Reinganum [1981] cited the empirical evidence against the CAPM as a reason to re-examine whether an equity's For the daily return data, the return on the portfolio decreased with increasing portfolio When monthly data was used, the market model estimator resulted in monthly returns that increased with the estimated The final concern of Reinganum was the implication of non-normality of returns on the interpretation of the test statistics. The portfolios were skewed and leptokurtic for both the monthly and daily returns. The Clare & Thomas [1994] looked at the average monthly mean return, standard deviation and market ## Roll [1977]## Before Roll's critique, tests of the CAPM were carried out on the assumption that the following questions had to be answered:- did high
*β*portfolios earn more than low*β*portfolios?; and - did zero-risk portfolios earn the same as the risk-free rate?
Roll [1977] - is the proxy market portfolio mean-variance efficient?
A 'revolution' in empirical work on the CAPM was triggered by Roll's critique of previous studies. Roll called into question both what was being tested and the methodology used. His argument was based on three main points. - There is only a single, testable hypothesis associated with either the Sharpe-Lintner CAPM or Black's generalisation. This hypothesis is that 'the market portfolio is mean-variance efficient'. Other implications of the model, such as the linearity of expected returns in
*β*coefficients, follow from market portfolio efficiency, and are not independently testable. - Using a proxy for the market portfolio induces the possibility of both type I errors (incorrectly rejecting the null hypothesis) and type II errors (failing to reject the null hypothesis). This results from the possibility of the proxy being mean-variance efficient when the market is not, or the proxy might be inefficient when the market portfolio is efficient. This problem is not accounted for by choosing proxies that have a high correlation with the market, as the efficiency characteristics of the two portfolios can nevertheless be very different. Therefore:
'the theory is not testable unless the exact composition of the true market portfolio is known'; and 'all individual assets are included in the sample.' - Moreover, even if everybody could agree on the composition of the market portfolio, testing for the (
*ex ante*) proxy's efficiency by using common regression techniques to verify the existence of a linear relationship between returns and*β*coefficients is unsatisfactory, since:- 'The two-parameter theory does not make a prediction about parameter values, but only about the (linear) form of the cross-sectional relationship. Thus, econometric procedures designed to obtain accurate parameter estimates are not very useful.'; and
- 'The widely-used portfolio grouping procedure can support the theory even when it is false. This is because individual asset deviations from exact linearity can cancel out when placed together in a portfolio.'
The immediate response to Roll's critique was largely a rejection of what was interpreted as a nihilistic message.
and that
Therefore, their conclusion was that:
However, Roll's critique changed the empirical work on the CAPM in two ways. It promoted the development of a new statistical methodology for testing the mean- variance efficiency of a given index, ## Fama & French [1992]Fama & French [1992] argue that the cross-section of average returns on US equities ^{37} gearing, earnings/price ratio, and book-to-market equity.^{38} Their main results can be summarised as follows: - The risk premium on
*β*is insignificantly different from zero when it is included by itself. - The risk premium on
*β*is also insignificantly different from zero when it is included as an explanatory variable. - Size appears to be negatively related to returns when it is included by itself or when it is included with
*β*. - Another important determinant of average returns appears to be the natural logarithm of the ratio of the book value of equity to the market value of equity. This ratio is positively related to returns regardless of whether it is included byitself or included with, for instance, net asset value.
- Average returns also appear to be positively related to companies earnings/priceratios, when that ratio is positive. The earnings/price effect, however, is not as strong as the size or book-to-market effects, becoming only marginally significant when the other two variables are included.
Black [1992; 1993] argued that any observed empirical effect which cannot be justified on
Black further argued that:
Kothari, Shanken & Sloan [1995, p. 186], using an alternative data set, found that:
Their results suggest important differences between their data set S&P industry level data covering 1947-1987 - and that from Compustat examined by Fama & French. The authors suggest that the strong relationship found by Fama & French between the ratio of the book value of equity to the market value of equity and average returns maybe due to a survivorship bias. This is further discussed in section 4.5. Their results suggest that companies which are included in CRSP but not Compustat over the period 1963-89 period have a substantially lower average return, lending some credence to this hypothesis. Kothari, Shanken & Sloan concluded their paper by summarising the case for and against - if the entire history of security returns in the US is used, the estimated average compensation for
*β*risk is 0.47% per month and is close to being significant. Moreover, the estimated compensation is not significantly different from the actual average risk premium^{40}of 0.76% per month; - if the study were to stop in 1982, the support for
*β*would be overwhelming. The last ten years, which have not been favourable for*β*'s explanatory role for returns, are an aberration; and - a close examination of the monthly behaviour of the estimated and actual premiums indicates a close association between the two variables, in line with the Sharpe-Lintner CAPM. For example, in months where the market falls steeply, high
*β*equities substantially underperform low*β*equities.
Conversely, against the case for - even with sixty years of data on returns, spanning many generations of money managers, the t-statistic for the estimated average compensation for
*β*risk is a modest 1.84; - the most recent period, more representative of current experience, provides much weaker support for
*β*. The strongest support for*β*, however, emerges from periods many years before Markowitz's ideas on how to form equities into efficient portfolios; and - including other variables may substantially diminish the role of
*β*in explaining security returns.
____________________________
γ underestimated._{β}
- Equities returns that were listed on the NYSE, AMEX, or NASDAQ with return data available from the CRSP database - NYSE and AMEX data was used for the period 1962-1989, NASDAQ data from 1973.
- Financial accounting data as available from the merged Compustat annual industrial files - Compustat data was available on the 1962-89 period.
They used the following estimation procedure:- Two-pass Fama & MacBeth cross-sectional regression technique;
- Cross-sectional regressions run on individual equity returns;
- Individual
*β*estimates based on the 'portfolio*β*' of the particular equity- Equities first sorted on the basis of size, into ten size-decile portfolios;
- Each size-portfolio was then sorted on the basis of pre-ranking
*β*s into ten*β*-decile portfolios. Portfolio returns were the equally-weighted monthly returns of the equity in the portfolio. Portfolio returns were regressed on the value-weighted CRSP benchmark returns (and lagged returns) to obtain portfolio ßs. Equities were assigned the*β*of the portfolio.
r._{f} |