MétaCan
Menu
Back to cohort
Record W7104265986 · doi:10.5281/zenodo.17538508

Artificial Intelligence for Adaptive Investment Strategies

2025· preprint· W7104265986 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typepreprint
Language
FieldDecision Sciences
TopicStock Market Forecasting Methods
Canadian institutionsnot available
Fundersnot available
KeywordsInvestment (military)SupervisorInvestment strategyAdaptive learningControl (management)Artificial neural network

Abstract

fetched live from OpenAlex

Master Thesis UQAM University (University Quebec in Montreal) by Michel Doucerain supervisor Petko Valchev Artificial Intelligence for Adaptive Investment Strategies

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0070.001
Scholarly communication0.0090.001
Open science0.0070.013
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.006

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.

Opus teacher head0.265
GPT teacher head0.400
Teacher spread0.134 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicStock Market Forecasting MethodsFrench-language works237,207