Anchoring Bias: Understanding Impact and Possible Mitigation Strategies

A promising renewable energy startup presented its pitch to a bustling venture capital (VC) firm. The founders confidently anchored their valuation at $50 million, supported by projections of a $10 billion total addressable market (TAM). Enthralled by the figures and the compelling narrative, the VC team issued a term sheet based on these anchors. However, months later, operational challenges and regulatory hurdles revealed that the startup's growth potential was far less than anticipated. Such reliance on assumptions is an example of anchoring bias—a cognitive tendency to overly rely on an initial piece of information when making judgments, even in the face of new evidence.

This story is not unique to venture investing. Anchoring bias also manifests in financial markets, influencing decisions made by retail investors, professional traders, and institutions. For instance, during the 2008 financial crisis, many investors anchored their expectations on pre-crisis stock valuations or historical price highs, failing to adjust to the rapidly changing economic environment. Similarly, during the 2021 meme stock frenzy, retail traders anchored on GameStop's $483 intraday high, holding positions long after the fundamentals deteriorated. These examples highlight how anchoring bias distorts decision-making in both private and public markets.

In venture capital or financial markets, anchoring bias leads to overvaluations, misjudged risks, and missed opportunities. However, evidence-based strategies can help mitigate its effects.

How Anchoring Bias Manifests in Venture Investing and Financial Markets

In Venture Investing

Anchoring bias in venture capital often stems from two primary sources:

1. Numerical Anchors: Pre-money valuations or TAM projections during early discussions can skew subsequent assessments. For example, a study found that 72% of Series A valuations clustered within 20% of their initial anchors, regardless of interim developments.

2. Narrative Anchors: Founders' pedigrees or hype surrounding emerging technologies (e.g., AI or blockchain) can anchor perceptions, leading investors to overlook operational risks or market readiness.

In Financial Markets

In public markets, anchoring bias manifests similarly but with broader systemic implications:

• Historical Price Anchors: Investors often fixate on past stock prices (e.g., 52-week highs or previous peaks) when making buy/sell decisions. During the dot-com bubble, for instance, NASDAQ composite P/E ratios remained above historical averages for 18 months post-peak as investors anchored to pre-crash valuations.

• Earnings Forecast Anchors: Analysts frequently anchor their earnings forecasts on prior performance rather than adjusting for new data. This lag in revisions can distort stock valuations and delay market corrections.

• Social Anchors: Retail investors often anchor on price targets shared on social media platforms or by influential figures without critically evaluating underlying fundamentals.

Strategies to Mitigate Anchoring Bias

1. Multi-Perspective Valuation Frameworks

To counteract numerical anchors in both venture investing and financial markets:

• Scenario-Based Modeling: Assign probability weights to multiple outcomes rather than relying on a single estimate. For example:

  • In venture investing: Model a biotech startup's value across varying FDA approval probabilities.

  • In financial markets: Use Monte Carlo simulations to assess potential price trajectories under different macroeconomic scenarios.

• Stage-Adjusted Multiples: Use sector-specific revenue or EBITDA multiples that reset at each funding round (for startups) or earnings cycle (for public companies).

Platforms like Carta (for private markets) and Bloomberg Terminal (for public markets) now integrate machine learning tools to flag discrepancies between updated financials and anchor-based projections.

2. Decoupling Due Diligence from Initial Impressions

Anchors often take hold early in evaluations. To minimize their influence:

• Blind Team Evaluations: In venture investing, anonymizing pitch decks prevents the founder's reputation or educational background from becoming anchors. Research shows this increases the selection of startups from underrepresented founders by 22%.

• Structured Analysis: In financial markets, systematic valuation models (e.g., discounted cash flow analysis) ensure that decisions are grounded in fundamentals rather than historical price anchors.

A 2024 study found that structured due diligence reduced anchor-driven term sheet issuance by 34% in venture capital and improved earnings forecast accuracy by 19% in equity research.

3. Anti-Anchoring Portfolio Construction

Investors can design portfolios that reduce reliance on anchors:

• Dynamic Position Sizing: Allocate capital based on milestone achievements (in venture investing) or real-time market conditions (in public markets). For example:

  • In VC: A startup initially valued at $20 million might receive additional funding at $45 million only after meeting growth targets.

  • In equities: Rebalance portfolios quarterly based on updated earnings reports rather than historical price levels.

• Anchor-Reset Clauses: For venture term sheets, include provisions requiring valuation reassessment if key assumptions change (e.g., delays in product launch). In public markets, use stop-loss orders to prevent anchoring on inflated price targets.

4. Behavioral Training for Cognitive Debiasing

Behavioral training programs can help investors recognize and counteract anchoring bias:

• Anchoring Recognition Drills: Reviewing past deals or trades to identify where anchors influenced decisions helps improve future judgment.

• Red Team Challenges: Encourage partners or analysts to use contrarian data to argue against their investment theses.

Maintaining an "Anchor Audit" ledger comparing initial assumptions with eventual outcomes has proven effective in identifying patterns of bias across VC portfolios and equity trades.

5. Leveraging Technology

Technological tools can further mitigate anchoring bias:

• AI-Powered Anchor Detection: Platforms like Alpha Sense analyze pitch meetings and earnings calls for signs of anchoring (e.g., overemphasis on historical metrics). Early adopters reduced anchor-influenced term sheets by 29% in VC and improved trading accuracy by 15% in equities.

• Blockchain for Transparent Valuation Histories: Distributed ledgers track valuation rationales and flag reliance on outdated anchors, ensuring accountability across funding rounds and trading cycles.

Challenges in Implementation

Despite these strategies, specific challenges persist:

1. The TAM Trap: Total addressable market estimates are standard anchors but are often inflated or speculative. Investors must discount TAM figures based on adoption barriers:

   • In VC: Validate TAM through bottom-up customer interviews rather than top-down analyst reports.

   • In equities: Adjust TAM estimates for competitive dynamics and regulatory risks.

2. Founder Pedigree Bias & Social Anchors:

   • In VC: Founders with prestigious educational backgrounds often receive higher valuations despite no correlation with success rates. Blinding educational backgrounds during evaluations can mitigate this bias.

   • In financial markets: Retail investors frequently anchor on social media-driven price targets without evaluating fundamentals.

Conclusion

Anchoring bias is intertwined with human cognition. The bias mitigation strategy involves structured frameworks, behavioral training, and technological tools. Whether in venture investing or financial markets—where uncertainty is high, and narratives are compelling—combating this bias requires continuous reassessment and adaptability. By implementing these strategies, investors can make more rational decisions and build resilient portfolios while avoiding the pitfalls of outdated anchors.

Key References:

Kahneman, D., Slovic, P., and Tversky, A. Eds. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge, MA, Cambridge University Press

Ang, Yu Qian, Andrew Chia, and Soroush Saghafian. "Using Machine Learning to Demystify Startups Funding, Post-Money Valuation, and Success." HKS Faculty Research Working Paper Series RWP20-028, August 2020. 

Oldham, S., Murawski, C., Fornito, A., Youssef, G., Yucel, M., and Lorenzetti, V., 2018, The anticipation and outcome phases of reward and loss processing: A neuroimaging meta‐analysis of the monetary incentive delay task. Hum Brain Mapp

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Spinnaker Life Sciences. (2024). How scenario planning empowers FP&A in pharma and biotech. Spinnaker Insights. Retrieved from https://www.spinnakerls.com/post/navigating-uncertainty-how-scenario-planning-empowers-fp-a-in-pharma-and-biotech#viewer-902ah899

Dealroom.co. (2025). Multiples, Dealroom Reports. Retrieved from https://dealroom.co/guides/multiples

Carta. (2025). How Carta uses machine learning to create market-driven compensation benchmarks. Carta Insights. Retrieved from https://carta.com/blog/compensation-bands/

FirstPro Inc. (2024). Implementing blind hiring to reduce unconscious bias. FirstPro Blog. Retrieved from https://www.firstproinc.com/tips-for-employers/leveling-the-playing-field-implementing-blind-hiring-to-reduce-unconscious-bias/

Inceptiv Law. (2023). Ten things you should do before you sign a term sheet. Inceptiv Law Blog. Retrieved from https://inceptiv.law/ten-things-you-should-do-before-you-sign-a-term-sheet/