Projecting a Firm’s Financial Performance

Along with analyzing past performance, an analyst will also project a firm’s future income and cash flows. A simple model to project a firm’s future performance starts with the forecast of GDP growth and forecasts the firm’s net income as follows:

  1. Forecast expected GDP growth rate. The analyst can use the GDP growth forecast supplied by outside research groups.
  2. Based on the historical relationships between the GDP and industry, forecast the industry’s expected sales. For example, if you are analysing consumer durables industry, then you can use the historical data about how the industry sales change with respect to change in GDP.
  3. Analyse whether the firm’s market share in the industry will remain same, decrease or increase.
  4. Based on the possible change in market share, forecast the company’s sales.
  5. Once you have the sales forecast of the firm, you need to forecast the income and cash flows. To forecast income, there are two approaches: for a stable firm, you can apply the historical margins to arrive at the net income forecast. Alternatively, you can project each expense item in the income statement based on separate assumption about them and forecast the net income. Any non-recurring items must be removed from the income forecast.
  6. Apart from net income, the analyst will also forecast the future cash flows. To do so, he will have to make several assumptions about sources and uses of cash such as changes in working capital, debt repayment, issue of new stock or debt, etc. The analyst will also estimate the interest expense, tax rates, and dividend payments in the future.

An analyst will make use of spread sheet modelling to build the forecasted financial statements. Typically the net income will be forecasted for the next 3 to 5 years.

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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
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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