Note: The financial figures in this article reflect data reported circa September 2019 and are intended to illustrate how each valuation metric works, not to provide current investment advice.
Why Quantitative Valuation Metrics Matter for Investors
Quantitative valuation frameworks give investors a systematic, repeatable way to assess whether a stock is cheap or expensive relative to its fundamentals. Rather than relying on intuition or a single ratio such as price-to-earnings, these composite models aggregate multiple signals to reduce the noise any one metric can introduce. This article explains the most widely used frameworks — Value Composite, Q.i. Value, Piotroski F-Score, Beneish M-Score, Magic Formula Rank, and ERP5 — using Splunk Inc. (NasdaqGS: SPLK) as a concrete reference point from 2019.
Value Composite Scores (VC1 and VC2)
Developed by quantitative investor James O’Shaughnessy, the Value Composite framework ranks stocks by combining several valuation ratios into a single score between 1 and 100. A score near 0 suggests a stock may be undervalued; a score near 100 suggests it may be overvalued relative to peers.
VC2: Six-Ratio Composite
The Value Composite 2 (VC2) incorporates the following six ratios:
- Price to Earnings (P/E) — measures how much investors pay per dollar of earnings.
- Price to Cash Flow (P/CF) — compares share price to operating cash flow per share.
- EBITDA to Enterprise Value (EV/EBITDA) — a capital-structure-neutral profitability measure.
- Price to Book Value (P/B) — compares market price to net asset value.
- Price to Sales (P/S) — useful for companies with volatile or negative earnings.
- Shareholder Yield — combines dividend yield and net share buyback yield.
At the time of the original report (September 2019), Splunk Inc. had a VC2 score of 82, indicating the stock appeared richly valued on a composite basis relative to the broader market universe being ranked.
VC1: Five-Ratio Composite
Value Composite 1 (VC1) removes shareholder yield from the calculation, leaving the five price-based ratios. For SPLK at the same date, VC1 stood at 81 — essentially consistent with VC2, suggesting that shareholder yield was not a meaningful differentiator in this case.
Q.i. Value: Identifying Undervaluation via Four Ratios
The Q.i. Value ranking uses four ratios focused on yield and liquidity:
- EBITDA Yield — EBITDA divided by enterprise value.
- Free Cash Flow (FCF) Yield — FCF divided by enterprise value or market cap.
- Earnings Yield — earnings per share divided by share price (inverse of P/E).
- Liquidity — a measure of how easily the company can meet short-term obligations.
The lower the Q.i. Value score, the more undervalued the company is considered to be. Splunk’s Q.i. Value of 58 in September 2019 placed it in a middling range — neither strongly undervalued nor severely overvalued on this particular set of yield-focused measures.
Piotroski F-Score: Balance Sheet Health
The Piotroski F-Score, developed by accounting professor Joseph Piotroski, assesses the financial strength of a company using nine binary tests derived from the financial statements. Each test that a company passes awards one point, for a maximum score of 9.
The nine criteria span three categories:
- Profitability: positive return on assets, positive operating cash flow, improving ROA, cash flow exceeding net income (accruals quality).
- Leverage and Liquidity: declining long-term debt ratio, improving current ratio, no new share dilution.
- Operating Efficiency: improving gross margin, improving asset turnover ratio.
Interpretation guidelines:
- 8–9: Strong financial position.
- 3–7: Moderate — further analysis warranted.
- 0–2: Weak — potential red flags.
Splunk’s F-Score of 5 at the time placed it in the middle range, suggesting a mixed but not alarming financial picture. For a high-growth SaaS company investing heavily in sales and R&D, a mid-range F-Score is not unusual.
Beneish M-Score: Detecting Earnings Manipulation
The Beneish M-Score, developed by Professor Messod Beneish, is a probabilistic model designed to flag whether a company may be manipulating its reported earnings. It uses eight financial ratios derived from the income statement and balance sheet — examining things like changes in receivables, gross margin, asset quality, sales growth, and accruals.
The critical threshold is -2.22:
- An M-Score greater than -2.22 raises a flag for possible manipulation.
- An M-Score below -2.22 suggests manipulation is less likely.
Splunk’s M-Score of -2.70 fell below the -2.22 threshold, indicating that the model did not flag earnings manipulation as a significant concern at that point in time. Investors should treat this as one signal among many rather than a definitive verdict.
Gross Margin Score: Stability and Growth Over Eight Years
This score, sometimes referred to as the Gross Margin (Marx) score, evaluates the stability and growth of a company’s gross margin over the trailing eight years. It is ranked on a scale of 1 to 100, where 1 is positive (consistent, growing gross margins) and 100 is negative (volatile or declining margins).
Splunk’s Gross Margin score of 32 suggested relatively stable gross margin performance over the measured period — a reasonable result for a software company that was transitioning to a subscription model and maintaining high gross margins typical of SaaS businesses.
Magic Formula Rank (MF Rank): Quality at a Fair Price
The Magic Formula was developed by hedge fund manager Joel Greenblatt and described in his book The Little Book That Beats the Market. The formula ranks companies using two ratios:
- Return on Invested Capital (ROIC) — measures how efficiently the company generates profit from capital.
- Earnings Yield — EBIT divided by enterprise value, indicating how cheap the stock is relative to its operating earnings.
Companies are ranked separately on each metric and the ranks are combined. The lower the combined MF Rank, the more attractive the stock is considered. Splunk’s MF Rank of 12,917 — out of a universe that can include tens of thousands of stocks — indicated that SPLK did not rank as an obvious Magic Formula pick at that time, which aligns with its high VC2 score.
ERP5 Rank: A Multi-Factor Value and Quality Screen
The ERP5 Rank extends the Magic Formula concept by adding two more dimensions:
- Earnings Yield
- ROIC
- Price to Book Value
- 5-Year Average ROIC — smooths out single-year anomalies in capital efficiency.
As with MF Rank, a lower ERP5 Rank is generally considered better. Splunk’s ERP5 Rank of 15,247 reinforced the picture painted by the other metrics: a high-quality business by some measures, but not a screaming bargain on valuation in late 2019.
Price Index Data: Momentum Signals
The price index is a simple momentum indicator calculated by dividing the current share price by the share price at a fixed point in the past. A value above 1.0 indicates price appreciation; below 1.0 indicates a price decline.
Splunk’s price index readings as of September 2019:
| Time Period | Price Index | Interpretation |
|---|---|---|
| 1 Month | 1.044 | Slight appreciation |
| 3 Month | 0.973 | Slight decline |
| 5 Month | 0.854 | Meaningful pullback |
| 10 Month | 1.229 | Strong appreciation |
| 12 Month | 1.001 | Essentially flat year-over-year |
| 24 Month | 1.755 | 75% gain over two years |
| 36 Month | 2.041 | Doubled over three years |
The longer-term indices showed strong multi-year momentum, while shorter-term readings suggested the stock had pulled back in the months immediately preceding the report — a common pattern in high-growth technology names.
Historical Volatility
Volatility figures measure the annualised standard deviation of price returns over a given window. Higher volatility means wider price swings — relevant for position sizing and risk management.
Splunk’s volatility readings as of September 2019:
- 3-Month Volatility: 36.55%
- 6-Month Volatility: 37.33%
- 12-Month Volatility: 48.07%
The declining volatility from the 12-month to the 3-month window suggests that SPLK’s price action had become somewhat more stable in the near term relative to the prior year — though all three figures remained well above typical large-cap averages, consistent with a high-growth SaaS stock.
Putting It All Together: How to Use These Metrics
No single metric tells the whole story. Combining multiple frameworks helps build a more complete picture:
- Use VC1/VC2 to establish whether a stock looks cheap or expensive on traditional valuation ratios.
- Use the Piotroski F-Score to assess whether the underlying business is improving or deteriorating financially.
- Use the Beneish M-Score as a quality-control check on the reliability of reported earnings.
- Use MF Rank and ERP5 Rank to compare a stock’s value-and-quality profile against a broad universe of peers.
- Use price index data to understand momentum context and whether recent price action aligns with the longer-term trend.
- Use volatility data to calibrate position size and set realistic expectations for price swings.
In Splunk’s case, the 2019 snapshot painted the picture of a high-quality, high-growth technology company that was not cheap by traditional valuation standards — a characterisation consistent with how the market was pricing cloud software businesses at that time.
This article is for educational purposes only and does not constitute investment advice. All figures cited are historical data points from approximately September 2019 and do not reflect current market conditions.