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.
Small number of composite factors
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.
本文由荷宝海外投资基金管理(上海)有限公司(“荷宝上海”)编制, 本文内容仅供参考, 并不构成荷宝上海对任何人的购买或出售任何产品的建议、专业意见、要约、招揽或邀请。本文不应被视为对购买或出售任何投资产品的推荐或采用任何投资策略的建议。本文中的任何内容不得被视为有关法律、税务或投资方面的咨询, 也不表示任何投资或策略适合您的个人情况, 或以其他方式构成对您个人的推荐。 本文中所包含的信息和/或分析系根据荷宝上海所认为的可信渠道而获得的信息准备而成。荷宝上海不就其准确性、正确性、实用性或完整性作出任何陈述, 也不对因使用本文中的信息和/或分析而造成的损失承担任何责任。荷宝上海或其他任何关联机构及其董事、高级管理人员、员工均不对任何人因其依据本文所含信息而造成的任何直接或间接的损失或损害或任何其他后果承担责任或义务。 本文包含一些有关于未来业务、目标、管理纪律或其他方面的前瞻性陈述与预测, 这些陈述含有假设、风险和不确定性, 且是建立在截止到本文编写之日已有的信息之上。基于此, 我们不能保证这些前瞻性情况都会发生, 实际情况可能会与本文中的陈述具有一定的差别。我们不能保证本文中的统计信息在任何特定条件下都是准确、适当和完整的, 亦不能保证这些统计信息以及据以得出这些信息的假设能够反映荷宝上海可能遇到的市场条件或未来表现。本文中的信息是基于当前的市场情况, 这很有可能因随后的市场事件或其他原因而发生变化, 本文内容可能因此未反映最新情况,荷宝上海不负责更新本文, 或对本文中不准确或遗漏之信息进行纠正。