AI-FINALYST: Multi-Agentic Debating Framework with Confidence-Weighting for Investment Decisions
Bibliographic record
Abstract
This paper presents AI-FINALYST, a novel framework for analysing financial information using six agents based on large language models. The agents engage in a multiturn debate that mimics investment decision-making by a team of human analysts. Each agent specialises in one form of equity analysis: fundamental, technical, sentiment, sector, news, and social media. The final call combines the scores awarded by all six agents. The model is back tested on the daily prices of five U.S. stocks for a period of five years for making buy, hold or sell investment decisions at quarterly intervals, and it outperforms buy and hold for four out of five stocks. An equally weighted portfolio of all five stocks based on AI-FINALYST calls achieves a 38% return per annum, exhibiting a drawdown reduction of 15.93%, as compared to the buy-and-hold strategy, which shows a 21.7% return per annum for the same volatility.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".