Equity Valuation - Free Cash Flow Model (FCFE)

We learned about how an analyst can value a stock using the dividend discount model, where the analyst considers the dividends investors expect to receive in the years to come. The idea behind this model is quite intuitive once you understand the concept of time value of money.

One problem with dividend discount models is that all companies do not pay dividends. In that case, how does one value a company? For example, Apple is a highly valuable company, however, at the time of this writing, it does not pay dividends to its shareholders. Instead of distributing dividends to its shareholders, Apple reinvests this money back in their business.

To value such companies, an alternative to dividends is to use the free cash flow model. The free cash flow model can also be useful for companies that do pay dividend but only a small portion of their earnings, and the dividends paid do not appropriately reflect the true capacity of the business.

What we are really concerned about here is the Free Cash Flow to Equity (FCFE). FCFE is defined as the amount of free cash flow the firm has after meeting all its obligations. This includes debt obligations, capital expenditure to maintain existing assets, and new asset purchases to maintain the growth rate assumed. This is the cash that can be paid to shareholders after paying for all expenses, debt repayments, and reinvestments.

FCFE is calculated using the following formula:

FCFE = Net Income – (Capital Expenditure – Depreciation) – Change in Non-cash Working Capital + (New Debt Issued – Debt Repayments)

The non-cash working capital is concerned with items such as inventory, and accounts receivables. We reduce the FCFE by any increase in non-cash working capital, for example, an increase in accounts receivables.

The value of the stock using FCFE can be calculated using the following formula:

V0=i=1FCFEt(1+ke)tV_{0}=\sum_{i=1}^{\infty }\frac{FCFE_{t}}{\left ( 1+k_{e} \right )^{t}}

Note that the required return on equity (k) is calculated using the CAPM model.

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Data Science in Finance: 9-Book Bundle

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Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
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  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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