Loss aversion and the sunk cost trap: 7 deadly sins of investing

Loss aversion and the sunk cost trap quietly erode returns and, paradoxically, help explain why momentum investing works.
Marcus Howes

Plato Investment Management

Plato Investment Management's 7 deadly sins of investing series continues this week, with the spotlight turning to loss aversion and the sunk cost trap.

This follows Part 1 & Part 2, written by my colleague, Plato Global Alpha Fund Portfolio Manager Dr David Allen, which you can read here.

Education
Seven deadly sins of investing: Part 1
Education
Seven deadly sins of investing: Part 2 - The hot-hand fallacy

Let's dive into this third deadly sin. 

A thought experiment

Read the scenario and commit to an answer before reading on.

Imagine you have booked two weekend getaways for the same long weekend. One is a trip to Noosa that cost you $2,000. The other is a trip to Byron Bay that cost $1,000. You realise you are going to enjoy Byron more. You cannot get a refund on either booking. 

Which trip do you take?

The rational answer is straightforward: go to Byron. Both bookings are paid for. The money is gone either way. The only question is which weekend you will enjoy more.

And yet, if you are like most people, Noosa is tugging at you. In a near-identical experiment run by psychologists Arkes and Blumer in 1985, 54% of respondents chose the more expensive, less enjoyable option. 

They could not bear the thought of "wasting" the bigger outlay, even though the money was gone regardless.

This is the sunk cost fallacy. A past expense, irrecoverable and economically irrelevant, overriding a clear-eyed assessment of what to do next.

Why losses hurt more than gains feel good

The deeper force at work here is loss aversion

Nobel Prize winners Daniel Kahneman and Amos Tversky demonstrated in their 1979 paper on prospect theory that we do not weigh gains and losses symmetrically. 

Losing $100 feels roughly twice as painful as gaining $100 feels good. This is not a quirk of the unsophisticated. It is hardwired into how our brains evaluate outcomes.

For investors, this asymmetry creates two damaging habits. We hold onto losing positions too long, because selling forces us to confront the pain of a realised loss. And we sell our winners too quickly, locking in the pleasure of a gain before the market can take it away.

Try another quick one. Note your gut reaction.

You are offered a coin flip. Heads, you win $150. Tails, you lose $100. Do you take the bet?

The maths is clearly in your favour, but most people refuse it. The potential pain of losing $100 outweighs the pleasure of gaining $150. That is loss aversion in action. Now imagine that dynamic playing out across an entire portfolio, on every position, every day.

The disposition effect

Behavioural finance researchers call this pattern the disposition effect, and it has been measured extensively. In a 1998 study, Terrance Odean analysed the trading records of 10,000 individual investors at a US brokerage and found that a stock sitting at a gain was roughly 50% more likely to be sold than one sitting at a loss. 

Worse, the winners those investors sold went on to outperform the losers they kept by an average of 3.4% over the following year.

Think about what that looks like in practice. 

Millions of Australians bought Telstra in the T2 float at $7.40. Over the years that followed, the stock drifted to $5, then $4, then $3, and at every step investors told themselves it would “come back.” The money tied up in Telstra was a sunk cost, exactly like the Noosa trip, but they could not bring themselves to crystallise the loss. 

More than two decades later, Telstra still trades below that T2 price. The rational question was never “will it get back to $7.40?” It was “given what I know today, is this the best use of my capital?”

Meanwhile, the same investor might have bought Afterpay at $10 and sold at $20, thrilled to have doubled their money. Afterpay went on to reach $160 before being acquired by Block. 

The gain felt good. The early sale felt prudent. But it was the disposition effect at work: grabbing the certainty of a win rather than letting a strong thesis play out.

Investors are not just making emotionally driven decisions. They are making expensive ones. The pattern has been replicated across markets globally, among both retail and professional investors. It is one of the most robust findings in all of behavioural finance.

Why this matters for momentum

Here is where it gets interesting. The disposition effect is not just a curiosity of individual behaviour. It is one of the leading explanations for why momentum works as an investment strategy.

The logic is intuitive. 

When good news arrives and a stock rises, many investors sell too early to lock in their gains. That selling pressure slows the stock's ascent toward fair value. The result is that the stock continues to drift upward over subsequent months as the information is gradually reflected in the price. 

The reverse applies to bad news: investors hold their losers, propping up the price, and the decline plays out more slowly than it should.

This creates a predictable pattern. Stocks that have been rising tend to keep rising, and stocks that have been falling tend to keep falling, in part because human loss aversion delays the adjustment to new information. 

Momentum strategies, which systematically buy recent winners and sell recent losers, profit directly from this behavioural drag.

Source: Barra Momentum

Source: Barra Momentum

The chart above shows the cumulative performance of a global momentum factor over three decades. 

The premium has not been smooth, and it has gone through painful drawdowns, but the long-run pattern is clear: the collective tendency of investors to sell winners too early and hold losers too long has created a persistent, exploitable return premium.

Know when to fold ‘em

Kenny Rogers had it right. The problem is that our biology makes it nearly impossible to do consistently. We anchor to sunk costs. We defer losses. We grab gains too early. And we do it even when we know better.

Awareness is a start, but it is not a solution. Even professional fund managers still exhibit the disposition effect. We are not immune. But one of the more effective defences is to take the human out of the most emotionally charged part of the process: the decision of when to sell. Rules-based frameworks and systematic disciplines do not feel the sting of a loss. They evaluate each position on its forward-looking merits, without sentiment.

That does not make the decision painless. It just ensures the pain does not get a vote.

Invest with Plato Investment Management

This article is part of series of investor letters sent to Plato Investment Management clients. 

Plato manages over $23 billion on behalf on institutions, financial advisers, SMSFs, and wholesale and retail investors. Its flagship strategies include the Plato Global Alpha Fund Complex ETF (ASX: PGA1), the Plato Australian Shares Income Fund, and the Plato Global Shares Income Fund

Click here to learn about opportunities to invest with Plato

........
This communication is prepared by Plato Investment Management Limited (‘Plato’) (ABN 77 120 730 136, AFSL 504616). Pinnacle Fund Services Limited (‘PFSL’) (ABN 29 082 494 362, AFSL 238371) is the product issuer of Plato Funds. PFSL is not licensed to provide financial product advice. PFSL is a wholly-owned subsidiary of the Pinnacle Investment Management Group Limited (‘Pinnacle’) (ABN 22 100 325 184). The Product Disclosure Statement (‘PDS’) and Target Market Determination (‘TMD’) of the Fund are available via the links below. Any potential investor should consider the PDS and TMD before deciding whether to acquire, or continue to hold units in, the Fund. Link to the Product Disclosure Statement Link to the Target Market Determination For historic TMD’s please contact Pinnacle client service Phone 1300 010 311 or Email [email protected] This communication is for general information only. It is not intended as a securities recommendation or statement of opinion intended to influence a person or persons in making a decision in relation to investment. It has been prepared without taking account of any person’s objectives, financial situation or needs. Any persons relying on this information should obtain professional advice before doing so. Past performance is for illustrative purposes only and is not indicative of future performance. Whilst Plato, PFSL and Pinnacle believe the information contained in this communication is reliable, no warranty is given as to its accuracy, reliability or completeness and persons relying on this information do so at their own risk. Subject to any liability which cannot be excluded under the relevant laws, Plato, PFSL and Pinnacle disclaim all liability to any person relying on the information contained in this communication in respect of any loss or damage (including consequential loss or damage), however caused, which may be suffered or arise directly or indirectly in respect of such information. This disclaimer extends to any entity that may distribute this communication. Any opinions and forecasts reflect the judgment and assumptions of Plato and its representatives on the basis of information available as at the date of publication and may later change without notice. Any projections contained in this presentation are estimates only and may not be realised in the future. Unauthorised use, copying, distribution, replication, posting, transmitting, publication, display, or reproduction in whole or in part of the information contained in this communication is prohibited without obtaining prior written permission from Plato. Pinnacle and its associates may have interests in financial products and may receive fees from companies referred to during this communication.

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Marcus Howes
Quantitative Research Analyst
Plato Investment Management

Marcus joined Plato in early 2023. With a practical understanding of machine learning and neural networks, Marcus specialises in leveraging Natural Language Processing (NLP) techniques and harnessing the capabilities of Large Language Models...

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