Loss aversion

Most people feel the pain of losing Rs 5,000 more sharply than the pleasure of gaining Rs 5,000. The workbook's own phrasing is exactly that: it is better not to lose Rs 5,000 than to gain Rs 5,000. That lopsided feeling is loss aversion.

It sounds harmless, even prudent. The trouble is what it does to decisions. An investor who weighs losses more heavily than gains will see high risk where the actual risk is low, and will stay away from opportunities that were perfectly suitable. The workbook says precisely this: such behaviour often leads people to stay away from profitable opportunities, due to the perception of high risks, even when the risk could be very low.

The bias was first identified by the psychologists Daniel Kahneman and Amos Tversky; Kahneman later won the Nobel Prize in Economics.

It is one of several biases the workbook lists — alongside the availability heuristic, confirmation bias, familiarity bias, herd mentality, overconfidence and recency bias — whose common damage is that the investor does not gather enough information to spot opportunities or evaluate risks properly.

Loss aversion works through the willingness leg of risk profiling. The workbook splits risk appetite into the need to take risk, the ability to take it, and the willingness to take it, and willingness is the psychological one. A strongly loss-averse investor can have a high need (the goal requires growth) and a high ability (a long horizon and stable income) and still refuse the product that fits.

It shows up in two typical ways:

The workbook's remedy for all such biases is the same: do as detailed an analysis as possible rather than taking shortcuts, and — to detach emotions from investments — take the opinion of a third person, a Registered Investment Adviser (RIA) or a Mutual Fund Distributor (MFD).

Rakesh, 32, is saving for his daughter's higher education in 15 years. He can invest Rs 15,000 a month. The figures below are illustrative, not workbook returns.

He watched a friend's equity fund fall 20 per cent and decides he cannot bear to see his money go down. So he keeps the whole amount in a recurring deposit.

Total invested in both cases: Rs 27 lakh (Rs 15,000 × 180 months), with returns compounded monthly.

The gap of roughly Rs 24.6 lakh is the price of avoiding a loss that, over a 15-year horizon, was never the main risk. The main risk was falling short of the goal — and loss aversion hid it.

The second form of the bias is just as costly. Suppose Rakesh does start the SIP, and in year 3 his holding of Rs 6 lakh drops to Rs 4.8 lakh. Selling now turns a Rs 1.2 lakh paper decline into a Rs 1.2 lakh real loss, and stops the SIP from buying units cheaply in the fall.

A distributor's job here is not to talk him out of his feelings, but to show him the rupee size of the likely temporary fall before he invests, and to choose a mix — perhaps hybrid rather than pure equity — whose falls he can sit through.

Chapter 1 (Investment Landscape), section 1.6, lists loss aversion among the behavioural biases and credits it to Kahneman and Tversky; section 1.7 then uses willingness to take risk as one of the three legs of risk profiling. Expect a definition question (the Rs 5,000 wording is the workbook's own), a question on who first identified it, and a scenario asking which bias explains an investor avoiding a low-risk opportunity.

• Loss aversion is about feeling losses more than equal gains — not about being unable to afford a loss. Inability to afford a loss is the ability leg of risk profiling.
• It can make low risk look high. The workbook says investors stay away "even when the risk could be very low".
• Identified by Kahneman and Tversky, psychologists; Kahneman won the Nobel Prize in Economics. Do not confuse with the authors of other finance theories.
• Recency bias is different. Recency bias extrapolates the latest event; loss aversion weighs losses more than gains regardless of timing.
• The workbook's fix is a third person — an RIA or MFD — to detach emotion, plus detailed analysis instead of shortcuts.
• The list of biases is "not exhaustive", in the workbook's own words.