LTM (LTM)
Quantitative Summary
DeterministicFinancial health is average: Piotroski 4/9.
Generated deterministically from quant metrics and financial statements. Not a recommendation.
Algorithmic Teardown
AI-GeneratedThe capital allocation efficiency appears robust, with a return on invested capital of 17.1% suggesting the firm generates returns exceeding typical cost of equity thresholds. However, the DuPont decomposition reveals an ROE of 109.2% driven almost entirely by financial leverage rather than operational momentum; specifically, an equity multiplier of 13.19x amplifies a modest net margin of 10.2% and moderate asset turnover of 0.81x. This structural reliance on debt contrasts sharply with the Piotroski F-Score of 4/9, which signals potential deterioration in fundamental quality or financial health, while the Beneish M-Score of -2.35 indicates low probability of earnings manipulation despite these mixed indicators.
Valuation metrics are currently compressed relative to historical norms and sector peers, as implied by a P/E ratio near its multi-year lows. This discount suggests the market is pricing in significant downside risk regarding the high leverage levels rather than rewarding the reported 11.2% revenue growth or gross margin expansion of 29.2%. A DCF analysis would likely yield a fair value significantly below current trading levels if future cash flows are discounted at rates commensurate with such elevated financial distress risks, implying the stock is priced for stagnation rather than the implied growth trajectory currently embedded in equity valuations.
The risk profile presents a dichotomy between defensive earnings quality signals and aggressive balance sheet leverage; while the low Beneish score offers some comfort regarding reporting integrity, the high debt load creates vulnerability to interest rate fluctuations or economic downturns that could compress margins further. The divergence between strong revenue growth and a mediocre Piotroski score suggests underlying operational fragility that warrants scrutiny before assuming stability in future cash flow generation.
Generated by LLM from quantitative data inputs. May contain inaccuracies. Not investment advice.
Price Chart with Moving Averages
Quant Health Deep Dive
Profitability & Value Creation
DuPont Analysis — ROE Decomposition
Breaking down Return on Equity to see how the company generates its ROE — efficiency, margins, or leverage.
Balance Sheet Health
Earnings Surprise History
EPS estimates vs actuals for the most recent reported quarters. Source: yfinance.
Underwater (Drawdown from Peak)
How far below the all-time high the price has been over time. Deeper = more pain for holders.
Rolling 60-Day Beta vs S&P 500 (VOO)
How the stock's sensitivity to market moves changes over time. β > 1 = more volatile than the market.
Fundamentals
Passive Flow Attribution
ETF Draft EffectWhen investors buy or sell ETFs like VWO or SPEM, the fund manager is mechanically forced to buy or sell LTM shares regardless of LTM's individual fundamentals. We estimate $64M of passive capital is structurally linked to LTM through 3 tracked ETFs. Passive flows have a limited but growing influence on LTM's daily trading dynamics.
Passive exposure = Σ (ETF AUM × stock weight in ETF) across 3 tracked ETFs. Actual passive ownership is larger (includes mutual funds). Not investment advice.
ETF Contagion Visualizer
Simulate a price drop in LTM to visualize passive redemption contagion across ETFs and collateral stocks.
If LTM (LTM) experiences a significant drawdown, ETF redemptions can create collateral selling pressure on co-held stocks. Our model identifies Taiwan Semiconductor Manufacturing Co. Ltd. (2330) as the most exposed collateral stock, sharing 1 ETFs with LTM. This is the "Passive Contagion" effect described in the Inelastic Market Hypothesis.
Contagion model based on shared ETF exposure and constituent weights across 3 tracked ETFs. Estimated selling pressure is a simplified model — actual impact depends on market liquidity, ETF redemption mechanics, and market-maker activity.
LTM Ownership Dynamics
ETFs with Highest LTM Exposure
Float lock-up computed from 3 ETFs tracked by SecuritiesDB. Actual passive ownership is higher (includes mutual funds, pension funds, etc.).
Fails-to-Deliver (FTD) History
SEC-reported settlement failures. Elevated FTDs can indicate high short-selling pressure, operational settlement issues, or naked shorting activity.
| Date | Failed Shares | Close Price | Notional Value |
|---|---|---|---|
| 2026-05-12 | 7,858 | $49.68 | $390,385.44 |
| 2026-05-07 | 76,792 | $51.85 | $4.0M |
| 2026-05-06 | 33,000 | $46.26 | $1.5M |
| 2026-05-05 | 17,826 | $45.26 | $806,804.76 |
| 2026-05-04 | 11,409 | $47.26 | $539,189.34 |
| 2026-04-29 | 501 | $48.50 | $24,298.5 |
| 2026-04-20 | 35 | $55.66 | $1,948.1 |
| 2026-04-10 | 2,121 | $53.10 | $112,625.1 |
| 2026-04-09 | 1,356 | $52.61 | $71,339.16 |
| 2026-04-02 | 5,119 | $50.80 | $260,045.2 |
| 2026-04-01 | 110,451 | $49.44 | $5.5M |
| 2026-03-31 | 78 | $46.22 | $3,605.16 |
| 2026-03-30 | 1,273 | $47.99 | $61,091.27 |
| 2026-03-27 | 2,361 | $48.69 | $114,957.09 |
| 2026-03-24 | 86 | $49.62 | $4,267.32 |
| 2026-03-16 | 151 | $47.62 | $7,190.62 |
| 2026-03-13 | 77 | $47.05 | $3,622.85 |
| 2026-03-12 | 86 | $50.34 | $4,329.24 |
| 2026-03-11 | 1,252 | $50.94 | $63,776.88 |
| 2026-03-10 | 8 | $48.76 | $390.08 |
| 2026-03-02 | 154 | $56.16 | $8,648.64 |
| 2026-02-17 | 403 | $59.71 | $24,063.13 |
| 2026-02-11 | 300 | $61.16 | $18,348 |
| 2026-02-09 | 5,176 | $64.84 | $335,611.84 |
| 2026-02-06 | 77,001 | $64.37 | $5.0M |
| 2026-02-05 | 422 | $64.14 | $27,067.08 |
| 2026-02-04 | 35,683 | $67.74 | $2.4M |
| 2026-02-03 | 16,476 | $68.46 | $1.1M |
| 2026-02-02 | 252 | $65.81 | $16,584.12 |
| 2026-01-27 | 74,096 | $64.49 | $4.8M |
Source: SEC Regulation SHO FTD data. Data is reported with a ~30 day delay. High FTD quantities relative to average daily volume may indicate settlement stress.
Compare LTM to Peers
Quant metrics computed deterministically from financial statements and price data. Updated: N/A.
SecuritiesDB provides programmatic data aggregation for informational purposes only. None of the metrics, summaries, or algorithmic flags constitute a recommendation to buy or sell any security.