PCA-Based Investor Attention Index and Its Impact on the KSE-100 Excess Returns
Bibliographic record
Abstract
The study employs principal component analysis (PCA) to construct an investor attention index derived from seven key variables: abnormal trading volume, extreme returns, past returns, nearness to the 52-week high, nearness to the historical high, Google search volume, and mutual fund inflows. Subsequently, the research examines the impact of the investor attention index on the KSE-100 index excess returns. The analysis covers monthly data from January 2004 to December 2024. The PCA identified four components and constructed attention indices: APCA1 has highest weights of nearness to the 52-week high, abnormal trading volume, past returns, and mutual funds inflows; APCA2 has major weights of abnormal trading volume, extreme returns, past returns, and Google search volume; APCA3 has nearness to the 52-week high, nearness to the historical high, extreme returns, and mutual funds inflows; and APCA4 has nearness to the historical high, extreme returns, Google search volume, and mutual funds inflows. The APCA1 and APCA4 have a positive and significant impact on the excess returns of the KSE-100 index. This suggests that when investors are more motivated to invest, herding behavior increases, leading to improved index performance and higher returns. Subsequently, APCA3 has a negative but significant impact on index returns, indicating that a lack of investor interest leads to reduced trading activity and weaker index performance. The findings of this study have important implications for policymakers, investors, and mutual fund managers to understand the patterns of investor attention, creating policies and procedures to make the financial markets more transparent and protect the investor’s rights.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".