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  3. Financial Forecasting in Python
  • 1

    Income statements

    Free

    In this chapter, we will learn the basics of financial statements, with a specific focus on the income statement, which provides details on our sales, costs, and profits. We will learn how to calculate profitability metrics and finish off what we have learned by building our profit forecast for Tesla!

  • 2

    Balance sheet and forecast ratios

    In this chapter, we will learn a bit more about the balance sheet, covering assets and liabilities and specific ratios to help evaluate the financial health and efficiency of a company, as well as how these ratios can assist us in building a great forecast.

  • 3

    Formatting raw data, managing dates and financial periods

    We have gotten a basic understanding of income statements and balance sheets. However, consolidating data for forecasting is complex, so in this chapter, we will look at some basic tools to help solve some of the complexities specifically relating to finance - working with dates and different financial periods, and formatting our raw data into the correct format for financial forecasting.

  • 4

    Assumptions and variances in forecasts

    In this chapter, we will be exploring two more aspects to creating a good forecast. First, we will look at assumptions, what drives them and what happens when an assumption changes? Next, we will look at variances, as a forecast is built at one point in time, but what happens when the actual results do not correspond to our forecast? We need to build a sensitive forecast that can be sensitive to changes in both assumptions and take into account variances, and this is what we will explore in this chapter.

Initializing

Exercise

Build a sensitivity analysis for the net profit

Txs Tools has provided the following forecast admin cost in USD based on full-time employees:

Jul = 1500 Aug = 1500 Sep = 1500

Build the forecast net profit forecast_net_profit when emp_leave = [6, 6, 0] and the cost per temp employee is 80 USD.

In addition to emp_leave and admin_usd, forecast gross profit has already been provided for you as forecast_gross_profit.

Instructions

100 XP
  • Create an index variable and initialize this index to 0.
  • Create the dependency by looping through the admin_usd list, using your index to access the correct month in your lists.
    • Set the number of temporary employees (temp) by referencing your emp_leave list at the current index
    • If there are temporary employees, your admin dependencies (admin_dep) should reflect the additional cost of the temporary employees (plus the standard admin costs)
    • Else, admin_dep should reflect the standard admin costs.
  • Calculate forecast_net_profit as forecast_gross_profit (at the index) minus the calculated admin_dep.
  • Print the forecast_net_profit.