Validity of the Capital Asset Pricing Model (CAPM) for Securities Trading at the Nairobi Securities Exchange (NSE)

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.