Een alternatief forecast bouwen

We gaan nu een alternatieve forecast maken voor Txs Tools. De forecast voor het nieuwe kwartaal is gebaseerd op daadwerkelijke data voor jul - aug en op aangepaste forecastdata voor september. De data (verkochte eenheden) is als volgt:

Jul = 700
Aug = 220
Sep = 520

De afhankelijkheidsberekeningen zijn al afgerond in de vorige oefening. De volgende informatie is van toepassing:

base_cost_price = 7
base_sales_price = 15

Deze oefening maakt deel uit van de cursus

Financiële forecasting in Python

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

def ____(____, ____, ____):
    res = []
    for sales in sales_usd:
        if sales >= 350:
            sales_dep = (350 * base_sales_price) + ((sales - 350) * (base_sales_price - 1))
        else:
            sales_dep = sales * base_sales_price
        if sales >= 500:
            cost_dep = (500 * base_cost_price) + ((sales - 500) * (base_cost_price + 2))
        else:
            cost_dep = sales * base_cost_price
        res.append(sales_dep - cost_dep)
    return ____

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Financiële forecasting in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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: 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 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: Gevoelige forecastmodellen bouwen Exercise 2: Gewogen waarschijnlijkheid Exercise 3: Marktsentiment Exercise 4: Afhankelijkheden en gevoeligheid Exercise 5: Afhankelijkheden instellen voor sales en COGS Exercise 6: Een gevoeligheidsanalyse bouwen voor brutowinst Exercise 7: Afhankelijkheden toekennen voor kosten Exercise 8: Bouw een gevoeligheidsanalyse voor de nettowinst Exercise 9: Werken met variaties in de forecast Exercise 10: Een alternatief forecast bouwen

Huidige oefening

Exercise 11: Een gap-analyse opbouwen tussen forecasts Exercise 12: Afhankelijkheden instellen voor Netflix Exercise 13: Een alternatief forecast voor Netflix berekenen