Advances in Financial Machine Learning¶
de Prado, M. L. (2018). Advances in Financial Machine Learning. Wiley.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- As-Of Join Rule
- The discipline that guards against this is genuine point-in-time correctness
This sourceRequires point-in-time data to be tied to when it was actually public, including release dates, distribution delays, and backfill corrections, rather than merely to an effective date.
- The discipline that guards against this is genuine point-in-time correctness
- Backtesting Against Known Cases
- The signature failure is look-ahead bias — quietly letting information that would not have been available at decision time leak into the historical replay, which flatters the layer with knowledge it could not really have had.
This sourceLook-ahead bias leaks information unavailable at the simulated decision time into a historical replay, overstating performance.
- The signature failure is look-ahead bias — quietly letting information that would not have been available at decision time leak into the historical replay, which flatters the layer with knowledge it could not really have had.
- Lag & Window Feature Extraction
- Its signature failure is look-ahead bias: a window or lag that includes information from after the prediction time, producing gorgeous backtests that evaporate live because the model was quietly shown the future.
This sourceDefines look-ahead bias as allowing information unavailable at the decision time into a historical model or backtest.
- Its signature failure is look-ahead bias: a window or lag that includes information from after the prediction time, producing gorgeous backtests that evaporate live because the model was quietly shown the future.
- Model Retuning
- The classic quiet error is look-ahead bias in validation — letting information from the evaluation period leak into the fit, so the re-fit looks great offline and disappoints live.
This sourceDefines look-ahead and test-set leakage and explains why they can inflate validation results while producing strategies that fail out of sample.
- The classic quiet error is look-ahead bias in validation — letting information from the evaluation period leak into the fit, so the re-fit looks great offline and disappoints live.
Verification¶
Does it exist? Not confirmed. The DOI recorded for this work did not resolve when it was last checked, so it is shown above but not linked. The citation may still be sound; the recorded identifier is not. Flagged for repair.
Does it back the claim? Not recorded. Neither this nor any other of the 4 citations of this work carries a recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 2 other ways.
- https://www.wiley.com/en-us/Advances+in+Financial+Machine+Learning-p-9781119482086 ×2
- https://doi.org/10.1002/9781119482086 ×1
Registry ID ref:0de9c90d5b63 · see in the full table