In the 1980s and 1990s, factors such as size, value, and momentum were shown to generate returns that could not be explained by the capital asset pricing model (CAPM). Since then, hundreds of other ‘factors’ have emerged in the literature, leading to a so-called ‘zoo’ of equity factors. This has given rise to a heated debate regarding how many distinct factors really exist.
In a new note, we explain our stance on this debate. We share the concerns expressed in several recent academic papers that many of the hundreds of factors that have been proposed over the past decades can be attributed to data mining. However, we do not find that the entire factor zoo can be reduced to just a handful of factors.
Although the small set of factors used in academic asset pricing models can serve as a very good starting point, that is not the end of the story. In our research, we find evidence of dozens of factors. These include factors that are wrongly dismissed or rejected, multiple factors needed to capture one broader phenomenon, factors based on non-standard data sources or with limited history, and ‘next generation’ factors, based on big data, machine learning or artificial intelligence.
That said, for practical implementation purposes, it is common to categorize factors into a small number of strategic composite factors. The low-risk factor contains metrics such as volatility and beta, measured using different lookback periods and different data frequencies, but also distress risk indicators, such as distance-to-default and credit spreads. The value factor consists of all variables which measure price relative to fundamentals, such as book value, earnings and cash-flows. These ratios can be adjusted for e.g. distress risk or environmental footprint.
The quality factor is essentially a mixed bag of company fundamentals, such as profitability, earnings quality and investment patterns. We agree with the academic perspective that these actually appear to be separate, distinct factors, but follow the industry convention to combine them under a common ‘quality’ header.
For the momentum factor it is basically the other way around. Academics and smart beta index providers tend to see momentum as a single factor (price momentum), but in our research we find that it is better understood as an entire family of different sentiment-related factors, most notably price momentum and analyst revisions.
Finally, there is another broad set of short-term factors. These are typically ignored by academics and index providers altogether, but we find them to be highly effective for trade timing purposes. This theme includes various reversal phenomena, signals based on short interest and signals derived from trading volume patterns. In sum, starting from an academic zoo consisting of hundreds of alleged factors, we narrow it down to several dozens that really work, which we organize into a small number of composite factors.
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