Other Proprietary Scores

Introduction to Other Proprietary Scores

Apart from single-factor scores and multi-factor scores, we have some other proprietary stock scores that are used as inputs in calculating factor scores.

These scores are also available to users for building stock screens and signals. We'll discuss them in detail now.

Earnings Momentum Score

Historically earnings momentum has been associated with positive risk premiums. In other words, a portfolio of stocks with higher earnings momentum outperforms a portfolio of stocks with lower earnings momentum, on average.

Earnings momentum score can be used as a standalone metric while screening or building a signal. We also use the earnings momentum score in conjunction with price momentum to calculate the final price and earnings momentum score.

Practitioners use different methodologies to calculate earnings momentum. The core idea is to capture a stock's momentum with respect to its earnings.

We use 3 metrics and apply our standard score-aggregate-score methodology to calculate a stock’s earnings momentum score.

1. Last quarter EPS growth - This is calculated as year-on-year growth in quarterly net income for the latest available quarterly data. If net income in the same quarter last year was negative or not available, this will be NA. In order to avoid working with stale data, previous quarter numbers are dropped as soon as the quarter changes.
2. EPS surprise - This measures the EPS beat with respect to the consensus EPS estimate of the most recent financial quarter. For example, if the consensus EPS estimate for March 2023 quarter for Infosys was Rs 15.72/share and actual EPS was Rs 14.79/share, the surprise would be negative 5.92%. In order to avoid working with stale data, previous quarter numbers are dropped as soon as the quarter changes.
3. Earnings upgrade (3M) - This is calculated by dividing the current EPS NTM by EPS NTM estimate 3 months ago. This gives an indication of how much the consensus EPS estimate has been revised upwards (or downwards). Earnings upgrade (positive number) is considered a good sign while downgrade (negative number) is considered bad.

For each metric, we calculate a score between 0 and 100 using our standard scoring methodology. We then calculate an aggregate score by summing up the individual scores (equal weight). The stocks are then again scored between 0 and 100 based on the aggregate score to arrive at the final earnings momentum score.

Some important scenarios need to be discussed here:

Avoiding stale data

Since both EPS growth (last quarter) and EPS surprise are set to NA as soon as the quarter changes, there will be cases (during the first few days after quarter end) when these metrics will be not available for all stocks in the universe. In this case, we assign a neutral score of 50.

The idea behind setting them to NA is to avoid mixing stocks that have the latest reported quarter with stocks for which we have data for the previous quarter. Momentum is a fast decaying signal and one should be careful to avoid using “stale” information.

Working with estimates

A lot of stocks do not have active analyst coverage and hence, EPS surprise and Earnings revision will not be available.

Again, a neutral score of 50 is assigned to stocks for which these metric values are not available.

We could have just dropped metrics that use analyst estimates. But in our view, and despite all the limitations of estimated data, upgrade and surprise do have valuable information and should ideally be used.

At the same time, we should not penalize stocks that do not have active analyst coverage and hence a neutral score of 50.

Earnings Quality Score

Earnings quality refers to the degree to which a company's reported earnings accurately reflect its true financial performance. In other words, it is an assessment of how reliable and informative a company's earnings statements are in terms of reflecting the underlying economic reality of the business.

Note that earnings quality is not restricted to absence of earnings management but includes multiple other factors such as earnings persistence, cash flows and so on. We have dedicated an entire article to potential “earnings” management by companies - the practice of manipulating a company's financial results to meet or exceed market expectations.

Earnings quality score is our attempt to quantify the “quality” of a firm’s earnings with respect to other stocks in the universe. And we attempt to capture not just potential earnings management, but also earnings persistence and cash flow accrual.

Earnings quality score can be used as a standalone metric while screening or building a signal. We also use the earnings quality score as a key input in calculating the final quality score.

We use 3 metrics and apply our standard score-aggregate-score methodology to calculate a stock’s earnings quality score.

• Beneish M-score - This is a metric developed by Prof. Messoud D. Beneish and is used to detect potential earnings manipulation by companies. M-score is calculated using 8 different variables to arrive at a single score. M-score of greater than -1.78 is considered a red flag and indicates that the company may be likely manipulating its earnings.
• Cash flow accrual ratio - Cash flow accrual ratio is a metric to detect if accrual earnings (net income) are significantly different from cash earnings. It can be used to assess a company’s earnings quality and reliability. Note that the ratio is constructed in such a manner that more negative is better.
• Earnings persistence - This measure indicates how repeatable and controllable a company's earnings are. This is normalized into a score between 0 and 100 where higher score implies healthier earnings. This is a proprietary metric created by our data partner FactSet and is calculated by looking at recurring, cash-basis income along with changes in net operating assets.

For each metric, we calculate a score between 0 and 100 using our standard scoring methodology. We then calculate an aggregate score by summing up the individual scores (equal weight). The stocks are then again scored between 0 and 100 based on the aggregate score to arrive at the final earnings quality score.