Una herramienta de ayuda para la inversión en small caps de EEUU
Bibliographic record
Abstract
[EN] The main objective of the present work is to create a system that helps users discover \npromising US Small Caps stocks in a personalized way. \nFor doing so, techniques from the fields of natural language processing, time series \nforecasting with deep learning, classical portfolio optimization, databases administration, web development and DevOps will be applied, with a strong focus on following \nvalue investing principles, by using financial statements data whenever possible. \nConcretely, the system will be able to daily extract data from an API REST, process \nit and store it in a database, to then be analyzed, transformed and converted into powerful insights for the investor, which include a forecast of the return on equity (ROE) \nfor a specified future quarter, the creation of a diversified and optimized portfolio that \nsatisfies the risk tolerance specified by the user, an earnings call analysis, composed of \nsentiment analysis, summarization, contradiction detection and relative text complexity \nmeasurement. Finally, the user will be able to, through a multiplatform website, specify his query \nparameters (which include selecting sectors and industries of the economy, risk level \ntolerance and the quarter to forecast ROE), create a portfolio and download a report with \nan analysis of the whole portfolio and each individual stock, in PDF format.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.018 |
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".