Macroeconomic determinants of the stock market: A comparative study of Anglosphere and BRICS
Bibliographic record
Abstract
• Uncovered contrasting stock market drivers in developed versus emerging economies. • BRICS stock prices rise with inflation, opposing an expected negative relationship. • Policy rates and money supply insignificantly impact stock prices in both groups. • Findings reveal new diversification strategies based on inflation dynamics. • Results challenge existing models, offering insights for investors and policymakers. This study examines and compares the macroeconomic determinants of stock markets in BRICS (Brazil, Russia, India, China, and South Africa) and Anglosphere (Australia, Canada, New Zealand, the United Kingdom, and the United States) countries given their different economic structures. Using quarterly data from 1995Q3 to 2023Q3, we employ a panel Autoregressive Distributed Lag (ARDL) cointegration approach to analyse the long-run relations between real stock prices and the key macroeconomic variables of real GDP, consumer price index (CPI), policy rates, and money supply. Our findings show that in Anglosphere countries, there is a significant positive elastic long-run relation between stock prices and real GDP, and a significant negative elastic relation with CPI. Thus, economic growth enhances stock market performance while inflation adversely affects it in these developed economies. For BRICS countries, we identify a significant positive inelastic long-run relation between stock prices and CPI, indicating that stock markets in these emerging economies act as an inflation hedge. Policy rates and money supply are not significant for either group. These results highlight that different macroeconomic dynamics influence stock markets across developed and emerging economies, implying different risk characteristics. The Anglosphere stock markets are driven by the competing macroeconomic effects arising from GDP and CPI, whereas for the BRICS stock markets, inflationary conditions are of primary importance. The study offers insights for investors and policymakers regarding asset allocation strategies and the formulation of policies tailored to different economic blocs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".