Author Name : Nishchaydeep Singh Kharbanda
Economics has a strangely convenient starting point: the rational individual. Give a person enough information, let them compare the costs and benefits of their choices, and, theoretically, they should choose whatever maximizes their expected utility. The model is elegant. The problem is that humans are not.Investors panic. They become greedy. They follow crowds. They become attached to decisions they have already made. They refuse to accept losses, become overconfident after a few successful predictions, and sometimes buy an asset simply because everyone else seems to be buying it. If we assume that investors are rational, these behaviours look like exceptions. If we accept that investors are human, they start looking much more predictable.That is what makes behavioral economics interesting to me. It does not necessarily reject traditional economics. Instead, it asks whether our models of economic decision-making are incomplete if we ignore the psychology of the person making the decision.One of the clearest examples is loss aversion, which forms an important part of Daniel Kahneman and Amos Tversky’s prospect theory. The basic idea is simple: people tend to
experience losses more intensely than equivalent gains. Losing ₹10,000 does not psychologically
feel like the opposite of gaining ₹10,000. The loss usually carries greater emotional weight. This can completely change how an investor behaves.Suppose I purchase a stock at ₹500. A few months later, it falls to ₹300. The economically relevant question should be: Given everything I know now, would I buy this stock at ₹300? But that is often not what happens.Instead, I might think, “I’ll hold it until it comes back to ₹500.”The problem is obvious once you step outside the investor’s head. Why does ₹500 matter? The company does not know that I bought its shares for ₹500. Its future value depends on expected earnings, cash flows, competitive conditions, management, interest rates, industry trends and a range of other variables. Yet I have made my original purchase price psychologically significant.
This is anchoring. An investor takes an initial reference point and gives it far more importance than it deserves. The anchor could be the purchase price, a previous market high, an analyst’s price target, or even a number mentioned repeatedly in the media. Once established, the anchor can influence decisions
long after it has stopped being economically relevant What makes this particularly dangerous is that the investor may not feel irrational. They may call their decision “patience.” But sometimes patience is simply a more comfortable word for refusing to admit that the original decision was wrong. Then comes overconfidence. A successful investment can be dangerous for exactly the same reason a failed investment can be painful: we attach a story to the outcome. Suppose an investor correctly predicts that a stock will rise. They may conclude that their analysis was exceptional. They then take larger positions,
trade more frequently, or begin believing they can consistently outperform the market.But there is a question investors should probably ask themselves more often: Was the decision good, or was the outcome good? Those are not the same thing.
Financial markets operate under uncertainty and incomplete information. A well-researched
decision can lose money because of an unexpected event. A terrible decision can make money
because of luck. If we judge our ability only by outcomes, we can easily confuse luck with skill.
Over time, that confusion can create excessive risk-taking.
This is where the concept of calibration becomes important. A well-calibrated investor should
understand the difference between confidence and certainty. Saying “I think there is a 70%
probability this investment will succeed” is fundamentally different from behaving as though
success is guaranteed.
Then there is herd behaviour, one of the most visible forms of behavioural bias in financial
markets.
Imagine a stock starts rising rapidly. You see people buying it. Your friends start talking about it.
Financial media starts covering it. You begin wondering whether everyone else knows
something that you don’t.
So you buy.
Your purchase contributes to the rise. More people see the rising price and become convinced
that the asset is valuable. They buy too. Eventually, the price itself becomes evidence for the
decision that caused the price to rise.
That is a feedback loop.
The important point is that the individual investors involved do not necessarily have to be
unintelligent. Their decisions can make sense given their limited information. If everyone around
you appears to possess information that you don’t, following them can seem safer than standing
alone. The irrationality emerges from the interaction of individually understandable decisions.
This is one reason bubbles are so difficult to identify while they are happening. The same
process can operate in reverse during a crash. Fear creates selling, selling pushes prices lower,
falling prices create more fear, and suddenly investors are not asking what an asset is
fundamentally worth. They are asking how quickly they can get out.
Another fascinating bias is the disposition effect, where investors tend to sell winning
investments relatively quickly while holding losing investments for longer.
Again, the psychology makes sense.
Selling a profitable investment gives you confirmation: I was right.
Selling a losing investment forces you to confront another possibility: I was wrong.
So an investor might continue holding a declining stock because they believe it will recover.
Perhaps it will. But the important question is whether they would purchase that same stock today
at its current price. If the answer is no, then holding it may have less to do with expected returns
and more to do with loss realization and regret aversion.
This is where behavioral economics becomes particularly powerful. It shows that economic
decisions are influenced not only by prices and probabilities, but also by reference dependence,
bounded rationality, emotions, social influence and cognitive biases.
And humans are not irrational in completely random ways.
That is the fascinating part.
Our mistakes are often systematic.
We anchor.
We overestimate ourselves.
We imitate crowds.
We avoid realizing losses.
We seek information that confirms what we already believe.
We give disproportionate importance to recent experiences.
These patterns can be studied, measured and, to some extent, incorporated into economic models .
This also explains why behavioral economics matters outside financial markets. The same
mechanisms influence how people borrow, save, spend, negotiate and respond to economic
policies. A consumer might buy something because it is advertised as “50% off,” even though
the original price was artificially inflated. Someone might continue funding a failing project
because they have already invested years into it. Another person might take an unnecessary
financial risk because everyone around them appears to be making money.
These are economic decisions, but they are also psychological decisions.
And that, ultimately, is why I find behavioral economics so compelling. Traditional economic
models can tell us what a rational individual should do under certain assumptions. Behavioral
economics forces us to confront what people actually do.
It also changes how I think about the word “rational.” Rationality does not necessarily mean
being emotionless. A person can have emotions and still make rational decisions. The real issue
is whether those emotions systematically distort the way they evaluate costs, benefits,
probabilities and future outcomes.
The solution, therefore, is not to pretend that humans can become perfectly rational.
It is to design systems that account for the fact that we aren’t.
An investor who knows they are vulnerable to loss aversion can establish predetermined rules for
exiting an investment. Someone aware of their overconfidence can diversify rather than
concentrating their portfolio around their own predictions. An investor worried about herd
behaviour can separate their research process from market noise and social pressure.
Behavioral economics, in that sense, is not simply about explaining why people make mistakes.
It is about understanding the conditions under which those mistakes become likely enough to
matter.
Markets are not machines.
They are collections of human decisions interacting with one another under uncertainty.
And perhaps that is why investing is so difficult. You are not simply trying to predict the market,
the economy, or the next movement in a stock.
At some point, you also have to predict yourself.
And there may be no market in which information is more incomplete, incentives are more
complicated, and the decision-maker is more difficult to understand.
