MacroSignal — An Enhanced Macro-Driven Machine Learning Framework for Equity Selection
Bibliographic record
Abstract
Classical equity selection prioritizes firm-specific attributes, such as valuation multiples and momentum signals. However, the background of macroeconomic dynamics is usually neglected. When changes in economic regimes, money policies, or unexpected inflation occur, usual factor models are no longer valid. This study develops a macro-driven machine learning framework: MacroSignal. It consists of six key macroeconomic indicators: GDP growth, CPI, M2, federal funds rate, unemployment rate, and industrial production, combined with the classical financial features to predict the 5-day stock returns. There are 21 stocks in the U.S market taken during different market cycles from 2015 to 2025. Based on similar attributes, two versions of modelling have been constructed: binary and multi-class. The binary model performs better than the multi-class version–by about 68% against 54%. Money supply growth is the most powerful predictor and thus implies that individual stocks are directly influenced by Fed Policy. About 11% excess return is generated by this framework during the turbulent times of 2020-2021, thereby indicating its truly practical value.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".