What this category covers
Machine-learning methods adapted to noisy, nonstationary financial data and cross-sectional or time-series prediction. Each concept page explains the quantitative meaning, how the idea is used in portfolio or market analysis, the relevant formula or analytical framework, variables, a compact example and the main limitations to keep in view.
Core concepts
Quick entry pointsAll Machine Learning & Quant Research concepts
32 entriesAccumulated Local Effects
Accumulated Local Effects is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceAgglomerative Clustering
Agglomerative Clustering is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceAlpha Model
Alpha Model is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceAnomaly Detection
Anomaly Detection is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceAsset Clustering
Asset Clustering is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceAttention Mechanism
Attention Mechanism is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceAutoencoder Anomaly Detection
Autoencoder Anomaly Detection is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceBatch Learning
Batch Learning is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceBatch Size
Batch Size is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceBayesian Optimization
Bayesian Optimization is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceBet Sizing Model
Bet Sizing Model is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceClass Imbalance
Class Imbalance is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceClassification Threshold
Classification Threshold is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceConcept Drift
Concept Drift is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceConvolutional Neural Network
Convolutional Neural Network is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceCross-Entropy Loss
Cross-Entropy Loss is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceCross-Sectional Features
Cross-Sectional Features is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceCross-Sectional Ranking Model
Cross-Sectional Ranking Model is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceData Drift
Data Drift is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceFactor Analysis
Factor Analysis is a factor-based concept used to describe a systematic source of return, risk or cross-sectional variation. Factor analysis separates broad common exposures from security-specific behavior so that portfolio bets can be measured and controlled more explicitly.
Quantitative FinanceGaussian Mixture Model
Gaussian Mixture Model is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceHyperparameter Optimization
Hyperparameter Optimization is a quantitative-finance concept used within machine learning & quant research. It provides a precise language for describing how market data, uncertainty, models or portfolio decisions are measured and tested.
Quantitative FinanceLocal Outlier Factor
Local Outlier Factor is a factor-based concept used to describe a systematic source of return, risk or cross-sectional variation. Factor analysis separates broad common exposures from security-specific behavior so that portfolio bets can be measured and controlled more explicitly.
Quantitative FinanceModel Averaging
Model Averaging is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceModel Drift
Model Drift is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceModel Explainability
Model Explainability is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceModel Interpretability
Model Interpretability is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceNonnegative Matrix Factorization
Nonnegative Matrix Factorization is a factor-based concept used to describe a systematic source of return, risk or cross-sectional variation. Factor analysis separates broad common exposures from security-specific behavior so that portfolio bets can be measured and controlled more explicitly.
Quantitative FinanceSequence-to-Sequence Model
Sequence-to-Sequence Model is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceSignal Model
Signal Model is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.
Quantitative FinanceSupport Vector Regression
Support Vector Regression is a regression-based technique used in quantitative finance to estimate how a target variable changes with one or more explanatory variables. In practice, the usefulness of the estimate depends on specification, stability and the treatment of time dependence and heteroskedasticity.
Quantitative FinanceTransformer Model
Transformer Model is a quantitative model or framework used in machine learning & quant research to convert assumptions and observed market information into a structured estimate, state or decision rule. Its value comes from making the relationships explicit enough to calibrate, test and compare rather than relying on intuition alone.