Bibliographic record
Abstract
The close connection between information and the price of an asset has long been discussed in the financial literature. In order for information to be incorporated into the asset price, investors must pay sufficient attention to the market. However, individuals have scarce cognitive abilities, and since there is a large amount of information, they tend to be selective and pay limited attention to their choices. So, investor attention should play an important role in capital markets. This relationship is potentially affected by the economic, cultural and regulatory characteristics of the markets, and by the existing informational advantages between local and non-local investors. Given this context, the objective of this research is to detect and measure how the attention of investors, with different levels of informational advantage (local and nonlocal), impacts return, volatility and trading volume, in capital markets of countries emerging and developed. To this end, closing price and volume data were collected from the main stock exchange indices for ten developed markets (Germany, Canada, Spain, United States, France, Holland, Italy, Japan, United Kingdom and Switzerland) and ten emerging markets. (South Africa, Brazil, China, India, Indonesia, Malaysia, Mexico, Pakistan, Russia and Turkey). To construct measures of local and non-local investor attention, Google Trends search volume was used, which tracks the volume of queries for each term/word during a given period of time and geographic location. The collection period was from January 2017 to December 2021 for the main models, and from January 2015 to December 2019 for the robustness tests. Based on these data, the characteristics of each variable were examined and a Panel vector autoregression model was used in six panels. From these, causal relationships and temporal precedence were estimated, impulse response functions and variance decompositions were generated to determine the impact of investor attention on return, volatility and trading volume. The empirical evidence found converges with the investor recognition hypothesis and indicated that local and foreign attention measures significantly affected return, volatility and abnormal trading volume. As far as market development is concerned, it has been found that stock exchanges in developed markets are more responsive to attention than those in emerging markets. The results also showed that it is not possible to attribute an informational advantage to local investors in relation to non-local ones.
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 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 | 0.001 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".