact sign convention (a use of cash is negative). This method negates them internally so that they represent positive magnitudes (amount spent / amount of the increase), matching the standard academic FCFF formula. See get_free_cash_flow_to_firm in the Intrinsic module for details.
• FCFF can be used together with the Weighted Average Cost of Capital in get_intrinsic_valuation (set cash_flow_type appropriately, or supply this result as a custom base cash flow) as an alternative to the reported Free Cash Flow.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_free_cash_flow_to_firm().loc["AAPL"]

Which returns:

get_free_cash_flow_to_equity

Free Cash Flow to Equity (FCFE) is the cash flow available to a company’s common equity holders after all operating expenses, reinvestment needs, and net payments to (or from) debt holders have been accounted for. Unlike FCFF, FCFE is a levered cash flow measure and should be discounted at the Cost of Equity, not the Weighted Average Cost of Capital, when used to value equity directly.

The formula is as follows:

Also known as: FCFE, levered free cash flow.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the FCFE and its components.

Notes:

• The Capital Expenditure and Change in Working Capital line items from the cash flow statement are stored using a cash-flow-impact sign convention (a use of cash is negative). This method negates them internally so that they represent positive magnitudes, matching the standard academic FCFE formula. Net Debt Issued (used as Net Borrowing) is already reported using the cash-flow-impact convention (a positive value means the company was a net borrower) and is therefore used as-is.
• FCFE is the natural cash-flow-based counterpart to Residual Income (get_residual_income) for equity-only valuation, in the same way FCFF is the counterpart to Economic Value Added for firm-level valuation.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_free_cash_flow_to_equity().loc["AAPL"]

Which returns:

get_gorden_growth_model

The Gordon Growth Model, also known as the Dividend Discount Model (DDM) with Constant Growth, is a method used to estimate the intrinsic value of a stock based on its expected future dividends. The model assumes that dividends will grow at a constant rate indefinitely.

The formula is as follows:

The formula essentially discounts the future expected dividends to their present value, taking into account the required rate of return and the growth rate. The numerator represents the expected dividend in the next period. The denominator represents the required rate of return minus the growth rate.

Investors often use the Gordon Growth Model to compare the intrinsic value of a stock with its current market price. If the intrinsic value is higher than the market price, some investors may interpret it as an indication that the stock is undervalued.

It’s important to note that the Gordon Growth Model is based on several assumptions, including the assumption of constant growth in dividends. It is most applicable to mature companies with stable and predictable dividend growth. If a company’s dividend growth is expected to fluctuate or if it does not pay dividends, alternative valuation models may be more appropriate.

The assumption of constant growth of dividends is often unrealistic. In reality, dividends may fluctuate or even be suspended. Therefore, the Gordon Growth Model should be used with caution and in conjunction with other valuation methods.

Also known as: Gordon Growth Model, DDM, dividend discount model, GGM.

Args:

• rate_of_return (float): The required rate of return.
• growth_rate (float): The growth rate of the dividends.
• project_periods (int, optional): The number of periods to project the the stock price. Defaults to 5.
• rounding (int, optional): The number of decimals to round the results to. Defaults to 4.

Returns:

pd.DataFrame: DataFrame containing the intrinsic value for each ticker over time.

Notes:

• The results are highly dependent on the input. Therefore, think carefully about each input parameter to ensure the results are accurate (given your beliefs)
• Each historical period is valued off that period’s actual Dividends per Share, and only the periods beyond the last one available are projected forward at the given growth rate. The first period of the historical window will therefore often be understated, since it only covers the part of the year that falls inside the requested date range and so captures only part of the year’s dividends. Use a start_date at least one full period before the first period you intend to read.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_gorden_growth_model(0.20, 0.05)

Which returns:

get_two_stage_dividend_discount_model

The Two-Stage Dividend Discount Model extends the (single-stage) Gordon Growth Model to companies that are not expected to grow at a constant rate forever. It explicitly projects and discounts dividends over an initial high-growth phase, and then values everything from the end of that phase onward as a perpetuity growing at a lower, more sustainable stable rate (a Gordon Growth terminal value).

The formula is as follows:

Also known as: two-stage DDM, two-stage dividend discount model.

Args:

• rate_of_return (float, list or dict): The required rate of return (discount rate) used to discount both phases. Can be one number to use for all tickers, or a list or dict that contains a rate of return for each ticker.
• high_growth_rate (float, list or dict): The constant growth rate applied to dividends during the explicit high-growth phase. Can be one number to use for all tickers, or a list or dict that contains a high growth rate for each ticker.
• stable_growth_rate (float, list or dict): The perpetual (terminal) growth rate applied to dividends from the end of the high-growth phase onward. Can be one number to use for all tickers, or a list or dict that contains a stable growth rate for each ticker.
• high_growth_periods (int, optional): The number of periods in the explicit high-growth phase. Defaults to 5.
• rounding (int, optional): The number of decimals to round the results to. Defaults to 4.

Returns:

pd.DataFrame: DataFrame containing the intrinsic value and its components for each ticker.

Notes:

• The results are highly dependent on the input. Therefore, think carefully about each input parameter to ensure the results are accurate (given your beliefs).
• The Rate of Return must be greater than the Stable Growth Rate, otherwise the Terminal Value formula divides by a non-positive number.
• The base dividend used for the projection is the most recent available Dividends Per Share for each ticker. For companies that do not pay a dividend, this model is not meaningful (the projections and resulting intrinsic value will be zero).

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_two_stage_dividend_discount_model(0.10, 0.12, 0.03).loc["AAPL"]

Which returns:

get_residual_income

Residual Income is a measure of the profit a company generates in excess of the return required by its equity holders. It is the equity-side counterpart to Economic Value Added (EVA), and underpins the Residual Income Model, an alternative equity valuation lens to a traditional Discounted Cash Flow (DCF) that is particularly useful when a company’s free cash flows are negative or unpredictable but its accounting earnings are more stable.

The formula is as follows:

Also known as: RI, economic profit (equity variant), abnormal earnings.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Residual Income and its components.

Notes:

• A positive Residual Income indicates that the company generated more profit than equity holders required given the capital they have invested. A negative Residual Income indicates the company failed to earn its equity holders’ required return, even if it still reported a positive Net Income.
• The Cost of Equity is approximated with the Capital Asset Pricing Model (CAPM), consistent with the Weighted Average Cost of Capital calculation elsewhere in this module.
• The Book Value of Equity is approximated as the average of Total Shareholder Equity minus Preferred Stock over the current and prior period, excluding preferred equity since Residual Income (like Net Income) belongs to common equity holders only.

References:

• Ohlson, James A. “Earnings, Book Values, and Dividends in Equity Valuation.” Contemporary Accounting Research, Vol. 11, No. 2, 1995, pp. 661-687.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_residual_income().loc["AAPL"]

Which returns:

get_altman_z_score

Calculates the Altman Z-Score, a financial metric used to predict the likelihood of a company going bankrupt. The Altman Z-Score is calculated using several financial ratios, including working capital to total assets, retained earnings to total assets, earnings before interest and taxes (EBIT) to total assets, market value of equity to book value of total liabilities, and sales to total assets.

The formula is as follows:

The Altman Z-Score can be interpreted as follows:

• A Z-Score of less than 1.81 indicates a high likelihood of bankruptcy.
• A Z-Score between 1.81 and 2.99 indicates a gray area.
• A Z-Score of greater than 2.99 indicates a low likelihood of bankruptcy.

Also known as: Altman Z-score, bankruptcy prediction, financial distress score.

Args:

• diluted (bool, optional): Whether to use diluted shares outstanding in the calculation. Defaults to True.
• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Altman Z-Score and its components.

Notes:

• The Altman Z-Score is a financial metric used to predict the likelihood of a company going bankrupt.
• The Z-Score is calculated using several financial ratios, including working capital to total assets, retained earnings to total assets, earnings before interest and taxes (EBIT) to total assets, market value of equity to book value of total liabilities, and sales to total assets.
• A Z-Score of less than 1.81 indicates a high likelihood of bankruptcy, while a Z-Score of greater than 2.99 indicates a low likelihood of bankruptcy.
• The Z-Score is most effective when used to analyze manufacturing companies with assets of $1 million or more.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

altman_z_score = toolkit.models.get_altman_z_score()

altman_z_score.loc["AAPL"]

Which returns:

get_piotroski_score

Calculate the Piotroski Score, a comprehensive financial assessment tool that helps investors and analysts evaluate a company’s financial health and fundamental strength.

The Piotroski Score was developed by Joseph Piotroski and is based on a set of nine fundamental financial criteria. Each criterion is assigned a score of 0 or 1, and the scores are then summed to calculate the Piotroski Score.

The nine criteria are categorized into three groups:

1. Profitability: - Return on Assets (ROA) Criteria: Measures the profitability of the company. - Operating Cash Flow Criteria: Evaluates the company’s ability to generate cash from its operations. - Change in ROA Criteria: Assesses the trend in ROA over time. - Accruals Criteria: Examines the quality of earnings.

2. Leverage, Liquidity, and Operating Efficiency: - Change in Leverage Criteria: Analyzes changes in the company’s leverage (debt). - Change in Current Ratio Criteria: Evaluates changes in the current ratio. - Number of Shares Criteria: Assesses the issuance of common shares.

3. Operating Efficiency and Asset Utilization: - Gross Margin Criteria: Examines the company’s gross margin, a measure of profitability. - Asset Turnover Ratio Criteria: Evaluates the efficiency of asset utilization and sales generation.

The Piotroski Score is calculated by summing the scores assigned to each of the nine criteria. The maximum possible score is 9, indicating the highest financial strength, while the minimum score is 0, suggesting potential financial weaknesses.

Note that the Piostroski Score has been developed many decades ago and that it is important to always compare the same sectors. E.g. it could be that it is quite normal that a firm issues shares each year which nets a lower score even though it is a normal practice in that sector.

Please see Piotroski, Joseph D. “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers.” Journal of Accounting Research, Vol. 38, Supplement, 2000, pp. 1-41.

Also known as: Piotroski F-score, financial strength, quality score.

Args:

• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Piotroski F-Score and its components.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_piotroski_score().loc["AAPL"]

Which returns:

Periods for which the financial statements have not been reported yet are returned as NaN rather than being scored zero across the board.

get_beneish_m_score

The Beneish M-Score is a probabilistic model, developed by Messod Beneish, that uses eight financial ratios derived from a company’s financial statements to identify whether a company has manipulated its earnings. It is a natural companion to the Altman Z-Score and Piotroski F-Score, using the same normalized financial statements as its input.

The formula is as follows:

M-Score = -4.84 + 0.92 * DSRI + 0.528 * GMI + 0.404 * AQI + 0.892 * SGI + 0.115 * DEPI - 0.172 * SGAI + 4.679 * TATA - 0.327 * LVGI

The eight variables are:

• DSRI: Days Sales in Receivables Index
• GMI: Gross Margin Index
• AQI: Asset Quality Index
• SGI: Sales Growth Index
• DEPI: Depreciation Index
• SGAI: Selling, General and Administrative Expenses Index
• TATA: Total Accruals to Total Assets
• LVGI: Leverage Index

Also known as: Beneish M-Score, earnings manipulation score.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Beneish M-Score and its components.

Notes:

• A M-Score greater than -1.78 suggests that the company is likely to be an earnings manipulator. A M-Score lower than -1.78 suggests the company is unlikely to be a manipulator.
• As with the Altman Z-Score and Piotroski F-Score, this is a probabilistic, not a definitive, indicator and should be combined with further fundamental analysis.
• Every component compares the current period to the prior period, so the very first period in the results will always be NaN.

References:

• Beneish, Messod D. “The Detection of Earnings Manipulation.” Financial Analysts Journal, Vol. 55, No. 5, 1999, pp. 24-36.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_beneish_m_score().loc["AAPL"]

Which returns:

get_ohlson_o_score

Calculates the Ohlson O-Score, a financial metric used to predict the likelihood of a company going bankrupt. Unlike the Altman Z-Score, which is built with multiple discriminant analysis, the O-Score’s coefficients come from a fitted logistic regression (logit) model. This is why the two models are usually reported side by side rather than one being treated as a replacement for the other: the Z-Score is only meaningful compared against Altman’s empirically derived threshold bands, while the O-Score is directly interpretable as a probability of bankruptcy once passed through the logistic transform.

The formula is as follows:

The Ohlson O-Score can be interpreted as follows:

• Ohlson’s (1980) original cutoff is a bankruptcy probability of 0.038 (3.8%), the threshold that minimized the sum of Type I and Type II misclassification errors on his sample. It is deliberately far below the naive 0.50 midpoint because bankruptcy is a rare event. Equivalently, in raw O-Score terms the cutoff sits at ln(0.038 / 0.962), i.e. approximately -3.23.
• A higher probability indicates a higher likelihood of bankruptcy.

Also known as: Ohlson O-Score, bankruptcy prediction, financial distress score.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Ohlson O-Score, its bankruptcy probability, and its components.

Notes:

• Ohlson’s (1980) original SIZE term deflates Total Assets by the US GNP price-level index (rebased to 1968), a US-specific 1970s macro series that isn’t available in this toolkit and isn’t meaningful outside the original US sample. This implementation simplifies/omits that deflator and uses ln(Total Assets) in nominal terms instead. This shifts the O-Score (and resulting probability) by roughly a constant amount across all observations in a given currency/period — it does not change the ranking of companies relative to one another, but the absolute probability estimate should not be compared directly to studies that apply the deflator.
• Ohlson’s original FUTL term uses “Funds from Operations”, an accounting-flow concept that predates standardized cash flow statements. This implementation approximates Funds from Operations with Operating Cash Flow, the standard simplification used in modern reproductions of the O-Score.
• The Beneish M-Score, Altman Z-Score, Piotroski F-Score and Ohlson O-Score are natural companions, using the same normalized financial statements as their input.
• As with the other bankruptcy and distress models, this is a probabilistic, not a definitive, indicator and should be combined with further fundamental analysis.

References:

• Ohlson, James A. “Financial Ratios and the Probabilistic Prediction of Bankruptcy.” Journal of Accounting Research, Vol. 18, No. 1, 1980, pp. 109-131.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

ohlson_o_score = toolkit.models.get_ohlson_o_score()

ohlson_o_score.loc["AAPL"]

Which returns:

Note that the first period is NaN because the Change in Net Income and Negative Income Indicator components require a prior period to compare against.

get_zmijewski_score

Calculates the Zmijewski Score, a financial metric used to predict the likelihood of a company going bankrupt. It is a simpler, three-variable model compared to both the Altman Z-Score and the Ohlson O-Score, and is commonly used as a robustness check when those two models disagree, since it is estimated on a different sample and with a different statistical technique (a probit model rather than discriminant analysis or logistic regression).

The formula is as follows:

The Zmijewski Score can be interpreted as follows:

• A higher X (and therefore a higher bankruptcy probability) indicates a higher likelihood of financial distress.

Also known as: Zmijewski Score, ZFC score, bankruptcy prediction, financial distress score.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Zmijewski Score, its bankruptcy probability, and its components.

Notes:

• Unlike the Ohlson O-Score, which uses a logistic (sigmoid) link to convert its score into a probability, the Zmijewski Score uses a probit link (the standard normal CDF). Applying a logistic transform to the Zmijewski Score instead would produce an incorrect probability.
• As with the Altman Z-Score, Piotroski F-Score, Beneish M-Score and Ohlson O-Score, this is a probabilistic, not a definitive, indicator and should be combined with further fundamental analysis.

References:

• Zmijewski, Mark E. “Methodological Issues Related to the Estimation of Financial Distress Prediction Models.” Journal of Accounting Research, Vol. 22, 1984, pp. 59-82.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

zmijewski_score = toolkit.models.get_zmijewski_score()

zmijewski_score.loc["AAPL"]

Which returns:

get_springate_score

Calculates the Springate Score, a financial metric used to predict the likelihood of a company going bankrupt. It follows the same multiple discriminant analysis methodology as the Altman Z-Score, but was calibrated on a smaller, Canadian-firm sample using four financial ratios instead of five.

The formula is as follows:

The Springate Score can be interpreted as follows:

• A Springate Score of less than 0.862 indicates a high likelihood of bankruptcy.
• A Springate Score of greater than 0.862 indicates a low likelihood of bankruptcy.

Also known as: Springate Score, S-Score, bankruptcy prediction, financial distress score.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Springate Score and its components.

Notes:

• As with the Altman Z-Score, Piotroski F-Score, Beneish M-Score, Ohlson O-Score and Zmijewski Score, this is a probabilistic, not a definitive, indicator and should be combined with further fundamental analysis.
• EBIT is approximated as Net Income + Income Tax Expense + Interest Expense, consistent with the Altman Z-Score calculation elsewhere in this module.

References:

• Springate, Gordon L.V. “Predicting the Possibility of Failure in a Canadian Firm.” Unpublished M.B.A. Research Project, Simon Fraser University, 1978.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_springate_score().loc["AAPL"]

Which returns:

get_grover_score

Calculates the Grover Score, a financial metric used to predict the likelihood of a company going bankrupt. It was developed by re-estimating the coefficients of a reduced-form Altman Z-Score and adding a Return on Assets term, using a sample that paired each of Altman’s original bankrupt firms with a matched non-bankrupt firm from the same industry and year.

The formula is as follows:

The Grover Score can be interpreted as follows:

• A Grover Score of -0.02 or lower indicates a high likelihood of bankruptcy.
• A Grover Score of 0.01 or higher indicates a low likelihood of bankruptcy (per some secondary sources), leaving a gray area in between the two thresholds.

Also known as: Grover Score, G-Score, bankruptcy prediction, financial distress score.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Grover Score and its components.

Notes:

• As with the Altman Z-Score, Springate Score, Ohlson O-Score and Zmijewski Score, this is a probabilistic, not a definitive, indicator and should be combined with further fundamental analysis.
• EBIT is approximated as Net Income + Income Tax Expense + Interest Expense, consistent with the Altman Z-Score calculation elsewhere in this module. Return on Assets uses the point-in-time Total Assets balance (not averaged), matching the original Grover (2001) specification.

References:

• Grover, Jeffrey S. “Validating the Grover Bankruptcy Model.” Doctoral dissertation, University of North Texas, 2003.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_grover_score().loc["AAPL"]

Which returns:

get_fulmer_h_score

Calculates the Fulmer H-Score, a financial metric used to predict the likelihood of a company going bankrupt. Unlike the Altman Z-Score, which was built on relatively large, listed manufacturing companies, the Fulmer H-Score was calibrated on a matched-pair sample of small, failed and non-failed U.S. companies, using nine financial ratios selected via step-wise multiple discriminant analysis.

The formula is as follows:

H-Score = 5.528 * V1 + 0.212 * V2 + 0.073 * V3 + 1.270 * V4 - 0.120 * V5 + 2.335 * V6 + 0.575 * V7 + 1.083 * V8 + 0.894 * V9 - 6.075

The nine variables are:

• V1: Retained Earnings to Total Assets
• V2: Sales to Total Assets
• V3: EBT to Total Equity
• V4: Cash Flow (Operating Cash Flow) to Total Liabilities
• V5: Total Debt to Total Assets
• V6: Current Liabilities to Total Assets
• V7: Log of Tangible Total Assets (Total Assets less Goodwill and Intangible Assets)
• V8: Working Capital to Total Liabilities
• V9: Log of EBIT to Interest Expense

The Fulmer H-Score can be interpreted as follows:

• An H-Score of less than 0 predicts failure.
• An H-Score of greater than 0 predicts non-failure.

Also known as: Fulmer H-Score, H factor, bankruptcy prediction, financial distress score.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• show_columns (list[str] \| None, optional): List of columns to show in the results. If None, all columns will be shown. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Fulmer H-Score and its components.

Notes:

• Because V7 and V9 involve a natural logarithm, periods where Tangible Total Assets is zero or negative, where EBIT and Interest Expense do not share the same sign, or where the company reports no Interest Expense at all, will produce NaN and therefore no H-Score for that period. This is a structural limitation of the log-linear Fulmer specification, not a bug — the same kind of limitation documented for the Graham Number elsewhere in this toolkit.
• Because V7 uses an un-normalized dollar figure, the H-Score is sensitive to the absolute scale of a company’s total assets; treat results for very large companies (far outside the small-firm sample the model was calibrated on) with additional caution.
• As with the Altman Z-Score, Springate Score and Grover Score, this is a probabilistic, not a definitive, indicator and should be combined with further fundamental analysis.

References:

• Fulmer, John G., James E. Moon, Thomas A. Gavin, and Michael J. Erwin. “A Bankruptcy Classification Model for Small Firms.” Journal of Commercial Bank Lending, Vol. 66, No. 11, 1984, pp. 25-37.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_fulmer_h_score().loc["AAPL"]

Which returns:

get_present_value_of_growth_opportunities

The Present Value of Growth Opportunities (PVGO) is a financial metric that represents the present value of a company’s future growth opportunities. It is calculated as the difference between the company’s current stock price and the discounted value of its future cash flows.

The formula is as follows:

Also known as: PVGO, growth value.

Args:

• calculate_daily (bool, optional): Whether to calculate the PVGO using daily historical data. Defaults to False.
• diluted (bool, optional): Whether to use diluted shares in the calculation. Defaults to True.
• include_dividends (bool, optional): Whether to deduct Preferred Dividends Paid from Net Income when calculating the Earnings per Share, so that the earnings figure reflects what is attributable to common shareholders only. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.

Returns:

pd.DataFrame: DataFrame containing the PVGO values.

Notes:

• The textbook PVGO discounts the no-growth value of the company (Earnings per Share / r) at the cost of equity, since both the share price and the Earnings per Share are equity-only, per-share quantities. This implementation discounts at the Weighted Average Cost of Capital instead, which blends in the (typically lower, tax-shielded) cost of debt and therefore generally understates PVGO. Prefer comparing PVGO across companies, or over time for the same company, over reading absolute levels literally.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_present_value_of_growth_opportunities()

Which returns:

get_sustainable_growth_rate

The Sustainable Growth Rate (SGR) is the maximum rate at which a company can grow its revenue, using internally generated funds only, without having to raise additional equity or increase its financial leverage.

The formula is as follows:

Also known as: SGR, self-sustainable growth rate.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Sustainable Growth Rate.

Notes:

• Growing faster than the SGR without external financing typically requires either improving profitability, reducing the dividend payout, or increasing leverage.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_sustainable_growth_rate()

Which returns:

get_internal_growth_rate

The Internal Growth Rate (IGR) is the maximum rate at which a company can grow its revenue using only its retained earnings, without raising any external financing (neither debt nor equity).

The formula is as follows:

Also known as: IGR.

Args:

• rounding (int, optional): The number of decimals to round the results to. Defaults to None.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.

Returns:

pd.DataFrame: DataFrame containing the Internal Growth Rate.

Notes:

• The IGR is more conservative than the Sustainable Growth Rate (SGR) since it assumes no additional debt is raised to fund growth, whereas the SGR assumes the company maintains its current level of financial leverage.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_internal_growth_rate()

Which returns:

get_graham_number

Calculate the Graham Number, a conservative estimate of a stock’s fair value based on its earnings and book value, as devised by Benjamin Graham.

The Graham Number is intended as an upper bound on the price a defensive investor should pay for a stock. It is most meaningful for stable, profitable companies with positive book value - for companies with negative earnings or negative book value the result is not meaningful (the square root of a negative number is undefined and will show up as NaN).

The formula is as follows:

Also known as: Graham fair value.

Args:

• diluted (bool, optional): Whether to use diluted shares in the calculation. Defaults to True.
• trailing (int \| None, optional): The trailing period to use for the calculation. Defaults to None.
• rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
• growth (bool, optional): Whether to calculate the growth of the values. Defaults to False.
• lag (int \| list[int], optional): The lag to use for the growth calculation. Defaults to 1.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.

Returns:

pd.DataFrame: DataFrame containing the Graham Number values.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.models.get_graham_number()

Which returns: