Numinor
Use Cases
Sentiment & Alpha
3 min readFebruary 12, 2026

Sentiment-Resistant Stocks: Finding Alpha in Emotional Market Noise

Not all stocks react equally to market mood swings. Discover how identifying sentiment-resistant companies creates a systematic edge by capturing fundamental value while others chase emotional trades.

Datasets Used
SmarTag News

The Question

Markets oscillate between greed and fear. Some stocks swing wildly with every sentiment shift—meme stocks, thematic plays, speculative names. Others remain stubbornly stable, their prices anchored to fundamentals even as narratives rage.

What if the alpha isn't in predicting sentiment, but in identifying stocks that ignore it?

Traditional sentiment models ask: Which stocks will benefit from positive news? This approach inverts the question: Which stocks are immune to sentiment noise—and why does that immunity predict outperformance?

The Approach

We construct a Sentiment Beta for every stock by regressing its daily returns against a market-wide sentiment index derived from SmarTag News. This measures how sensitive each stock is to aggregate mood swings.

High Sentiment Beta stocks amplify emotional moves—they soar on optimism, crash on pessimism. Low Sentiment Beta stocks drift independently, barely reacting to the crowd's emotional state.

Traditional CAPM logic suggests high-beta stocks should deliver higher returns (risk premium). But sentiment isn't fundamental risk—it's mispricing risk. Stocks that react strongly to sentiment are being pulled away from fair value by behavioral noise.

The factor construction flips this: we rank stocks by the inverse of absolute Sentiment Beta. Stocks with near-zero sentiment sensitivity—those trading on fundamentals, not emotion—get the highest scores.

The Finding

The strategy delivered 17%+ annualized returns in long-short portfolios with a Sharpe ratio above 2.0. Monthly information coefficients approached 4% with IC information ratios near 1.8 for holding periods longer than 20 days.

Stocks minimally affected by market sentiment significantly outperformed those heavily influenced by it. The effect was strongest among mid-cap names, where behavioral biases dominate price discovery more than in large caps (where institutions anchor to fundamentals) or small caps (where liquidity constraints matter more than sentiment).

The alpha mechanism appears to be anti-herding. When the market panics and sells everything, sentiment-resistant stocks hold their ground—suggesting informed holders or structural buyers (index funds, long-term institutions). When euphoria lifts all boats, these stocks lag—but avoiding the subsequent crash more than compensates.

Crucially, low Sentiment Beta isn't the same as low market beta. Some stocks are both sentiment-resistant and volatile—they move on idiosyncratic fundamentals (earnings surprises, M&A) but ignore the market's emotional backdrop. These stocks contributed most of the alpha.

Try It Yourself

This strategy works best as a portfolio overlay: within your existing universe, tilt toward sentiment-resistant names during periods of high market volatility or extreme sentiment readings (captured via aggregate news volume and tone).

Applications include:

  • Defensive equity portfolios: Reduce drawdowns without sacrificing long-term returns by selecting fundamentally-driven stocks
  • Factor combination: Pair with traditional value or quality factors—sentiment resistance amplifies their effectiveness
  • Regime switching: Dynamically adjust sentiment exposure based on market conditions (crowd behavior intensifies during bubbles and crashes)

Want to build a sentiment beta model for your coverage universe? Book a call to discuss data integration and factor construction.

Want to explore this with your own data?

We'll walk you through the methodology, provide sample code, and help you adapt this approach to your specific research questions.

Book a Call

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