What has held me back (and I suspect many others) on my investing journey isn’t a lack of information…….it’s the reactive decisions I’ve made in hot, emotional moments where I FOMO into a trend or panic sell into a falling market. Those choices weren’t random; they were driven by hard‑wired psychological biases. Below, I’ve outlined the biases I’ve repeatedly fallen into. Then I show how, when they stack, they create predictable behavioural patterns that damage returns.
The 17 biases I’ve tripped over
- Loss aversion
We’re wired to feel the pain of a loss roughly twice as strongly as the pleasure of an equivalent gain. That pushes us to sell at the worst moments, avoid realising small losses, and under‑allocate to volatile but fairly priced opportunities. - Endowment effect
Once investors own an asset, they tend to value it more than outsiders and demand a higher price to part with it. That attachment can blind them to changing fundamentals, cycles, and better uses of capital. - Sunk cost fallacy
Past expenditures are irrecoverable and shouldn’t affect current decisions, but our brains hate “wasting” them. This keeps investors adding to losing positions or prolonging projects even when expected value has deteriorated. - Regret aversion
To avoid the sting of being wrong, investors freeze or choose “blameless” options. Ironically, this leads to missed attractive entries or chasing late to avoid the feeling of missing out. - Anchoring (reference points)
First numbers (entry price, prior high, target) stick and we adjust too little when new information arrives. Anchors turn into arbitrary “must reach” thresholds that hijack sell, sizing, and exit decisions. - Framing effect
Logically equivalent statements feel different (“80% success” vs “20% failure”), flipping risk appetite. Wording, labels, and reference points shape perceived safety and value even when the underlying facts are unchanged. - Availability bias
Recent, vivid, or viral information crowds out boring base‑rate statistics and fundamentals. We overweight what’s top‑of‑mind and underweight what the long‑run data implies. - Confirmation bias
We preferentially seek and trust information that supports our existing view while discounting disconfirming evidence. Echo chambers feed overconfidence and delay necessary course corrections. - Overconfidence & optimism (self‑attribution)
We overestimate our forecasting skill and attribute wins to skill while blaming losses on bad luck. This narrows our confidence intervals, encourages overtrading, and leads to oversized bets. - Law of small numbers (gambler’s & hot‑hand fallacies)
We expect tiny samples to mirror the long run, so short streaks look like signals or we think reversals are “due.” This produces premature scaling, mistimed entries, and poor risk control. - Recency bias
We overweight the most recent returns and project them forward, ignoring cycles and mean reversion. That tempts investors to buy high and sell low as market leadership rotates. - Herding / social proof
When uncertain, we copy the crowd or defer to influencers because it feels safer than standing alone. Safety‑in‑numbers crowds trades and can pack everyone into the same exits. - Familiarity & home bias
We concentrate in markets, tickers, or ecosystems we “know,” mistaking familiarity for safety. This reduces diversification and raises exposure to idiosyncratic, correlated risks. - Status quo / inertia
We default to doing nothing because change is effortful or uncomfortable, even when inaction raises risk. Drift and neglect create unintended bets and stale portfolios. - Present bias
We prefer immediate gratification over higher long‑term expected value, so we overtrade and react to short‑term noise. Fees, taxes, slippage, and missed compounding are the hidden costs. - Mental accounting (house‑money, break‑even)
We put identical dollars into different “buckets” (profits, principal, “fun money”) and change risk‑taking based on the label. We also fixate on “getting back to even,” which can trap capital in low‑quality positions. - Disposition effect
We systematically sell winners too early (banking small gains for relief) and hold losers too long (avoiding realised losses). Over time, this tilts the portfolio toward weak assets and erodes overall returns.
The behaviour patterns these biases create
In most cases, it’s not a single bias that derails an investment decision, but a combination of biases working together. Addressing one bias in isolation isn’t enough…..you need to understand how different biases combine to drive a specific behaviour, and then have clear actions in place to counter that behaviour.
Below are the key behaviours I’ve fallen into in the past and occasionally still do today…..along with examples from my own experience.
FOMO
Crowd heat, rising prices, and nonstop chatter create urgency that makes late entries feel safer than patience. Investors swap discipline for speed and crowd into trades with poor reward‑to‑risk.
Related biases:
- 12) Herding / social proof;
- 7) Availability bias;
- 11) Recency bias;
- 4) Regret aversion;
- 15) Present bias
My personal mistake:
During the 2021 crypto boom, I jumped on the NFT train far too late, buying in when prices were already within 15% of their peak. When the market inevitably turned, the hype evaporated and some of the NFTs I’d purchased lost more than 80% of their value. This has been one of my most costly mistakes to-date and something that at the time was very painful.
Noise Over Signal
Investors are pulled toward what’s loud, recent, and vivid…..headlines, push alerts, and trending tickers…..while slow, base‑rate data gets ignored. This shortens time horizons, encourages reaction over analysis, and leads to chasing what just moved.
Related biases:
- 7) Availability bias;
- 11) Recency bias
My personal mistake:
Post-2022 bear market, when the narrative turned negative on Meta, I exited my Facebook stock almost at the bottom. I panic sold because I got caught up in the noise and broader market fear, rather than focusing on the fundamentals of one of the most successful companies of the past 25 years. Since then, Meta has rebounded over 700% to its highs today……gains I completely missed by letting short-term sentiment override long-term perspective.Nowadays to avoid this I follow the principle The Market is a Voting Machine in the Short Term and a Weighing Machine in the Long Run as a result I very seldom sell into a falling market as this is short term volatility and noise that doesn’t affect longterm returns.
Story Over Stats
Compelling stories, charismatic founders, huge TAMs, hot themes….feel concrete even when data is thin. Investors then anchor to the narrative, seek confirming evidence, and over-read a few wins as proof.
Related biases:
- 5) Anchoring;
- 8) Confirmation bias;
- 10) Law of small numbers;
- 11) Recency bias
My personal mistake:
For two Bitcoin cycles in a row, I fell hard into the “Story Over Stats” trap. I anchored to the narrative that crypto was going straight to the moon and Bitcoin would hit $1 million, ignoring clear historical evidence of cyclical boom-and-bust patterns. I sought out bullish voices and dismissed any analysis suggesting a downturn, over-reading short-term wins as proof the story was playing out. It wasn’t until I made the same costly mistake twice that I accepted Bitcoin is still an asset class subject to normal bull and bear market cycles…..one that may reach $1 million someday, but only after enduring the usual drawdowns along the way.
Framing Over Facts
Identical information framed differently (“80% success” vs “20% failure”) can flip risk appetite, and reference points like entry price distort value judgments. Losses loom large, ownership creates attachment, and framing nudges decisions away from expected value.
Related biases:
- 1) Loss aversion;
- 2) Endowment effect;
- 4) Regret aversion;
- 5) Anchoring;
- 6) Framing effect
My personal mistake:
When Terra’s Anchor Protocol offered a 19.5% APY on its UST algorithmic stablecoin, I focused almost entirely on the eye-catching yield and downplayed the risk of a depeg. My natural half-glass-full mindset had me leaning into the upside story while overlooking the fragility of the peg and the structural flaws in the system. When UST eventually depegged and collapsed, I lost more than 60% of my investment.
Now Over Later
Near‑term outcomes feel urgent and vivid, so investors trade for quick hits or relief instead of long‑term expected value. Meanwhile, inertia delays beneficial but uncomfortable tasks like rebalancing and saving.
Related biases:
- 15) Present bias;
- 14) Status quo / inertia
My personal mistake:
I still fall into this trap, often jumping into a crypto or share rally and deploying all my available capital at once. While I usually catch some of the upside, the inevitable pullback often takes prices below my entry, erasing those gains. These are typically medium-term holds, so it’s not catastrophic, but if I stuck to one of my principles Be Fearful When Others Are Greedy and Greedy When Others Are Fearful a disciplined “buy the dips, sell into rallies” approach, my returns would be far stronger.
Luck Over Skill
After a few wins, investors often mistake randomness for edge and scale up too quickly; after losses, they blame bad luck instead of a weak process. Small samples and fresh outcomes shrink perceived uncertainty and fuel overtrading.
Related biases:
- 9) Overconfidence & optimism;
- 10) Law of small numbers;
- 11) Recency bias;
- 8) Confirmation bias
My personal mistake:
My most costly financial lesson came in my second cryptocurrency bull cycle. Holding a large amount of Ether, I tried to boost my gains with 10x leveraged derivatives trades. A few early wins across a small handful of trades gave me false confidence, and I increased my bet sizes thinking I’d found an edge. When the market peaked and pulled back, those leveraged positions turned against me, wiping out 40% of my crypto portfolio.
Commitment Over Exit
Once time, money, or ego is invested, investors tend to escalate, bucket money, and wait for break‑even instead of reallocating. This locks capital in weak ideas and lets losers crowd out better opportunities.
Related biases:
- 3) Sunk cost fallacy;
- 16) Mental accounting;
- 17) Disposition effect;
- 14) Status quo / inertia;
- 5) Anchoring (to entry price)
My personal mistake:
When Silvergate Bank began showing clear signs of trouble (late 2022), amid a broader push by US Democrats to clamp down on crypto…the warning lights were flashing for two to three months. The market was pricing in these risks, yet instead of taking a manageable 20% loss, I held on, anchored to my purchase price and hoping the situation would somehow reverse. That “wait to get back to even” mindset turned a small haircut into a much deeper loss as the bank slid toward administration. I eventually sold losing 98% of my initial investment.
Comfort Over Diversification
Familiar markets, sectors, and tickers feel safer, so investors over‑allocate to what they know and under‑allocate to risk‑reducing exposures. Comfort concentrates correlation and leaves the portfolio exposed when that narrow slice stumbles.
Related biases:
- 13) Familiarity & home bias;
- 14) Status quo / inertia;
- 8) Confirmation bias
My personal mistake:
In my early investing years, I was heavily concentrated, most of my wealth tied up in the company I ran and the rest deployed into crypto. I dismissed share investing as “too old school” and saw real estate as requiring too much capital to get started. Looking back, this was a costly mistake: a simple NASDAQ ETF purchased in 2005 would have delivered an annual return of 15%+, and entering the Bondi property market seven years earlier than I did would have significantly increased my net worth today.
Conclusion
Recognising my psychological biases and the behaviours they trigger, has been one of the most important steps in improving my investment decision-making. These patterns aren’t quirks unique to me; they’re hard-wired tendencies that affect almost every investor.
Left unchecked, they lead to chasing hype, holding losers too long, selling winners too early, and confusing luck with skill. The key isn’t trying to eliminate bias entirely…….that’s impossible, but building processes, like my 3×3 Loop, that keep decisions anchored in data, discipline, and pre-defined rules rather than emotion. By naming these behaviours, owning my past mistakes, and putting safeguards in place, I give myself the best chance of staying rational through the noise of markets and compounding wealth over the long term.
TheBucket 