Quantitative Methods
A practical methods library for applied quantitative research and investment practice
SlackQuant Quantitative Methods explains the statistical, econometric, portfolio, and machine-learning tools that appear across SlackQuant Research. Each article connects intuition, formal definitions, reproducible implementation, common misuse, and the research settings in which the method is actually used.
Current articles
Out-of-Sample Forecast Evaluation
Can a forecasting procedure predict outcomes that were unavailable when each forecast was made? It covers information sets, repeated forecast origins, pseudo-out-of-sample versus genuine real-time evaluation, leakage boundaries, and a small example that separates development fit from held-out performance.
Rolling vs Expanding Windows
How much historical data should a repeated forecasting procedure carry forward? It separates rolling forecast origins from rolling estimation windows, explains the adaptation-versus-estimation trade-off, and shows how the two window designs respond differently after a structural break.
Look-Ahead Bias and Data Leakage
When can a historical test accidentally see the future? It distinguishes look-ahead bias from broader data leakage, maps common contamination paths, and shows how a centered feature can appear stronger simply because it crosses the forecast-origin information boundary.
Diebold–Mariano Test
Does a lower realized forecast loss represent a statistically distinguishable advantage? It builds the test from paired loss differentials, explains long-run variance and the HLN small-sample adjustment, and shows why a lower RMSE need not imply statistically distinguishable predictive accuracy.
Model Confidence Set
When does the evidence justify a single winning model, and when should uncertainty remain set-valued? It explains sequential equal-performance testing, elimination rules, and bootstrap uncertainty, then shows how five ranked models can narrow to a two-model confidence set rather than a forced single winner.
Block Bootstrap
How should uncertainty be resampled when observations are serially dependent? It explains moving-block bootstrap intuition and block-length sensitivity, then shows how IID resampling can erase serial dependence that matters for uncertainty estimates.
Library principles
The library is paper-driven rather than encyclopedic. Methods are added when they materially help readers understand a research design, empirical result, or investment decision. Articles favor plain-language intuition before unnecessary formalism, but do not simplify away assumptions that matter for validity.
Each computed illustration is accompanied by the data and code needed to reproduce it. Synthetic examples are clearly labeled as illustrations rather than presented as empirical evidence.
Next methods
Future additions remain paper-driven and will be added when they materially support active SlackQuant research.