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Kurs

Finanzprognosen mit Python

MittelSchwierigkeitsgrad

Aktualisiert 12/2021

In diesem Kurs schlüpfst du in eine CFO-Rolle, in der du den Vorstand zu wichtigen Kennzahlen berätst und Finanzprognosen erstellst.

Kurs kostenlos starten

PythonApplied Finance

4 Std.

12 Videos

49 Übungen

4,050 XP

13,126

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Kursbeschreibung

In Finanzprognosen mit Python schlüpfst du in die Rolle der CFO und lernst, wie du einen Aufsichtsrat zu wichtigen Kennzahlen berätst, während du eine Finanzprognose erstellst, die Grundlagen von Gewinn- und Verlustrechnung sowie Bilanz kennenlernst und unordentliche Finanzdaten bereinigst. Im Kurs arbeitest du mit realen Datensätzen von Netflix, Tesla und Ford und nutzt dabei das Paket pandas. Nach dem Kurs kannst du Finanzkennzahlen berechnen, mit Annahmen und Abweichungen arbeiten und deine eigene Prognose in Python aufsetzen!

Voraussetzungen

Introduction to Python Intermediate Python

1

Income statements

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!

Introduction to financial statements

Calculating gross profit

Calculating net profit

Elements within net profit & gross profit

Calculating sales & Cost of Goods Sold (COGS)

Calculating sales

Forecasting sales with a discount

Calculating COGS

Calculating the break-even point

Working with raw forecast datasets

Tesla income statement

Forecasting profit for Tesla

Kapitel starten

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.

Introduction to the balance sheet

Calculating accounts receivable (debtors)

Calculating accounts payable (creditors)

Understanding accounts payable and receivable

Balance sheet efficiency ratios - Part 1

Debtor days ratio

Days payable outstanding

Balance sheet efficiency ratios - Part 2

Days in inventory and asset turnover ratio

Understanding ratios

Calculating balance sheet ratios for Ford

Forecasting the balance sheet for Ford

Kapitel starten

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.

Financial periods and how to work with them

Converting quarters into months

Merging months into quarters

The datetime library

Working with the datetime library

Converting date formats - simple

Converting date formats - explicit

Tips and tricks when working with datasets

Working with datasets - month totals

Working with datasets - combining datasets

Working with datasets - exporting data

Kapitel starten

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.

Building sensitive forecast models

Weighted probability

Market sentiment

Dependencies and sensitivity

Assigning dependencies for sales and COGS

Building a sensitivity analysis for gross profit

Assigning dependencies for expenses

Build a sensitivity analysis for the net profit

Working with variances in the forecast

Building an alternate forecast

Building a gap analysis between forecasts

Setting dependencies for Netflix

Calculating an alternative forecast for Netflix

Kapitel starten

Finanzprognosen mit Python

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