Behavioural Finance explains Market Anomalies
This sub‑topic explores how behavioural finance explains the existence of market anomalies that appear to contradict the Efficient Market Hypothesis. Understanding these anomalies helps an investment adviser recognise patterns that may affect client portfolios and exam questions. The content links psychological biases to observable market effects and shows how to evaluate them in the Indian context.
Learning Objectives
- 1Identify the major behavioural biases that generate market anomalies.
- 2Describe the most common anomalies observed in Indian equity markets.
- 3Apply the holding‑period return formula to quantify an anomaly.
- 4Interpret exam‑style questions that test the link between bias and anomaly.
Behavioural Foundations of Market Anomalies
Behavioural finance studies how real‑world investors deviate from the rational‑agent model assumed by classical finance. It highlights systematic psychological tendencies—such as overconfidence, loss aversion, and herd behaviour—that influence buying and selling decisions.
These tendencies create predictable patterns in asset prices, known as market anomalies. An anomaly is a statistically significant deviation from the price behaviour expected under the Efficient Market Hypothesis (EMH). In the Indian market, anomalies are often observed in small‑cap stocks, sectoral rotations, and calendar effects.
For the NISM exam, you must be able to link a specific bias to its resulting anomaly and recognise why regulators like SEBI stress disclosure of such patterns when advising clients.
- Behavioural bias – the root cause.
- Anomaly – the observable market outcome.
Common Market Anomalies in Indian Markets
Several anomalies have been documented in Indian equities and debt markets. The January effect refers to higher average returns in January, especially for small‑cap stocks, due to tax‑loss harvesting and new‑year inflows. The Monday effect shows lower returns on Mondays, often linked to negative sentiment after weekend news.
Momentum is another robust anomaly: stocks that performed well over the past 3‑12 months tend to continue outperforming, while past losers keep lagging. Conversely, the value effect (or value premium) describes higher returns for stocks with low price‑to‑earnings or price‑to‑book ratios, reflecting investors’ tendency to over‑react to recent news and then under‑price fundamentals.
Exam questions frequently ask you to identify which bias explains a given anomaly. For example, the disposition effect—selling winners too early and holding losers—helps explain why investors may create a short‑term reversal pattern after a sharp price move.
Key Market Anomalies, Associated Behavioural Bias, and Typical Pattern in India
| Anomaly | Dominant Behavioural Bias | Typical Pattern |
|---|---|---|
| January Effect | Tax‑loss harvesting & overconfidence | Higher Jan returns, especially in small‑caps |
| Monday Effect | Negative sentiment after weekend | Lower Monday returns across indices |
| Momentum | Representativeness & herd behaviour | Past 3‑12‑month winners keep winning |
| Value Effect | Anchoring & loss aversion | Low P/E or P/B stocks outperform |
| Disposition Effect | Loss aversion & mental accounting | Early sale of winners, holding losers |
How Biases Lead to Anomalies
Overconfidence causes investors to over‑estimate the precision of their information, leading to excessive trading. When many investors act on similar over‑optimistic forecasts, price trends can be amplified, creating the momentum anomaly.
Loss aversion makes investors fear losses more than they value gains. This bias drives the disposition effect, where investors hold onto losing stocks hoping for a rebound while prematurely locking in gains, which can generate short‑term reversal patterns.
Herd behaviour emerges when investors follow the crowd rather than independent analysis. In the Indian market, herd buying during festive seasons often fuels the January effect, while herd selling after negative news can intensify the Monday effect. Recognising these causal links is essential for answering scenario‑based questions in the NISM exam.
Students often think that any observed anomaly disproves the EMH. The correct view is that anomalies highlight market inefficiencies that may be temporary; EMH still provides the baseline framework for evaluating them.
Quantifying Anomaly Returns
Where:
P_{0}= Opening price of the security at the start of the period (₹)P_{1}= Closing price at the end of the period (₹)D= Dividends or distributions received during the period (₹)Worked Example
Given P_{0}=200, P_{1}=230, D=5: Step 1: HPR = (230 - 200 + 5) / 200 Step 2: HPR = 35 / 200 = 0.175 Verification: (230 - 200 + 5) / 200 = 0.175.
To assess the strength of a momentum anomaly, advisers often compute the HPR for a portfolio of past winners versus past losers over a 6‑month horizon. A higher average HPR for the winner basket confirms the anomaly.
When answering NISM questions, remember to include dividends in the numerator; ignoring them under‑states the true return and may lead to an incorrect answer.
Note that statistical significance (p‑value < 0.05) is required to claim an anomaly, but the exam typically tests conceptual understanding rather than detailed regression output.
Average 6‑Month Holding‑Period Returns (2018‑2022)
Practical Scenario – January Effect
Scenario
Rohit, an investment adviser, is preparing a portfolio for a retail client who wishes to invest ₹1,00,000 in January. The client asks whether allocating a larger portion to small‑cap mutual funds is advisable given the historical January effect.
Solution
Step 1: Identify the anomaly – the January effect suggests higher average returns for small‑caps in January, typically around 2‑3% excess over the benchmark. Step 2: Quantify expected excess using HPR: assume a small‑cap fund price rises from ₹100 to ₹103 (no dividend). HPR = (103‑100)/100 = 0.03 or 3%. Step 3: Compare with the benchmark (e.g., Nifty 50) rising from ₹10,000 to ₹10,150, HPR = 1.5%. Step 4: Advise the client that, based on historical patterns, a modest tilt to small‑caps could add ~1.5% extra return for the month, but also note higher volatility and that past performance does not guarantee future results. Step 5: Ensure compliance – disclose the behavioural basis of the recommendation and document the client’s risk tolerance as required by SEBI (Regulation 5 of the Investment Advisers Regulations, 2022).
Conclusion
The adviser can justify a small‑cap tilt using the January effect, but must balance the potential extra return against higher risk and regulatory disclosure requirements.
Students sometimes treat the January effect as a guaranteed profit source. The correct answer emphasises that anomalies can weaken over time as markets adapt.
Implications for Investment Advisers
Advisers should incorporate behavioural insights when constructing portfolios. Recognising that clients may be prone to the disposition effect helps the adviser design exit strategies that mitigate premature selling of winners.
Regulatory guidance from SEBI requires advisers to disclose material behavioural risks, such as herd‑driven market timing, especially when recommending strategies that exploit anomalies.
For the exam, remember that the adviser’s duty of care includes explaining why an anomaly may or may not be reliable, and documenting the client’s understanding in the suitability report.
Testing Anomalies – Empirical Evidence
Academic studies test anomalies using historical price data and statistical tests such as t‑tests or regression analysis. A statistically significant coefficient (p‑value < 0.05) indicates that the anomaly is unlikely to be due to random chance.
In the Indian context, research on the momentum effect (e.g., 12‑month past returns predicting next 3‑month returns) shows an average excess return of about 4% per annum, after accounting for transaction costs.
Exam questions may present a simplified data table and ask you to identify which anomaly is being tested, or to choose the correct interpretation of a p‑value.
⭐Exam Takeaways
- Behavioural biases such as overconfidence, loss aversion, and herd behaviour create predictable market anomalies.
- Common Indian anomalies include the January effect, Monday effect, momentum, value premium, and disposition effect.
- Holding‑Period Return (HPR) = (P1 − P0 + D) ÷ P0 quantifies the return generated by an anomaly.
- Anomalies do not disprove EMH; they indicate temporary inefficiencies that may fade as markets adapt.
- Advisers must disclose behavioural risks, document client suitability, and avoid assuming that past anomalies guarantee future profits.
Practice Questions
9 questions on Behavioural Finance explains Market Anomalies
A market anomaly is best described as:
Which behavioural bias is identified as a dominant cause of the January effect in Indian markets?
What is the formula for Holding‑Period Return (HPR)?
Using the HPR formula, calculate the holding‑period return when P0 = 200, P1 = 230, and D = 5.
The value effect in Indian equities is associated with which characteristic of stocks?
According to the study material, which day of the week typically exhibits lower returns due to negative sentiment after weekend news?
An adviser compares a small‑cap fund that rises from ₹100 to ₹103 (no dividend) with a benchmark that rises from ₹10,000 to ₹10,150. What is the excess holding‑period return of the small‑cap fund over the benchmark?
Which behavioural bias is most directly linked to the creation of the momentum anomaly?
How does the study material describe the relationship between market anomalies and the Efficient Market Hypothesis?