Validity of the Capital Asset Pricing Model (CAPM) for Securities Trading at the Nairobi Securities Exchange (NSE)
Bibliographic record
Abstract
This research undertakes an empirical analysis of the validity of the Capital Asset Pricing Model (CAPM) for securities trading at the Nairobi Securities Exchange (NSE). Based on the critical conditions of the CAPM model, the specific objectives of the research were: to evaluate the level of systematic risks for firms listed on the NSE, to evaluate the rate of return for individual stocks listed on the NSE, to evaluate the rate of return for the NSE, to analyze the relationship between systematic risk and expected returns for firms listed on the NSE, and to evaluate the value of the intercept term for firms listed on the NSE. Fama & Macbeth’s two-pass regression method is applied to a sample of eighteen firms trading at the NSE, with the most recent data (May 2013 –May 2016) being used. By virtue of finding a beta value that is statistically different from zero, the study concludes that the CAPM is not a valid model for explaining risk-return relationships at the NSE. Other critical conditions which the findings violate include: the hypothesized linear risk-return relationship, and the hypothesized zero value for the intercept. Some of the failures of the CAPM are attributed to its theoretical failings, and specifically, its many unrealistic and simplifying assumptions. Although this study addresses the methodological weaknesses of prior studies by basing analysis on portfolios rather than individual stocks (thus correcting measurement error problems) and carrying out month-by-month cross-section regression (thus correcting residual errors); the methodology adopted still fails to account for anomalies in asset pricing. Therefore, in addition to recommending that future studies adopt methodologies that account for pricing anomalies, this study also recommends that future studies consider expanding the number of firms to study as well as the period of study. This can help to generate more observations, and therefore, better data fit.
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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 | 0.002 | 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 | 0.001 | 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".