Behavioural Biases in Investing
After Portfolio Allocation Examples for Indian Investors, the next question is why investors often fail to follow disciplined allocation and rebalancing rules. Kahneman & Tversky's Prospect Theory showed that investors are not rational maximisers. Understanding these biases helps you avoid them - and helps you understand market inefficiencies. In interviews, this matters because examples like LIC, Paytm, Yes Bank, and DHFL let you move from textbook definitions to practical investing traps.
- Kahneman & Tversky's Prospect Theory showed that investors are not rational maximisers.
- Anchoring means over-weight first information seen, such as buying 'at 52-week low' without fundamental basis.
- Herding means follow the crowd, visible in LIC IPO: 2.9x subscribed despite overvaluation and the Paytm rush.
- Loss aversion means losses hurt 2x more than gains, such as holding Yes Bank or DHFL long after red flags.
- Recency bias means extrapolate recent trend indefinitely, such as buying at market peak in Jan 2008 or Jan 2022 expecting trend to continue.
- Overconfidence shows up when retail traders do F&O - 90% lose money in the SEBI study.
- Confirmation bias means seek info that confirms view, such as only reading bull thesis on a stock.
Big Picture Overview
Behavioural biases are systematic and predictable investing traps. They affect how investors process prices, crowds, losses, recent trends, their own ability, and confirming information.
Behavioural Biases That Affect Investing Decisions
The practical use of behavioural finance is not just to name a bias, but to identify the decision error and build a rule to avoid it. The table below connects each bias with its definition, an Indian market example, and a practical avoidance method.
Why Behavioural Biases Create Market Inefficiencies
Understanding these biases helps you avoid them - and helps you understand market inefficiencies. When investors anchor to a price, follow a crowd, refuse to book losses, or extrapolate a recent trend indefinitely, market prices can move away from fundamentals.
In India-focused investing conversations, the strongest answers connect the bias to a real decision rule. For example, use DCF intrinsic value, not price anchors; build independent thesis before checking market consensus; set stop-loss at purchase; rebalance rules-based; check 10-year CAGR; compare to fundamentals; track all trades; compare vs benchmark; and actively seek bear case; build variant view.
When asked 'Tell me about a behavioral bias that affects markets,' use the Paytm IPO example: 'Herding and anchoring combined - investors anchored to the ₹2,150 IPO price and herded in despite negative unit economics. Post-listing, the stock fell to ₹450. A rational DCF at IPO using realistic LTV:CAC would have flagged overvaluation.' This shows analytical depth.
Structuring a Behavioural Biases in Investing Interview Answer
"Tell me about a behavioral bias that affects markets."
The strongest answer does not stop at naming a bias. Link the bias to an Indian market example, the investor mistake, and the rule that would have avoided it.
The most frequent error is treating behavioural biases as definitions to memorise, not practical investing traps. That costs points because it misses the interview angle: explain how the bias affected markets, then show how DCF intrinsic value, independent thesis, stop-loss, rules-based rebalancing, benchmarks, or a bear case would have avoided the mistake.
Conclusion
Behavioural biases in investing explain why investors are not rational maximisers and why markets can become inefficient. Use Indian examples like LIC, Paytm, Yes Bank, and DHFL to show the trap, the decision error, and the rule that helps avoid it.