Financial periods and how to work with them

1. Financial periods and how to work with them

Hi, and welcome back. In this chapter, we will go into more detail on financial periods and how to work with them. All forecasts are based on specific financial periods, so let's have a look.

2. The financial year

A company works in a specific framework when it comes to reporting its numbers. This is called the financial year. It is important to have this structure in order to be able to compare years and companies. A financial year does not have to be the same as a calendar year, January to December, but can start and end at any month. For example, the financial year of Microsoft runs from the 1st of July to the 30th of June.

3. Months and quarters

A financial year is broken up into months and quarters. Months are pretty explanatory and quarters are chunks of three months at a time. For example, the first quarter of a calendar year is January to March. However, even when companies have a different starting date to their financial year than 1 January, they almost always have their year start and end at one of the calendar quarters, so either 1 April until 31 March, 1 July until 30 June, or 1 October until 30 September.

4. Abbreviations

In a financial statement or forecast, the months are usually represented by numbers, with the number 1 corresponding to January no matter when the financial year starts. They are not dependent on the financial year. For quarters, the abbreviations Q1, Q2, etc. are used, and these are based on the financial year. Taking the example of Microsoft again, their first quarter would be July until September. The year can either be abbreviated to two digits or the full four digits, depending on the length of time examined. The year is based on the financial year end, for example, Microsoft has a year-end of June; therefore the 2018 financial year would be from 1 July 2017 until 30 June 2018.

5. Let's practice!

Now let's see some examples.

This exercise is part of the course

Financial Forecasting in Python

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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!

Exercise 1: Introduction to financial statements Exercise 2: Calculating gross profit Exercise 3: Calculating net profit Exercise 4: Elements within net profit & gross profit Exercise 5: Calculating sales & Cost of Goods Sold (COGS)Exercise 6: Calculating sales Exercise 7: Forecasting sales with a discount Exercise 8: Calculating COGS Exercise 9: Calculating the break-even point Exercise 10: Working with raw forecast datasets Exercise 11: Tesla income statement Exercise 12: Forecasting profit for Tesla

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.

Exercise 1: Introduction to the balance sheet Exercise 2: Calculating accounts receivable (debtors)Exercise 3: Bad debts Exercise 4: Calculating accounts payable (creditors)Exercise 5: Understanding accounts payable and receivable Exercise 6: Balance sheet efficiency ratios - Part 1 Exercise 7: Debtor days ratio Exercise 8: Days payable outstanding Exercise 9: Balance sheet efficiency ratios - Part 2 Exercise 10: Days in inventory and asset turnover ratio Exercise 11: Understanding ratios Exercise 12: Calculating balance sheet ratios for Ford Exercise 13: Forecasting the balance sheet for Ford

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.

Exercise 1: Financial periods and how to work with them

Current Exercise

Exercise 2: Converting quarters into months Exercise 3: Merging months into quarters Exercise 4: The datetime library Exercise 5: Working with the datetime library Exercise 6: Converting date formats - simple Exercise 7: Converting date formats - explicit Exercise 8: Tips and tricks when working with datasets Exercise 9: Working with datasets - month totals Exercise 10: Working with datasets - combining datasets Exercise 11: Working with datasets - exporting data

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.

Exercise 1: Building sensitive forecast models Exercise 2: Weighted probability Exercise 3: Market sentiment Exercise 4: Dependencies and sensitivity Exercise 5: Assigning dependencies for sales and COGS Exercise 6: Building a sensitivity analysis for gross profit Exercise 7: Assigning dependencies for expenses Exercise 8: Build a sensitivity analysis for the net profit Exercise 9: Working with variances in the forecast Exercise 10: Building an alternate forecast Exercise 11: Building a gap analysis between forecasts Exercise 12: Setting dependencies for Netflix Exercise 13: Calculating an alternative forecast for Netflix