Behavioral Finance

Efficient Market Hypothesis (EMH)

The Efficient Market Hypothesis (EMH) is the foundation of traditional finance. It proposes that asset prices accurately reflect all available information at any given time. This implies you can't consistently beat the market through skill or insight because prices already reflect everything.

There are three forms:

• Weak form: Prices reflect PAST data.

• Semi-Strong form: Prices reflect PUBLIC data.

• Strong form: Prices reflect all info INCLUDING INSIDER information.

The true EMH suggests technical analysis is useless, insider trading won't work, and active managers can't outperform index funds.

Issues with EMH

Markets don’t always behave efficiently as demonstrated by:

• Dot-com bubble: Tech stocks soared above fundamentals.

• GameStop 2021: Meme traders ignored valuations and massively moved the price.

Limits to Rational Arbitrage

Behavioral finance accepts that some investors are rational, but they’re often unable or unwilling to act because of key factors:

1. NOISE TRADER RISK (Vishny 1997):

   • Irrational traders can push prices further from fair value and cause losses for arbitrageurs. Example: the GameStop short squeeze wiping out rational hedge fund short-sellers.

2. HORIZON RISK:

   • Even if you’re confident that mispricing will eventually correct, you might not be able to wait it out--a client might pull capital, or you might go bankrupt. Example: LTCM 1994, their trades were sound but collapsed due to liquidity issues before prices normalized.

3. MODEL RISK:

   • If your idea of ‘fair value’ is wrong due to a faulty model, you could misprice the asset yourself.

4. IMPLEMENTATION COSTS:

   • Even perfect trades have costs e.g. fees eat profits, or trading large volumes can move the market.

5. PRINCIPLE-AGENT PROBLEM (institutional):

   • Institutions can also be biased – more so than individuals – due to incentives or job risk. E.g., fund managers may care more about beating their peers than beating the market.

   • The agent (fund manager) may avoid risky trades if they make them look bad in the short-run - they ‘closet index’ or HERD with other funds to protect their job.

   • (Xu 1999) shows that institutional managers suffer from:

     • Overconfidence

     • Representativeness bias (chasing past wins)

     • Herding (buying what peers buy)

Traditional Finance vs. Behavioral Finance

Traditional finance assumes that people are rational agents and make decisions based on EXPECTED UTILITY THEORY (EUT). They know what they want, consistently choose the objectively best option, and update their beliefs correctly with new info.

Expected Utility Theory

EUT posits that we make decisions by weighing each outcome by its probability and utility, then choosing the highest score. For example, a rational investor would never prefer a 50% chance of losing £10 to a guaranteed £0.

Behavioral Finance says real people don’t behave this way, including professionals.

EUT is based on Savage’s Axioms – rules that define ‘rational’ choice under uncertainty. Key Axioms include:

• TRANSITIVITY: if you prefer A to B, and B to C, then you must prefer A to C

• INDEPENDENCE: If you prefer A over B, then you must prefer A + X over B + X

• COMPLETENESS: you can rank any two outcomes; you’re never indifferent forever

• SURE-THING PRINCIPLE: if you’d choose A whether X happens or not, you should still choose A even if you don’t know whether X happens

(Tversky 1974) shows that people regularly violate these axioms, not by mistake, but systematically, undermining the logic of classical finance.

Prospect Theory

PROSPECT THEORY (developed by Tversky 1974) explains how people actually make decisions under risk, replacing the idea of objective utility with psychological reality.

Four key ideas:

1. REFERENCE DEPENDENCE: people judge gains and losses relative to a reference point (usually current wealth or expectation), not in absolute terms.

2. LOSS AVERSION: losses hurt twice as much as equivalent gains feel good.

3. DIMINISHING SENSITIVITY: the further you move from the reference point, the less each gain or loss matters (diff between 0 and 100 feels huge, but 900 to 1000 feels less huge).

4. PROBABILITY WEIGHING: people overweigh small probabilities (e.g. buying lottery tickets) and underweight large probabilities (ignoring very likely risks).

Behavioral Phenomena linked to PROEPECT THEORY

• FRAMING EFFECTS: people choose differently depending on how options are described. E.g., 10% mortality vs. 90% survival.

• MENTAL ACCOUNTING: people keep money in ‘mental buckets’ and treat it differently, which leads to suboptimal decisions e.g. gambling vs saving.

• DISPOSITION EFFECT: investors sell winners too early on to ‘lock in’ gains and hold losers too long to avoid ‘defeat’

• REALISATION UTILITY: people enjoy the act of realising a gain, even if it hurts long-term performance.

Heuristics and Biases

Heuristics

Heuristics are mental shortcuts we use to make decisions when faced with uncertainty; they're efficient, but not always accurate.

In Behavioral Finance, heuristics explain why people make predictable errors when evaluating risk, pricing assets, or reacting to news.

Key Heuristics (Dale 2015)

1. REPRESENTATIVENESS: We judge probabilities based on how much something resembles a stereotype, not on actual statistical likelihood. E.g., momentum investing based on past performance, despite no guarantees of future returns.

2. AVAILABILITY: We judge something based on how easily it comes to mind, not how likely it actually is. E.g., overreacting to dramatic market headlines.

3. ANCHORING: We rely too heavily on the first piece of information (the ‘anchor’) when making decisions, even if it's irrelevant. E.g., investors anchor to a previous stock high instead of reassessing its real value.

(Dale 2015) introduces heuristics and shows how they lead to misjudging risk, overreaction to news, and market volatility.

Cognitive Biases

Cognitive Biases are systematic decision errors, the behavioral consequences of heuristics. (Baker 2013) shows that investor biases influence corporate financing.

1. OVERCONFIDENCE: The belief that one’s skills or predictions are better than they are.

   • Leads to excessive trading and low diversification.

2. CONFIRMATION BIAS: The tendency to seek information that supports your beliefs.

   • Leads to rigid views and poor revision of models.

3. FRAMING EFFECT: Decisions change based on how information is presented.

4. DISPOSITION EFFECT: Holding losers too long, selling winners too early.

   • Driven by realisation utility & loss aversion.

Psychological barriers are non-fundamental price points – like round numbers – that traders treat as important, even if they aren’t. E.g., a stock breaks £100 – traders think it’s a breakout; a stock drops to £50 – psychological ‘support’ kicks in.

The Relationship

Heuristics lead to Biases which affect Market-Level Effects.

This explains anomalies that EMH can't, and how cognitive shortcuts create predictable inefficiencies.

Behavioral Portfolio Theory (BPT)

Modern Portfolio Theory (MPT)

MODERN PORTFOLIO THEORY (MPT) assumes that investors are rational; they optimise a single portfolio by balancing expected return vs. risk (variance); preferences are stable and mathematically consistent. However, in real life, investors are not always rational.

They don’t always diversify optimally, they hold low-risk cash AND high-risk bets in the same portfolio; they chase goals like safety, growth, or even status.

Behavioral Portfolio Theory (BPT)

BEHAVIOURAL PORTFOLIO THEORY (BPT), introduced by (Statman 2000), is a psychologically realistic alternative to MPT.

Investors mentally divide their wealth into layers, each tied to a specific goal. Two key layers:

1. SAFETY LAYER: Capital preservation (e.g., bonds, cash)

2. ASPIRATION LAYER: Big wins and risk-taking (e.g., stocks, crypto)

These aren’t integrated like in MPT, they’re psychologically separated accounts via mental accounting.

This implies investors take excessive risk in aspiration layers, hold onto too much cash in the safety layer (under diversification), and explains barbell portfolios (very risky & very safe assets).

Behavioral Corporate Finance (BCF)

(Baker 2013) defined the two key frameworks of Behavioral Corporate Finance

1. MARKET TIME APPROACH: Managers are rational, but markets are irrational.

   • Firms issue equity when prices are overvalued; they cater to investor sentiment, not long-term fundamentals, e.g., issuing equity during bull markets, like during the Dotcom boom, even if capital isn’t urgently needed.

2. MANAGERIAL BIAS APPROACH: Managers themselves are biased.

   • Common biases: Overconfidence, optimism, loss aversion.

   • It Affects capital structure, investment, and M&A, e.g., overconfident CEOs overestimate returns – aggressive M&A, overinvestment; loss-averse managers avoid debt – debt conservatism, even when leverage is optimal; optimistic forecasts lead to projects that later underperform.

Behavioral Game Theory (BGT)

BEHAVIOURAL GAME THEORY (BGT) brings behavioral insights into strategic interactions. In real-world negotiations and contracts, people care about:

• Fairness, reciprocity, and social image violates classic game theory, which assumes people always maximise material payoff.

• E.g., ULTIMATUM GAME: player A offers a split 90/10, Player B rejects unfair offers, even if they get nothing.

• Fairness norms trump strict rationality, e.g., founders reject ‘fair’ deals if they feel undervalued.

Neurofinance

Neurofinance explores how brain chemistry, hormones, and neural systems influence financial decision-making.

Traditional finance assumes we think like computers. Neurofinance says we think like biological organisms with:

• Hormonal cycles, stress responses, emotional bias, social triggers, etc.

It moves from the cognitive to the physiological.

• CORTISOL: Stress hormone

  • Rises during market crashes or volatility.

  • Linked to increased risk aversion: traders become defensive sell quickly and avoid new positions, e.g., the Financial crisis 2008, market-wide panic ^ elevated cortisol and deleveraging.

• TESTOSTERONE: Dominance hormone

  • Linked to overconfidence, risk-seeking, and momentum trading.

  • E.g., Trader wine – testosterone spike – bigger trades – bubble risk.

  • WINNER EFFECT - Biologically documented in humans and animals.

• OXYTOCIN: Trust hormone

  • Build feelings of connection, bonding, and cooperation: makes people more trusting.

  • Good for team dynamics and collaboration but increases fraud vulnerability (Zak 2008).

(Zak 2008) Shows neurochemical shifts drive financial behaviour: risk avoidance, overconfidence, excessive trust.

Adaptive Market Hypothesis (AMH)

ADAPTIVE MARKET HYPOTHESIS: (Lo 2005) proposed AMH to bridge EMH and behavioral finance. Lo proposes that EMH is too rigid; markets are shaped by competition, adaption, and irrational learning. Applies AMH to real-world failures (2008 crisis), criticises financial models that ignore emotion and feedback loops.

Traditional markets say people are rational, markets are efficient, and anomalies are temporary; however, AMH suggests:

• Markets are like ecosystems and evolve.

• Investors adapt to the environment by trial and error.

• Behavioral traits (e.g., herding) are survival strategies.

Features of AMH

• Market efficiency isn’t fixed; it depends on competition, innovation, and the environment.

• Investors are boundedly rational and learn over time.

• Anomalies may appear and then disappear once exploited (like strategies in evolution), e.g., momentum profits – people exploit them – returns fall – the anomaly disappears – the market evolves.

Key Research

• Session 1 – EMH & Arbitrage Limits

  • Fama (1998): defends EMH, suggests anomalies like bubbles are statistical ‘noise’, and behavioural finance overstates its importance

  • Vishny (1997): irrational traders can push prices further from fair value – cause losses for arbitrageurs (GAMESTOP 2021)

• Session 2/3 – Rationality & Prospect Theory

  • Tversky (1974) – people regularly violate Savage’s Axioms, systematically not by mistake. People consistently break the rules of rational choice

• Session 4/5 – Heuristics & Biases

  • Dale (2015) – introduces 3 HEURISTICS, shows how they lead to misjudging risk, overreaction to news, and market volatility

  • Barberis (2013) - “explains how availability and representativeness cause market anomalies

• Session 6 – Portfolios & Corporate Behaviour

  • Statman (2000) – introduces BPT as a realistic alternative to MPT. outlines 2 main layers of behavioural portfolio theory

  • Baker (2013) – define the 2 main frameworks of Behavioural corporate finance

• Session 8/9 – Neurofinance & Adaptive Markets

  • Sapra & Zak (2008) - Hormones effect financial decisions

  • Lo (2005) – introduces AMH, Markets evolve like ecosystems and aren’t strictly rational.

Efficient Market Hypothesis (EMH)

The Efficient Market Hypothesis (EMH) is a cornerstone of traditional finance, asserting that asset prices fully reflect all available information at any given time. This implies that consistently outperforming the market through skill or insight alone is not possible, as prices already incorporate all known factors.

The EMH exists in three primary forms, each with varying degrees of informational inclusion:

• Weak Form: This form posits that current stock prices already reflect all historical market data, including past prices and trading volumes. Consequently, technical analysis, which relies on historical patterns, is deemed ineffective for predicting future price movements.

• Semi-Strong Form: This level suggests that prices incorporate all publicly available information, such as financial statements, news articles, and economic reports. Thus, neither technical nor fundamental analysis can provide a competitive edge, as the market instantaneously adjusts to new public information.

• Strong Form: The most stringent version of EMH, it claims that prices reflect all information, whether public or private (insider). Under this form, even insider trading would not result in abnormal profits, as such information is already embedded in the prices.

Essentially, the true EMH implies that technical analysis is futile, insider trading is unprofitable, and active fund managers cannot surpass the performance of passively managed index funds over the long term.

Issues with EMH

Despite its theoretical appeal, markets often deviate from perfect efficiency. Notable examples include:

• Dot-com Bubble: During the late 1990s, technology stocks experienced unprecedented growth, with valuations far exceeding fundamental metrics. This irrational exuberance led to an unsustainable bubble that eventually burst, causing significant losses for investors.

• GameStop 2021: The surge in GameStop's stock price in early 2021, driven by retail investors coordinating through online forums, demonstrated how meme-driven trading could defy traditional valuation models. This event highlighted the potential for irrational behavior to influence market prices.

Limits to Rational Arbitrage

Behavioral finance acknowledges the presence of rational investors but recognizes that they often face constraints that limit their ability to correct market mispricings:

1. NOISE TRADER RISK (Vishny 1997): Noise traders, driven by irrational sentiments, can exacerbate price distortions, causing losses for arbitrageurs attempting to exploit mispricings. The GameStop short squeeze exemplified this risk, as rational hedge funds incurred substantial losses due to coordinated buying by retail investors.

2. HORIZON RISK: Even if an arbitrageur correctly identifies a mispricing, they may be unable to capitalize on it due to time constraints. Clients may withdraw funds, or the arbitrageur may face financial distress before the mispricing converges to its fair value. The collapse of Long-Term Capital Management (LTCM) in 1998 illustrates this risk, as the firm's positions were ultimately sound but succumbed to liquidity pressures before prices normalized.

3. MODEL RISK: Arbitrage strategies rely on models to estimate fair value. However, if these models are flawed or incomplete, they can lead to mispricings and losses. Model risk is particularly relevant in complex financial instruments where accurate valuation is challenging.

4. IMPLEMENTATION COSTS: Transaction costs, such as brokerage fees and bid-ask spreads, can erode the profitability of arbitrage strategies. Additionally, large trading volumes can move market prices, reducing the effectiveness of arbitrage opportunities.

5. PRINCIPLE-AGENT PROBLEM (institutional): Institutional investors, such as fund managers, may face conflicts of interest that hinder their ability to act rationally. Fund managers may prioritize short-term performance or peer-group rankings over long-term value maximization. This can lead to herding behavior, where managers mimic the investment decisions of their peers to avoid underperformance.

   • The agent (fund manager) may avoid risky trades if they make them look bad in the short-run - they ‘closet index’ or HERD with other funds to protect their job.

   • (Xu 1999) shows that institutional managers suffer from:-

     • Overconfidence

     • Representativeness bias (chasing past wins)

     • Herding (buying what peers buy)

Traditional Finance vs. Behavioral Finance

Traditional finance operates on the assumption that individuals make rational decisions based on Expected Utility Theory (EUT). According to EUT, individuals know their preferences, consistently select the objectively best option, and update their beliefs accurately with new information.

Expected Utility Theory

Expected Utility Theory (EUT) suggests that decision-makers weigh potential outcomes by their probabilities and associated utilities, opting for the choice that yields the highest expected utility. For instance, a rational investor would unequivocally favor a guaranteed £0 over a 50% chance of losing £10.

However, behavioral finance contends that real-world decision-making often deviates from EUT principles. Even professionals exhibit behaviors inconsistent with rational choice.

EUT is rooted in Savage's Axioms, which define the rules governing rational choice under uncertainty. Key axioms include:

• TRANSITIVITY: If an individual prefers option A to option B and option B to option C, then they must prefer option A to option C. This ensures consistency in preferences.

• INDEPENDENCE: If an individual prefers option A over option B, then they must prefer option A + X over option B + X, where X is any additional outcome. This axiom implies that preferences are independent of irrelevant alternatives.

• COMPLETENESS: Individuals can rank any two outcomes, without being indefinitely indifferent. Completeness ensures that decision-makers can always express a preference between alternatives.

• SURE-THING PRINCIPLE: If an individual would choose option A regardless of whether event X occurs or not, then they should still choose option A even if the occurrence of event X is unknown. This principle emphasizes the importance of focusing on relevant factors in decision-making.

(Tversky 1974) demonstrates that individuals systematically violate these axioms, not due to errors, but as a result of psychological biases. These violations challenge the foundations of classical finance.

Prospect Theory

Prospect Theory, developed by Tversky in 1974, offers an alternative framework for understanding decision-making under risk. It replaces the notion of objective utility with psychological reality, acknowledging that individuals evaluate outcomes based on subjective perceptions.

Four key ideas underpin Prospect Theory:

1. REFERENCE DEPENDENCE: Individuals assess gains and losses relative to a reference point, typically their current wealth or expectations, rather than in absolute terms. This implies that perceived value is context-dependent.

2. LOSS AVERSION: Losses exert a disproportionately larger impact on individuals than equivalent gains. The pain of a loss is approximately twice as intense as the pleasure derived from a comparable gain.

3. DIMINISHING SENSITIVITY: The marginal impact of gains and losses diminishes as individuals move further away from the reference point. The difference between £0 and £100 is more pronounced than the difference between £900 and £1000.

4. PROBABILITY WEIGHING: Individuals tend to overweight small probabilities and underweight large probabilities. This can lead to risk-seeking behavior in the face of low-probability gains and risk-averse behavior in the face of high-probability losses.

Behavioral Phenomena linked to PROEPECT THEORY

• FRAMING EFFECTS: Choices vary depending on how options are presented. For example, individuals may respond differently to a medical treatment described as having a 10% mortality rate versus a 90% survival rate.

• MENTAL ACCOUNTING: Individuals compartmentalize money into separate mental accounts, treating funds differently based on their source or intended use. This can lead to suboptimal financial decisions, such as excessive gambling or insufficient saving.

• DISPOSITION EFFECT: Investors tend to sell winning investments prematurely to lock in gains while holding onto losing investments for too long to avoid recognizing losses. This behavior is driven by a desire to realize gains and avoid acknowledging defeats.

• REALISATION UTILITY: Individuals derive pleasure from realizing gains, even if it compromises long-term performance. This can lead to suboptimal investment decisions, as investors prioritize immediate gratification over future wealth accumulation.

Heuristics and Biases

Heuristics

Heuristics are mental shortcuts employed to expedite decision-making in uncertain situations. While efficient, they may not always yield accurate outcomes.

In behavioral finance, heuristics explain why individuals make predictable errors when assessing risk, pricing assets, or interpreting news.

Key Heuristics (Dale 2015)

1. REPRESENTATIVENESS: Probabilities are assessed based on the degree to which an event resembles a stereotype, rather than on actual statistical likelihood. Momentum investing, which relies on past performance, exemplifies this heuristic, despite the absence of guarantees regarding future returns.

2. AVAILABILITY: Judgments are based on the ease with which information comes to mind, rather than on its actual likelihood. Overreacting to dramatic market headlines illustrates this heuristic.

3. ANCHORING: Decision-making overly relies on the initial piece of information (the anchor), even if it is irrelevant. Investors may anchor on a previous stock high instead of reassessing its intrinsic value.

(Dale 2015) elucidates how heuristics can lead to misjudging risk, overreacting to news, and contributing to market volatility.

Cognitive Biases

Cognitive biases represent systematic decision-making errors stemming from heuristics. (Baker 2013) demonstrates how investor biases influence corporate financing decisions.

1. OVERCONFIDENCE: Overestimating one's skills or predictions. This leads to excessive trading and inadequate diversification.

2. CONFIRMATION BIAS: Seeking information that confirms existing beliefs. This results in rigid perspectives and inadequate model revision.

3. FRAMING EFFECT: Decisions are influenced by how information is presented.

4. DISPOSITION EFFECT: Investors hold onto losers too long and sell winners too early, driven by realization utility and loss aversion.

Psychological barriers are non-fundamental price levels, such as round numbers, that traders perceive as significant, even if they lack intrinsic importance. For example, traders may interpret a stock breaking £100 as a breakout or view £50 as a psychological support level.

The Relationship

Heuristics give rise to Biases, which in turn affect Market-Level Effects.

This framework explains anomalies that EMH cannot account for and elucidates how cognitive shortcuts can generate predictable inefficiencies.

Behavioral Portfolio Theory (BPT)

Modern Portfolio Theory (MPT)

Modern Portfolio Theory (MPT) assumes that investors are rational and strive to optimize a single portfolio by balancing expected return and risk (variance). Preferences are stable and mathematically consistent. However, real-world investors often deviate from rationality.

They may not diversify optimally, holding both low-risk cash and high-risk bets in the same portfolio and pursuing goals such as safety, growth, or even status.

Behavioral Portfolio Theory (BPT)

Behavioral Portfolio Theory (BPT), introduced by (Statman 2000), offers a psychologically realistic alternative to MPT.

Investors mentally allocate their wealth into distinct layers, each linked to a specific goal. Two key layers include:

1. SAFETY LAYER: Aimed at capital preservation (e.g., bonds, cash).

2. ASPIRATION LAYER: Focused on high-potential gains and risk-taking (e.g., stocks, crypto).

These layers are not integrated as in MPT but are psychologically separated via mental accounting.

This implies that investors take excessive risk in aspiration layers, hold onto too much cash in the safety layer (under diversification), and explains barbell portfolios (comprising very risky and very safe assets).

Behavioral Corporate Finance (BCF)

(Baker 2013) delineates two key frameworks within Behavioral Corporate Finance

1. MARKET TIME APPROACH: Managers are rational, but markets are irrational. Firms issue equity when prices are overvalued, catering to investor sentiment rather than long-term fundamentals. For instance, issuing equity during bull markets, such as the Dotcom boom, even if capital is not urgently needed.

2. MANAGERIAL BIAS APPROACH: Managers themselves are biased. Common biases include overconfidence, optimism, and loss aversion.

   • It Affects capital structure, investment, and M&A, e.g., overconfident CEOs overestimate returns – aggressive M&A, overinvestment; loss-averse managers avoid debt – debt conservatism, even when leverage is optimal; optimistic forecasts lead to projects that later underperform.

Behavioral Game Theory (BGT)

Behavioral Game Theory (BGT) integrates behavioral insights into strategic interactions. In real-world negotiations and contracts, individuals prioritize:

• Fairness, reciprocity, and social image, which contradicts classic game theory's assumption that individuals always maximize material payoff.

• E.g., ULTIMATUM GAME: player A offers a split 90/10, Player B rejects unfair offers, even if they get nothing.

• Fairness norms take precedence over strict rationality. For example, founders may reject 'fair' deals if they perceive themselves as undervalued.

Neurofinance

Neurofinance explores the influence of brain chemistry, hormones, and neural systems on financial decision-making.

Traditional finance assumes that individuals think like computers. Neurofinance suggests that individuals think like biological organisms influenced by:

• Hormonal cycles, stress responses, emotional biases, social triggers, etc.

It shifts the focus from the cognitive to the physiological.

• CORTISOL: A stress hormone that rises during market crashes or volatility.

  • Linked to increased risk aversion: traders become defensive sell quickly and avoid new positions, e.g., the Financial crisis 2008, market-wide panic ^ elevated cortisol and deleveraging.

• TESTOSTERONE: A dominance hormone linked to overconfidence, risk-seeking, and momentum trading.

  • E.g., Trader wine – testosterone spike – bigger trades – bubble risk.

  • WINNER EFFECT - Biologically documented in humans and animals.

• OXYTOCIN: A trust hormone that fosters feelings of connection, bonding, and cooperation, making individuals more trusting.

  • Good for team dynamics and collaboration but increases fraud vulnerability (Zak 2008).

(Zak 2008) demonstrates how neurochemical shifts drive financial behavior, influencing risk avoidance, overconfidence, and excessive trust.

Adaptive Market Hypothesis (AMH)

Adaptive Market Hypothesis: (Lo 2005) proposed AMH to bridge EMH and behavioral finance. Lo proposes that EMH is too rigid; markets are shaped by competition, adaption, and irrational learning. Applies AMH to real-world failures (2008 crisis), criticises financial models that ignore emotion and feedback loops.

Traditional markets suggest that individuals are rational, markets are efficient, and anomalies are temporary. However, AMH suggests:

• Markets are like ecosystems and evolve.

• Investors adapt to the environment by trial and error.

• Behavioral traits (e.g., herding) are survival strategies.

Features of AMH

• Market efficiency is not fixed but depends on competition, innovation, and the environment.

• Investors are boundedly rational and learn over time.

• Anomalies may emerge and then disappear once exploited (like strategies in evolution). For instance, momentum profits may decline as they are exploited, leading to the disappearance of the anomaly and the evolution of the market.

Key Research

• Session 1 – EMH & Arbitrage Limits-

  • Fama (1998): defends EMH, suggests anomalies like bubbles are statistical ‘noise’, and behavioural finance overstates its importance

  • Vishny (1997): irrational traders can push prices further from fair value – cause losses for arbitrageurs (GAMESTOP 2021)

• Session 2/3 – Rationality & Prospect Theory-

  • Tversky (1974) – people regularly violate Savage’s Axioms, systematically not by mistake. People consistently break the rules of rational choice

• Session 4/5 – Heuristics & Biases-

  • Dale (2015) – introduces 3 HEURISTICS, shows how they lead to misjudging risk, overreaction to news, and market volatility

  • Barberis (2013) - “explains how availability and representativeness cause market anomalies

• Session 6 – Portfolios & Corporate Behaviour-

  • Statman (2000) – introduces BPT as a realistic alternative to MPT. outlines 2 main layers of behavioural portfolio theory

  • Baker (20

Efficient Market Hypothesis (EMH)

The Efficient Market Hypothesis (EMH) is a cornerstone of traditional finance, asserting that asset prices fully reflect all available information at any given time. This implies that consistently outperforming the market through skill or insight alone is not possible, as prices already incorporate all known factors.

The EMH exists in three primary forms, each with varying degrees of informational inclusion:

• Weak Form: This form posits that current stock prices already reflect all historical market data, including past prices and trading volumes. Consequently, technical analysis, which relies on historical patterns, is deemed ineffective for predicting future price movements.

• Semi-Strong Form: This level suggests that prices incorporate all publicly available information, such as financial statements, news articles, and economic reports. Thus, neither technical nor fundamental analysis can provide a competitive edge, as the market instantaneously adjusts to new public information.

• Strong Form: The most stringent version of EMH, it claims that prices reflect all information, whether public or private (insider). Under this form, even insider trading would not result in abnormal profits, as such information is already embedded in the prices.

Essentially, the true EMH implies that technical analysis is futile, insider trading is unprofitable, and active fund managers cannot surpass the performance of passively managed index funds over the long term.

Issues with EMH

Despite its theoretical appeal, markets often deviate from perfect efficiency. Notable examples include:

• Dot-com Bubble: During the late 1990s, technology stocks experienced unprecedented growth, with valuations far exceeding fundamental metrics. This irrational exuberance led to an unsustainable bubble that eventually burst, causing significant losses for investors.

• GameStop 2021: The surge in GameStop's stock price in early 2021, driven by retail investors coordinating through online forums, demonstrated how meme-driven trading could defy traditional valuation models. This event highlighted the potential for irrational behavior to influence market prices.

Limits to Rational Arbitrage

Behavioral finance acknowledges the presence of rational investors but recognizes that they often face constraints that limit their ability to correct market mispricings:

1. NOISE TRADER RISK (Vishny 1997): Noise traders, driven by irrational sentiments, can exacerbate price distortions, causing losses for arbitrageurs attempting to exploit mispricings. The GameStop short squeeze exemplified this risk, as rational hedge funds incurred substantial losses due to coordinated buying by retail investors.

2. HORIZON RISK: Even if an arbitrageur correctly identifies a mispricing, they may be unable to capitalize on it due to time constraints. Clients may withdraw funds, or the arbitrageur may face financial distress before the mispricing converges to its fair value. The collapse of Long-Term Capital Management (LTCM) in 1998 illustrates this risk, as the firm's positions were ultimately sound but succumbed to liquidity pressures before prices normalized.

3. MODEL RISK: Arbitrage strategies rely on models to estimate fair value. However, if these models are flawed or incomplete, they can lead to mispricings and losses. Model risk is particularly relevant in complex financial instruments where accurate valuation is challenging.

4. IMPLEMENTATION COSTS: Transaction costs, such as brokerage fees and bid-ask spreads, can erode the profitability of arbitrage strategies. Additionally, large trading volumes can move market prices, reducing the effectiveness of arbitrage opportunities.

5. PRINCIPLE-AGENT PROBLEM (institutional): Institutional investors, such as fund managers, may face conflicts of interest that hinder their ability to act rationally. Fund managers may prioritize short-term performance or peer-group rankings over long-term value maximization. This can lead to herding behavior, where managers mimic the investment decisions of their peers to avoid underperformance.

   • The agent (fund manager) may avoid risky trades if they make them look bad in the short-run - they ‘closet index’ or HERD with other funds to protect their job.

   • (Xu 1999) shows that institutional managers suffer from:-

     • Overconfidence

     • Representativeness bias (chasing past wins)

     • Herding (buying what peers buy)

Traditional Finance vs. Behavioral Finance

Traditional finance operates on the assumption that individuals make rational decisions based on Expected Utility Theory (EUT). According to EUT, individuals know their preferences, consistently select the objectively best option, and update their beliefs accurately with new information.

Expected Utility Theory

Expected Utility Theory (EUT) suggests that decision-makers weigh potential outcomes by their probabilities and associated utilities, opting for the choice that yields the highest expected utility. For instance, a rational investor would unequivocally favor a guaranteed £0 over a 50% chance of losing £10.

However, behavioral finance contends that real-world decision-making often deviates from EUT principles. Even professionals exhibit behaviors inconsistent with rational choice.

EUT is rooted in Savage's Axioms, which define the rules governing rational choice under uncertainty. Key axioms include:

• TRANSITIVITY: If an individual prefers option A to option B and option B to option C, then they must prefer option A to option C. This ensures consistency in preferences.

• INDEPENDENCE: If an individual prefers option A over option B, then they must prefer option A + X over option B + X, where X is any additional outcome. This axiom implies that preferences are independent of irrelevant alternatives.

• COMPLETENESS: Individuals can rank any two outcomes, without being indefinitely indifferent. Completeness ensures that decision-makers can always express a preference between alternatives.

• SURE-THING PRINCIPLE: If an individual would choose option A regardless of whether event X occurs or not, then they should still choose option A even if the occurrence of event X is unknown. This principle emphasizes the importance of focusing on relevant factors in decision-making.

(Tversky 1974) demonstrates that individuals systematically violate these axioms, not due to errors, but as a result of psychological biases. These violations challenge the foundations of classical finance.

Prospect Theory

Prospect Theory, developed by Tversky in 1974, offers an alternative framework for understanding decision-making under risk. It replaces the notion of objective utility with psychological reality, acknowledging that individuals evaluate outcomes based on subjective perceptions.

Four key ideas underpin Prospect Theory:

1. REFERENCE DEPENDENCE: Individuals assess gains and losses relative to a reference point, typically their current wealth or expectations, rather than in absolute terms. This implies that perceived value is context-dependent.

2. LOSS AVERSION: Losses exert a disproportionately larger impact on individuals than equivalent gains. The pain of a loss is approximately twice as intense as the pleasure derived from a comparable gain.

3. DIMINISHING SENSITIVITY: The marginal impact of gains and losses diminishes as individuals move further away from the reference point. The difference between £0 and £100 is more pronounced than the difference between £900 and £1000.

4. PROBABILITY WEIGHING: Individuals tend to overweight small probabilities and underweight large probabilities. This can lead to risk-seeking behavior in the face of low-probability gains and risk-averse behavior in the face of high-probability losses.

Behavioral Phenomena linked to PROEPECT THEORY

• FRAMING EFFECTS: Choices vary depending on how options are presented. For example, individuals may respond differently to a medical treatment described as having a 10% mortality rate versus a 90% survival rate.

• MENTAL ACCOUNTING: Individuals compartmentalize money into separate mental accounts, treating funds differently based on their source or intended use. This can lead to suboptimal financial decisions, such as excessive gambling or insufficient saving.

• DISPOSITION EFFECT: Investors tend to sell winning investments prematurely to lock in gains while holding onto losing investments for too long to avoid recognizing losses. This behavior is driven by a desire to realize gains and avoid acknowledging defeats.

• REALISATION UTILITY: Individuals derive pleasure from realizing gains, even if it compromises long-term performance. This can lead to suboptimal investment decisions, as investors prioritize immediate gratification over future wealth accumulation.

Heuristics and Biases

Heuristics

Heuristics are mental shortcuts employed to expedite decision-making in uncertain situations. While efficient, they may not always yield accurate outcomes.

In behavioral finance, heuristics explain why individuals make predictable errors when assessing risk, pricing assets, or interpreting news.

Key Heuristics (Dale 2015)

1. REPRESENTATIVENESS: Probabilities are assessed based on the degree to which an event resembles a stereotype, rather than on actual statistical likelihood. Momentum investing, which relies on past performance, exemplifies this heuristic, despite the absence of guarantees regarding future returns.

2. AVAILABILITY: Judgments are based on the ease with which information comes to mind, rather than on its actual likelihood. Overreacting to dramatic market headlines illustrates this heuristic.

3. ANCHORING: Decision-making overly relies on the initial piece of information (the anchor), even if it is irrelevant. Investors may anchor on a previous stock high instead of reassessing its intrinsic value.

(Dale 2015) elucidates how heuristics can lead to misjudging risk, overreacting to news, and contributing to market volatility.

Cognitive Biases

Cognitive biases represent systematic decision-making errors stemming from heuristics. (Baker 2013) demonstrates how investor biases influence corporate financing decisions.

1. OVERCONFIDENCE: Overestimating one's skills or predictions. This leads to excessive trading and inadequate diversification.

2. CONFIRMATION BIAS: Seeking information that confirms existing beliefs. This results in rigid perspectives and inadequate model revision.

3. FRAMING EFFECT: Decisions are influenced by how information is presented.

4. DISPOSITION EFFECT: Investors hold onto losers too long and sell winners too early, driven by realization utility and loss aversion.

Psychological barriers are non-fundamental price levels, such as round numbers, that traders perceive as significant, even if they lack intrinsic importance. For example, traders may interpret a stock breaking £100 as a breakout or view £50 as a psychological support level.

The Relationship

Heuristics give rise to Biases, which in turn affect Market-Level Effects.

This framework explains anomalies that EMH cannot account for and elucidates how cognitive shortcuts can generate predictable inefficiencies.

Behavioral Portfolio Theory (BPT)

Modern Portfolio Theory (MPT)

Modern Portfolio Theory (MPT) assumes that investors are rational and strive to optimize a single portfolio by balancing expected return and risk (variance). Preferences are stable and mathematically consistent. However, real-world investors often deviate from rationality.

They may not diversify optimally, holding both low-risk cash and high-risk bets in the same portfolio and pursuing goals such as safety, growth, or even status.

Behavioral Portfolio Theory (BPT)

Behavioral Portfolio Theory (BPT), introduced by (Statman 2000), offers a psychologically realistic alternative to MPT.

Investors mentally allocate their wealth into distinct layers, each linked to a specific goal. Two key layers include:

1. SAFETY LAYER: Aimed at capital preservation (e.g., bonds, cash).

2. ASPIRATION LAYER: Focused on high-potential gains and risk-taking (e.g., stocks, crypto).

These layers are not integrated as in MPT but are psychologically separated via mental accounting.

This implies that investors take excessive risk in aspiration layers, hold onto too much cash in the safety layer (under diversification), and explains barbell portfolios (comprising very risky and very safe assets).

Behavioral Corporate Finance (BCF)

(Baker 2013) delineates two key frameworks within Behavioral Corporate Finance

1. MARKET TIME APPROACH: Managers are rational, but markets are irrational. Firms issue equity when prices are overvalued, catering to investor sentiment rather than long-term fundamentals. For instance, issuing equity during bull markets, such as the Dotcom boom, even if capital is not urgently needed.

2. MANAGERIAL BIAS APPROACH: Managers themselves are biased. Common biases include overconfidence, optimism, and loss aversion.

   • It Affects capital structure, investment, and M&A, e.g., overconfident CEOs overestimate returns – aggressive M&A, overinvestment; loss-averse managers avoid debt – debt conservatism, even when leverage is optimal; optimistic forecasts lead to projects that later underperform.

Behavioral Game Theory (BGT)

Behavioral Game Theory (BGT) integrates behavioral insights into strategic interactions. In real-world negotiations and contracts, individuals prioritize:

• Fairness, reciprocity, and social image, which contradicts classic game theory's assumption that individuals always maximize material payoff.

• E.g., ULTIMATUM GAME: player A offers a split 90/10, Player B rejects unfair offers, even if they get nothing.

• Fairness norms take precedence over strict rationality. For example, founders may reject 'fair' deals if they perceive themselves as undervalued.

Neurofinance

Neurofinance explores the influence of brain chemistry, hormones, and neural systems on financial decision-making.

Traditional finance assumes that individuals think like computers. Neurofinance suggests that individuals think like biological organisms influenced by:

• Hormonal cycles, stress responses, emotional biases, social triggers, etc.

It shifts the focus from the cognitive to the physiological.

• CORTISOL: A stress hormone that rises during market crashes or volatility.

  • Linked to increased risk aversion: traders become defensive sell quickly and avoid new positions, e.g., the Financial crisis 2008, market-wide panic ^ elevated cortisol and deleveraging.

• TESTOSTERONE: A dominance hormone linked to overconfidence, risk-seeking, and momentum trading.

  • E.g., Trader wine – testosterone spike – bigger trades – bubble risk.

  • WINNER EFFECT - Biologically documented in humans and animals.

• OXYTOCIN: A trust hormone that fosters feelings of connection, bonding, and cooperation, making individuals more trusting.

  • Good for team dynamics and collaboration but increases fraud vulnerability (Zak 2008).

(Zak 2008) demonstrates how neurochemical shifts drive financial behavior, influencing risk avoidance, overconfidence, and excessive trust.

Adaptive Market Hypothesis (AMH)

Adaptive Market Hypothesis: (Lo 2005) proposed AMH to bridge EMH and behavioral finance. Lo proposes that EMH is too rigid; markets are shaped by competition, adaption, and irrational learning. Applies AMH to real-world failures (2008 crisis), criticises financial models that ignore emotion and feedback loops.

Traditional markets suggest that individuals are rational, markets are efficient, and anomalies are temporary. However, AMH suggests:

• Markets are like ecosystems and evolve.

• Investors adapt to the environment by trial and error.

• Behavioral traits (e.g., herding) are survival strategies.

Features of AMH

• Market efficiency is not fixed but depends on competition, innovation, and the environment.

• Investors are boundedly rational and learn over time.

• Anomalies may emerge and then disappear once exploited (like strategies in evolution). For instance, momentum profits may decline as they are exploited, leading to the disappearance of the anomaly and the evolution of the market.

Key Research

• Session 1 – EMH & Arbitrage Limits-

  • Fama (1998): defends EMH, suggests anomalies like bubbles are statistical ‘noise’, and behavioural finance overstates its importance

  • Vishny (1997): irrational traders can push prices further from fair value – cause losses for arbitrageurs (GAMESTOP 2021)

• Session 2/3 – Rationality & Prospect Theory-

  • Tversky (1974) – people regularly violate Savage’s Axioms, systematically not by mistake. People consistently break the rules of rational choice

• Session 4/5 – Heuristics & Biases-

  • Dale (2015) – introduces 3 HEURISTICS, shows how they lead to misjudging risk, overreaction to news, and market volatility

  • Barberis (2013) - “explains how availability and representativeness cause market anomalies

• Session 6 – Portfolios & Corporate Behaviour-

  • Statman (2000) – introduces BPT as a realistic alternative to MPT. outlines 2 main layers of behavioural portfolio theory

  • Baker (20

Efficient Market Hypothesis (EMH)

The Efficient Market Hypothesis (EMH) is a cornerstone of traditional finance, asserting that asset prices fully reflect all available information at any given time. This implies that consistently outperforming the market through skill or insight alone is not possible, as prices already incorporate all known factors.

The EMH exists in three primary forms, each with varying degrees of informational inclusion:

• Weak Form: This form posits that current stock prices already reflect all historical market data, including past prices and trading volumes. Consequently, technical analysis, which relies on historical patterns, is deemed ineffective for predicting future price movements. Specifically, weak form efficiency suggests that analyzing past stock prices and trading volumes will not provide any edge in predicting future price movements, as this information is already reflected in the current prices. Charting and other technical analysis tools are considered useless under this form.
• Semi-Strong Form: This level suggests that prices incorporate all publicly available information, such as financial statements, news articles, and economic reports. Thus, neither technical nor fundamental analysis can provide a competitive edge, as the market instantaneously adjusts to new public information. This implies that once information is released to the public, it is immediately incorporated into the prices, making it impossible for investors to profit from it using publicly available data.
• Strong Form: The most stringent version of EMH, it claims that prices reflect all information, whether public or private (insider). Under this form, even insider trading would not result in abnormal profits, as such information is already embedded in the prices. In essence, no one can consistently achieve above-average returns, as all information, including private or insider information, is already priced into the assets.

Essentially, the true EMH implies that technical analysis is futile, insider trading is unprofitable, and active fund managers cannot surpass the performance of passively managed index funds over the long term.

Issues with EMH

Despite its theoretical appeal, markets often deviate from perfect efficiency. Notable examples include:

• Dot-com Bubble: During the late 1990s, technology stocks experienced unprecedented growth, with valuations far exceeding fundamental metrics. This irrational exuberance led to an unsustainable bubble that eventually burst, causing significant losses for investors. Specifically, companies with little to no earnings or viable business models saw their stock prices skyrocket, driven by speculative investments and the fear of missing out (FOMO).
• GameStop 2021: The surge in GameStop's stock price in early 2021, driven by retail investors coordinating through online forums, demonstrated how meme-driven trading could defy traditional valuation models. This event highlighted the potential for irrational behavior to influence market prices. The short squeeze in GameStop, orchestrated by retail investors buying shares and call options, forced hedge funds with short positions to cover their bets, driving the price to unprecedented levels. This highlighted the power of social media and coordinated retail investing in disrupting traditional market dynamics.

Limits to Rational Arbitrage

Behavioral finance acknowledges the presence of rational investors but recognizes that they often face constraints that limit their ability to correct market mispricings:

• NOISE TRADER RISK (Vishny 1997): Noise traders, driven by irrational sentiments, can exacerbate price distortions, causing losses for arbitrageurs attempting to exploit mispricings. The GameStop short squeeze exemplified this risk, as rational hedge funds incurred substantial losses due to coordinated buying by retail investors. Noise traders are often characterized by their lack of information or reliance on rumors and speculation. Their actions can create significant price volatility and make it difficult for rational arbitrageurs to profit from mispricings.
• HORIZON RISK: Even if an arbitrageur correctly identifies a mispricing, they may be unable to capitalize on it due to time constraints. Clients may withdraw funds, or the arbitrageur may face financial distress before the mispricing converges to its fair value. The collapse of Long-Term Capital Management (LTCM) in 1998 illustrates this risk, as the firm's positions were ultimately sound but succumbed to liquidity pressures before prices normalized. LTCM's strategies relied on the convergence of prices in various fixed-income securities. However, the Russian financial crisis in 1998 triggered a flight to quality, widening spreads and causing LTCM's positions to deteriorate rapidly. The firm was ultimately bailed out to prevent a wider financial meltdown.
• MODEL RISK: Arbitrage strategies rely on models to estimate fair value. However, if these models are flawed or incomplete, they can lead to mispricings and losses. Model risk is particularly relevant in complex financial instruments where accurate valuation is challenging. For example, models used to price mortgage-backed securities before the 2008 financial crisis failed to adequately account for the risk of widespread defaults. This led to mispricings and ultimately contributed to the crisis.
• IMPLEMENTATION COSTS: Transaction costs, such as brokerage fees and bid-ask spreads, can erode the profitability of arbitrage strategies. Additionally, large trading volumes can move market prices, reducing the effectiveness of arbitrage opportunities. High-frequency traders, who execute a large number of trades in a short period, are particularly sensitive to implementation costs. Even small transaction fees can significantly impact their profitability.
• PRINCIPLE-AGENT PROBLEM (institutional): Institutional investors, such as fund managers, may face conflicts of interest that hinder their ability to act rationally. Fund managers may prioritize short-term performance or peer-group rankings over long-term value maximization. This can lead to herding behavior, where managers mimic the investment decisions of their peers to avoid underperformance. The agent (fund manager) may avoid risky trades if they make them look bad in the short-run - they ‘closet index’ or HERD with other funds to protect their job. Fund managers may engage in "window dressing," where they buy popular stocks at the end of a quarter to improve their portfolio's appearance, even if these stocks are not fundamentally attractive.

(Xu 1999) shows that institutional managers suffer from:

• Overconfidence
• Representativeness bias (chasing past wins)
• Herding (buying what peers buy)

Traditional Finance vs. Behavioral Finance

Traditional finance operates on the assumption that individuals make rational decisions based on Expected Utility Theory (EUT). According to EUT, individuals know their preferences, consistently select the objectively best option, and update their beliefs accurately with new information.

Expected Utility Theory

Expected Utility Theory (EUT) suggests that decision-makers weigh potential outcomes by their probabilities and associated utilities, opting for the choice that yields the highest expected utility. For instance, a rational investor would unequivocally favor a guaranteed £0 over a 50% chance of losing £10.

However, behavioral finance contends that real-world decision-making often deviates from EUT principles. Even professionals exhibit behaviors inconsistent with rational choice.

EUT is rooted in Savage's Axioms, which define the rules governing rational choice under uncertainty. Key axioms include:

• TRANSITIVITY: If an individual prefers option A to option B and option B to option C, then they must prefer option A to option C. This ensures consistency in preferences. If an investor prefers stocks to bonds and bonds to cash, then they should also prefer stocks to cash.
• INDEPENDENCE: If an individual prefers option A over option B, then they must prefer option A + X over option B + X, where X is any additional outcome. This axiom implies that preferences are independent of irrelevant alternatives. If an investor prefers receiving £100 over receiving £50, then they should also prefer a lottery ticket that gives them a £100 prize with probability p over a lottery ticket that gives them a £50 prize with the same probability p.
• COMPLETENESS: Individuals can rank any two outcomes, without being indefinitely indifferent. Completeness ensures that decision-makers can always express a preference between alternatives. An investor can always state whether they prefer investing in the stock market or in real estate, or whether they are indifferent between the two.
• SURE-THING PRINCIPLE: If an individual would choose option A regardless of whether event X occurs or not, then they should still choose option A even if the occurrence of event X is unknown. This principle emphasizes the importance of focusing on relevant factors in decision-making. If an investor would choose to invest in a particular stock regardless of whether interest rates rise or fall, then they should still choose to invest in that stock even if they are unsure about the future direction of interest rates.

(Tversky 1974) demonstrates that individuals systematically violate these axioms, not due to errors, but as a result of psychological biases. These violations challenge the foundations of classical finance.

Prospect Theory

Prospect Theory, developed by Tversky in 1974, offers an alternative framework for understanding decision-making under risk. It replaces the notion of objective utility with psychological reality, acknowledging that individuals evaluate outcomes based on subjective perceptions.

Four key ideas underpin Prospect Theory:

12. REFERENCE DEPENDENCE: Individuals assess gains and losses relative to a reference point, typically their current wealth or expectations, rather than in absolute terms. This implies that perceived value is context-dependent. For example, an investor who buys a stock at £50 and sees it rise to £60 may feel less satisfied than an investor who buys the same stock at £40 and sees it rise to £60, even though both investors have made a £10 profit.
13. LOSS AVERSION: Losses exert a disproportionately larger impact on individuals than equivalent gains. The pain of a loss is approximately twice as intense as the pleasure derived from a comparable gain. The emotional distress experienced from losing £100 is generally more significant than the happiness experienced from gaining £100.
14. DIMINISHING SENSITIVITY: The marginal impact of gains and losses diminishes as individuals move further away from the reference point. The difference between £0 and £100 is more pronounced than the difference between £900 and £1000. Winning £100 when you have nothing feels like a significant gain, but winning £100 when you already have £900 feels less impactful.
15. PROBABILITY WEIGHING: Individuals tend to overweight small probabilities and underweight large probabilities. This can lead to risk-seeking behavior in the face of low-probability gains and risk-averse behavior in the face of high-probability losses. People often buy lottery tickets despite the extremely low probability of winning, and they may be overly cautious about risks with high probabilities, such as driving without a seatbelt.

Behavioral Phenomena linked to PROEPECT THEORY

• FRAMING EFFECTS: Choices vary depending on how options are presented. For example, individuals may respond differently to a medical treatment described as having a 10% mortality rate versus a 90% survival rate. The way information is framed can significantly influence decisions, even if the underlying facts remain the same.
• MENTAL ACCOUNTING: Individuals compartmentalize money into separate mental accounts, treating funds differently based on their source or intended use. This can lead to suboptimal financial decisions, such as excessive gambling or insufficient saving. An individual might be more willing to spend money they perceive as "found money" (e.g., a tax refund) than money they earned through hard work, even though both amounts have the same economic value.
• DISPOSITION EFFECT: Investors tend to sell winning investments prematurely to lock in gains while holding onto losing investments for too long to avoid recognizing losses. This behavior is driven by a desire to realize gains and avoid acknowledging defeats. An investor might sell a stock that has increased in value by 20% to secure the profit, while holding onto a stock that has decreased in value by 20% in the hope that it will recover.
• REALISATION UTILITY: Individuals derive pleasure from realizing gains, even if it compromises long-term performance. This can lead to suboptimal investment decisions, as investors prioritize immediate gratification over future wealth accumulation. An investor may sell a stock that has appreciated in value simply to experience the satisfaction of realizing a gain, even if they believe the stock will continue to rise in the future.

Heuristics and Biases

Heuristics

Heuristics are mental shortcuts employed to expedite decision-making in uncertain situations. While efficient, they may not always yield accurate outcomes.

In behavioral finance, heuristics explain why individuals make predictable errors when assessing risk, pricing assets, or interpreting news.

Key Heuristics (Dale 2015)

20. REPRESENTATIVENESS: Probabilities are assessed based on the degree to which an event resembles a stereotype, rather than on actual statistical likelihood. Momentum investing, which relies on past performance, exemplifies this heuristic, despite the absence of guarantees regarding future returns. Investors might assume that a stock that has performed well in the past will continue to perform well in the future, even if there is no fundamental reason to believe this is the case.
21. AVAILABILITY: Judgments are based on the ease with which information comes to mind, rather than on its actual likelihood. Overreacting to dramatic market headlines illustrates this heuristic. Investors might overestimate the risk of a stock market crash after reading a series of negative news articles, even if the overall economic outlook remains positive.
22. ANCHORING: Decision-making overly relies on the initial piece of information (the anchor), even if it is irrelevant. Investors may anchor on a previous stock high instead of reassessing its intrinsic value. If an investor initially bought a stock at £100, they might be reluctant to sell it for less than £100, even if its intrinsic value has declined significantly.

(Dale 2015) elucidates how heuristics can lead to misjudging risk, overreacting to news, and contributing to market volatility.

Cognitive Biases

Cognitive biases represent systematic decision-making errors stemming from heuristics. (Baker 2013) demonstrates how investor biases influence corporate financing decisions.

23. OVERCONFIDENCE: Overestimating one's skills or predictions. This leads to excessive trading and inadequate diversification. An overconfident investor might believe they can consistently pick winning stocks and may trade more frequently than is necessary, resulting in higher transaction costs and lower overall returns.
24. CONFIRMATION BIAS: Seeking information that confirms existing beliefs. This results in rigid perspectives and inadequate model revision. An investor who believes a particular stock is a good investment might only seek out positive information about the stock, while ignoring any negative information that might challenge their belief.
25. FRAMING EFFECT: Decisions are influenced by how information is presented.
26. DISPOSITION EFFECT: Investors hold onto losers too long and sell winners too early, driven by realization utility and loss aversion.

Psychological barriers are non-fundamental price levels, such as round numbers, that traders perceive as significant, even if they lack intrinsic importance. For example, traders may interpret a stock breaking £100 as a breakout or view £50 as a psychological support level.

The Relationship

Heuristics give rise to Biases, which in turn affect Market-Level Effects.

This framework explains anomalies that EMH cannot account for and elucidates how cognitive shortcuts can generate predictable inefficiencies.

Behavioral Portfolio Theory (BPT)

Modern Portfolio Theory (MPT)

Modern Portfolio Theory (MPT) assumes that investors are rational and strive to optimize a single portfolio by balancing expected return and risk (variance). Preferences are stable and mathematically consistent. However, real-world investors often deviate from rationality.

They may not diversify optimally, holding both low-risk cash and high-risk bets in the same portfolio and pursuing goals such as safety, growth, or even status.

Behavioral Portfolio Theory (BPT)

Behavioral Portfolio Theory (BPT), introduced by (Statman 2000), offers a psychologically realistic alternative to MPT.

Investors mentally allocate their wealth into distinct layers, each linked to a specific goal. Two key layers include:

27. SAFETY LAYER: Aimed at capital preservation (e.g., bonds, cash).
28. ASPIRATION LAYER: Focused on high-potential gains and risk-taking (e.g., stocks, crypto).

These layers are not integrated as in MPT but are psychologically separated via mental accounting.

This implies that investors take excessive risk in aspiration layers, hold onto too much cash in the safety layer (under diversification), and explains barbell portfolios (comprising very risky and very safe assets).

Behavioral Corporate Finance (BCF)

(Baker 2013) delineates two key frameworks within Behavioral Corporate Finance

29. MARKET TIME APPROACH: Managers are rational, but markets are irrational. Firms issue equity when prices are overvalued, catering to investor sentiment rather than long-term fundamentals. For instance, issuing equity during bull markets, such as the Dotcom boom, even if capital is not urgently needed.
30. MANAGERIAL BIAS APPROACH: Managers themselves are biased. Common biases include overconfidence, optimism, and loss aversion. It Affects capital structure, investment, and M&A, e.g., overconfident CEOs overestimate returns – aggressive M&A, overinvestment; loss-averse managers avoid debt – debt conservatism, even when leverage is optimal; optimistic forecasts lead to projects that later underperform.

Behavioral Game Theory (BGT)

Behavioral Game Theory (BGT) integrates behavioral insights into strategic interactions. In real-world negotiations and contracts, individuals prioritize:

Fairness, reciprocity, and social image, which contradicts classic game theory's assumption that individuals always maximize material payoff.

E.g., ULTIMATUM GAME: player A offers a split 90/10, Player B rejects unfair offers, even if they get nothing.

Fairness norms take precedence over strict rationality. For example, founders may reject 'fair' deals if they perceive themselves as undervalued.

Neurofinance

Neurofinance explores the influence of brain chemistry, hormones, and neural systems on financial decision-making.

Traditional finance assumes that individuals think like computers. Neurofinance suggests that individuals think like biological organisms influenced by:

Hormonal cycles, stress responses, emotional biases, social triggers, etc.

It shifts the focus from the cognitive to the physiological.

• CORTISOL: A stress hormone that rises during market crashes or volatility. Linked to increased risk aversion: traders become defensive sell quickly and avoid new positions, e.g., the Financial crisis 2008, market-wide panic ^ elevated cortisol and deleveraging.
• TESTOSTERONE: A dominance hormone linked to overconfidence, risk-seeking, and momentum trading. E.g., Trader wine – testosterone spike – bigger trades – bubble risk.

WINNER EFFECT - Biologically documented in humans and animals.

• OXYTOCIN: A trust hormone that fosters feelings of connection, bonding, and cooperation, making individuals more trusting. Good for team dynamics and collaboration but increases fraud vulnerability (Zak 2008).

(Zak 2008) demonstrates how neurochemical shifts drive financial behavior, influencing risk avoidance, overconfidence, and excessive trust.

Adaptive Market Hypothesis (AMH)

Adaptive Market Hypothesis: (Lo 2005) proposed AMH to bridge EMH and behavioral finance. Lo proposes that EMH is too rigid; markets are shaped by competition, adaption, and irrational learning. Applies AMH to real-world failures (2008 crisis), criticises financial models that ignore emotion and feedback loops.

Traditional markets suggest that individuals are rational, markets are efficient, and anomalies are temporary. However, AMH suggests:

• Markets are like ecosystems and evolve.
• Investors adapt to the environment by trial and error.
• Behavioral traits (e.g., herding) are survival strategies.

Features of AMH

Market efficiency is not fixed but depends on competition, innovation, and the environment.

Investors are boundedly rational and learn over time.

Anomalies may emerge and then disappear once exploited (like strategies in evolution). For instance, momentum profits may decline as they are exploited, leading to the disappearance of the anomaly and the evolution of the market.

Key Research

Session 1 – EMH & Arbitrage Limits

• Fama (1998): defends EMH, suggests anomalies like bubbles are statistical ‘noise’, and behavioural finance overstates its importance
• Vishny (1997): irrational traders can push prices further from fair value – cause losses for arbitrageurs (GAMESTOP 2021)

Session 2/3 – Rationality & Prospect Theory

• Tversky (1974) – people regularly violate Savage’s Axioms, systematically not by mistake. People consistently break the rules of rational choice

Session 4/5 – Heuristics & Biases