Contents
Definition
Anchoring bias is the cognitive tendency to rely disproportionately on an initial piece of information — the "anchor" — when making subsequent judgments, even when the anchor is irrelevant or arbitrary. The definitive empirical demonstration is Tversky and Kahneman's 1974 paper Judgment under Uncertainty: Heuristics and Biases (Science, 185(4157), 1124–1131), which showed that subjects spun a wheel of fortune numbered 0–100 and then estimated African nations in the UN: groups who spun high numbers gave higher estimates than groups who spun low numbers. In markets, the anchor is typically: the purchase price of a security, a 52-week high or low, a round-number index level, or an IPO issue price. Anchoring causes insufficient adjustment — investors update their views, but not enough relative to the magnitude of new information. This produces price stickiness around psychologically salient reference points and explains, in part, why stocks often pause at 52-week highs.
How it manifests in Indian retail investing
The single most common Indian expression of anchoring is "I'll sell when it comes back to my buy price." An investor who purchased a stock at ₹500 and watches it fall to ₹300 anchors on the ₹500 cost price as a target, often holding through further deterioration. This combines anchoring with loss aversion. The second manifestation is round-number anchoring: the Nifty 50 at 20,000, 21,000, 22,000 — each level generates disproportionate media attention and retail order clustering. Options strike price selection in India also exhibits anchoring: SEBI's 2024 F&O study found that ATM (at-the-money) options represent 58% of retail options purchases, with clustering at round strikes (e.g., Nifty 21,500 vs. 21,450), consistent with anchoring on round numbers. IPO grey-market premiums anchor investor expectations: a ₹50 GMP on a ₹300 issue creates a ₹350 anchor that causes panic selling when listing is at ₹320.
What the data shows
Campbell and Sharpe (2009, "Anchoring Bias in Consensus Forecasts and Its Effect on Market Prices," Journal of Financial and Quantitative Analysis) found that professional economic forecasters exhibit significant anchoring: CPI forecast revisions are systematically smaller than the actual data change warrants, biased toward the prior forecast. In Indian equity data, Bombay Stock Exchange records show that stocks trade in notably higher volume within 2% of their 52-week high — a round-number anchor — than at random price levels, controlling for volatility. SEBI's 2024 retail study found that 67% of loss-making retail F&O traders report their primary metric for position exit decisions as the entry price rather than the underlying fundamental or technical thesis, consistent with cost-price anchoring.
Worked example
In January 2023, an investor buys 100 shares of a mid-cap IT company at ₹1,200 per share (total: ₹1,20,000). Over 6 months the stock falls to ₹780 due to US tech-sector order cuts that materially impair earnings. The investor's anchor is ₹1,200. They decline to review the position at ₹780 because the thesis feels "temporary" relative to the anchor. By December 2023, three analyst downgrades occur and the stock is at ₹520. The investor still holds, anchored on ₹1,200, now down 57%. An anchoring-aware review process would have asked: "Given current information, would I buy this at ₹780?" (or ₹520) — separating the current decision from the sunk-cost anchor. This is the "pre-mortem" reframing used in institutional investment committees.
How to recognise it in yourself
Anchoring diagnostic: (1) Is the primary reference point for evaluating a security its purchase price rather than its current fundamental value? (2) Does a "good price" feel defined by a past high or low rather than a DCF or peer comparison? (3) Are sell decisions triggered by round numbers (₹500, ₹1,000, ₹10,000 index levels) rather than a pre-specified valuation target? (4) When new information materially changes the fundamental outlook, does the target price update proportionally or remain close to the original estimate? Research shows anchoring is particularly strong for the first price encountered — the IPO price, the price at which a tip was received, or the screen price at first lookup.
See also
Primary sources
- Tversky, A. & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124–1131.
- Campbell, S.D. & Sharpe, S.A. (2009). Anchoring Bias in Consensus Forecasts. Journal of Financial and Quantitative Analysis, 44(2), 369–402.
- SEBI Circular SEBI/HO/MRD/MRD-PoD-1/P/CIR/2024/12.
MintByte (ARN-314872 / APMI APRN-01658) is a SEBI-registered MFD and GIFT City wealth management firm. This glossary entry is educational and does not constitute investment advice.