Economics, Finance & Coding Glossary

Excess return above the benchmark.

Return expressed as a yearly rate, regardless of holding period.

Simulating a strategy on historical data to evaluate performance.

A measure of a stock's sensitivity to market movements.

Closed-form price for European options.

A bond's price sensitivity to interest-rate changes.

Expected return as risk-free rate plus beta times market premium.

Output as a function of capital and labour with constant returns.

Two non-stationary prices whose linear combo is stationary.

Gains from trade arise from differences in opportunity cost.

Linear relationship between two variables, from −1 to +1.

Splitting data into train/test folds to evaluate model generalisation.

Named temporary result set defined with WITH … AS (…).

Pandas 2D table structure with labelled rows and columns.

Peak-to-trough decline in portfolio value.

Prices already reflect all available information.

Average loss in the worst tail beyond VaR.

String with embedded expressions: f"value is {variable}".

Extends CAPM with size (SMB) and value (HML) factors.

Links nominal interest rates, real rates, and inflation.

Single-number measure of income or wealth inequality (0–1).

Values a stock as next dividend over (required return − growth).

Total market value of goods & services produced in an economy.

Aggregates rows that share a column value.

Year-over-year percentage rise in a basket of consumer prices.

Active return divided by tracking error.

The discount rate that makes a project NPV exactly zero.

Combines rows from two tables based on a matching condition.

The bet size that maximises long-run logarithmic growth.

How an initial spending injection cascades into larger GDP change.

Window functions that look at the previous / next row.

Fit a straight line (or hyperplane) that minimises the sum of squared errors: ordinary least squares (OLS).

Compact syntax to build a list from an iterable.

Classification model that turns a linear score into a probability through the sigmoid.

Buy winners (long) and sell losers (short) simultaneously.

Optimal portfolio weighting that maximises return per unit of risk.

The tendency of a price or spread to return to its average.

The tendency of recent winners to keep outperforming.

A state where no player can gain by changing strategy alone.

Today's value of future cash flows, discounted for time and risk.

Bell-shaped probability distribution defined by mean and std dev.

Fast, fixed-type numerical array - the backbone of scientific Python.

Empirical link between unemployment and the output gap.

Probability of observing the result if the null hypothesis is true.

1D labelled array - like a single DataFrame column.

Short-run inverse relationship between inflation and unemployment.

How responsive quantity demanded is to a change in price.

Exchange rates should equalise the price of identical goods.

A no-arbitrage link between calls, puts, the stock, and a bond.

Money supply × velocity equals price level × real output.

Proportion of variance in the outcome explained by the model.

Python ML library: classifiers, regressors, scalers, pipelines.

Risk-adjusted return: excess return divided by volatility.

Like Sharpe, but penalises only downside volatility.

Average spread of values around the mean.

A query nested inside another query.

A dollar today is worth more than a dollar tomorrow.

Excess return per unit of market (systematic) risk.

Worst expected loss over a horizon at a confidence level.

Standard deviation of returns - a measure of risk.

Blended after-tax cost of a firm’s equity and debt.

Performs a calculation across rows related to the current row.