Real-world examples of recency bias in financial markets

The best examples of recency bias in financial markets today

If you want to understand behavioral finance, start with the stories investors tell themselves after a big move. The best examples of recency bias in financial markets all share one thing: people assume that what just happened will keep happening.

You can see this in:

• How investors piled into tech after 2020
• How they abandoned bonds after 2022
• How they treated meme stocks and crypto like permanent money machines

These are not abstract textbook cases. They’re real examples that moved trillions of dollars.

Tech stocks after the pandemic: a textbook example of recency bias

The post‑2020 tech boom is one of the clearest examples of recency bias in financial markets.

From March 2020 to late 2021, the Nasdaq 100 more than doubled. Big Tech names like Apple, Microsoft, Amazon, and Tesla were on a tear. Work‑from‑home, e‑commerce, and cloud computing looked unstoppable.

What did many investors do?

• They extrapolated those returns far into the future.
• They raised their tech allocations dramatically.
• They dumped lagging sectors like energy and financials.

That’s classic recency bias: overweighting the most recent performance and underweighting long-term cycles and valuations.

When rates started rising in 2022, tech sold off hard. The Nasdaq 100 dropped more than 30% from its peak. Investors who had chased the recent winners were left with concentrated, volatile portfolios.

Academic work has documented this kind of return-chasing behavior for decades. A well-known paper by Barberis, Shleifer, and Vishny (1998) models how investors overreact to recent news and price trends, contributing to market booms and corrections. You can find related research on investor behavior and expectations via the National Bureau of Economic Research at nber.org.

Meme stocks and social media: the viral examples of recency bias

When people ask for real examples of recency bias in financial markets, meme stocks are usually near the top of the list.

Think back to early 2021. GameStop and AMC exploded higher in a matter of days. Screenshots of huge gains flooded Reddit and Twitter. For many new traders, the only data point that mattered was: “This stock just doubled (or tripled).”

Recency bias showed up in a few ways:

• New traders assumed the recent vertical move was the “new normal.”
• Many ignored fundamentals and focused only on recent price action and viral narratives.
• The most recent winners attracted the most new money, even as volatility went off the charts.

This is a modern, social‑media‑amplified example of recency bias. The dramatic recent performance overshadowed any sober assessment of risk, valuation, or long‑term prospects.

Regulators have been increasingly focused on how social media and gamified trading platforms influence investor behavior. The U.S. Securities and Exchange Commission (SEC) has published investor alerts and bulletins on speculative trading and meme stocks, available at investor.gov, which is a helpful resource if you want to see how authorities think about these behavioral patterns.

Crypto booms and busts: repeated examples of recency bias in financial markets

Cryptocurrencies offer some of the clearest examples of recency bias in financial markets, because the cycles are so extreme and so fast.

During the 2017 crypto boom, and again in 2020–2021, many investors:

• Saw Bitcoin and other coins rising sharply.
• Assumed those recent gains were a sign of inevitable long‑term dominance.
• Allocated money based on short‑term charts rather than long‑term risk tolerance.

When prices fell 50–80% in subsequent bear markets, recency bias flipped direction:

• Recent losses led investors to assume crypto was “dead.”
• Some sold near the bottom because the latest price action felt like the only reality that mattered.

The pattern is the same: people overweight the very recent trend, whether it’s up or down, and underweight the full history of volatility, drawdowns, and cycles.

If you want a sober view on financial risk and speculative assets, the Federal Reserve’s research and Financial Stability Reports at federalreserve.gov provide data and analysis that contrast sharply with the short‑term narratives that dominate social media.

Inflation scares and bond markets: a quieter example of recency bias

Not all examples of recency bias in financial markets are loud and dramatic. Some are slow and quiet, like the behavior of bond investors after a bad year.

In 2022, U.S. bonds had one of their worst years in modern history as interest rates surged. Many balanced portfolios that relied on bonds for stability were shocked by the drawdowns.

Recency bias kicked in:

• Investors started to say, “Bonds are broken.”
• Some shifted heavily into cash or stocks, assuming bonds would keep performing poorly.

But rising yields actually increase expected future returns for new bond purchases. The very moment when bonds felt most painful was also the moment when their forward-looking prospects improved.

This is a subtle example of recency bias: extrapolating one historically bad year into a long‑term story, instead of recognizing that markets are cyclical. Long-term research from institutions like the Federal Reserve and academic finance departments (e.g., data sets referenced through harvard.edu affiliated research centers) shows that bonds have gone through multiple rate cycles and still played a stabilizing role over decades.

Housing market optimism: recency bias in real estate investing

Real estate investors are not immune. The housing market offers repeated examples of recency bias in financial markets, even though it moves more slowly than stocks.

After long stretches of rising home prices, buyers and investors often:

• Assume recent appreciation will continue indefinitely.
• Stretch on price, leverage, or both, based on the belief that “real estate always goes up.”

You saw this behavior before the 2008 financial crisis and again in many U.S. markets during 2020–2022, when ultra‑low mortgage rates and pandemic‑era demand pushed prices sharply higher.

Recency bias shows up when people:

• Ignore historical periods of flat or declining prices.
• Underestimate the impact of rising rates on affordability.
• Treat the last few years of gains as a reliable forecast, rather than a data point.

Housing data and long‑term trends from the Federal Reserve’s FRED database at the St. Louis Fed (stlouisfed.org) are a useful antidote. They show that, while real estate can build wealth over time, it does not move in a straight line.

Professional managers aren’t immune: fund flows and recent performance

It’s tempting to think recency bias is a “retail investor problem.” It isn’t. Even professionals provide examples of recency bias in financial markets when you look at mutual fund and ETF flows.

Morningstar and other research firms have documented that investors often allocate more to funds that have recently outperformed and withdraw from funds that have lagged, even when long-term performance and fees suggest the opposite would be smarter.

This behavior shows up as:

• Chasing last year’s top‑performing sector or thematic fund.
• Abandoning diversified strategies after a short period of underperformance.

Over time, this kind of performance chasing can lead to a “buy high, sell low” pattern at the portfolio level. Academic research on the behavior gap—the difference between investment returns and investor returns—often points to timing decisions driven by recent performance. The SEC’s educational materials at investor.gov discuss this gap and encourage long‑term, diversified approaches.

Personal portfolios: everyday examples of recency bias

Some of the most important examples of recency bias in financial markets happen inside individual retirement accounts and brokerage accounts, far from the headlines.

Common patterns include:

• Increasing stock allocations after a multi‑year bull market because “stocks always come back.”
• Cutting equity exposure sharply after a bear market because “I can’t handle this anymore,” based on the latest pain.
• Rotating into whatever sector ETF or theme fund has the hottest recent chart.

This is where behavioral finance meets real life. Long-term planning gets hijacked by short‑term emotions, powered by recency bias.

Organizations like the Financial Industry Regulatory Authority (FINRA) provide investor education on risk, diversification, and behavioral pitfalls at finra.org, which can help investors recognize and counter these tendencies.

How to recognize and counter recency bias in your investing

Seeing these examples of recency bias in financial markets is only useful if you translate them into better decisions.

You can’t eliminate the bias, but you can build guardrails around it:

Anchor decisions to written rules, not recent prices.
Create an investment policy statement (even a simple one) that spells out your target asset allocation, rebalancing rules, and risk limits. Refer to that document when markets are volatile instead of relying on your feelings about the latest move.

Use long-term data, not one-year charts.
When evaluating an asset class or fund, look at 10–20 years of returns, volatility, and drawdowns. Ask: “Is my view based on the last 12 months or the last 20 years?” If it’s mostly the last 12 months, recency bias is probably in the driver’s seat.

Rebalance systematically.
Rebalancing forces you to trim recent winners and add to recent losers, which is the opposite of what recency bias pushes you to do. A rules‑based rebalancing schedule (for example, annually or when allocations drift by a set percentage) can help.

Limit news‑driven trading.
Short‑term financial news is optimized for attention, not for your long‑term returns. If you find yourself trading based on headlines or the last week’s market move, that’s a sign recency bias is active.

Stress‑test your assumptions.
When you catch yourself saying “this time is different” or “this will keep going,” ask: “What if the next five years look nothing like the last two?” Building scenarios that contradict the recent trend helps weaken recency bias.

FAQ: examples of recency bias in financial markets

What are some simple examples of recency bias in investing?
A straightforward example of recency bias is an investor increasing their stock exposure after a long bull market because recent gains make stocks feel safer, then cutting exposure after a sharp decline because recent losses make stocks feel riskier. Another is chasing a hot sector ETF because it has outperformed for the last year, without considering valuation or long‑term history.

How is recency bias different from momentum investing?
Recency bias is a cognitive bias—an emotional and psychological tendency to overweight recent information. Momentum investing is a deliberate strategy that uses rules to buy assets with strong recent performance and sell those with weak performance. The difference is discipline and process. Recency bias is usually reactive and unstructured; momentum strategies are systematic and tested.

Can you give an example of recency bias during a market crash?
A common example of recency bias during a crash is an investor selling long‑term holdings near the bottom because the latest price declines feel like they will continue indefinitely. Even if long‑term fundamentals are intact, the recent pain dominates their decision-making.

Are professional investors less prone to recency bias?
They may be more aware of it, but they are not immune. Fund managers face career pressure, client redemptions, and benchmarking, all of which can push them toward short‑term, recent‑performance‑driven decisions. Studies of fund flows and manager behavior show that even professionals chase recent winners and avoid recent losers.

How can I check if I’m falling for recency bias in my portfolio?
Look at your last few trades or allocation changes. If most of them moved money toward recent winners and away from recent losers, without a clear long‑term plan or valuation rationale, you’re likely being influenced by recency bias. Comparing your behavior to long-term data and to a written investment plan is a practical way to spot this pattern.

When you study these real examples of recency bias in financial markets—from tech booms and meme stocks to bond sell‑offs and housing cycles—you start to see the same script playing out. The challenge isn’t spotting the bias in hindsight. It’s recognizing it in real time, when recent returns are screaming in your ear and long‑term data is whispering in the background.