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Read more22 January 2018
In the epic, the Mahabharata, on the final day of the lessons from his guru, Parashurama, Karna bravely bore a scorpionâs sting while Parashurama was resting his head upon his lap. As the blood from Karnaâs thigh trickled down and disturbed the sleeping Parashurama, he realized that only a Kshatriya (a warrior) could bear such pain without a murmur and that he had been tricked by Karna into believing he was a Brahmana (of the priestly class). Karna explained that he was actually a Suta (a mixture of Brahmana and Kshatriya) in an attempt to placate his irate teacher. Furious at being deceived, Parashurama cursed Karna that he would be unable to use weapons, especially the Brahmastra, when he needed them the most. This curse came into effect when Karna was unable to remember the incantation to invoke the Brahmastra while fighting against Arjun on the battlefield.
I often wonder if investors suffer from a similar curse that Parashurama gave to Karna. One can read all the books on behavioural finance and listen to advice from the legends of investing but what really matters is how one applies this knowledge at the most crucial times. While many have the intelligence to understand behavioural biases, few have the wisdom to apply them in real-life scenarios.
Here are some scenarios where I have observed that it is very difficult to stay unbiased and think rationally
1. When a high quality Company goes through a company specific crisis.
In my investing career, I have seen many high quality companies going through a crisis. Be it the Maruti Manesar crisis, the MCX NSEL crisis, the Nestle Maggi crisis, the Diviâs USFDA crisis, etc.
2. When a high quality Company faces sector-specific headwinds.
There are also many themes like Pharmaceutical, Information Technology, micro-finance which I have seen go through periods of extremely positive and extremely negative investor sentiment.
3. A high quality Company with a large addressable market going through a period of overvaluation.
There are times when a good Company with a large addressable market trades at absurd valuations. For example, Infosys at its 2000 peak had a market cap of INR 92,000 crores and its FY2000 PAT was INR 300 crores.
4. The market turns agnostic to quality and determines cheapness just based on P/E ratios
There are times in the bull market when Stock A is considered cheaper than Stock B just because it trades at a lower P/E and no attention is paid towards comparing the balance sheet, cash-flow generations or return ratios of the two companies.
While the lessons from behavioural finance are generally useful, it is in situations like the ones mentioned above that they become absolutely vital. And, it is precisely at these moments that they are most difficult to follow. Why is that so?
In the excellent book âHalo effectâ by Phil Rosenzweig, the author states âthere will always be a temptation to tell a neat story that makes everything sensible and logical and that suggests why the deserving succeeded while the wicked or the arrogant failedâ. And this tendency of humans to justify to themselves the stock price performance (positive or negative) is what leads to their inability to think rationally during such times. Neat stories are published in broker reports, newspapers, and business magazines glorifying the companies whose stock price has shot up in the last few months and demonising those companies whose stock price has not performed. Most people think of stock price as a result of these stories but my observation is that these stories are actually a result of the stock price. Like Warren Buffett commented in the late 1960s âWe live in an investment world, populated not by those who must be logically persuaded to believe, but by the hopeful, credulous and greedy, grasping for an excuse to believe.â
There is also an angle of human biology to such a behaviour. In the excellent book âThe Hour between Dog and Wolfâ by Dr. John Coates, the author explains how human biology contributes to the alternating cycles of irrational exuberance and pessimism. Given below is an excerpt from the book:-
âMost models in economics and finance assume that risk preferences are a stable trait, much like your height. But this assumption, as our studies suggest, is misleading. Humans are designed with shifting risk preferences. They are an integral part of our response to stress, or challenge.
When opportunities abound (bull markets), a potent cocktail of dopamine â a neurotransmitter operating along the pleasure pathways of the brain â and testosterone encourages us to expand our risk taking, a physical transformation I refer to as âthe hour between dog and wolf.â
Under conditions of extreme volatility, such as a crisis, traders, investors and indeed whole companies can freeze up in risk aversion (through the release of the stress hormone â cortisol), and this helps push a bear market into a crash. Unfortunately, this risk aversion occurs at just the wrong time, for these crises are precisely when markets offer the most attractive opportunities, and when the economy most needs people to take risksâ.
Thus, one needs to understand that in such extreme times, the market participants are actually under the influence of a naturally produced narcotic that transforms them into different people. And thus, while itâs easy to explain things in hindsight after the dust has settled, itâs extremely difficult to stay brutally rational and unbiased in a live volatile situation.
Just as Karna could not remember the mantras he needed to invoke his weapon, perhaps so too do investors forget to invoke the teachings of their financial gurus and remain steadfast to their original investment plans when the need arises the most. Fortunately for them, lifting âthe curse of Karnaâ remains in their power.
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Sr. No. |
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Pending at the end of last month |
Received |
Resolved* |
Total Pending # |
Pending complaints >Â 3 months |
Average Resolution time^ (in days) |
1 |
Directly from Investors |
0 |
0 |
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SEBI (SCORES) |
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0 |
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Other Sources (if any) |
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0 |
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* Inclusive of complaints of previous months resolved in the current month.
# Inclusive of complaints pending as on the last day of the month
^ Average Resolution time is the sum total of time taken to resolve each complaint in days, in the current month divided by total number of complaints resolved in the current month.
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Sr. No. |
Month |
Carried forward from previous month |
Received |
Resolved* |
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1 |
April, 2024 |
0 |
0 |
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2 |
May, 2024 |
0 |
0 |
0 |
0 |
3 |
June, 2024 |
0 |
0 |
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4 |
July, 2024 |
0 |
0 |
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5 |
August, 2024 |
0 |
0 |
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0 |
0 |
0 |
0 |
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SN |
Year |
Carried forward from previous year |
Received |
Resolved* |
Pending# |
1 |
2020-21 |
0 |
0 |
0 |
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2 |
2021-22 |
0 |
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0 |
0 |
3 |
2022-23 |
0 |
0 |
0 |
0 |
4 |
2023-24 |
0 |
0 |
0 |
0 |
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Grand Total |
0 |
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0 |
0 |
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