Section 7 - Chapter 1 - Behavioral Finance

Chapter 1 – Behavioral Finance
Section 7 – Behavioral Finance
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Agenda
Behavioral Finance
Introduction to Behavioral Finance and
Prospect Theory
Belief Preservation Biases
Information Processing Biases
Emotional Biases
Behavioral Biases and Chart Patterns
Case Study

Prospect Theory
Prospect Theory, developed by Daniel Kahneman and Amos Tversky (1979), describes how people make decisions
under risk and uncertainty. It challenges traditional Expected Utility Theory by showing that people value gains and
losses differently.
Key Facts & Concepts
1. Reference Dependence
o People evaluate outcomes relative to a reference point (not absolute wealth).
o Gains and losses are perceived differently based on this reference.
2. Loss Aversion
o Losses hurt more than equivalent gains feel good.
o Rule of thumb: Losses feel about 2x as painful as equivalent gains feel pleasurable ( ≈
𝛌
3. Diminishing Sensitivity
o The impact of changes in wealth decreases as the amount grows.
o Example: Gaining $100 to $200 feels more significant than $10,000 to $10,100

Prospect Theory
4. Probability Distortion
o People overweight small probabilities (e.g., lottery tickets).
o People underweight large probabilities (e.g., insurance).
5. Certainty Effect
o People prefer sure outcomes over probabilistic ones, even when the expected
value is lower.
6. Fourfold Pattern of Risk Attitudes
Probability Gains Losses
High (certain) Risk-averse (take sure gains) Risk-seeking (avoid sure losses)
Low (rare) Risk-seeking (lottery effect) Risk-averse (buy insurance)

Prospect Theory
7. Endowment Effect
o People assign higher value to things they own compared to identical
items they don’t own.
8. Framing Effect
o Different ways of presenting the same problem affect decision-making.
o Example: "90% survival rate" vs. "10% mortality rate" influences
perception.
9. Mental Accounting
o People categorize money into separate "accounts" (e.g., salary vs.
lottery winnings) and treat them differently.

Prospect Theory
Interpretation & Applications
🔹 Finance & Investing
• Investors hold onto losing stocks too long (risk-seeking in losses).
• People prefer guaranteed returns over higher expected value (e.g., fixed deposits vs. stocks).
🔹 Marketing & Pricing
• "Save $5" sounds better than "Avoid a $5 loss" (framing effect).
• Free trials take advantage of the endowment effect—people don't want to "lose" access.
🔹 Behavioral Economics & Policy
• Insurance purchases show overweighting of small probabilities.
• Governments use framing to encourage behaviors (e.g., "Tax rebate" vs. "Penalty for late
payment").

Behavioral Finance
Behavioral Finance
Behavioral Finance explores how psychological biases and emotions influence financial decision-
making, often leading people to act irrationally in markets. It challenges the traditional assumption of
rational investors in classical finance theories.
Key Concepts & Biases
1. Loss Aversion (Prospect Theory)
o Losses hurt more than equivalent gains feel good (~2x impact).
o Example: Investors hold losing stocks too long to avoid realizing a loss.
2. Overconfidence Bias
o People overestimate their knowledge and ability to predict markets.
o Leads to excessive trading, reducing long-term returns.

Behavioral Finance
3. Herding Behavior
o Investors follow the crowd instead of independent analysis.
o Leads to bubbles (e.g., Dot-com Bubble, Bitcoin mania) and crashes.
4. Confirmation Bias
o People seek information that supports their beliefs and ignore contrary data.
o Leads to poor investment decisions based on biased research.
o People treat money differently depending on its source (salary vs. lottery winnings).
o Example: Spending a bonus frivolously while being frugal with salary.

Behavioral Finance
6. Anchoring Bias
o Relying too much on an initial value (anchor) when making decisions.
o Example: A stock that fell from $100 to $50 seems cheap, even if its fair
value is $40.
7. Recency Bias
o People give more weight to recent events than historical trends.
o Example: After a market crash, investors believe stocks will always decline.
8. Disposition Effect
o Investors sell winners too early and hold losers too long due to fear of regret.

Behavioral Finance
🔹 Investing & Trading
• Investors often panic sell during downturns (recency bias, herding).
• Overconfidence leads to active trading, reducing profits due to fees and mistakes.
🔹 Corporate & Business Decisions
• CEOs may make overly optimistic projections (overconfidence bias).
• Companies use anchoring in pricing (e.g., "Was $100, now $50!").
🔹 Marketing & Consumer Behavior
• "Limited-time offers" exploit loss aversion to drive purchases.
• Framing a discount as "Save $20" instead of "Now $80" feels better.
🔹 Policy & Government Regulations
• Retirement savings plans use automatic enrollment to counter procrastination.
• Governments frame tax incentives in ways that encourage positive behaviors.

Belief Preservation Biases
Belief Preservation Biases refer to cognitive biases that make people cling to their
existing beliefs despite new, conflicting evidence. These biases cause irrational decision-
making, especially in finance, investing, and everyday life.
Key Biases & Concepts
1. Confirmation Bias
o People seek and interpret information that confirms their existing beliefs.
o Example: An investor bullish on Tesla only reads positive news and ignores risks.
2. Conservatism Bias
o People slowly update their beliefs even when presented with new, strong evidence.
o Example: An analyst ignores new earnings reports and sticks to old forecasts.

3. Illusion of Validity
o Overestimating the accuracy of one's judgment due to past success or experience.
o Example: A stock trader believes they can predict the market because they made money before.
4. Representativeness Bias
o People classify new information based on stereotypes rather than actual probabilities.
o Example: Assuming a company with a great CEO must also have great financials.
5. Hindsight Bias
o Believing past events were predictable when, in reality, they weren’t.
o Example: "I knew the 2008 financial crisis was coming!" (when they actually didn't predict it).

6. Self-Attribution Bias
o People credit themselves for success but blame external factors for failure.
o Example: An investor takes credit for a profitable stock pick but blames the
Fed for a bad one.
7. Cognitive Dissonance
o Discomfort from holding conflicting beliefs, leading to rationalizing or
ignoring contradictions.
o Example: A crypto investor dismisses fraud allegations against their favorite
coin as "FUD."

8. Status Quo Bias
o Preference for keeping things as they are rather than making changes.
o Example: An employee sticks to an old 401(k) plan instead of optimizing investments.
9. Endowment Effect
o Overvaluing what one already owns just because they own it.
o Example: A homeowner refuses to lower the selling price of their house despite market
conditions.
• Overestimating one's knowledge or abilities, leading to excessive risk-taking.
• Example: A day trader believes they can outperform hedge funds.

• Confirmation bias leads investors to ignore risks.
• Overconfidence results in excessive trading and poor portfolio performance.
🔹 Business & Decision-Making
• Conservatism bias prevents companies from adapting to market changes.
• Hindsight bias leads to faulty strategic planning.
🔹 Marketing & Consumer Behavior
• Endowment effect makes people overvalue personal belongings, increasing brand loyalty.
• Status quo bias is why people resist changing products or services.
🔹 Policy & Public Perception
• Cognitive dissonance fuels political and ideological divisions.
• Representativeness bias influences stereotyping and social judgments.

Information Processing Biases
Information Processing Biases occur when individuals misinterpret or mishandle information,
leading to flawed decision-making. These biases distort how people perceive, analyze, and act
on data, particularly in investing, business, and everyday choices.
1. Anchoring Bias
o Relying too much on an initial value (anchor) when making decisions.
o Example: If a stock falls from $100 to $50, an investor might think it’s a bargain, even if its
fair value is $30.
2. Availability Bias
o Giving too much weight to easily recalled information rather than complete analysis.
o Example: A person invests in a company because it was recently in the news, ignoring
financial fundamentals.

3. Representativeness Bias
o Assuming something is likely based on stereotypes or past patterns, rather than real probability.
o Example: A startup with a charismatic founder is assumed to be the "next Tesla," despite weak
financials.
4. Framing Effect
o Decisions are influenced by how information is presented rather than the facts themselves.
o Example: "90% survival rate" sounds better than "10% mortality rate," even though both mean the
same.
5. Hindsight Bias
o Believing that past events were predictable, even though they weren’t.
o Example: "I knew the market crash was coming!" (when in reality, they didn’t predict it
beforehand).

6. Recency Bias
o Giving more weight to recent events than historical data.
o Example: After a stock market crash, an investor believes markets will keep crashing and avoids
investing.
7. Self-Attribution Bias
o Taking credit for successes but blaming external factors for failures.
o Example: An investor attributes their gains to skill but blames losses on "market manipulation."
8. Illusion of Control
o Overestimating one’s ability to influence random events.
o Example: A trader believes they can consistently beat the market through skill, despite market
randomness.

9. Gambler’s Fallacy
o Believing that past independent events influence future probabilities.
o Example: After five coin flips land on heads, expecting the next flip to be
tails.
• Treating money differently based on where it comes from rather than its
actual value.
• Example: A person spends their tax refund freely but is extremely frugal
with their salary.

• Anchoring bias makes investors hold onto losing stocks at a set price.
• Recency bias can lead to panic selling during downturns.
• Availability bias makes investors focus on recent news rather than
fundamental analysis.
• Framing effect is used in marketing to influence consumer choices.
• Hindsight bias leads to overconfidence in strategic decisions.

🔹 Consumer Behavior & Marketing
• Mental accounting explains why people spend bonuses differently than
regular income.
• Gambler’s fallacy influences risky betting behavior.
🔹 Policy & Economics
• Self-attribution bias affects leadership decisions and economic policy-
making.
• Illusion of control can lead policymakers to believe they can precisely
manage economic cycles.

Emotional Biases
Emotional Biases arise from feelings and psychological reactions, leading to irrational decision-making
in investing, business, and everyday choices. Unlike cognitive biases, which stem from errors in
processing information, emotional biases are driven by fear, greed, pride, or regret.
1. Loss Aversion
o Losses feel twice as painful as equivalent gains feel good.
o Example: An investor refuses to sell a losing stock, hoping to "break even."
o Overestimating one’s knowledge, skills, or control over outcomes.
o Example: A trader believes they can consistently beat the market, leading to
excessive trading.

Emotional Biases
3. Endowment Effect
o Overvaluing assets simply because one owns them.
o Example: A homeowner refuses to sell their house at market price, believing it’s worth more.
4. Self-Control Bias
o Short-term temptations overpower long-term financial goals.
o Example: Choosing immediate spending over retirement savings.
5. Regret Aversion Bias
o Avoiding decisions due to fear of making a wrong choice.
o Example: Not investing in stocks because of past investment losses.
6. Status Quo Bias
o Preferring things to stay the same rather than making changes.
o Example: Keeping all money in cash rather than investing due to fear of risk.

Emotional Biases
7. Fear of Missing Out (FOMO)
o Jumping into trends due to fear of missing profits.
o Example: Buying crypto at an all-time high because "everyone else is doing it."
8. Greed Bias
o Chasing high returns without considering risks.
o Example: Holding a stock too long in hopes of even higher gains, leading to losses.
9. Pride Bias
o Holding onto investments to prove one is "right."
o Example: Refusing to sell a losing stock out of ego.
10. Guilt Bias
• Making financial decisions based on emotional obligation rather than logic.
• Example: Giving money to family at the expense of personal financial security.

Emotional Biases
• Loss aversion leads to holding onto bad investments for too long.
• FOMO & greed bias cause investors to chase bubbles (e.g., crypto mania).
• Overconfidence bias leads to excessive risk-taking and poor diversification.
• Status quo bias prevents companies from adapting to market changes.
• Regret aversion bias stops leaders from making bold but necessary choices.
🔹 Consumer Behavior & Marketing
• Fear & greed drive impulsive purchases in sales and promotions.
• Endowment effect makes people unwilling to trade or sell personal belongings.
🔹 Policy & Economics
• Loss aversion makes people oppose beneficial reforms (e.g., pension restructuring).
• Self-control bias leads to low savings rates and poor retirement planning.

Confirmation Bias & Analyst Recommendations
Key Facts About Confirmation Bias
🔹 Definition: The tendency to seek, interpret, and remember information that supports existing beliefs
while ignoring contradictory evidence.
🔹 Impact: Leads to biased decision-making and poor investment choices due to selective information
processing.
🔹 Common in: Investing, business strategy, hiring decisions, politics, and everyday life.
📌 Examples of Confirmation Bias in Analyst Ratings
✅ Dot-Com Bubble (1999-2000) – Analysts continued issuing "Buy" ratings on failing tech companies.
✅ Enron Scandal (2001) – Despite red flags, analysts maintained strong ratings on Enron until its
collapse.
✅ 2008 Financial Crisis – Analysts ignored housing market risks, leading to inflated ratings on
mortgage-backed securities.

Confirmation Bias in Analyst Recommendations
📌 How It Affects Investors
• Investors seek out analyst reports that confirm their opinions on a stock.
• They ignore negative reports or dismiss them as "wrong" or "biased."
• Leads to holding bad investments too long or overinvesting in risky assets.
📌 How It Affects Analysts
• Analysts may favor bullish reports due to personal biases or pressure from firms.
• They tend to underweight risks and focus on positive data points that align with their past
forecasts.
• Leads to overly optimistic price targets and delayed downgrades of stocks.

Interpretation & Practical Takeaways
🔹 For Investors
✔ Always read both bullish & bearish reports before making a decision.
✔ Look for conflicting viewpoints to challenge assumptions.
✔ Use objective metrics (e.g., P/E ratios, cash flow) instead of relying on analyst opinions alone.
🔹 For Analysts
✔ Avoid anchoring on previous forecasts—reassess based on new data.
✔ Be transparent about risks & uncertainties, not just potential upside.
✔ Recognize the impact of external pressures (e.g., investment banks pushing positive ratings).
🔹 For Businesses & Policymakers
✔ Encourage independent reviews to challenge internal biases.
✔ Implement checks & balances to avoid misleading forecasts.
✔ Train decision-makers to recognize and mitigate confirmation bias.

Mental Accounting & Overconfidence
Key Facts About Mental Accounting
🔹 Definition: The tendency to treat money differently based on its source, purpose, or
category, rather than viewing it as part of a total financial picture.
🔹 Impact: Leads to irrational financial decisions, poor budgeting, and inefficient
investing.
🔹 Common Examples:
• Windfall Effect: Spending a bonus freely while being frugal with salary.
• Sunk Cost Fallacy: Holding onto bad investments because of past money spent.
• Bucket System: Separating money into categories (e.g., rent, fun, savings) instead
of optimizing total resources.

Key Facts About Overconfidence Bias
🔹 Definition: The tendency to overestimate one’s knowledge, skills, or control over outcomes.
🔹 Impact: Leads to excessive risk-taking, poor diversification, and financial losses.
🔹 Common Examples:
• Overtrading: Believing one can consistently beat the market.
• Stock Picking Bias: Thinking personal research is superior to professional advice.
• Entrepreneurial Overconfidence: Underestimating risks and overestimating success probabilities.
Real-World Examples
✅ Mental Accounting: Lottery winners often spend winnings recklessly instead of saving.
✅ Overconfidence: Many retail investors overtrade, believing they can outperform professional fund
managers.

Interpretation & Practical Takeaways
🔹 Mental Accounting
✔ Treat all money as fungible—evaluate financial decisions holistically.
✔ Focus on total wealth optimization rather than rigid mental categories.
✔ Avoid sunk cost fallacy—make decisions based on future value, not past losses.
🔹 Overconfidence Bias
✔ Use data-driven analysis rather than gut feelings for investments.
✔ Diversify investments instead of betting too much on one stock.
✔ Acknowledge uncertainty—nobody can predict markets perfectly.

Chapter 1 - Market Sentiment and Technical Analysis
Next Section 8 – Sentiment Analysis
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Section 7 - Chapter 1 - Behavioral Finance

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