The Capital Asset Pricing Model (CAPM) of 1961 says expected return on any portfolio (or stock) should earn a premium above the risk-free rate. In simpler words, low risk meant lower return and vice versa. William Sharpe, Harry Markowitz and Merton Miller got the 1990 Nobel Memorial Prize in economics for work on this.
James Montier rechristened CAPM as Completely Redundant Asset Pricing (CRAP) in a research paper. No doubt, behavioural experts had insights into the market behaviour, but somewhere there is an "academic bias" that creeps in, making academicians more positively biased about their body of work. History is full of literature where new academic theorists have not been very objective about the previous body of work. Mandelbrot called the bell curve nonsense; Fama asked how this stuff (behavioural finance) ever got published among others.
Montier's take
Apart from the fact that Montier wanted to justify the "academic bias", the author strengthens his case against CAPM assumptions by illustrating the low-beta and high-beta portfolio behaviour. He illustrates Fama and French's 2004 review of CAPM.
"Each December from 1923 to 2003 they estimate a beta for every stock on the NYSE, AMEX and NASDAQ, using two to five years of prior monthly returns. Ten portfolios are then formed based on beta and the returns, and tracked over the next 12 months. The figure plots the average return for each decile against its average beta. The straight line shows the predictions from the CAPM. The model predictions are clearly violated. CAPM woefully under predicts the returns in the low beta stocks and massively overestimates the returns in high-beta stocks."
This might suggest that investors might be well advised to consider a strategic tilt towards low-beta and against high-beta, a strategy first suggested by Fishcher Black in 1993. Suggesting simply that low-risk could deliver higher return and vice versa.
Do Fama and French make CAPM redundant?
Fama and French improved the model by adding value, size (capitalisation) variables to the CAPM variables. Though testing suggested that the new variables enhanced the understanding of the market behaviour, the model was still offering better guidelines to understand asset prices but was still not unequivocal in its findings. Even newer models with momentum as a variable failed to establish rules and relegate CAPM into redundancy. The model still worked in a few cases and was still valid.
Is it not all about divergence?
A lot of our financial models are still looking at snapshots of data, rather than studying any dynamic evolution in market behaviour. A lot of data interpretation focuses on causally explaining mean reversion failures, or simply putting divergence from idealised cases. This is why a divergence from CAPM made CAPM a poor idealisation. We continue to seek better idealised scenarios, but somewhere we forget that markets are not made of one idealisation, but a set of idealisations. In this case both CAPM and Fama and French being two sets of idealisations.
If it's about mean reversion failure, it's all about models failing to explain divergence. Could it be that simple? This is what we explained in our re-take on Thaler's "End of Behavioural Finance" that this was a psychological explanation of cases of mean reversion failure.
The power of proxy
In our paper on data universality, we explained the power of proxy and how data behaviour is universal, irrespective of the variables, be it financial or non-financial. A simple performance ranking can be a good proxy to explain value, growth, momentum, reversion, low beta, high beta, volatility, etc, in a certain universe of assets prices or simply any natural data set. We took a proxy percentile performance ranking of worst (bottom fifth) and best in a group of assets (top fifth) for the S&P 100 components. The test was made for 20 days to 1,200 days. And, even after 1,200 days of holding nearly 20 per cent of the worst losers and best winners, they continued to remain worst and best, respectively.
This proved that though there was a tendency for the worst to outperform and for the best to underperform, this was not a rule. This could be extended to the idea of how low-beta stocks could continue to stagnate. Which in other words, meant low-risk could continue to deliver low-return or simply suggesting that CAPM was not crap but a relevant case of market behaviour.
The author is CMT and founder, Orpheus Risk Management Indices
James Montier rechristened CAPM as Completely Redundant Asset Pricing (CRAP) in a research paper. No doubt, behavioural experts had insights into the market behaviour, but somewhere there is an "academic bias" that creeps in, making academicians more positively biased about their body of work. History is full of literature where new academic theorists have not been very objective about the previous body of work. Mandelbrot called the bell curve nonsense; Fama asked how this stuff (behavioural finance) ever got published among others.
Montier's take
Apart from the fact that Montier wanted to justify the "academic bias", the author strengthens his case against CAPM assumptions by illustrating the low-beta and high-beta portfolio behaviour. He illustrates Fama and French's 2004 review of CAPM.
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"Each December from 1923 to 2003 they estimate a beta for every stock on the NYSE, AMEX and NASDAQ, using two to five years of prior monthly returns. Ten portfolios are then formed based on beta and the returns, and tracked over the next 12 months. The figure plots the average return for each decile against its average beta. The straight line shows the predictions from the CAPM. The model predictions are clearly violated. CAPM woefully under predicts the returns in the low beta stocks and massively overestimates the returns in high-beta stocks."
This might suggest that investors might be well advised to consider a strategic tilt towards low-beta and against high-beta, a strategy first suggested by Fishcher Black in 1993. Suggesting simply that low-risk could deliver higher return and vice versa.
Do Fama and French make CAPM redundant?
Fama and French improved the model by adding value, size (capitalisation) variables to the CAPM variables. Though testing suggested that the new variables enhanced the understanding of the market behaviour, the model was still offering better guidelines to understand asset prices but was still not unequivocal in its findings. Even newer models with momentum as a variable failed to establish rules and relegate CAPM into redundancy. The model still worked in a few cases and was still valid.
Is it not all about divergence?
A lot of our financial models are still looking at snapshots of data, rather than studying any dynamic evolution in market behaviour. A lot of data interpretation focuses on causally explaining mean reversion failures, or simply putting divergence from idealised cases. This is why a divergence from CAPM made CAPM a poor idealisation. We continue to seek better idealised scenarios, but somewhere we forget that markets are not made of one idealisation, but a set of idealisations. In this case both CAPM and Fama and French being two sets of idealisations.
If it's about mean reversion failure, it's all about models failing to explain divergence. Could it be that simple? This is what we explained in our re-take on Thaler's "End of Behavioural Finance" that this was a psychological explanation of cases of mean reversion failure.
The power of proxy
In our paper on data universality, we explained the power of proxy and how data behaviour is universal, irrespective of the variables, be it financial or non-financial. A simple performance ranking can be a good proxy to explain value, growth, momentum, reversion, low beta, high beta, volatility, etc, in a certain universe of assets prices or simply any natural data set. We took a proxy percentile performance ranking of worst (bottom fifth) and best in a group of assets (top fifth) for the S&P 100 components. The test was made for 20 days to 1,200 days. And, even after 1,200 days of holding nearly 20 per cent of the worst losers and best winners, they continued to remain worst and best, respectively.
This proved that though there was a tendency for the worst to outperform and for the best to underperform, this was not a rule. This could be extended to the idea of how low-beta stocks could continue to stagnate. Which in other words, meant low-risk could continue to deliver low-return or simply suggesting that CAPM was not crap but a relevant case of market behaviour.
The author is CMT and founder, Orpheus Risk Management Indices