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...
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".