Analyzing Financial Time Series: Stock Prices and Returns

Financial data can be highly volatile and misleading if you only look at raw stock prices. To perform accurate analysis, risk assessment, or algorithmic modeling, you must understand how to convert prices into returns. This text-only course guides you through the foundational mathematical and practical concepts of financial time series. You will learn how to clean, transform, and analyze stock data, transitioning from raw price tracking to sophisticated return calculations that form the basis of modern quantitative finance. What you'll learn: - Understand the core differences between stock prices, arithmetic returns, and logarithmic returns. - Calculate simple and log returns using modern programming practices and data libraries. - Analyze the statistical properties of financial time series, including volatility and distribution. - Handle common real-world data issues such as missing values, market holidays, and corporate actions. - Apply time-series modeling concepts to evaluate portfolio performance and risk metrics. Starting with fundamental definitions of financial assets, you will progress step-by-step through mathematical formulas, practical code-based implementations, and real-world data cleaning scenarios. This course is designed for beginner financial analysts, data scientists, and programming enthusiasts looking to enter quantitative finance. No prior experience with financial modeling is required. Start reading today to build a solid, math-backed foundation in financial data analysis.