The Hidden Biases That Distort Your Crypto Trading Decisions

This is the second of a four-part series examining the “Psychology of Risk”, particularly in the context of cryptocurrency trading.

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In our previous article, “Why Smart Crypto Traders Still Make Dumb Decisions – The Psychology Of Risk”, we had discussed, at a high level, how our experiences, cognitive biases and emotional mindsets affect our perception of risk. In this article we shall delve a bit deeper into the primary cognitive biases that impact our decision making in the face of uncertainty.

Cognitive biases are systematic patterns of deviation from rationality in judgment. They often distort how we perceive and respond to risk, leading to decisions that may not align with our best financial interests. We list here some of the primary biases to be mindful of from the perspective of a trader/investor and some common ways they are manifested in crypto.

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Confirmation Bias

The tendency to favor information that confirms pre-existing beliefs, while ignoring evidence that contradicts them. 

Examples: 

• Following only bullish/bearish influencers depending on one's position.
• Participating in echo chamber communities that reinforce existing beliefs.

Risk Implications: Can blind investors to genuine market risks or opportunities that don't fit their narrative.

Recency Bias‍

Tendency to overweight recent market events and underweight historical patterns.

Examples: 

• Investors who entered during the 2021 bull run might have formed unrealistic expectations about "normal" returns.
• Conversely, during the 2022 - 2023 downturn, investors might have missed accumulation opportunities due to recent negative experience.

Risk Implications: Can lead to poor risk assessment during both bull and bear markets by assuming recent conditions will persist. Particularly dangerous in crypto markets due to their cyclical nature and extreme volatility.

Anchoring Bias‍

Mental fixation on specific reference points when making decisions.

Examples: 

• An investor might refuse to sell a stock they bought at $100 because they are anchored to that price, even if its fair value is now much lower.
• Investors anchoring to historical price levels, like previous cycle peaks.

Risk Implications: Can lead to poor risk management by basing decisions on psychologically significant but potentially irrelevant price points.

Loss Aversion Bias‍

The tendency to fear losses more than valuing equivalent gains.

Examples: 

• Holding on to losing investments too long to avoid realizing a loss.
• Avoiding high-risk, high-reward opportunities entirely.

Risk Implications: Investors need to be mindful of "revenge trading" to recover losses and have a disciplined sell strategy. 

Availability Bias‍

Overestimating the probability of events that are easily remembered.

Examples: 

• Notable "buy the dip" success stories leading to underestimation of prolonged bear market risks.
• After the Mt. Gox hack, many users became overly cautious about Japanese exchanges specifically.

Risk Implications: Can lead to either excessive caution or unwarranted risk-taking depending on the event in memory.

The Gambler’s Fallacy‍

Believing that past events influence independent future outcomes.

Examples: 

• Assuming a string of losses must be followed by a win.
• Believing that multiple dips mean a bounce is "due".
• Increasing position sizes after losses to "average down".

Risk Implications: Can lead to false pattern recognition in market cycles and ignoring fundamental factors in favor of probability-based assumptions. 

Normalcy Bias‍

Tendency to expect future events to follow historical patterns.

Examples: 

• “Bitcoin Always Recovers": Investors assuming Bitcoin will always bounce back from major drawdowns because it has historically done so.
• Assuming established protocols with high TVL (Total Value Locked) are automatically safe.

Risk Implications: Assuming historical market cycles will repeat can lull one into a false sense of complacency. 

Attribution Bias‍

Tendency to attribute successful outcomes to skill and failures to external factors.

Examples: 

• Attributing trading success to skill and superior analysis, and failures to market manipulation, or “whales moving the market”.
• Attributing project selection to foresight when projects succeed and to FUD when they fail.

Risk Implications: Leads to failure to learn from mistakes by blaming poor results on external factors. 

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Recognizing these biases is crucial for crypto traders and investors who want to improve their decision-making and risk management. Awareness of cognitive biases helps traders avoid emotional pitfalls, maintain objectivity, and develop a more disciplined approach to market analysis. Turns out there is more to mindfulness than just meditation! 

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Coming up next: In Part Three we explore the “Loss Aversion Bias”

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