Class Topics

Module 1

In the first module, we introduce Python fundamentals for financial analysis. You’ll learn how to set up your Python environment, create Jupyter notebooks, define functions, perform numerical calculations, and organize data effectively. To demonstrate practical applications, we’ll calculate and visualize bond prices across different discount rates and analyze stock return data—essential skills for quantitative finance work.

Module 2

In the second module, we will explore how to analyze financial data using Python. You’ll learn to download stock data from Yahoo! Finance using libraries like yfinance, which create Pandas data frames. We’ll cover how to calculate financial returns from stock prices using Pandas’ percentage change functions. Additionally, we’ll demonstrate how to compute descriptive statistics, extract implied dividends, and perform time-series analysis on stock return data.

Module 3

In the third module, we examine why stocks earn different returns. We’ll identify systematic risk factors that explain these differences across companies. You’ll learn why certain trading strategies consistently outperform others without necessarily indicating market inefficiency. The module covers key factors that drive stock returns: company size, value characteristics, momentum effects, profitability metrics, and corporate investment policies. Understanding these factors provides a framework for analyzing and predicting patterns in equity returns.

Module 4

In the fourth module, we explore portfolio optimization—how investors balance risk and return. Using data analysis and numerical techniques, we’ll examine methods to evaluate this fundamental trade-off in investment management, where risk is measured as return volatility.

Investors face two key challenges: understanding the available investment possibilities (the opportunity set) and determining their optimal risk-return balance. We’ll demonstrate how, at the optimal portfolio allocation, investors must align the market’s risk-return trade-off rate (marginal rate of transformation) with their personal risk-return preferences (marginal rate of substitution).

Module 5

In the final module, we analyze time-series properties of financial data. First, we examine whether past returns can predict future returns - a phenomenon called serial-correlation or autocorrelation in finance. We test this by regressing current returns against past returns, revealing that some stocks show daily serial correlation even after accounting for multiple lagged returns.

Next, we explore mean-reversion in stationary time series like interest rates. This occurs when rates that are above their long-run average tend to decrease, while those below tend to increase. We’ll discover this pattern is persistent but challenging to empirically validate.

Finally, we study time-varying volatility. We’ll learn that volatility has three key characteristics: it reverts to a long-term average, responds to recent market shocks, and exhibits autoregressive properties (past volatility predicts future volatility). Understanding these volatility dynamics is crucial for implementing effective portfolio strategies in uncertain markets.