Mutual funds in North and South America: Relationship between news and Mutual funds Returns
Bibliographic record
Abstract
This paper investigates the impact of news sentiment on mutual fund returns across several countries in the Americas, examining both developed and developing markets. By leveraging advanced Natural Language Processing (NLP) and machine learning tech- niques, the study integrates news sentiment into asset pricing models, thereby enhancing their precision. A Vector Error Correction Model (VECM) is used to analyze the com- plex interactions between news sentiment and mutual fund returns, addressing potential endogeneity and capturing the dynamic relationships over time. Though variations in the effects of news across different countries were expected, developed markets exhibit more consistent and synchronized reactions. It is observed that the effect of news sentiment on the first lag is generally lower than that of their respective stock market indices for both Canada and the USA. In contrast, mutual funds in developing countries such as Brazil, Argentina, and Chile show greater divergence in their reactions, both among themselves and relative to their market indices. Notably, Mexico exhibits characteristics of both de- veloped and developing markets, with early responsiveness to news similar to the USA but with larger coefficients. This study offers valuable insights into regional differences in...
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".