Basics
Content Category: Factor/Company Reported Data
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Using HOLT’s methodology, the HOLT Factor Library provides a unique, differentiated, and theoretically tractable factor set. The HOLT Scorecard consolidates key metrics and concepts from the HOLT framework into a set of factors for use in screening, portfolio analytics, and as signals in quantitative models.
The HOLT Scorecard distils a variety of metrics into three composite factors for Quality, Momentum, and Valuation. It also provides an Overall Scorecard Factor Percentile which equally blends the three factors and offers an Investment Style classification based on combinations of factor ranks. Growth and Risk are additional descriptive factors that are helpful for screening and portfolio analytics.
HOLT’s comprehensive global data covers publicly traded companies in developed and emerging markets.
Data Overview
Asset Class: Public Companies
Data Frequency: Weekly/Monthly
Delivery Frequency: Weekly/Monthly
History: Data available back to the 1970s
Data Methodology
Factor Percentile is the standardized value of a HOLT metric, on a 0-to-100 scale from least to most desirable. It preserves both the order and distribution of the underlying data and, therefore, captures the magnitude of differences between firms. For single factors such as CFROI, Factor Percentile is the standardized value of the factor, while for composite factors, such as Quality, Factor Percentile is the weighted average of its contributing Factor Percentiles.
It is calculated using the normal cumulative distribution function (CDF) parameterized using the median and standard deviation of a company’s peer set.
Peer Rank, the dense rank of a company’s Factor Percentile expressed as a ratio, is the ordinal ranking of the Factor Percentile on a 0-to-100 scale from least to most desirable.
For single and composite factors, Peer Rank represents the position of a company within a peer set as ordered by Factor Percentile. A higher Factor Percentile value translates to a higher Peer Rank.
Use Cases
Factor Percentiles should be favored over Peer Ranks in the following scenarios:
- Aggregate Analysis – Quantifying the relative valuation gap between the Energy and Utilities sectors.
- Portfolio Analysis – Quantifying the Quality of a portfolio relative to a benchmark.
- Quantitative Modeling – Incorporating a HOLT metric directly into a multi-factor stock-selection model.
- Analyzing Trade-offs – Maximizing the Momentum exposure of a portfolio within a tracking error constraint (optimization) or quantifying the trade-off between the Quality of a company and its Valuation.
- Input to Further Calculations – Building composite factors such as Quality from multiple inputs such as CFROI, CFROI Median, and CFROI Range.
Peer Ranks should be favored over Factor Percentiles in the following scenarios:
- Screening – Dividing a universe by convenient partitions, such as identifying stocks in the highest Quality 20% of the peer set or the bottom 10% of the peer set, based on Momentum.
- Grouping or Categorizing Companies – HOLT Investment Styles are based on a firm’s combination of Quality, Momentum, and Valuation Peer.
The details provided above are as of January 2021.
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