Quantitative Investing

Quantitative investing is an approach to portfolio management that uses mathematical models, statistical techniques, and large datasets to make investment decisions. Instead of relying on human judgment or …

Factor Investing

Factor investing is a systematic approach that targets specific characteristics, called factors, which have historically been associated with higher returns or lower risk. Common equity factors include Momentum, …

Alpha

Alpha refers to the portion of investment returns that cannot be explained by overall market movements. It represents value added through strategy design, security selection, or portfolio construction …

Beta

Beta measures a portfolio’s sensitivity to overall market movements. A beta of 1 means the portfolio tends to move in line with the market, while a beta above …

Momentum Investing

Momentum investing is based on the observation that stocks which have performed well recently tend to continue outperforming in the near future, while recent losers often keep underperforming. …

Value Investing

Value investing focuses on identifying stocks that trade at low prices relative to their fundamentals, such as earnings, book value, or cash flows. The underlying idea is that …

Quality Factor

The Quality factor targets companies with strong financial health, stable earnings, and efficient capital usage. Typical metrics include Return on Equity, profit margins, low debt levels, and consistent …

Low Volatility Investing

Low Volatility investing focuses on stocks that exhibit smaller price fluctuations compared to the broader market. Contrary to traditional finance theory, lower-risk stocks have historically delivered competitive, and …

Multifactor Models

Multifactor models combine several factors, such as Momentum, Value, Quality, and Low Volatility, into a single investment framework. Instead of relying on one return driver, multifactor strategies diversify …

Portfolio Optimization

Portfolio optimization is the process of converting stock signals into actual portfolio weights while balancing expected returns and risk. This is typically done using mathematical techniques such as …

Backtesting in Quant Finance

Backtesting involves simulating a strategy’s performance using historical data to evaluate its behavior across different market conditions. It is a foundational step in quantitative research. A proper backtest …

Drawdown

Drawdown measures the decline from a portfolio’s peak value to its lowest point before recovery. It represents the magnitude of losses investors experience during downturns. Maximum drawdown is …

Sharpe Ratio

The Sharpe Ratio measures risk-adjusted returns by dividing excess return over the risk-free rate by portfolio volatility. A higher Sharpe Ratio indicates more efficient returns per unit of …

Market Regimes

Market regimes refer to distinct environments characterized by trends, volatility, and risk sentiment. Common regimes include bull markets, bear markets, and high-volatility phases. Quant strategies often use regime …

Systematic Investing

Systematic investing relies on predefined rules to guide every investment decision, from stock selection to rebalancing. This contrasts with discretionary approaches driven by human judgment. Systematic frameworks enhance …

Risk Management in Quant Strategies

Risk management is central to quantitative investing. It encompasses position sizing, diversification, drawdown controls, volatility targeting, and regime-based exposure adjustments. Quant risk frameworks continuously monitor portfolio behavior and …

Portfolio Turnover

Portfolio turnover measures how much of a portfolio is bought and sold over a given period, usually expressed as a percentage. A turnover of 50% means that half …

Rebalancing Frequency

Rebalancing frequency refers to how often a portfolio updates its holdings based on new signals or changes in market conditions. Common schedules include weekly, monthly, or quarterly rebalancing. …

Stock Universe Selection

Stock universe selection defines which securities are eligible for inclusion in a strategy. This is one of the most important yet underestimated steps in quantitative investing. A universe …

Overfitting in Quant Models

Overfitting occurs when a model captures random noise in historical data rather than genuine patterns. Such models perform exceptionally well in backtests but fail when deployed in live …

Out-of-Sample Testing

Out-of-sample testing evaluates a model on data that was not used during its development. This step helps determine whether observed performance reflects real predictive power or merely overfitting. …

Z-Score Normalization

Z-score normalization is a statistical technique used to standardize data by converting values into deviations from their mean, measured in standard deviations. In quant investing, Z-scores allow different …

Correlation vs Causation

Correlation measures how two variables move together. Causation implies that one variable directly influences the other. In quantitative finance, confusing the two is a common and costly mistake. …

Factor Neutralization

Factor neutralization is the process of removing unintended factor exposures from a portfolio or signal so that performance reflects the desired sources of return. In quantitative investing, stocks …

Exposure Management

Exposure management refers to actively controlling how much risk a portfolio takes across different dimensions such as market beta, sectors, factors, or individual stocks. In quantitative strategies, exposures …

Mean Reversion

Mean reversion is the tendency of asset prices or financial metrics to move back toward their long-term average after extreme deviations. In investing, this concept suggests that stocks …

Trend Following

Trend following is an investment approach that seeks to profit from sustained price movements, buying assets that are rising and reducing exposure to those that are falling. It …

Signal Decay

Signal decay refers to the gradual loss of predictive power of an investment signal over time. A factor or indicator that once generated strong returns may weaken as …

Ensemble Models

Ensemble models combine multiple individual models or signals into a single framework to improve stability and predictive power. Instead of relying on one strategy, ensembles aggregate several approaches, …

Risk Parity

Risk parity is a portfolio construction method that allocates capital so that each asset or component contributes equally to overall portfolio risk, rather than allocating based on expected …

Optimization Constraints

Optimization constraints are rules imposed during portfolio construction to ensure practical, diversified, and risk-aware allocations. Without constraints, mathematical optimizers may produce extreme or unrealistic portfolios. Common constraints include …

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Quant Glossary

Quantitative Investing

Factor Investing

Alpha

Beta

Momentum Investing

Value Investing

Quality Factor

Low Volatility Investing

Multifactor Models

Portfolio Optimization

Backtesting in Quant Finance

Drawdown

Sharpe Ratio

Market Regimes

Systematic Investing

Risk Management in Quant Strategies

Portfolio Turnover

Rebalancing Frequency

Stock Universe Selection

Overfitting in Quant Models

Out-of-Sample Testing

Z-Score Normalization

Correlation vs Causation

Factor Neutralization

Exposure Management

Mean Reversion

Trend Following

Signal Decay

Ensemble Models

Risk Parity

Optimization Constraints

Liquidity Filters

Slippage Modeling

Market Impact

PCA in Quant Finance

Feature Engineering

Regime Detection Models

Model Drift

Portfolios

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