Behavioral Finance Flashcards
Analyses of “real” person’s thought process (by Raiffa)
- Normative analysis is concerned with the rational solution to the problem at hand. (traditional finance)
- Descriptive analysis is concerned with the nanner in which real people actually make decisions. (behavioral finance explanations)
- Prescriptive analysis is concerned with practical advice and tools that might help people achieve normative results. (efforts to use behavioral finance in practice)
Behavioral biases
– cognitive errors stem from basic statistical, information–processing, or memory errors.
– Emotional biases stem from impulse or intuition; emotional biases may be considered to result from reasoning influenced by feelings.
Traditional finance assumptions
Investors
- are rational;
- make decisions consistent with utility theory and revise expectations consistent with Bayes’ formula.
- are risk-averse;
- are self interested;
- have access to perfect information;
- are unbiased when processing all available information
Utility theory
People maximize the present value of utility (level of relative satisfaction received from the consumption of goods and services) subject to a present value budget constraint.
Decision-makers choose between risky or uncertain prospects by comparing their expected utility values. They maximize their expected utility – the weighted sum of the utility values of outcomes multiplied by their respective probabilities – subject to their budget constraints.
Axioms of Utility Theory
The basic axioms of utility theory:
– Completeness (Individual has well-defined preferences. Individual can clearly choose btwn A and B or be indif btwn the two choices.)
– Transitivity (individual is consistent in his decisions. If A is preferred to C, and B lays btwn A and C, then B is preferred to C)
– Independence (If A is preferred to B and some amount, x, of C is added to A and B, then A+ xC is preferred to B+ xC)
– Continuity (continuous indifference curves says that an individual is indifferent btwn all points representing combinations of choices on a single indifference curve)
Bayes’ formula
Is a mathematical rule explaining how existing probability beliefs should be changed given new information. This formula is an application of conditional probabilities. All possible events must be mutually exclusive and exhaustive events with known probabilities.
P(A|B) = [P(B|A)/P(B)] P(A)
where:
P(A|B) - updated prob of A given the new info B
P(B|A) - updated prob of new info B, given event A
P(B) - prior (unconditional) prob of info B
P(A) - prior prob of event A, wo new info B. base prob/rate of event A.
Bayes’ Formula example:
You have two identical urns, U1 and U2. U1 has 2 red balls (R) and 3 white balls (W). U2 has 4 red balls and 1 white ball. You randomly choose one of the urns to pick out a ball. A Red ball is pulled out first. What is the probability that you picked U1, based on the fact that a red ball was pulled out first, P(U1|R)?
P(U1|R) = [P(R|U1)/P(R)] P(U1) P(R|U1) = 2 red/ 5 total = 40% P(R) = 6 red / 10 total = 60% P(U1) = 1/2
P(U1|R) = (.4/.6) * .5 = 33.3%
Certainty equivalent
Given an opportunity to participate or to forgo to participate in an event for which the outcome is uncertain, it is the max sum of money a person would pay to participate or a min sum of money a person would accept to not participate in the opportunity.
The difference between the certainty equivalent and the expected value is called the risk premium.
Bounded rationality
Is proposed as an alternative to the assumptions of perfect information and perfect rationality. It relaxes the assumptions of expected utility theory and perfect information to more realistically represent human economic decision making. Bounded rationality assumes that individuals’ choices are rational but are subject to limitations of knowledge and cognitive capacity. Bounded rationality is concerned with ways in which final decisions are shaped by the decision-making process itself.
Double inflection utility function
a utility function that changes based on levels of wealth. It is concave up to inflection point B, then becomes convex until inflection point C, after which it becomes concave again.
Prospect theory
Prospect theory describes how individuals make choices in situations in which they have to decide between alternatives that involve risk (e.g, financial decisions) and how individuals evaluate potential losses and gains. Prospect theory considers how prospects (alternatives) are perceived based on their framing, how gains and losses are evaluated, and how uncertain outcomes are weighted.
Satisfice (bounded rationality)
Combines “satisfy” and “suffice” and describes decisions, actions, and outcomes that may not be optimal, but they are adequate. To satisfice is to find a solution in a decision-making situation that meets the needs of the situation and achieves the goals of the decision-maker.
Satisficing is finding an acceptable solution as opposed to optimizing, which is finding the best (optimal) solution.
Heuristics (bounded rationality): means-ends analysis
Rather than taking a holistic approach, heuristics may use more of an incremental approach.
Examples:
1. means-ends analysis - The problem solver is at a current state and decides on the goal state. Rather than looking for alternatives to achieve the goal, the decision-maker moves towards the goals in stages. Decisions are made progressively until the goal is achieved: The first decision is made to get one step closer to the goal state, the next decision results in getting still closer to the goal, and decisions continue to be made until the goal state is met.
Heuristics (bounded rationality): divide–and-conquer procedure
a problem or issue is divided into components. In this case, rather than attempt to find alternatives to solve the issue of problem, the decision-makers attempt to find satisfactory solutions for each sub-problem
Prospect theory: editing stage – codification
People perceive outcomes as gains and losses rather than final states of wealth and welfare. A gain or loss is, of course, defined with respect to some reference point. The location of the reference point affects whether that outcomes are coded as gains and losses. Prospects are coded as such that the probability is initially at 100%