Chapter 2: Quantitative Methods

In this section

• Rates and Returns
  Explore how interest rates function as a required rate of return, discount rate, and opportunity cost, understand nominal vs. real rates and various risk premiums, and learn about essential return measures such as HPR, TWRR, MWRR, and continuous compounding for robust portfolio performance evaluation.
• Time Value of Money in Finance
  Learn how to calculate present and future values, annuities, perpetuities, and effectively discount or compound cash flows to manage investments more confidently.
• Statistical Measures of Asset Returns
  Dive into key statistical measures—mean, median, dispersion, skewness, and kurtosis—essential for evaluating and interpreting asset return data in finance.
• Probability Trees and Conditional Expectations
  Explore the fundamentals of probability trees, conditional expectations, and their practical applications in investment decision-making and risk management.
• Portfolio Mathematics
  Explore the core concepts and mathematical framework behind portfolio returns, variance, correlation, diversification, shortfall risk, and safety-first principles to design resilient investment portfolios.
• Simulation Methods
  Explore Monte Carlo techniques, bootstrap resampling, and the differences between normal and lognormal distributions, and learn how to apply simulation methods in finance for pricing, risk assessment, and portfolio analysis.
• Estimation and Inference
  Explore core sampling methods, the Central Limit Theorem, resampling techniques, and confidence intervals to build reliable financial estimations and inferences.
• Hypothesis Testing
  Explore the fundamentals of hypothesis testing, from formulating null and alternative hypotheses to interpreting p-values, avoiding errors, and applying confidence intervals in financial decision-making.
• Parametric and Non-Parametric Tests of Independence
  Learn how parametric and non-parametric tests help evaluate independence in financial data, including t-tests, Spearman’s rank correlation, and chi-square methods.
• Simple Linear Regression
  Learn how Simple Linear Regression uses Ordinary Least Squares (OLS) to estimate relationships between a single independent variable and a dependent variable. Explore key assumptions, interpret coefficients, and evaluate model fit using R², SEE, and ANOVA.
• Introduction to Big Data Techniques
  Explore how Big Data reshapes modern finance, distinguish AI from ML, and learn the essential machine learning approaches. Discover how data science powers predictive analytics, algorithmic trading, and more.