Cognitive Biases in Stock Market Investing
📌 Confirmation Bias
🔹 Tendency to seek, interpret, and remember information that confirms our pre-existing beliefs while ignoring contradictory data.
📌 Anchoring Bias
🔹 Fixating on an initial reference point (such as a stock’s purchase price) and basing future decisions on that reference, even when conditions have changed.
📌 Representativeness Bias
🔹 Assuming that a past pattern will continue in the future without considering fundamental factors. Example: Believing that a stock that has risen significantly will keep rising indefinitely.
📌 Availability Bias
🔹 Making decisions based on the most easily recalled information rather than the most relevant.
📌 Overconfidence Bia
🔹 Believing that one has above-average skills in predicting the market, which can lead to excessive trading or taking unnecessary risks.
📌 Loss Aversion Bias
🔹 Feeling more pain from a loss than satisfaction from an equivalent gain, leading to irrational decisions such as holding losing stocks too long or panic selling.
📌 Herd Mentality Effect
🔹 Following the majority’s decisions without conducting independent analysis. This contributes to market bubbles and sharp declines.
📌 Status Quo Bias
🔹 Preference for keeping a portfolio unchanged, even when better opportunities exist in the market.
📌 Illusion of Control
🔹 Believing one can control or predict market events that are actually random.
📌 Mental Accounting Bias
🔹 Treating money differently depending on its source or intended use rather than evaluating it globally. Example: Refusing to sell a losing stock while waiting to "recover losses."
📌 Sunk Cost Fallacy
🔹 Clinging to an investment simply because money has already been lost, instead of assessing whether it remains a good choice.
📌 Recency Bias
🔹 Giving more weight to recent information than historical data, potentially leading to impulsive decisions.
📌 Hindsight Bias
🔹 Believing that what happened was "obvious" after the fact, reinforcing overconfidence in future predictions.
📌 Survivorship Bias
🔹 Focusing only on successful cases while ignoring failures, leading to unrealistic expectations.
📌 Disposition Effect
🔹 Selling winning stocks too early while holding onto losing stocks for too long.
📌 Optimism Bias
🔹 Believing things will turn out better than statistically probable, leading to overvaluing investments and taking unnecessary risks.
📌 Pseudo-Certainty Bias
🔹 Preferring investments with guaranteed outcomes (even with low returns) over those with higher potential but more uncertainty.
📌 Halo Effect
🔹 Judging an investment based on a positive characteristic of the company (e.g., a well-known CEO) instead of analyzing it objectively.
🔹 Blindly trusting expert recommendations without conducting independent analysis.
📌 Familiarity Bias
🔹 Investing only in well-known companies (like Apple, Tesla, etc.) without considering other potentially more profitable opportunities.
📌 Endowment Effect
🔹 Assigning higher value to a stock simply because it is already owned, rather than analyzing it objectively.
📌 Inertia Bias
🔹 Avoiding investment decisions out of fear of making mistakes, even when good opportunities exist.
📌 Money Illusion Bias
🔹 Failing to consider inflation when evaluating investment returns. A 10% gain in a year with 8% inflation is not the same as a 10% gain in a year with 2% inflation.
📌 Illusion of Market Control Bias
🔹 Believing that technical analysis or a specific strategy guarantees control over market behavior.
📌 Anchoring to Initial Information Bias
🔹 Making decisions based on the first piece of information received about a stock without updating the analysis with new data.
📌 Excessive Intuition Bias
🔹 Relying too much on intuition without data to support investment decisions.
📌 Accessibility Bias
🔹 Giving more weight to recent or striking information instead of the most relevant data.
📌 Memory Effect Bias
🔹 Making decisions based on personal past experiences, even when current data suggests otherwise.
📌 Overreaction Bias
🔹 Exaggerating the importance of news or market events and making hasty decisions.
📌 Too Big to Fail Bias
🔹 Believing that a large and well-established company can never go bankrupt or face a crisis.
📌 Self-Attribution Bias
🔹 Attributing stock market successes to personal skill while blaming external factors for losses.
📌 Contagion Effect
🔹 Being emotionally influenced by market sentiment without solid fundamentals.
📌 Surprise Bias
🔹 Ignoring the possibility of unexpected events that can affect the market (black swan events).
📌 External Attribution Bias
🔹 Blaming the government, the Fed, banks, or news for losses instead of reviewing one’s strategy.
📌 "I Don’t Want to Know" Effect
🔹 Avoiding portfolio review to avoid seeing losses, leading to delayed or poor decisions.
📌 Action Bias
🔹 The tendency to feel that "doing something" is better than "doing nothing," even when the best option might be to hold and wait.
📌 Hot-Hand Fallacy
🔹 Believing that because an investment has performed well repeatedly, it will continue to do so in the future.
📌 Reference Point Bias
🔹 Comparing an investment’s performance to an arbitrary benchmark (e.g., comparing a portfolio to the S&P 500 even when it has assets with different risk levels).
📌 All-or-Nothing Bias
🔹 Thinking that an investment is either 100% good or 100% bad, without considering nuances and intermediate risks.
📌 Lottery Fallacy Bias
🔹 Believing that investing in very cheap stocks (penny stocks) will yield massive returns, like a lottery.
📌 Cyclical Behavior Bias
🔹 Believing that past market cycles will repeat exactly the same way in the future, without considering changes in the economy or technology.
📌 Too Good to Be True Effect
🔹 Distrusting a good investment opportunity just because it seems too good, without analyzing it rationally.
📌 False Correlation Bias
🔹 Assuming that two market events are related just because they happened at the same time, without a true causal relationship.
📌 Bandwagon Effect Bias
🔹 Investing in assets just because many others are (market trends like Bitcoin, meme stocks, etc.).
📌 "I Can’t Be Wrong" Bias
🔹 Believing that a strategy that worked in the past will continue to work without adjustments.
📌 Pattern Recognition Illusion Bias
🔹 Seeing patterns in price charts where none actually exist, leading to decisions based on imaginary trends.
📌 Multiple Choice Effect Bias
🔹 Thinking that having more investment options is always better, which can lead to analysis paralysis.
📌 Paralysis by Analysis
🔹 Occurs when an investor becomes stuck and fails to make decisions due to an excess of information or overanalysis of data. Instead of taking action, the investor continues evaluating options, searching for the "perfect decision," and as a result, misses important opportunities.
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- Too much information: The investor consumes so many data points, reports, and analyses that making a decision becomes difficult.
- Fear of making mistakes: Uncertainty about risks and the desire to avoid losses lead to inaction.
- Searching for the "perfect" investment: Looking for an ideal entry point or a risk-free asset, which rarely exists.
- Excessive tools and strategies: Using too many technical indicators or financial models can make decisions more confusing instead of clearer.
- Missed opportunities: While the investor analyzes, the market keeps moving, and opportunities vanish.
📌 "I Can’t Lose" Bias
🔹 Refusing to accept that an investment may be bad and refusing to sell at a loss, hoping for a recovery that may never come.
📌 Expert Bias
🔹 Believing that because an investor is experienced or famous (like Warren Buffett or Michael Burry), they are always right.
📌 "If Only I Had Stayed" Bias
🔹 Regretting selling a stock before a big price increase, which can lead to emotional decision-making in future investments.
📌 Exaggerated Panic Bias
🔹 Overreacting to negative news and selling prematurely without deep analysis.
📌 Past Performance Trap Bias
🔹 Choosing investments based only on past performance without analyzing future potential.
📌 Quick Money Illusion Bias
🔹 Believing that making easy money in the stock market is always possible with little effort, without considering risks.
📌 Underestimation of Risk Bias
🔹 Ignoring investment risks simply because they have recently performed well.
📌 Novelty Bias
🔹 Overvaluing a company or technology simply because it is new, without assessing its long-term viability.
📌 The Monte Carlo Fallacy, also known as the Gambler's Fallacy
🔹 A cognitive bias that occurs when a person mistakenly believes that a random event is more or less likely based on past events, even though the outcomes are statistically independent.