Relationship Between Selected Macroeconomic Variables and the Financial Performance of Investment Banks in Kenya
Bibliographic record
Abstract
Currently, investment banks in Kenya are facing a lot of challenges due to persistence losses. However, the available studies are inadequate to aid investment banks in overcoming these challenges in Kenya due to mixed findings, resulting in rising uncertainty on equity investments’ performance, leading to massive losses among investment banks. This study, therefore, sought to model the relationship between inflation, GDP, interest rates, exchange rates, and financial performance of investment banks. Arbitrage pricing theory, Modern portfolio theory as well as classical economic theory (flow-oriented model) was used. A causal research design was adopted. The study found that inflation has negative significant influence on financial performance of equity investments among investment banks in Kenya. Also, GDP has positive and significant influence on financial performance of equity investments among investment banks in Kenya. Interest rate was also found to have negative and significant influence on financial performance of equity investments among investment banks in Kenya. In addition, exchange rate has negative significant influence on financial performance of equity investments among investment banks in Kenya. The study therefore recommends any investor including financial investors to methodically analyze inflation trends and understand how it affects the company’s financial performance. Investors must also be in a position to predict the future concerning inflation changes.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".