Overview

Quantitative screening models give investors a structured way to compare companies across multiple financial dimensions simultaneously. This article breaks down a set of quant signals for two NYSE-listed companies — ServiceNow, Inc. (NYSE:NOW), an enterprise cloud software platform, and Waste Management, Inc. (NYSE:WM), a large-cap environmental services business — covering valuation composites, price momentum, return quality, and earnings integrity metrics.

Q.i. Value: Identifying Undervaluation

The Q.i. Value ranks companies using four ratios: EBITDA Yield, Free Cash Flow (FCF) Yield, Liquidity, and Earnings Yield. A lower score generally suggests the company is more undervalued relative to peers.

• ServiceNow (NOW): Q.i. Value of 55.00
• Waste Management (WM): Q.i. Value of 39.00

On this metric, Waste Management scores materially lower, suggesting it appeared relatively more undervalued than ServiceNow at the time of measurement.

Value Composite Score (VC1 and VC2)

Developed by James O’Shaughnessy, the Value Composite (VC) score aggregates five valuation ratios: price-to-earnings (P/E), price-to-cash-flow (P/CF), EBITDA-to-enterprise value (EV), price-to-book (P/B), and price-to-sales (P/S). Scores run from 1 to 100, where scores closer to 0 indicate potential undervaluation and scores near 100 suggest overvaluation.

A second variant, Value Composite 2 (VC2), adds shareholder yield as a sixth input.

ServiceNow’s VC1 score of 80 and VC2 of 82 placed it toward the expensive end of the spectrum at snapshot date. Waste Management’s VC1 of 57 and VC2 of 48 indicated a more balanced to moderately valued profile, with shareholder yield improving its relative attractiveness in the VC2 calculation.

ERP5 Rank

The ERP5 Rank combines four factors — Earnings Yield, Return on Invested Capital (ROIC), Price-to-Book, and 5-Year Average ROIC — to help identify undervalued companies. Lower ranks indicate greater potential undervaluation.

• ServiceNow (NOW): ERP5 Rank of 14,508
• Waste Management (WM): ERP5 Rank of 8,113

Waste Management ranked considerably lower (better) on the ERP5 scale, consistent with its lower Q.i. and VC scores.

Historical Volatility

Historical volatility measures how much a stock’s price has deviated over a given period. Higher volatility generally implies greater risk and uncertainty. All figures below are percentage-based annualised volatility readings at the snapshot date.

ServiceNow exhibited roughly 2.5× the historical volatility of Waste Management across all measured periods, reflecting the higher price risk typically associated with high-growth software stocks versus mature, cash-generative industrials.

Price Index (Momentum)

The Price Index is calculated by dividing the current share price by the share price at a prior date. A value above 1.0 indicates price appreciation; below 1.0 indicates depreciation over that period.

ServiceNow (NOW)

Waste Management (WM)

Over longer horizons, ServiceNow significantly outpaced Waste Management in price appreciation — a 3-year index of 3.63 versus 1.90 — though this came with substantially higher volatility. Both stocks showed slight short-term weakness in their 1-month readings.

Return Metrics: ServiceNow (NOW)

Return on Assets (ROA)

Return on Assets (ROA) measures net income after tax divided by total assets, indicating how efficiently a company uses its asset base to generate profit.

• ServiceNow ROA: 0.000805 (approximately 0.08%)

This near-zero ROA is consistent with ServiceNow’s growth-phase profile at the time, where the company was investing heavily in sales, marketing, and R&D rather than optimising for near-term profitability.

Return on Invested Capital (ROIC)

ROIC is calculated by dividing net operating profit (EBIT) by employed capital (total assets minus current liabilities). The ROIC Quality ratio divides the 5-year average ROIC by the standard deviation of the 5-year ROIC — a higher figure indicates more consistent returns.

• ROIC (current): -0.015148
• ROIC 5-Year Average: -0.140710
• ROIC Quality: 0.306066

Negative ROIC figures reflect ServiceNow’s investment-heavy operating model at this point in its growth cycle, where operating losses were being generated while scaling the business.

FCF Yield 5-Year Average

The FCF Yield 5-Year Average divides the 5-year average free cash flow by the current enterprise value (market cap + debt + minority interest + preferred shares − cash). This gives a longer-term view of cash generation relative to business size.

• ServiceNow FCF Yield 5yr Avg: 0.003484 (approximately 0.35%)

Gross Margin Score

The Gross Margin Score (Marx) evaluates gross margin stability and growth over the previous eight years. The scale runs from 1 (best) to 100 (worst).

• ServiceNow Gross Margin Score: 8.00 — indicating strong, consistent gross margin improvement over the measured period.

Quality and Earnings Integrity Metrics: Waste Management (WM)

Current Ratio

The Current Ratio (current assets ÷ current liabilities) measures short-term liquidity. Very high ratios may suggest inefficient use of working capital; very low ratios may indicate difficulty meeting near-term obligations.

• Waste Management Current Ratio: 1.70 — indicating adequate short-term liquidity.

Gross Margin Score

• Waste Management Gross Margin Score: 7.00 — also near the top of the scale (1 = best), suggesting a highly stable gross margin profile over the prior eight years.

Beneish M-Score (Earnings Manipulation Indicator)

The M-Score, developed by accounting professor Messod Beneish, uses eight financial statement variables to estimate the likelihood that a company has manipulated its reported earnings. The eight inputs are:

• Days’ Sales in Receivables Index
• Gross Margin Index
• Asset Quality Index
• Sales Growth Index
• Depreciation Index
• SG&A Expenses Index
• Leverage Index
• Total Accruals to Total Assets

A score above −1.78 is a flag that earnings manipulation may be present. A score well below −1.78 indicates a lower probability of manipulation.

• Waste Management M-Score: −2.853 — below the −1.78 threshold, suggesting no strong signal of earnings manipulation at this date.

Piotroski F-Score

The Piotroski F-Score, developed by accounting professor Joseph Piotroski, rates financial strength on a scale of 1 to 9. It evaluates nine binary criteria across three categories:

• Profitability: ROA, operating cash flow, change in ROA, accruals (earnings quality)
• Leverage & Liquidity: Change in long-term debt ratio, change in current ratio, absence of new share issuance
• Operating Efficiency: Change in gross margin, change in asset turnover

A score of 9 indicates strong financial health; a score of 1 indicates weakness.

• Waste Management F-Score: 5 — a middling score suggesting a mix of positive and neutral signals, neither strongly bullish nor bearish on financial health criteria.

Summary Comparison

At the time of this snapshot, Waste Management scored more favourably on most traditional value metrics, while ServiceNow demonstrated far stronger long-term price momentum despite appearing expensive by composite valuation measures. These two stocks represent contrasting archetypes: a high-growth, high-multiple software company versus a durable, cash-generative industrial business.

Important Disclaimer

All data in this article reflects a single point-in-time snapshot from August 2019. Quantitative signals are screening tools, not buy or sell recommendations. Investors should conduct their own due diligence and consult a qualified financial adviser before making any investment decisions. Past price performance does not guarantee future results.

The Problem with DevOps Adoption Statistics

Industry surveys love to report adoption numbers. According to VersionOne’s 2017 State of Agile Report, 71 percent of businesses had adopted some form of DevOps to streamline software development and deployment. A separate RightScale report cited Docker container technology as the most popular DevOps tool, used by 35 percent of respondents.

These numbers make for compelling slide decks. But according to Nicole Forsgren, CEO and Chief Scientist of DevOps Research and Assessment (DORA), they are almost useless as a guide to strategy.

“We don’t say things like, ’27 percent of the industry is using configuration management,'” Forsgren said. A peek into competitors’ playbooks can be “interesting,” she acknowledged, but knowing what tools others have purchased tells you nothing about whether those organizations are actually succeeding. “Those other reports cannot say what is predictive of improvement.”

DORA’s approach is different. Instead of simply cataloguing tool adoption, DORA studies the specific practices companies use and—critically—measures their outcomes. The goal is to identify which DevOps capabilities reliably predict better business results.

Why Adoption Rates Are Not a Roadmap

The gap between adoption and success is well-documented across technology disciplines. Forsgren pointed to research from Gartner indicating that roughly 70 percent of big data initiatives fail. If an organization’s strategy amounts to surveying what tools the market is buying and then purchasing the same software, the probability of success remains low regardless of how popular a given tool may be.

DevOps is no exception. Despite high adoption rates, many organizations struggle to realize the friction-free software delivery pipeline the methodology promises. A survey by Shippable found that 68 percent of DevOps practitioners cited poor connectivity in the DevOps toolchain as a significant obstacle, while 27 percent pointed to siloed departments as a barrier to the cross-functional collaboration DevOps requires.

High adoption rates, in other words, mask a wide variance in actual performance. Knowing that a majority of companies use a given practice or tool does not help an organization understand whether adopting it will improve their own delivery speed, stability, or business outcomes.

DORA’s Scientific Approach to DevOps Research

DORA was founded on the premise that DevOps research should meet the same standards of rigor applied in academic disciplines. The organization partnered with Puppet Labs to produce the annual State of DevOps Report, differentiating the tools and methods that produce measurable results from those that generate noise.

The data DORA collects must pass rigorous review before informing its conclusions. Compelling anecdotes about a technology or an operational method do not qualify as evidence. Every capability included in DORA’s model must demonstrably predict organizational performance.

“It lets us say, ‘If we focus on core capabilities, they will predict organizations’ ability to develop and deliver quality software with both speed and stability,'” Forsgren explained. DORA frames DevOps not as a collection of tools, but as a discipline grounded in lean and agile principles combined with technology and automation.

Debunking the Speed-vs-Stability Trade-off

One of DORA’s most significant findings challenges a widely held assumption: that faster software delivery necessarily comes at the cost of system stability. For years, this trade-off was treated as an engineering fact of life—to ship faster, you accept more instability.

DORA’s research found no evidence for this trade-off in high-performing organizations. “We thought that in order to get stability, you had to slow down. It doesn’t show up anywhere in the data,” Forsgren said. “High performers get both.”

This is consistent with the broader principle that counterintuitive findings sometimes emerge when you measure outcomes rigorously rather than assuming causal relationships. High-performing engineering organizations achieve high deployment frequency and low change-failure rates simultaneously—a combination that survey-based research built on adoption counts alone would be unlikely to surface.

Applying Research: Assessments and Practical Consulting

DORA’s work is not purely academic. The organization offers assessments and consultations that apply its research findings to specific organizational contexts.

“We offer an assessment using these strong, scientifically based measures that we have prepared and refined over four years of rigorous academic research,” Forsgren said. Assessments gather data from all relevant roles within an organization—development, testing, operations, quality assurance, information security, vendors, contractors, and consultants—and then benchmark results against industry data.

The output is specific and actionable: “I can—algorithmically—tell you what your bottleneck is, what your constraint is, where you should start to accelerate your performance,” Forsgren said.

Forsgren cited Capital One as an example of a DORA client. According to her account, Capital One conducted an assessment across more than a dozen lines of business. DORA focused the organization on two capabilities out of a possible twenty. The result, as reported by Forsgren, was a 20x improvement in deployment frequency within two months.

Key Takeaways for DevOps Practitioners

• Adoption statistics are descriptive, not prescriptive. Knowing what percentage of organizations use a given tool or practice does not tell you whether adopting it will improve your outcomes.
• Outcome measurement matters. Effective DevOps research measures deployment frequency, lead time, change-failure rate, and mean time to restore—not just tool adoption rates.
• The speed-stability trade-off is a myth for high performers. DORA’s data shows that elite organizations achieve both high velocity and high stability simultaneously.
• Focus beats breadth. Rather than adopting every available DevOps practice, identifying the specific capabilities that act as bottlenecks in your organization and improving those yields disproportionate results.
• Scientific rigor applies to operations. Applying the same standards to DevOps strategy that you would apply to a technical hypothesis—forming measurable predictions and testing them—produces better decisions than following industry trend reports.

Nicole Forsgren was featured in theCUBE’s Women in Tech series at the time of this interview. DORA was subsequently acquired by Google in 2018.