Übersicht über quantitative Methoden im CFA Level 1

Key Topics You Need to Focus On

1. Übersicht über das Thema

Quantitative Methoden is one of the most important and foundational subjects in the Chartered Financial Analyst Level I program.

This subject mainly covers core concepts such as:

• Time value of money
• Present and future value of cash flows
• Probability and statistics
• Hypothesis testing

A strong understanding of these topics will give you a major advantage when studying more advanced subjects like asset valuation and portfolio management later in the CFA program.

Although the volume of material is relatively large, Quantitative Methods is often considered a “scoring subject” because:

• The concepts are not overly tricky
• Many questions are calculation-based
• It is especially advantageous if you have a solid math background

Exam weight: Approximately 8–12% of the CFA Level I exam

2. Core Topics and Important Concepts

The Quantitative Methods section typically includes several modules (note: curriculum may change slightly each year). Below is a structured overview:

Modul 1: Zeitwert des Geldes

• Understand interest rates and their components
• Calculate the Effective Annual Rate (EAR)
• Master present value (PV) and future value (FV) of cash flows
• Use a financial calculator efficiently

Module 2: Organizing, Visualizing, and Describing Data

• Types and classification of data
• Frequency distributions
• Contingency tables and confusion matrices
• Data visualization techniques
• Measures of central tendency: mean, median, mode
• Arithmetic mean vs geometric mean
• Quantiles and percentiles
• Streuungsmaße: Bereich, Varianz, Standardabweichung
• Variationskoeffizient
• Schiefe und Kurtosis
• Korrelationskoeffizient

Modul 3: Wahrscheinlichkeitskonzepte

• Zufallsvariablen und Grundlagen der Wahrscheinlichkeit
• Bedingte vs. unbedingte Wahrscheinlichkeit
• Additions- und Multiplikationsregeln
• Erwartungswert, Kovarianz, Korrelation
• Anwendungen in Portfolio-Rendite und Risiko
• Bayes’ Formel
• Fakultäten, Permutationen und Kombinationen

Module 4: Common Probability Distributions

• Diskrete vs. kontinuierliche Zufallsvariablen
• Binomialverteilung
• Gleichverteilung
• Normalverteilung
• Lognormalverteilung
• Shortfall-Risiko und Roys Safety-First-Kriterium
• Diskrete vs. kontinuierliche Zinseszinsen
• Student's t-Verteilung, F-Verteilung
• Monte-Carlo-Simulation

Modul 5: Stichproben und Schätzung

• Zufallsstichprobenmethoden
• Stichprobenfehler und geschichtete Stichproben
• Zentrale Grenzwertsatz
• Standardfehler des Mittelwerts
• Konfidenzintervalle
• Resampling-Methoden (Bootstrap, Jackknife)
• Stichprobenverzerrung

Module 6: Hypothesis Testing

• Hypothesentestrahmen
• Null- und Alternativhypothesen
• Einseitige vs. zweiseitige Tests
• Type I and Type II errors
• Test statistics and significance level
• p-value interpretation
• Testing population parameters (mean, variance, etc.)

Module 7: Introduction to Linear Regression

• Simple linear regression model
• Dependent and independent variables
• Regression equation, slope, and intercept
• Cross-sectional vs time-series regression
• Assumptions of regression models
• Key metrics: SST, RSS, SSE, R²
• Common hypothesis tests (F-test, t-test)
• Predicted values and confidence intervals
• Extensions of linear regression models