Investor Behaviour Mistakes: What the Research Actually Shows

The most common investing mistakes are not about picking the wrong stock. They are about behaviour: trading too much, selling winners too early and holding losers too long, piling into whatever has just gone up, and selling in a panic after a fall. Peer-reviewed research has measured several of these costs directly, and they are large enough to matter.

In short: behavioural finance studies the predictable ways real people depart from the rational investor of textbook models. The best-documented mistakes are overtrading (driven by overconfidence), the disposition effect (selling winners and keeping losers), loss aversion (losses hurt more than equal gains feel good), and chasing past performance. The size of the resulting “behaviour gap” is real but often exaggerated in marketing, so the numbers deserve a careful read.

Loss aversion: why losses feel bigger

In 1979, psychologists Daniel Kahneman and Amos Tversky published prospect theory in Econometrica. One of its central ideas is loss aversion: people weigh losses more heavily than gains of the same size. In a 1992 follow-up, Tversky and Kahneman estimated that, in their experiments, losses were weighted a bit more than twice as heavily as equivalent gains.

For investors, loss aversion shows up as:

Refusing to sell a losing position because selling makes the loss “real.”
Checking a portfolio often and feeling every dip, which makes holding stocks feel riskier than it is over long horizons.
Selling everything after a fall to stop the pain, then missing the recovery.

Later research has debated how universal and how large loss aversion is, and the exact ratio varies a lot between studies and settings. The direction of the effect is well established; a precise “2 to 1” rule is not.

The disposition effect: selling winners, keeping losers

In a 1998 paper in the Journal of Finance, “Are Investors Reluctant to Realize Their Losses?”, Terrance Odean studied 10,000 accounts at a US discount broker from 1987 to 1993. He measured how often investors sold a stock that was up versus one that was down, relative to the opportunities they had.

Investors realised 14.8% of their available gains.
They realised only 9.8% of their available losses.

So they were about 50% more likely to sell a winner than a loser. That is the disposition effect.

It was also costly. Odean found that the winners investors sold went on to outperform the losers they kept by about 3.4 percentage points over the following year. The behaviour is backwards for taxes too: in most cases, realising losses and deferring gains is what reduces your tax bill. Interestingly, the pattern weakened in December, when tax-loss selling gave people a reason to sell losers. Tax-loss selling has its own traps, covered in wash sale and superficial loss rules.

Overtrading and overconfidence

In “Trading Is Hazardous to Your Wealth” (Journal of Finance, 2000), Brad Barber and Terrance Odean examined 66,465 households with accounts at a large US discount broker from 1991 to 1996.

Group	Annual return, net of costs
Market	17.9%
Average household	16.4%
Households that traded the most (top 20% by turnover)	11.4%

The most active traders underperformed the market by about 6.5 percentage points a year. The paper attributes much of that shortfall to the costs of trading. The authors argued that overconfidence explains why people trade so much despite the cost.

A follow-up, “Boys Will Be Boys” (Quarterly Journal of Economics, 2001), used the same type of data for more than 35,000 households and found that men traded about 45% more than women, and that trading reduced men’s net returns more than women’s. The authors linked this to research showing men tend to be more overconfident in areas like finance.

A worked example: what turnover costs

Imagine a $50,000 portfolio and two investors. One buys a broad index fund and holds it. The other turns over 200% of the portfolio a year, meaning $100,000 of buys and $100,000 of sells.

Even with zero commissions, every round trip crosses the bid-ask spread. If the average half-spread plus price impact on each trade is 0.15% (hypothetical but plausible for mid-sized stocks), the cost is:

$200,000 of trades × 0.15% = $300 a year, or 0.6% of the portfolio.

Over 25 years at a 6% gross return, 0.6% a year is the difference between about $214,600 and about $186,200, a difference of more than $28,000. And that ignores taxes on realised short-term gains, which for frequent traders can cost far more than the spread. The compounding arithmetic is in how compound growth works.

Recency bias and chasing performance

Recency bias is giving recent events too much weight. After a strong year, people expect another one; after a crash, they expect more falls. In practice this leads to performance chasing: buying funds and stocks after they have gone up and selling after they have gone down.

Two pieces of evidence:

Fund flows. Academic research on mutual funds has repeatedly found that money flows heavily into funds after strong past returns, while past winners do not reliably keep winning. This is one reason most active funds struggle to beat their benchmarks over time; see active vs passive investing.
Dollar-weighted returns. In a 2007 paper in the American Economic Review, Ilia Dichev compared the buy-and-hold returns of major stock markets with the dollar-weighted returns that investors as a group actually earned, given when money flowed in and out. He found investors’ actual returns were systematically lower than buy-and-hold returns in nearly every major market he studied, because more money tended to arrive after good periods. Other researchers have since argued that the size of this effect is sensitive to method, so treat the exact gap with caution.

Anchoring and herding

Two more patterns are widely documented in psychology and show up constantly in markets, even where their exact cost is harder to measure.

Anchoring is fixing on a reference number that shouldn’t matter. The most common anchor is the price you paid. A stock you bought at $80 that now trades at $50 is not “cheap” or “due to come back” because of your purchase price; the market doesn’t know or care what you paid. The useful question is whether you would buy it today at $50 with fresh money. If the answer is no, the purchase price is just an anchor. A 52-week high works the same way: “it used to be $120” is not, by itself, a reason to expect it to get there again.

Herding is doing what everyone else seems to be doing, because a crowd feels like information. Sometimes it is. But when buying is driven mainly by other people buying, prices can move far from what the underlying business is worth, in both directions. Social media makes herding faster and more visible than ever, which is part of what made the GameStop episode possible.

A simple check for both: write down, before you trade, the reason in one sentence. If the sentence is about your purchase price or about what other people are doing, rather than about the investment itself, pause.

The “behaviour gap” and the DALBAR numbers

You may have seen a claim that the average fund investor earns several percentage points a year less than the S&P 500 because of bad timing. That figure usually comes from DALBAR’s annual Quantitative Analysis of Investor Behavior (QAIB), a commercial report.

The idea behind it is sound, but its methodology has been criticised in detail. Critics, including retirement researcher Wade Pfau, have argued that the QAIB compares a lump sum invested at the start of the period with investors who actually added money gradually over time. When markets rise over the period, money added later has less time to grow, so a gap appears even if investors never made a single timing mistake. Other critics have reproduced large “gaps” in simulations where investors behaved perfectly. DALBAR has disputed these criticisms.

A fair reading:

Behaviour does cost many investors money. The peer-reviewed studies above show it with transaction-level data.
The very large gaps in marketing materials probably overstate it, partly because of how they are calculated.
Be wary when a large behaviour-gap number is used to sell a product or service.

What can go wrong when you try to fix your behaviour

Swapping one bias for another. Deciding never to sell anything is not a strategy either.
Overcorrecting with complexity. More rules and more monitoring can mean more trading.
Believing you are the exception. Overconfidence research consistently finds most people rate themselves as above average.

Things that tend to help, according to the logic of the research:

Decide in advance. A written plan for how much to hold in stocks and when to rebalance.
Automate. Regular contributions on a schedule remove the temptation to time each one; see dollar-cost averaging explained.
Look less often. Frequent checking amplifies loss aversion.
Count costs. Write down what each trade costs in spread, commission, and tax before making it.

The short version

The research is clear that real investors make predictable mistakes. Odean found investors sold winners about 50% more readily than losers, and the losers they kept underperformed. Barber and Odean found the most active traders trailed the market by about 6.5 points a year. Investors as a group tend to buy after good periods and sell after bad ones. The commonly quoted behaviour-gap figures exaggerate the problem, but the problem is real. A plan written in calm conditions, automated contributions, and less trading address most of it.