South African Real Estate Investment Trusts Prefer Tuesdays
Bibliographic record
Abstract
This study examines the day-of-the-week effect on the returns of different classifications of South African REITs. Ordinary least squares regression (OLS), generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) (2,1), and Kruskal–Wallis (KW) tests were performed on data obtained from the IRESS Expert database from 2013 to 2021. We found statistical differences in the day-of-the-week effects for SAREITs; the best day to invest in office REITs is Friday, for diversified REITs Thursday, and for industrial REITs Friday. Generally, Wednesday was found to be the least profitable day to invest in all REIT classifications because it had the least average daily return. Tuesdays were the most profitable days for all REIT classifications, with the highest average daily return. REITs traded the most on Fridays, while REITs traded the least on Mondays. Returns were the most volatile on Monday, while volume was the least volatile on Thursday. The KW test revealed a statistically significant difference between the median returns across days of the week. Based on the above, profitability is expressed on Tuesdays in South African REITs. By recognizing the day-of-the-week effect, investors can buy and sell South African REITs more effectively. This study, apart from being the first in the context of South African REITs, provides updated evidence of the contested calendar anomaly issues.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".