Indice dei giorni di incasso crediti

Il primo rapporto che vediamo è quello dei giorni di incasso crediti (debtor days). Indica in quanti giorni incassiamo i crediti dai nostri clienti. Di solito si calcola su un periodo di 1 esercizio.

La formula è la seguente:

\(Debtor\,Days = \frac{Ending\,Balance\,Debtors}{Sales} \times Days\,in\,Financial\,Year\)

Hai le seguenti informazioni:

Vendite dell'anno: 12.500 USD
Saldo crediti a fine periodo: 650

Questo esercizio fa parte del corso

Previsioni finanziarie in Python

Visualizza il corso

Istruzioni dell'esercizio

Crea le variabili per il saldo crediti di fine periodo debtors_end e per le vendite totali dell'anno sales_tot.
Calcola l'indice dei giorni di incasso crediti ddays_ratio.
Stampa il risultato.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create the variables
____ = ____
____ = ____

# Calculate the debtor days variable
____ = (____/____) * 365

# Print the result
print("The debtor days ratio is {}.".format(____))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Previsioni finanziarie in Python

IntermediárioNível de habilidade

4.8+

Inizia il corso gratis

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: Introduzione allo stato patrimoniale Exercise 2: Calcolare i crediti verso clienti (debtors)Exercise 3: Perdite su crediti Exercise 4: Calcolare i debiti verso fornitori (creditori)Exercise 5: Capire crediti e debiti commerciali Exercise 6: Indicatori di efficienza di bilancio - Parte 1 Exercise 7: Indice dei giorni di incasso crediti

Esercizio in corso

Exercise 8: Days Payable Outstanding Exercise 9: Indicatori di efficienza del bilancio - Parte 2 Exercise 10: Giorni di giacenza e indice di rotazione degli asset Exercise 11: Capire gli indici Exercise 12: Calcolo dei rapporti di bilancio per Ford Exercise 13: Prevedere lo stato patrimoniale per 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