The South African Fear and Greed Index and Its Connectedness to the U.S. Index
Bibliographic record
Abstract
This study investigates the cross-country spillover effects of investor sentiment, specifically Fear and Greed, between the United States and South Africa, within the broader context of increasing global financial integration and behavioral finance. Using monthly data from June 2007 to June 2024, this research constructs and tests the validity of a South African Fear and Greed Index, adapted from CNN’s U.S.-centric index, to better capture the unique dynamics and contribute to an alternate sentiment index for an emerging market. Employing the Diebold and Yilmaz (DY) connectedness framework, this study quantifies both static and dynamic spillover effects via a vector autoregression-based variance decomposition model. The results reveal significant bidirectional sentiment transmission, with the U.S. acting as a dominant net transmitter and South Africa as a net receiver, along with notable cross-country effects closely linked to the global economic trend. Spillover intensity escalates during periods of global economic stress, such as the 2008 financial crisis and the COVID-19 pandemic. The findings highlight that the USA significantly influences South Africa and that the adapted SA Fear and Greed Index better accounts for South African market conditions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".