Calculating accounts receivable (debtors)

When we sell something on credit, the credit portion is in the balance sheet under ‘Accounts Receivable’ or ‘Debtors’. For example, if credit sales are made in January with a 60-day payback period, they would be recorded in our ‘Debtors’ account in January, but only be paid (released) in March, and so on.

In this exercise, we will create the following lists:

The credit sales in the month credits, which in this exercise is 60% of the sale value.
The total accounts receivable debtors, to be calculated as the credits for the current month, plus the credits of the month before, minus the credits of two months before (as we assume the credits from 2 months ago or 60 days, will be repaid by then).

We have set an index for the variable month. The month value is set at 0.

This exercise is part of the course

Financial Forecasting in Python

Exercise instructions

Create a blank credits list and blank debtors list.
Complete the for loop:
- Calculate the credit of the month by multiplying the sales value (set to variable mvalue) by the percentage credit (60%).
- If the month is greater than 0, append the debtors as current month credits plus credits of prior month.
- If the month is not greater than 0, append the debtors as the current month credits.
Print the debtors list.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create the list for sales, and empty lists for debtors and credits
sales = [500, 350, 700]
____ = [] 
____ = []

# Create the statement to append the calculated figures to the debtors and credits lists
for mvalue in sales: 
    credits.append(mvalue * ____)
    if month > 0:
        ____.append(credits[____] + credits[month-1])
    else:
        ____.append(credits[____]) 
    month += 1
# Print the result
print("The ‘Debtors’ are {}.".format(_____))

Edit and Run Code

This exercise is part of the course

Financial Forecasting in Python

IntermediateSkill Level

4.7+

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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)

Current Exercise

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 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