Quant glossary - 30 quant finance terms explained simply

Quantooo Lab · 2026-10-22

Quant finance has its own vocabulary. Here are thirty terms you will meet most often, explained in one or two sentences each, with links to longer explanations where we have them.

What are the most important quant terms to know?

Start with these six: return, volatility, Sharpe ratio, information coefficient, backtest and overfitting. Almost every quant discussion uses them. The full list follows, grouped by theme.

Returns and risk

Return: the percentage change in value over a period, including dividends.
Excess return: return minus a benchmark's return, for example a stock's return minus the S&P 500's.
Volatility: how much returns swing, usually annualised. 20% means typical yearly swings of about plus or minus 20%. See forecasting volatility.
Drawdown: the fall from a previous peak. A drawdown of −20% means 20% below the highest value so far.
Beta: how much an asset tends to move with the market. A beta of 1.5 means about 1.5% for every 1% market move.
Alpha: return not explained by market exposure. Often claimed, rarely proven.
Sharpe ratio: return per unit of volatility, per year. Above 1 is strong; around 0 means no reward for the risk.

Signals and factors

Signal: a number used to rank or select assets, such as a score.
Factor: a characteristic linked to returns across many assets, such as momentum, value or low volatility.
Momentum: the tendency of recent winners to keep winning for a while. See does momentum investing still work?
Universe: the set of assets a strategy can choose from, for example the S&P 100.
Long-short portfolio: buy the top-ranked assets and sell short the bottom-ranked, so the result depends on the ranking, not the market's direction.
Rebalancing: resetting a portfolio to its target holdings, for example weekly.
Turnover: how much of a portfolio is traded per year. Drives costs.

Testing and evaluation

Backtest: running a rule on past data as if it had been used at the time.
Point-in-time data: data stored with the date it became known, so a backtest only sees what was public.
Look-ahead bias: using information before it was available. See look-ahead bias.
Survivorship bias: testing only on assets that still exist today. See survivorship bias.
Overfitting: fitting noise instead of a real pattern. See overfitting.
Walk-forward testing: train on the past, test on the next period, roll forward. See walk-forward testing.
Holdout: data kept aside until the end and tested once. See sealed holdout.
Information coefficient (IC): correlation between a ranking and later returns. See the IC explained.
t-statistic: how many standard errors a result is from zero. Below about 2, it could easily be luck.
Newey-West adjustment: a correction to the t-statistic when observations overlap, as with 20-day returns measured daily.
Deflated Sharpe ratio: a Sharpe ratio adjusted for how many strategies were tried before picking the best.
Random control: the same test on random rankings, to show what luck alone produces.
Regime: a market environment such as calm or stressed, in which strategies can behave differently.

Risk models

HAR model: a standard volatility forecast using average volatility over the last day, week and month.
GARCH: a classic model of volatility clustering, where turbulent days follow turbulent days.
QLIKE: a loss function for comparing volatility forecasts. Lower is better.

Where can you see these terms in use?

Every term above is used with real numbers on this site. The math page gives the exact formula for each, and the beginner's guide shows how they fit together on a stock page.

Open questions for students and researchers

Questions without a single right answer. Pick one and share your reasoning in the discussion below.

Which two terms on this list are most often confused with each other, and what goes wrong when they are?
Define the Sharpe ratio and the information ratio. When can a strategy have a high Sharpe but a low information ratio?
Which term would you add to this list, and why does a beginner need it?

Debate: three perspectives

An illustrative debate written by the author to lay out the main viewpoints. The three voices are roles, not real people. Your view is welcome in the discussion below.

The Sceptic: Jargon hides weak ideas. Many impressive-sounding terms describe simple things.

The Practitioner: Shared vocabulary speeds up work. Knowing exactly what 'IC' or 'drawdown' means avoids long misunderstandings.

The Academic: Precise definitions matter because the same word is used differently across papers. Always check how a term is computed, not just what it is called.

Which view is closest to yours, and what evidence would change your mind?