Effects of Interest Rate on Stock Market Returns in Kenya
Bibliographic record
Abstract
Debate on the stochastic behaviour of stock market returns, 3-month Treasury Bills rate, lending rate and their cointegrating residuals remains unsettled. This study examines the stochastic properties of the macroeconomic variables, stock market returns and their cointegrating residuals using an Autoregressive Fractionally Integrated Moving Average (ARFIMA) model. It also investigates Granger causality between the two measures of interest rate and stock market returns. The study uses monthly data from 1st January 1993 to 31st December 2015. The results indicate that the 3-month Treasury Bills rate, lending rate and stock market returns are fractionally integrated which implies that shocks to the variables persist but eventually disappear. The results also reveal that the cointegrating residuals are fractionally integrated which suggests that a new and harmful long-run equilibrium might be established when each of the measures of interest rate is driven away from stock market returns. Additionally, the results indicate that the 3-month Treasury Bills rate and lending rate negatively Granger cause stock market returns in the long run. This suggests that stocks and Treasury Bills are competing investment assets. On the other hand, ARFIMA-based Granger causality reveals that stock market returns lead the 3-month Treasury Bills rate and lending rate with a negative sign in the short run. This implies that a prosperous stock market results into a favorable macroeconomic environment. A key contribution of this study is that it is the first to empirically examine fractional cointegration and ARFIMA-based Granger Causality between interest rate and stock market returns in Kenya.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".