Behavioural Finance explains Bubbles and Crashes
Behavioural finance explains why asset prices sometimes deviate far from fundamentals, creating bubbles and subsequent crashes. The sub‑topic links psychological biases to market dynamics and is a high‑weight area in the NISM Series X‑B exam. Understanding these concepts helps you answer scenario‑based questions on price anomalies and SEBI’s regulatory stance. This content ties the theory to Indian market examples such as the 2020‑21 equity rally and the 2022 crypto correction.
Learning Objectives
- 1Identify key behavioural biases that drive bubble formation.
- 2Explain the feedback loop between sentiment, price, and fundamentals.
- 3Describe the typical crash pattern and its impact on investors.
- 4Apply the CAPM formula to differentiate rational pricing from a bubble.
Behavioural Biases that Fuel Bubbles
Overconfidence leads investors to over‑estimate their ability to pick winners, causing them to ignore valuation metrics and keep buying rising assets. In the Indian context, retail investors often chase high‑growth stocks without assessing price‑to‑earnings multiples, which inflates demand.
Herding occurs when investors mimic the trades of a perceived majority, believing the crowd must be right. Social media platforms and WhatsApp groups amplify herding, especially in small‑cap segments where information asymmetry is high.
Anchoring makes investors fixate on a recent price level (e.g., the all‑time high of a stock) and treat it as a reference point, disregarding underlying earnings. This bias sustains upward pressure even when fundamentals turn weak.
- Availability heuristic – recent news of big gains makes similar opportunities seem more likely, pushing prices up.
- Confirmation bias – investors seek data that supports their bullish view, filtering out negative signals.
Students often mark both herding and overconfidence as the same bias. Remember: herding is about following the crowd, while overconfidence is about self‑belief in superior skill. The exam may ask you to pick the bias that best explains a sudden surge in buying volume.
Feedback Loops and Market Sentiment
Positive sentiment creates a self‑reinforcing loop: rising prices attract more buyers, which pushes prices even higher. This is often called a "price‑momentum feedback" and is amplified by algorithmic trading that follows short‑term trends.
When prices exceed intrinsic value, rational investors may still stay invested because they expect to sell at a higher price later – a phenomenon known as the "greater fool theory." In India, the 2020‑21 equity rally saw many investors buying on the belief that a "greater fool" would appear.
Exam relevance: questions may present a price chart and ask which feedback mechanism is at work. Look for language such as "buy‑more‑because‑price‑rises" to identify momentum‑driven bubbles.
Key Behavioural Biases and Their Impact on Asset Prices
| Bias | Description | Typical Effect on Prices |
|---|---|---|
| Overconfidence | Excessive belief in personal forecasting ability | Prices rise sharply as investors ignore valuation |
| Herding | Mimicking the majority’s trades | Rapid price escalation due to collective buying |
| Anchoring | Fixating on a past price level | Sustained high prices despite deteriorating fundamentals |
| Availability Heuristic | Relying on recent vivid information | Spikes in demand after prominent news |
Bubble Formation Process
The bubble lifecycle typically follows four stages: (1) displacement, where a new technology or policy shift captures attention; (2) boom, driven by optimism and the biases described earlier; (3) euphoria, where prices detach completely from fundamentals; and (4) distress, when reality sets in and prices collapse.
During the displacement stage, SEBI may issue advisory notes, but the regulatory impact is limited because behavioural forces dominate. In the boom stage, leverage – both margin trading and debt‑financed investments – magnifies price moves, making the bubble larger than the underlying sentiment alone would produce.
For the exam, remember the four‑stage model and be ready to map a scenario to the correct stage. A common mistake is to label the "euphoria" stage as a "crash" – they are distinct phases.
Illustrative Bubble: Actual Price vs. Fundamental Value (2020‑2023)
Crash Dynamics
A crash is the rapid correction of inflated prices, often triggered by a negative shock such as earnings disappointment, regulatory action, or a liquidity squeeze. The speed of the decline is amplified by forced selling from leveraged positions and margin calls.
Behaviourally, panic selling is driven by loss aversion – the pain of a loss outweighs the pleasure of a gain. This leads investors to exit positions at any price, creating a steep downward spiral.
Exam tip: when a question mentions "margin calls" or "liquidity crunch," the correct answer will involve panic‑driven selling rather than a rational re‑pricing based on fundamentals.
Do not select "rational re‑pricing" for crash scenarios that mention panic, margin calls, or loss aversion. These are behavioural triggers, not pure market efficiency adjustments.
Where:
E(R_i)= Expected return of the asset i (in % per annum)R_f= Risk‑free rate (e.g., 10‑year Indian government bond yield)\beta_i= Beta of asset i relative to the marketE(R_m)= Expected market return (in % per annum)Worked Example
Given R_f = 6%, \beta_i = 1.2, and E(R_m) = 12%: Step 1: Risk premium = E(R_m) - R_f = 12% - 6% = 6% Step 2: \beta_i \times risk premium = 1.2 \times 6% = 7.2% Step 3: E(R_i) = R_f + 7.2% = 6% + 7.2% = 13.2% Verification: 6% + 1.2 \times (12% - 6%) = 13.2%.
Scenario
Rohan, a retail investor, notices that XYZ Technologies rose from ₹500 to ₹1,800 in 18 months. The company's earnings per share (EPS) grew only from ₹15 to ₹20 in the same period. Rohan wants to know if the price movement reflects a bubble.
Solution
Step 1: Compute the price‑to‑earnings (P/E) ratio at the start: 500/15 = 33.3. Step 2: Compute the P/E ratio at the end: 1800/20 = 90. Step 3: The P/E has nearly tripled while earnings grew only 33%, indicating a large valuation gap. Step 4: Apply CAPM to find the rational expected return. Assuming R_f = 6%, \beta = 1.1, and market return = 12%, the expected return is 6% + 1.1\times6% = 12.6%. Step 5: Compare the implied return from price growth (approximately 150% over 1.5 years ≈ 84% p.a.) with the CAPM return; the excess suggests a bubble driven by behavioural bias. Step 6: Rohan should be cautious and consider de‑risking his position.
Conclusion
The mismatch between the soaring P/E and modest earnings growth, together with an implied return far above CAPM expectations, signals a classic bubble scenario that the NISM exam frequently tests.
Regulatory Perspective – SEBI’s Role
SEBI monitors market conduct to curb excessive speculation that can lead to bubbles. It issues circulars on "excessive margin utilisation" and "unusual price volatility" to alert participants.
When a bubble is identified, SEBI may impose higher margin requirements, tighten short‑selling rules, or launch awareness campaigns. The regulator’s focus is on protecting retail investors from herd‑driven panic.
For the exam, remember that SEBI’s interventions are preventive (e.g., advisory notices) rather than corrective after a crash. Questions may ask which regulatory tool is appropriate for a bubble scenario – the answer is usually a "circuit breaker" or "margin tightening" recommendation.
Exam Tips and Memory Aids
Mnemonic for the four stages of a bubble: Displacement, Boom, Euphoria, Distress – remember "DBED" as "Debate" to recall the order.
When a question mentions "greater fool" or "price‑momentum feedback," instantly link it to herding and overconfidence biases. This shortcut saves time in scenario‑based items.
Always cross‑check any numerical answer with the CAPM formula if the question involves expected return versus observed price rise. A common mistake is to use simple average return instead of the risk‑adjusted CAPM return.
⭐Exam Takeaways
- Behavioural biases such as overconfidence, herding, anchoring and availability drive price deviations that create bubbles.
- A bubble follows the stages of displacement, boom, euphoria and distress; leverage amplifies each stage.
- Feedback loops arise when rising prices attract more buyers, reinforcing momentum and detaching prices from fundamentals.
- Crashes are triggered by panic selling, loss aversion, and forced liquidation, not by rational re‑pricing.
- CAPM provides a benchmark for rational expected return; large gaps between CAPM return and observed price growth indicate a bubble.
- SEBI intervenes with margin tightening, circuit breakers and investor awareness to mitigate bubble risks.
- Use the "DBED" mnemonic to recall bubble stages and watch for keywords like "greater fool" to spot behavioural triggers.
Practice Questions
8 questions on Behavioural Finance explains Bubbles and Crashes
Which behavioural bias is described as investors fixing on a recent price level and treating it as a reference point?
What does the mnemonic "DBED" help candidates recall in the context of bubbles?
Which bias is primarily about following the crowd rather than believing in one's own forecasting skill?
In a price‑momentum feedback loop, which statement best captures the mechanism?
SEBI wants to curb an emerging bubble. Which regulatory measure is most directly mentioned as a preventive tool for such a scenario?
A stock’s price rises from ₹500 to ₹1,800 in 18 months while EPS grows only from ₹15 to ₹20. Which behavioural bias most plausibly explains this price surge?
Using the CAPM (R_f=6%, β=1.1, E(R_m)=12%), the expected return is 12.6%. The observed price increase implies an 84% annualised return. Does this suggest a bubble?
Which behavioural trigger is identified as the primary driver of a market crash, rather than a rational re‑pricing?