Trade Ideas February 19, 2026

FactSet: Deep-Value Entry as AI-Driven Sentiment Creates an Opportunity

Subscription durability, strong cash flow and a beaten-down multiple make a measured long here — with clear stop and targets.

By Hana Yamamoto FDS
FactSet: Deep-Value Entry as AI-Driven Sentiment Creates an Opportunity
FDS

FactSet has been swept up in a broader 'AI-phobia' rotation that punished legacy data and software names. The business still generates strong cash flow ($647M FCF), sports a healthy ROE (27.7%) and carries a modest leverage profile (debt/equity 0.63). At roughly $7.38B market cap and a P/E near 12-12.6, the stock offers a favorable risk/reward for a swing trade as technicals show oversold conditions and institutional buying has appeared during the pullback.

Key Points

  • FactSet trades at attractive multiples: ~9.6x EV/EBITDA and ~12x P/E with market cap ~$7.38B.
  • The business produces strong free cash flow ($647.4M) and high returns (ROE ~27.7%), supporting dividends (~2.2%) and financial flexibility.
  • Technicals show oversold conditions (RSI ~29.8) and elevated short activity, creating a tactical entry opportunity.
  • Trade plan: long at $199.02, stop $180.00, target $260.00 on a mid-term (45 trading days) horizon.

Hook & thesis
FactSet (FDS) dropped hard from last year’s peak as AI enthusiasm morphed into AI-phobia, and investors punished incumbents they fear will be disintermediated. That selloff left a mature, subscription-heavy business trading at a single-digit EV/EBITDA (about 9.6x) and a P/E near 12. That combination of entrenched customers, predictable cash flow and an attractive valuation argues for a tactical long: buy the durable cash-flow engine at beaten-up multiples and let the AI conversation evolve rather than overreact to it.

Why the market should care
FactSet is not a momentum software name — it is a mission-critical data and analytics platform used across the buy-side, sell-side and wealth channels. Its revenue primarily comes from subscriptions for workstations, analytics, enterprise data and research management, which creates high renewal rates and switching costs. The company’s free cash flow of $647,393,000 and a return on equity of 27.66% demonstrate the profitability and cash generation behind those subscriptions.

Investors focused on AI narratives should note two things: first, FactSet’s platform is a distribution point for data and analytics that can integrate AI tooling; second, if AI tools reduce labor intensity, customers may be more likely to expand enterprise spend on integrated data and workflow platforms rather than abandon them. Put simply: AI is both a risk and a lever for FactSet’s product roadmap.

Business snapshot and fundamentals
FactSet serves portfolio managers, research analysts, investment bankers, risk managers and wealth advisors with a configurable desktop, APIs, enterprise feeds and analytics. The company's organizational shift to align offerings by firm type - Institutional Buyside, Dealmakers, Wealth and Partnerships - is meant to sharpen go-to-market execution and product investments.

Key fundamentals:

  • Market cap: about $7.38B.
  • Price-to-earnings: ~12.3 (recent prints ~12.25-12.59).
  • Free cash flow: $647.4M.
  • Return on equity: 27.66%; return on assets: 14.21%.
  • Debt-to-equity: 0.63 (modest leverage).
  • Dividend yield: ~2.2% with ex-dividend on 02/27/2026 and payable 03/19/2026.

Valuation framing
At roughly $7.38B market capitalization and an enterprise value near $8.44B, FactSet trades at about 9.6x EV/EBITDA and ~3.1x price-to-sales. Those multiples sit well below where many high-quality data and analytics peers traded during the AI froth. Even compared to FactSet’s own 52-week extremes, the current price near $199 (recent close $199.02) is far from the $474.79 52-week high reached on 05/16/2025; that prior high reflected inflated multiple expansion on momentum, not purely fundamental improvement.

Put differently: you are buying a cash-generative franchise with a durable subscription base at a valuation that implies low growth expectations. That creates asymmetric upside if FactSet stabilizes revenue and margins, which is plausible given an enterprise product portfolio and steady renewal dynamics.

Technical context
Technically the stock is oversold: the relative strength index sits near 29.8 and short-volume has been elevated across recent sessions, indicating a lot of pessimism is already priced in. Short interest has moved around 1.8M-2.7M over past months with days-to-cover in the 2-3.5 range. While momentum is bearish (MACD negative), oversold readings can precede a snapback rally — a useful backdrop for an entry with a defined stop.

Trade plan (actionable)

  • Trade direction: Long.
  • Entry price: $199.02 (current price).
  • Stop loss: $180.00.
  • Target price: $260.00.
  • Time horizon: mid term (45 trading days). Expect this trade to play out over a mult-week window as sentiment stabilizes and buyers return to beaten-down, high-quality data franchises. If momentum resumes in the name, consider holding to longer targets or trimming into strength.

Rationale: the stop at $180 sits below the $185 52-week low from 02/17/2026 and gives room for short-term volatility while protecting against a structural break in subscription dynamics. The $260 target is achievable if the market re-rates the stock modestly toward higher EV/EBITDA multiples (closer to low-teens) and the company shows steady cashflow and resilient renewal metrics. This gives roughly +31% upside from the entry and -9.5% downside to the stop, a favorable risk/reward for a swing trade.

Catalysts (2-5)

  • Quarterly results or guidance that show resilient subscription renewals and stable enterprise bookings - this would undercut the narrative that AI will immediately displace incumbents.
  • Product announcements or partnerships that position FactSet as an enterprise AI-enablement layer (APIs, marketplace integrations) which can increase addressable spend per client.
  • Improvement in macro risk sentiment or a broader sector rotation back into quality data/software names — historical patterns show beaten-down cyclicals can rally when risk-on returns.
  • Institutional buys (large-scale insider-like purchases or fund accumulation), similar to the reported $24M purchase in Q4, could attract momentum and multiple expansion.

Risks and counterarguments
Any investment here is not without material risks. Below I list the key downside scenarios and a direct counterargument to my bullish stance.

  • AI-led disintermediation: The core bear case is that AI-native vendors will stitch together data, models and execution in a way that meaningfully lowers FactSet’s renewals or pricing power. If customers find lower-cost point tools adequate for workflows, revenue growth and margins could compress beyond current expectations.
  • Multiple compression persists: Market sentiment can remain punitive for longer than fundamentals warrant. Even if cash flow holds, the stock could trade sideways or lower if investors prefer AI-native growth names over legacy subscription models.
  • Macroeconomic / market drawdown: Broader market weakness, especially in a midterm election year dynamic noted by strategists, could trigger selloffs that take quality names lower along with cyclical pressure on asset management budgets.
  • Execution missteps: If management’s reorg by firm type fails to accelerate sales or if product investments miss client needs, growth could slow and margins could be pressured—this is a classic execution risk.
  • Short-term technical failure: Elevated short interest and recent bearish momentum mean the name can be volatile; rapid down moves could force stop-outs before fundamentals reassert themselves.

Counterargument to the thesis
It’s plausible the market is right that incumbents face structural margin deterioration as AI commoditizes parts of the workflow. If FactSet cannot convert its product roadmap into higher-margin, AI-enabled enterprise offerings, the multiple could compress further and cash flow could decline. That scenario would invalidate the idea of buying into durable margins and would require taking a more defensive stance or waiting for clearer signs of stabilizing revenue.

What would change my mind
I would reduce conviction or close the position if one of the following occurs: a) consecutive quarters of declining subscription revenues or materially weaker renewal rates, b) management cuts guidance materially or signals slower enterprise uptake, or c) the company reports meaningful customer churn in core segments. Conversely, stronger-than-expected subscription growth, improved bookings and concrete AI-integration wins would increase my conviction and warrant holding or adding.

Valuation snapshot table

Metric Value
Market cap $7.38B
Enterprise value $8.44B
P/E ~12.3x
EV/EBITDA ~9.6x
Free cash flow $647.4M

Conclusion
FactSet is a classic high-quality, subscription-driven business that has been oversold in an environment where investors are aggressively punishing names they see as vulnerable to AI disruption. The company’s strong free cash flow, high ROE and modest leverage argue for a tactical long at current levels with a defined stop. This trade is not a blind value bet; it relies on the view that renewal economics remain intact and that the market will re-rate a mature cash-generative franchise as the AI debate matures.

Execute the trade as a mid-term swing: enter at $199.02, stop at $180.00, target $260.00. If the stock breaks below the stop or if fundamental signals deteriorate (renewals, guidance), re-evaluate quickly. If FactSet reports durable subscription health or demonstrates AI integration that expands enterprise spend, consider extending the holding toward higher-band targets.

Risks

  • AI-native competitors could disintermediate parts of FactSet’s workflow and weaken renewal/pricing power.
  • Valuation compression could persist if the market prefers high-growth AI names over legacy subscription models.
  • Broader market declines or macro uncertainty in 2026 could pressure asset-management budgets and platform spending.
  • Execution risk from management’s organizational changes; failure to monetize AI integrations would hurt upside potential.

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