Lecture 1
Summary of Behavioral Economics and Finance Content
This comprehensive content introduces Behavioral Economics
(BE) and Behavioral Finance (BF), exploring their foundations, theoretical models,
key concepts, and applications in understanding economic and financial decision-
making. It contrasts the standard economic model with behavioral insights and
develops the framework of Expected Utility Theory (EUT), highlighting its axioms,
applications, limitations, and extensions.
1. Overview of Behavioral Economics and Finance
Behavioral Economics focuses on understanding why people make the
economic choices they do, questioning the assumptions of the standard
economic model.
It integrates insights from psychology, laboratory experiments, and social
sciences to explain economic behavior.
Behavioral economics is not new; its roots trace back to Adam Smith’s
“Theory of Moral Sentiments” (1759).
The standard economic model, dominant after Vilfredo Pareto’s argument to
separate economics from psychology, assumes rationality and self-interest.
Behavioral economics regained prominence in the late 20th century through
contributions by Herbert Simon (bounded rationality) and Kahneman and
Tversky (cognitive biases and framing effects).
Key Nobel Prizes:
Year Laureate(s) Contribution
1978 Herbert A. Simon Decision-making within economic organizations
2002 Vernon L. Smith Laboratory experiments in economic analysis
2002 Daniel Kahneman Integration of psychology into economics
2017 Richard H. Thaler Contributions to behavioral economics
Behavioral Finance is a subfield of behavioral economics, applying
psychological insights to understand financial decisions made by investors,
markets, and managers.
It challenges traditional finance models based on neoclassical
economics and rational utility maximization, addressing anomalies such
as bubbles and financial crises unexplained by classical theories.
,2. Standard Economic Model and Its Assumptions
The neoclassical economic model assumes agents:
Are fully rational and self-interested.
Have complete information and known preferences.
Make choices consistent with utility maximization.
Agents are presumed to process all available information and make decisions
that maximize expected utility.
The model is both normative (how people should behave)
and descriptive (how people actually behave), often leading to tensions
because real behavior deviates from the model.
Limitations:
People often satisfice (accept a good enough option) rather than maximize
utility.
Information is frequently incomplete or unavailable.
People may fail to process information correctly.
Systematic deviations exist not only at an individual level but also within firms.
3. Risk, Uncertainty, and Notation in Economic Decisions
Risk involves situations with known probabilities; uncertainty refers to
unknown or unobservable probabilities.
Probabilities are numbers between 0 and 1, summing to 1 for all possible
outcomes.
Decision problems often involve binary prospects or lotteries, represented as
(x, p; y), where x and y are outcomes and p is the probability of outcome x.
Preferences between prospects use symbols such as ∼ (indifference) and ≻
(strict preference).
4. Expected Value Theory (EVT)
EVT calculates the value of a prospect as the weighted average of possible
outcomes multiplied by their probabilities.
Example calculations demonstrate how expected values are computed for
various gambles and investments.
EVT’s main limitation is illustrated by the St. Petersburg paradox, where the
expected value of a certain lottery is infinite, but people’s willingness to pay for
it is finite, contradicting EVT predictions.
,5. Expected Utility Theory (EUT)
Developed as a solution to the St. Petersburg paradox by Daniel Bernoulli
(1738).
EUT proposes that people maximize expected utility rather than expected
monetary value, recognizing diminishing marginal utility of wealth.
Utility measures satisfaction or pleasure derived from consumption or wealth.
Choices reveal preferences; utility functions help quantify these preferences
numerically.
Utility functions are typically concave, reflecting risk aversion (diminishing
marginal utility).
Mathematical representation:
Concept Formula
Expected Value (EV) ( EV = \sum p_i x_i )
Expected Utility (EU) ( EU = \sum p_i U(x_i) ), with ( U’(x) > 0, U’'(x) < 0 )
EUT is a normative model for rational decision-making under uncertainty.
Von Neumann and Morgenstern (1947) axiomatized EUT with four key
axioms:
Completeness: Preferences are always defined between prospects.
Transitivity: Preferences are consistent across comparisons.
Continuity: Intermediate preferences exist between ordered prospects.
Independence: Preferences between prospects are unaffected by mixing
with a third prospect.
6. Risk Attitudes and Certainty Equivalent
Individuals can be:
Risk averse: Prefer a certain amount less than the expected value of a
gamble.
Risk neutral: Indifferent between certain outcomes and gambles with the
same expected value.
Risk seeking: Prefer gambles over certain outcomes even if expected
values are equal.
, Certainty Equivalent (CE): The guaranteed amount that an individual
considers equally desirable as a risky prospect.
Relationship between CE and EV:
Condition Interpretation
CE < EV Risk averse
CE = EV Risk neutral
CE > EV Risk seeking
Graphical representation of utility versus wealth demonstrates concave utility
for risk-averse individuals.
7. Measuring Risk Aversion
Risk aversion is quantified through two coefficients:
Coefficient Definition
Absolute Risk ( ARA = - \frac{U’‘(w)}{U’(w)} ); measures dislike for risk in
Aversion (ARA) absolute wealth terms
Relative Risk Aversion ( RRA = - w \times \frac{U’‘(w)}{U’(w)} ); measures risk
(RRA) aversion relative to wealth
Types of risk aversion based on these coefficients:
Decreasing Absolute Risk Aversion (DARA): Willingness to take more
absolute risk as wealth increases.
Decreasing Relative Risk Aversion (DRRA): Willingness to risk a
larger proportion of wealth as wealth increases.
Constant Absolute Risk Aversion (CARA): Risk tolerance does not
change with wealth.
Constant Relative Risk Aversion (CRRA): Willingness to risk the same
proportion of wealth regardless of wealth level.
Empirical evidence suggests most individuals exhibit DARA and CRRA.
8. Illustrative Example of Risk Aversion
Using the utility function ( u(x) = \ln(x) ), examples show:
Summary of Behavioral Economics and Finance Content
This comprehensive content introduces Behavioral Economics
(BE) and Behavioral Finance (BF), exploring their foundations, theoretical models,
key concepts, and applications in understanding economic and financial decision-
making. It contrasts the standard economic model with behavioral insights and
develops the framework of Expected Utility Theory (EUT), highlighting its axioms,
applications, limitations, and extensions.
1. Overview of Behavioral Economics and Finance
Behavioral Economics focuses on understanding why people make the
economic choices they do, questioning the assumptions of the standard
economic model.
It integrates insights from psychology, laboratory experiments, and social
sciences to explain economic behavior.
Behavioral economics is not new; its roots trace back to Adam Smith’s
“Theory of Moral Sentiments” (1759).
The standard economic model, dominant after Vilfredo Pareto’s argument to
separate economics from psychology, assumes rationality and self-interest.
Behavioral economics regained prominence in the late 20th century through
contributions by Herbert Simon (bounded rationality) and Kahneman and
Tversky (cognitive biases and framing effects).
Key Nobel Prizes:
Year Laureate(s) Contribution
1978 Herbert A. Simon Decision-making within economic organizations
2002 Vernon L. Smith Laboratory experiments in economic analysis
2002 Daniel Kahneman Integration of psychology into economics
2017 Richard H. Thaler Contributions to behavioral economics
Behavioral Finance is a subfield of behavioral economics, applying
psychological insights to understand financial decisions made by investors,
markets, and managers.
It challenges traditional finance models based on neoclassical
economics and rational utility maximization, addressing anomalies such
as bubbles and financial crises unexplained by classical theories.
,2. Standard Economic Model and Its Assumptions
The neoclassical economic model assumes agents:
Are fully rational and self-interested.
Have complete information and known preferences.
Make choices consistent with utility maximization.
Agents are presumed to process all available information and make decisions
that maximize expected utility.
The model is both normative (how people should behave)
and descriptive (how people actually behave), often leading to tensions
because real behavior deviates from the model.
Limitations:
People often satisfice (accept a good enough option) rather than maximize
utility.
Information is frequently incomplete or unavailable.
People may fail to process information correctly.
Systematic deviations exist not only at an individual level but also within firms.
3. Risk, Uncertainty, and Notation in Economic Decisions
Risk involves situations with known probabilities; uncertainty refers to
unknown or unobservable probabilities.
Probabilities are numbers between 0 and 1, summing to 1 for all possible
outcomes.
Decision problems often involve binary prospects or lotteries, represented as
(x, p; y), where x and y are outcomes and p is the probability of outcome x.
Preferences between prospects use symbols such as ∼ (indifference) and ≻
(strict preference).
4. Expected Value Theory (EVT)
EVT calculates the value of a prospect as the weighted average of possible
outcomes multiplied by their probabilities.
Example calculations demonstrate how expected values are computed for
various gambles and investments.
EVT’s main limitation is illustrated by the St. Petersburg paradox, where the
expected value of a certain lottery is infinite, but people’s willingness to pay for
it is finite, contradicting EVT predictions.
,5. Expected Utility Theory (EUT)
Developed as a solution to the St. Petersburg paradox by Daniel Bernoulli
(1738).
EUT proposes that people maximize expected utility rather than expected
monetary value, recognizing diminishing marginal utility of wealth.
Utility measures satisfaction or pleasure derived from consumption or wealth.
Choices reveal preferences; utility functions help quantify these preferences
numerically.
Utility functions are typically concave, reflecting risk aversion (diminishing
marginal utility).
Mathematical representation:
Concept Formula
Expected Value (EV) ( EV = \sum p_i x_i )
Expected Utility (EU) ( EU = \sum p_i U(x_i) ), with ( U’(x) > 0, U’'(x) < 0 )
EUT is a normative model for rational decision-making under uncertainty.
Von Neumann and Morgenstern (1947) axiomatized EUT with four key
axioms:
Completeness: Preferences are always defined between prospects.
Transitivity: Preferences are consistent across comparisons.
Continuity: Intermediate preferences exist between ordered prospects.
Independence: Preferences between prospects are unaffected by mixing
with a third prospect.
6. Risk Attitudes and Certainty Equivalent
Individuals can be:
Risk averse: Prefer a certain amount less than the expected value of a
gamble.
Risk neutral: Indifferent between certain outcomes and gambles with the
same expected value.
Risk seeking: Prefer gambles over certain outcomes even if expected
values are equal.
, Certainty Equivalent (CE): The guaranteed amount that an individual
considers equally desirable as a risky prospect.
Relationship between CE and EV:
Condition Interpretation
CE < EV Risk averse
CE = EV Risk neutral
CE > EV Risk seeking
Graphical representation of utility versus wealth demonstrates concave utility
for risk-averse individuals.
7. Measuring Risk Aversion
Risk aversion is quantified through two coefficients:
Coefficient Definition
Absolute Risk ( ARA = - \frac{U’‘(w)}{U’(w)} ); measures dislike for risk in
Aversion (ARA) absolute wealth terms
Relative Risk Aversion ( RRA = - w \times \frac{U’‘(w)}{U’(w)} ); measures risk
(RRA) aversion relative to wealth
Types of risk aversion based on these coefficients:
Decreasing Absolute Risk Aversion (DARA): Willingness to take more
absolute risk as wealth increases.
Decreasing Relative Risk Aversion (DRRA): Willingness to risk a
larger proportion of wealth as wealth increases.
Constant Absolute Risk Aversion (CARA): Risk tolerance does not
change with wealth.
Constant Relative Risk Aversion (CRRA): Willingness to risk the same
proportion of wealth regardless of wealth level.
Empirical evidence suggests most individuals exhibit DARA and CRRA.
8. Illustrative Example of Risk Aversion
Using the utility function ( u(x) = \ln(x) ), examples show:
