RIFT: Pretraining and Applications for Representations of Interrelated Financial Time Series
Bibliographic record
Abstract
This article introduces a new financial time-series representation model called representations of interrelated financial time series (RIFT). RIFT combines a novel pretraining task and neural network architecture to create generalized representations of multiple financial time-series inputs. The network uses a Siamese architecture to predict pairwise future correlations of securities; the encoder can then be used to create representations of individual securities for downstream tasks. Similar to successful applications of transfer learning in other domains, the authors test the representations on several downstream tasks common in quantitative finance, including dimensionality reduction, portfolio optimization, and portfolio reconstruction. In particular, the article introduces neural hierarchical risk parity (HRP), an improvement on the HRP algorithm, the current state of the art for portfolio optimization, and shows promising results across a variety of assessment criteria, including a 6.0% relative improvement in annualized returns and a 5.6% relative improvement in the Sharpe ratio.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".