Do psychological factors exert greater influence on investment decisions than physiological factors? Evidence from Borsa Istanbul
Bibliographic record
Abstract
We investigate the impact of Ramadan, the Holy month of fasting, on the phenomenon of post-earnings announcement drift (PEAD) in Borsa Istanbul. Utilizing quarterly earnings announcements and employing both the analyst forecast method and time series forecast method as surprise measures, our study spans the earnings period from the first quarter of 2007 to the last quarter of 2018. Our objective is to bridge the gap between studies highlighting the negative impacts of physiological factors and the positive impacts of psychological factors on economic decision-making. Given the anticipated opposing effects of physiological and psychological factors on PEAD during Ramadan, our findings offer a unique opportunity to compare the influences of physiology and psychology on investment decisions. Contrary to the expected positive impact resulting from physiological factors causing attention deficit, our research reveals that earnings announcements made during Ramadan exhibit, on average, a 16–25% lower drift in the subsequent 60-day period, depending on the method used. We attribute this unexpected outcome to the dominance of psychological factors, such as heightened sentiment, self-awareness, and spirituality, over physiological factors. This finding holds significance for policymakers, as activities that may seem to have a harmful impact on the economy due to visible physiological or physical factors can unexpectedly yield positive effects due to less tangible psychological factors. Additionally, our results hold importance for investors, as a hedge strategy using earnings surprises results in a negative return if the announcements are made during Ramadan. Our findings remain robust across different measures of PEAD and various intensities of physiological factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".