Dr. CH. RAVI VARMA MBA-Behavioural Finance-study material

UNIT – 1

Introduction to Behavioral finance – Nature, Scope, Objectives And Applications; Investment

Decision Cycle; Judgment Under Uncertainty; Cognitive Information Perception –

Peculiarities (Biases) Of Quantitative And Numerical Information Perception –

Representativeness – Anchoring – Exponential discounting – Hyperbolic discounting.

1.1. Introduction, Nature, Scope, Objectives And Applications of Behavioral Finance

Introduction: Behavioural finance is relatively a new field which seeks to provide explanation

for people’s economic decisions. It is a combination of behavioural and cognitive

psychological theory with conventional economics and finance. Inability to maximise the

expected utility (EU) of rational investors leads to growth of behavioural finance research

within the efficient market framework. An underlying assumption of behavioural finance is

that, the information structure and characteristics of market participants systematically

influence the individual’s investment decisions as well as market outcomes. Investor, as a

human being, processes information using shortcuts and emotional filters. This process

influences financial decision makers such that they act seemingly in irrational manner, and

make suboptimal decision, violate traditional finance claim of rationality. The impact of this

suboptimal financial decision has ramification for the efficiency of capital markets, personal

wealth, and the performance of corporations. Irrational decision could be either due to

processing of wrong information or interpretation with inconsistent decisions.

Behaviour finance focuses upon how investors interpret and act on information to make

informed investment decisions. Investors do not always behave in a rational, predictable and

an unbiased manner indicated by the quantitative models. Behavioural Finance places an

emphasis upon investor behaviour leading to various market anomalies.

Definitions:

Linter G.(1998) has defined behavioural finance as “being study of how human interprets and

act on information to make informed investment decisions.”

Olsen R. (199828) asserts that “behavioural finance seeks to understand and predict

systematic financial market implications of psychological decision process.”

According to Shefrin “Behavioural Finance is the application of psychology to financial

behaviour-the behaviour of practitioner.”

According to Fromlet “Behavioural finance closely combines individual behaviour and

market phenomena and uses knowledge taken from both the psychological field and financial

theory”

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Assumptions of Behavioural Finance:

• Loss aversion: The characteristics of seeking to limit the size of the potential loss rather

than seeking to minimise the variability of the potential returns.

• Bounded rationality: The manner in which human being behave, limits their rationality.

• Denial of risk: They may know statistical odds but refuse to believe these odds.

Meaning:

Behavioural finance is a discipline that attempts to explain and increase understanding

regarding how the cognitive errors (mental mistakes) and emotions of investors influence the

decision making process. It integrates the field of psychology, sociology, and other

behavioural sciences to explain individual behaviour, to examine group behaviour, and to

predict financial markets. According to behavioural finance people are not always rational:

many investors fail to diversify trade too much, and seem to selling winners and holding

losers. Not only that, but they deviate from rationality in predictable ways.

Behaviour Finance is an integration of various fields:

Scope of Behavior Finance:

 • To understand the Reasons of Market Anomalies: (Like creation of bubbles, the effect of

any event, calendar effect etc)

• To Identify Investor’s Personalities: Various new financial instruments can be developed to

hedge unwanted biases created in financial markets.

• Helps to identify the risks and their hedging strategies

• Provides an explanation to various corporate activities: Effect of good or bad news, stock

split, dividend decision etc.

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• To enhance the skill set of investment advisors: Done by better understanding of investor’s

goal, maintaining a systematic approach to advise.

Behavior finance as a science:

• Science is a systematic and scientific way of observing, recording, analyzing and

interpreting any event.

• Behavioural Finance has got its inputs from traditional finance which is a systematic and

well designed subject based on various theories.

Behavior finance as an art:

• In art we create our own rules and not work on rules of thumb as in science.

• Art helps us to use theoretical concepts in the practical world.

• Behavioural finance focuses on the reasons that limit the theories of standard finance and

also the reasons for market anomalies created.

• It provides various tailor made solutions to the investors to be applied in their financial

planning.

Objectives of Behavior Finance:

• To review the debatable issues in Standard Finance

• To protect the interest of stakeholders in volatile investment scenario

• To examine the relationship between theories of Standard Finance and Behavioral Finance.

• To analyse the influence of biases on the investment process because of different

personalities in the financial markets.

• To examine the various social responsibilities of the subject.

• To discuss emerging issues in the financial world.

• To discuss the development of new financial instruments to hedge the conventional

instruments against various market anomalies

• To get the feel of trend of changed events over years, across various economies.

• To examine the contagion effect of various events.

• An effort towards more elaborated identification of investor’s personalities.

• More elaborated discussion on optimum Asset Allocation according to behavioral aspects as

well as aspects like age, gender, income etc of the investor.

Application of Behavior Finance:

Behavioural finance actually equips finance professionals with a set of new lenses, which

allows them to understand and overcome many proven psychological traps that are present

involving human cognition and emotions. This includes corporate boards and managers,

individual and institutional investors, portfolio managers, analysts, advisors, and even policy

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makers. Behavioural traps exist and occur across all decision spectrums because of the

psychological phenomena of heuristics and biases. These phenomena and factors are

systematic in nature and can move markets for prolonged periods. It applies to:

1. Investors

2. Corporations

3. Markets

4. Regulators

5. Educations

1.2. Investment Decision Cycle

Investment decisions are not one off decisions.  Public and private actors repeatedly or

continuously make choices that shape investments and have impacts on higher development

goals. The cycle is a way to conceptualize different phases of investment decision making.

Taking this step-by-step approach is proposed to improve the quality and enhance the results

of ongoing and future investment operations. The cycle distinguishes four phases. Different

organizations and approaches have further split these phases into more steps, but the overall

coverage of issues and sequence is broadly equivalent.  

1.  Plan Strategically : Assess, set and communicate sector priorities, and identify projects for

implementation.

2. Design Investment: Analyze context and alternatives and carry out detailed project

design.

3. Implement and Monitor: Get the job done, monitor and communicate progress towards

objectives, make necessary adjustments.

4.  Evaluate and Capitalize : Review and evaluate implementation experience to inform scaling

up and future plans and projects.

1.3. Judgment under Uncertainty

Heuristics: Heuristics are referred as rule of thumb, which applies in decision making to

reduce the cognitive resources to solve a problem. These are mental shortcuts that simplify

the complex methods to make a judgment. Investor as a decision maker confronts a set of

choices within certainty and limited ability to quantify results. This leads identification and

understanding of all heuristics that affect financial decision making. Some of heuristics are

representativeness, anchoring & adjustments, familiarity, overconfidence, regret aversion,

conservatism, mental accounting, availability, ambiguity aversion and effect. Heuristics help

to make decision.

Various heuristics and biases are:

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The two systems of mind

Familiarity and related heuristics

Representativeness and related heuristics

Anchoring

Irrationality and adaptation

Hyperbolic discounting

1.3.1. Two systems of mind;

Psychologists Keith Stanovich and Richard West refer to the two systems in mind as System1

and System2

System 1 operates automatically and rapidly. It required little or no effort and is not amenable

to voluntary control.

System 2 is effortful, deliberate and slow. It required mental activities that may be demanding

including complex calculations. The operations of system 2 are associated with the subjective

experience of agency, choice, and concentration.

Causes and consequences of cognitive ease:

Jumping to conclusion (Halo Effect)

What you see is all there is

Answering an easier question

Substitute questioning: if a satisfactory answer to a hard question is not found quickly,

system 1 will find a related question that is easier and will answer it.

The Affect Heuristic: the likes and dislikes of people determine their beliefs about the world.

The affect heuristic is an example of substitution. A harder question (How do I thing about

it?) is substituted by an easier question (How do I feel about it?). it seems that the emotional

tail wags the rational dog.

The law of small numbers

Cause and chance

Magical thinking

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Wishful thinking

Bounded rationality

Cognitive ease is caused when something is displayed clearly or repeated, or printed. It is

also induced when you are in a good mood. Conversely, cognitive strain is caused when you

read instructions in a poor font or worded in a convoluted language or when you are in a

peevish mood.

1.3.2. Familiarity and related Heuristics:

People are comfortable with things that are familiar to them. The human brain often uses the

familiarity shortcut in making choices.

Familiar: people have a preference for the familiar.

Ambiguity Aversion: ambiguity aversion drives people to choose risk to uncertainty. Risk

exists when the probability distribution is known and uncertainty exists when the probability

distribution is unknown.

Diversification Heuristic: means when choices are not mutually exclusive people like to try a

little bit of everything. Ex: Buffet dining.

Functional fixation: the tendency to latch on to a single object in a habitual way is referred to

by behaviouralists as functional fixedness.

Status quo bias and Endowment effect: it implies that people are comfortable with the

familiar and would like to keep things the way they have been. The fear of regret that may

follow, if the status quo is altered, makes people resistant to change.

The endowment effect says that people tend to place greater value on what belongs to them

relative to the value they would place on the same thing, if it belongs to someone else.

1.3.3. Representativeness and related biases:

Representativeness refers to the degree of similarity that an event has with its parent

population or the degree to which an event resembles its population. Representativeness

refers to the tendency to form judgements based on stereotypes. While representativeness

may be a rule of thumb, it can also lead people astray.

Innumeracy: people have difficulty with numbers (confuse between nominal changes and real

changes called as money illusion).

Probability matching: The class of symmetric path-independent models with experimenter-

controlled events is considered in conjunction with two-choice probability learning

experiments. Various refinements of the notion of probability matching are defined, and the

incidence of these properties within this class is studied

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Conjunction Fallacy: conjunction fallacy is a variant of representativeness.

Base Rate Neglect: Base rate fallacy, or base rate neglect, is a cognitive error whereby too

little weight is placed on the base (original) rate of possibility (e.g., the probability of A given

B). In behavioral finance, it is the tendency for people to erroneously judge the likelihood of

a situation by not taking into account all relevant data and focusing more heavily on new

information without acknowledging how the new information impacts the original

assumptions.

Bayesian Updating: Named after Thomas, Bayes’ theorem addresses the question: how

should we modify our belief in the wake of additional information.

Availability, Recency, and Salience Bias: sample data are often assigned undue importance

compared to population parameters. This tendency is accentuated when the data are easily

available. More so, when the event has occurred recently and is salient.

Cognitive Information Perception

Cognitive perception includes, aside from the senses listening, seeing, smelling, tasting and

feeling, the way in which we deal with information. While perception refers to ways of

obtaining information from our environment, cognition describes processes such as

remembering, learning, solving problems and orientation.

The cognitive perspective applies a nomothetic approach to discover human cognitive

processes, but have also adopted idiographic techniques through using case studies. Cognitive

psychology is also a reductionist approach. This means that all behaviour, no matter how

complex can be reduced to simple cognitive processes, like memory or perception.

1.3.4. Anchoring:

Anchoring  is a cognitive bias that describes the common human tendency to rely too heavily

on the first piece of information offered (the "anchor") when making decisions.

During decision making, anchoring occurs when individuals use an initial piece of

information to make subsequent judgments. Once an anchor is set, other judgments are made

by adjusting away from that anchor, and there is a bias toward interpreting other information

around the anchor. In the context of investing, one consequence is that market participants

with an anchoring bias tend to hold investments that have lost value because they have

anchored their fair value estimate to the original price rather than to fundamentals. As a

result, market participants assume greater risk by holding the investment in the hope the

security will go back up to its purchase price

1.4. Exponential discounting:

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In economics exponential discounting is a specific form of the discount function, used in the

analysis of choice over time (with or without uncertainty). Formally, exponential discounting

occurs when total utility is given by

Exponential discounting implies that the marginal rate of substitution between consumption

at any pair of points in time depends only on how far apart those two points are. Exponential

discounting is not dynamically inconsistent.

For its simplicity, the exponential discounting assumption is the most commonly used in

economics. However, alternatives like hyperbolic discounting have more empirical support.

1.5. Hyperbolic discounting

Hyperbolic discounting refers to the tendency for people to increasingly choose a smaller-

sooner reward over a larger-later reward as the delay occurs sooner rather than later in time.

When offered a larger reward in exchange for waiting a set amount of time, people act less

impulsively (i.e., choose to wait) as the rewards happen further in the future. Put another way,

people avoid waiting more as the wait nears the present time. Hyperbolic discounting has

been applied to a wide range of phenomena. These include lapses in willpower, health

outcomes, and consumption choices over time, and personal finance decisions.

UNIT - 2

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Utility/preference Functions: Expected Utility theory (EUT) and rational thought: Decision

making under risk and uncertainty – Expected utility as a basis for decision-making –

Theories based on Expected Utility Concept – Investor rationality and market efficiency.

2.1. Expected Utility theory (EUT) and rational thought:

Expected Utility Theory (EUT) states that the decision maker (DM) chooses between risky or

uncertain prospects by comparing their expected utility values, i.e., the weighted sums

obtained by adding the utility values of outcomes multiplied by their respective probabilities.

This elementary and seemingly commonsensical decision rule raises at once the most

important questions in contemporary decision theory.

2.2. Decision making under risk and uncertainty

Decision making under certainty:

A condition of certainty exists when the decision-maker knows with reasonable certainty

what the alternatives are, what conditions are associated with each alternative, and the

outcome of each alternative. Under conditions of certainty, accurate, measurable, and reliable

information on which to base decisions is available. The cause and effect relationships are

known and the future is highly predictable under conditions of certainty. Such conditions

exist in case of routine and repetitive decisions concerning the day-to-day operations of the

business.

Decision making under risk and uncertainty

The simplest definition of risk is that it is the variability of possible returns. An important

aspect of this definition is that the actual outcome could be better or worse than.

Another popular definition is that risk = probability × impact

This definition emphasises an important aspect of risk management - i.e. that we need to

identify and assess potential sources of risk both in terms of their potential impact on the

business and how likely they are.

Finally, some authors make a distinction between risk and uncertainty:

Risk is where there are a number of possible outcomes and the probability of each

outcome is known. (For example, based on past experience of digging for oil in a

particular area, an oil company may estimate that they have a 60% chance of finding oil

and a 40% chance of not finding oil.)

Uncertainty occurs when there are a number of possible outcomes but the probability of

each outcome is not known. (For example, the same oil company may dig for oil in a

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previously unexplored area. The company knows that it is possible for them to either find

or not find oil, but it does not know the probabilities of each of these outcomes.)

When investors make choices or decisions under risk or uncertainty, they must somehow

incorporate this risk into their decision-making process. Conditions of risk occur when an

investor must make a decision for which the outcome is not known with certainty. Under

conditions of risk, the investor can make a list of all possible outcomes and assign

probabilities to the various outcomes.

Uncertainty exists when a decision maker cannot list all possible outcomes and/or cannot

assign probabilities to the various outcomes. To measure the risk associated with a decision,

the manager can examine several characteristics of the probability distribution of outcomes

for the decision.

The various rules for making decisions under risk require information about several different

characteristics of the probability distribution of outcomes: (1) the expected value (or mean) of

the distribution, (2) the variance and standard deviation, and (3) the coefficient of variation.

While there is no single decision rule that managers can follow to guarantee that profits are

actually maximized, a number of decision rules that decision makers can use to help them

make decisions under risk: (1) the expected value rule, (2) the mean–variance rules, and (3)

the coefficient of variation rule. These rules can only guide investors in their analysis of risky

decision making. The actual decisions made by a decision maker will depend in large

measure on his willingness to take on risk. Investor’s propensity to take on risk can be

classified in one of three categories: risk averse, risk loving, or risk neutral.

Expected utility theory explains how managers can make decisions in risky situations. The

theory postulates that managers make risky decisions with the objective of maximizing the

expected utility of profit. The manager's attitude for risk is captured by the shape of the utility

function for profit. If a manager experiences diminishing (increasing) marginal utility for

profit, the manager is risk averse (risk loving). If marginal utility for profit is constant, the

manager is risk neutral.

If a manager maximizes expected utility for profit, the decisions can differ from decisions

reached using the three decision rules discussed for making risky decisions. However, in the

case of a risk-neutral manager, the decisions are the same under maximization of expected

profit and maximization of expected utility of profit. Consequently, a risk-neutral decision

maker can follow the simple rule of maximizing the expected value of profit and

simultaneously also be maximizing utility of profit.

2.3. Expected utility as a basis for decision-making

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Use of research techniques to reduce uncertainty

Market research is an important means of assessing and reducing uncertainty. For example,

about the likely responses of customers to new products, new advertising campaigns and

price changes.

A number of research techniques are available:

Focus Groups

Desk research (secondary research).

Field research (primary research). This includes:

o motivational and

o measurement research.

Quantitative methods of   incorporating risk and uncertainty

In addition to the research techniques, the following methods can be used to address risk or

uncertainty.

Sensitivity Analysis looks at varying key estimates to see how much safety margin we have

before the decision we are making will change. For example, based on a selling price of £5 a

project is worthwhile but if it drops by more than 10% to below £4.50, then the project

should be rejected.

Simulation is a method where random numbers are used to generate different possible

scenarios, which can then be solved. The process is re-run many times to then get an idea of

the spread of possible outcomes

Expected Values (EV) are essentially averages

Payoff tables are a simple way of showing the different possible scenarios and their

respective payoffs - i.e. the profit or loss of each.

Maximax, maximin and minimax regret are different perspectives that can be applied to

payoff tables. Once we know the different possible outcomes we can identify which decision

is best for a particular investor based on their risk aversion.

Decision Trees are a way of representing more complex decisions, probabilities and

outcomes.

In the case of uncertainty, decision science can provide very little guidance to managers

beyond offering them some simple decision rules to aid them in their analysis of uncertain

situations. Four basic rules for decision making under uncertainty are: (1) the maximax rule,

(2) the maximin rule, (3) the minimax regret rule, and (4) the equal probability rule.

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(1) The maximax rule: In decision theory, the optimistic (aggressive) decision

making rule under conditions of uncertainty. It states that the decision maker should select

the course of action whose best (maximum) gain is better than the best gain of all other

courses of action possible in given circumstances.

(2) The maximin rule: Rawls claims that people use the maximin rule to choose principles of

justice in his original position. According to the maximin rule we should compare alternatives

by the worst possible outcome under each alternative, and we should choose one which

maximize the utility of the worst outcome.

(3) The minimax regret rule: The minimax regret strategy is the one that minimises the

maximum regret. It is useful for a risk-neutral decision maker. Essentially, this is the

technique for a 'sore loser' who does not wish to make the wrong decision. 'Regret' in this

context is defined as the opportunity loss through having made the wrong decision.

(4) The equal probability rule: Two events are mutually exclusive or disjoint if they cannot

occur at the same time. The probability that Event A occurs, given that Event B has occurred,

is called a conditional probability. The conditional probability of Event A, given Event B, is

denoted by the symbol P(A. B).

2.4. Theories based on Expected Utility Concept

Expected utility theory is concerned with people’s preferences with respect to choices that

have uncertain outcomes (gambles). According to this theory, if certain axioms are fulfilled,

the subjective value of a gamble for an individual is the statistical expectation of the value the

individual assigns to the outcomes of that gamble.

Certain conditions have to be satisfied for an individual to have rational preferences. To

understand these conditions, let us introduce some notation. Suppose an individual is faced

with a choice between two outcomes, A and B. the symbol > indicates strong preference, thus

A>B means that A is always s preferred to B. The symbol ˜ indicates indifference so that A˜B

means the individual values the two outcomes equally. Finally the symbol ≥ suggests weak

preference, so that A≥B means that the individual prefers A or is indifferent between A and

B.

2.4.1. The non Neumann-Morgenstern Axioms:

According to expected utility theory, the following axioms define a rational decision maker.

These axioms are referred to as non Neumann-Morgenstern axioms as they were laid down

by John non Neumann and Oskar Morgenstern.

Completeness: The individual has well defined preferences and can always choose between

any two alternatives.

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Axiom: for every A and B either A>B or A=B or A<B

Transitivity: As an individual decides according to the completeness axiom, the individual

also decides consistently.

Axiom: For every A,B and C with A≥B and B≥C we must have A≥C

In words, if the individual prefers A to B, and B to C, then he must prefer A to C.

Independence: If two gambles are mixed with a third one, the individual will maintain the

same preference order as and when the two are presented independently of the third one.

Axiom: Let A, B and C be three lotteries with A≥B, and let t€(0,1); then tA+(1-t)

C>tB+(1-t)C

Continuity: when there are three lotteries (A, B, C) and the individual prefers A to B and B to

C, then it should be possible to mix A and C in such a manner that the individual is

indifferent between this mix and the lottery B.

Axiom: Let A, B and C be lotteries with A≥B≥C; then there exists a probability p

such that p(A+(1-p)C is equally good as B.

Omission and irrelevant Alternatives: The individual ignores irrelevant laternatives in

deciding between alternatives. For example, in evaluating two (or more) alternatives, the

individual ignores outcomes that occur with equal probability under both alternatives being

considered.

Frame Independence: The individual cares only about outcomes and the probabilities with

which they occur and not how they are presented or bundled.

2.4.2. Utility Maximization:

Utility reflects the satisfaction derived from a particular outcome – ordinarily an outcome is

represented by a “bundle” of goods. The utility function, denoted as U(*) assigns numbers to

possible outcomes such that preferred choices are assigned higher numbers. A rational

individual will consider all possible bundles of goods that satisfy his budget constraint and

then choose the bundle that maximizes his utility.

When only a single good is being considered, then ranking under certainty is simple. Given

the principle of non-satiation, the more the better. As an example, consider the utility of

wealth. Mathematically, the utility of wealth can be defined in various ways. One of the

mathematical functions commonly used is the logarithmic function. This means that the

utility derived from wealth w is U(w). Example for Logarithmic utility of wealth is

Wealth (in Rs. 10,000) U(w)=In(w)1 02 0.6931

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5 1.60947 1.945910 2.302620 2.995730 3.401250 3.9120100 4.6052

2.4.3. Expected Monetary Value:

In real world, however, there is a great deal of uncertainty about outcomes. How should one

decide when faced with risky gambles? Economists, mathematicians, and philosophers, have

long pondered over this question. For long, mathematicians had assumed that gambles are

assessed by their expected monetary value (EMV). For example, the EMV of a gamble which

pays 10,000 with probability of 0.70 and 1000 with a probability of 0.3 is:

0.7 x 10,000 + 0.3 x 1,000 = 7,300

In 1713, Nicholas Bernoulli exposed the weakness of the EMV criterion. He asked what si

the value of a gamble that pays two pounds if you toss a coin and it comes up head once, or

four pounds if it comes up heads twice in a row, or eight pounds if it come up heads twice in

a row, or eight pounds if it come up heads thrice in a row, so on and so forth? The expected

value of such a gamble is

(1/2 x 2) + (1/4 x 4) + (1/8 x 8) +… = 1+1+1…= ∞

His seems crazy because no one would pay that much for such a gamble.

2.4.4. Daniel Bernoulli’s Solution:

Daniel Bernoulli, a younger cousin of Nicholas Bernoulli, suggested a solution to that

problem 25 years later in 1738, and published it in the St. Peterburg Journal (that is why it

was called St. Peterburg paradox). Daniel suggested that the solution to the paradox was

simply that further increments in expected wealth don’t increase utility in the same

proportion. Put differently, expected wealth has diminishing marginal utility. This means that

the utility function in concave as in below example

(Diagram 2.2.)

Daniel Bernoulli argued that diminishing marginal value of wealth is what explains risk

aversion. Ex:

Wealth (Million) 1 2 3 4 5 6 7Utility (units) 10 18 25 31 36 40 43

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Daniel Bernoulli offered a solution to the famous “St. Petersburg paradox”. More important

his analysis of risk attitudes in terms of preferences for wealth is still part of economic

analysis even after almost 300 years.

2.4.5. Positive Theory:

Expected utility theory proposed by John von Neumann and Oskar Morgenstern is a

normative theory as it prescribes how people should behave rationally. A positive theory, on

the other hand, describes how people actually behave.

2.4.6. Modern Portfolio theory:

The expected utility theory says that in the face of uncertainty individuals maximize the

utility expected across possible states of the world. For a financial asset, like an equity stock,

that has innumerable possible outcomes, it is not a manageable proposition. However, if we

assume that investor are risk averse and investor preferences can be defined in terms of the

mean and variance of returns, it is possible to quantify the tradeoff between risk and return.

This is what the modern portfolio theory and the CAPM do.

Portfolio theory, originally proposed by Harry Markowitz in the 1950s, was the first formal

attempt to quantify the risk of a portfolio and develop a methodology for determining the

optimal portfolio. Prior to the development of portfolio theory, investors dealt with the

concepts of returns and risk somewhat loosely. Intuitively smart investors know the benefits

of diversification.

Harry Markowitz was the first person to show quantitatively why and how diversification

reduces risk. In recognition of his seminal contributions in this field he was awarded the

Nobel Prize in Economics in 1990.***Concept of Expected Risk, and Return of a Portfolio, Return, Risk tradeoff, efficient portfolio and efficient frontier*** -

follow SAPM material III Semester..

***Capital Asset Pricing Model, Capital Market Line and Security Market Line*** - follow SAPM material III Semester..

2.5. Investor rationality and market efficiency

According to Andrei Shliefer, any of the following three conditions will lead to market

efficiency:

1. Investor rationality

2. Independent deviation from rationality and

3. Effective arbitrage

1. Investor Rationality: Rational investors value each security at its fundamental value, the

net present value of future cash flows discounted at the risk-adjusted rate of return. When

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such investors learn something that has a bearing on fundamental values of securities, they

quickly respond to such information by bidding up the prices when the news is favourable

and bidding down the prices when the news is adverse. As a result, security prices reflect

fundamental values. The EMH is thus a consequence of equilibrium in competitive markets

thronged by rational investors.

2. Independent deviation from rationality:Remarkably, investor rationality is not a necessary

condition for the EMH. The markets can be efficient even if the investors are not rational. In

a commonly considered scenario, the irrational investors in the market trade in a random

fashion.

3. Effective Arbitrage: Even if the trading strategies of the irrational traders are correlated, a

case can be made for the EMH. This case, as argued by Milton Friedman and Eugene Fama,

is based on arbitrage, which is clearly one of the most intuitively appealing and plausible

arguments in economics. William Sharpe and Gordon Alexander define arbitrage as ‘the

simultaneous purchase and sale of the same, or essentially similar, security in two different

markets at advantageously different prices.’

Arbitrage has another implication. As irrational investors buy overpriced securities and sell

underpriced securities, they earn inferior returns compared to arbitrageurs or even passive

investors. Irrational investors lose money relative to their peers. As Milton Friedman pointed

out, since irrational investors cannot lose money forever they eventually disappear from the

market. Thus, in the long run, arbitrage and competitive selection ensure market efficiency.

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UNIT – 3

Behavioral Factors and Financial Markets: The Efficient Market Hypothesis – Fundamental

Information and Financial Markets – Information available for Market Participants and

Market Efficiency – Market Predictability – The Concept of Limits of Arbitrage Model –

Asset Management and Behavioral Factors – Active Portfolio Management; return statistics

and Sources of systematic underperformance – Fundamental information and technical

analysis – the case for psychological influence.

3.1. The Efficient Market Hypothesis:

The efficient market hypothesis (EMH) is an investment theory that states it is impossible to

"beat the market" because stock market efficiency causes existing share prices to always

incorporate and reflect all relevant information. According to the EMH, stocks always trade

at their fair value on stock exchanges, making it impossible for investors to either

purchase undervalued stocks or sell stocks for inflated prices. As such, it should be

impossible to outperform the overall market through expert stock selection or market timing,

and the only way an investor can possibly obtain higher returns is by purchasing riskier

investments.

1. Quick and accurate reaction to information

Defining the Forms of EMH

There are three forms of EMH: Weak, Semi-strong and Strong. Here's what each says about

the market.

Weak Form EMH: Suggests that all past information is priced into securities. Fundamental

analysis of securities can provide an investor with information to produce returns above

market averages in the short term but there are no "patterns" that exist. Therefore

fundamental analysis does not provide long-term advantage and technical analysis will not

work.

Semi-Strong Form EMH: Implies that neither fundamental analysis nor technical analysis

can provide an advantage for an investor and that new information is instantly priced in to

securities.

Strong Form EMH: Says that all information, both public and private, is priced into stocks

and that no investor can gain advantage over the market as a whole. Strong Form EMH

does not say some investors or money managers are incapable of capturing abnormally

high returns but that there are always outliers included in the averages.

2. Prices do not react to non-information

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3.2. Theoretical Challenges to the EMH:

Shortly after Jensen’s claim, the EMH was challenged, theoretically as well empirically.

Although initially the primary challenge was empirical, we will begin with the theoretical

challenges to the EMH and then turn to the empirical evidence.

The EMH has been challenged theoretically on three grounds:

Investor irrationality

Correlated investor behaviour

Limits to arbitrage

Price Behaviour

Investor Irrationality:

Attitude towards risk

non-bayesian formation of expectation

Sensitivity of Decision Making to how the problems are framed.

Correlated Investor Behaviour:

Investors tend to be overconfident and hence assume more risk.

Investors tend to extrapolate past time series and hence chase trends.

They tent to put lesser weight on base rates and more weight on new information and

hence overreact to news.

They follow market gurus and forecasts and act in a similar fashion.

Given the correlated behavior of noise traders, their actions lead to aggregate shifts in

demand.

Limits to Arbitrage: one can expect the irrationality of ‘noise traders’ to be countered by the

rationality of ‘arbitrageurs’ as the latter are supposed to be guided by fundamentals and

immune to sentiments. However, arbitrage in the real world is limited by two types of risk.

The first risk is fundamental. Buying ‘undervalued’ securities tends to be risky

because market may fall further and inflict losses. The fear of such a loss may restrain

arbitrageurs from taking large enough long positions that will push price to fully conform to

fundamentals.

The second risk is resale price risk and it arises mainly from the fact that arbitrageurs

have finite horizons. Why” there are two principal reasons.

1. Arbitrageurs usually borrow money or securities to implement their trades and therefore,

have to pay fees periodically. So they can ill-afford to keep an open position over a long

horizon.

2. Portfolio managers are evaluated every few months. This limits their horizon of arbitrage.

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Price Behavior: Given the substantial presence of noise traders whose behaviour is correlated

and the limits to arbitrage, investor sentiment does influence prices. In such a market, prices

often vary more than what is warranted by changes in fundamentals.

3.3. Empirical challenges to the EMH

Chronologically, the EMH was challenged empirically before it was criticized theoretically.

An important early challenge came from Robert Shiller’s (1981) work on stock market

volatility. Shiller showed that stock market prices fluctuate far more than could be justified

by a simple model in which prices are equated with the net present value of dividends. Shiller

made some specific assumptions about the dividend process and computed the net present

value of dividends using a constant discount rate. Though his work was criticized for mis-

specifying the fundamental value (Merton, 1987), he showed the way for a whole new area of

research.

1. Criticism of the weak form Efficiency: the proposition that past price information cannot

be used to earn excess returns was challenged by Werner DeBondt and Richard Thaler

(1985). They formed portfolios of the best and the worst performing stocks over the previous

three years, for each year since 1933. They then computed the returns on these portfolios over

the following five years. They found that loser portfolios delivered relatively high average

post-formation returns and winner portfolios deliverd relatively low average post formation

returns.

Researchers have identified some more ways to use past returns to predict future returns. An

important study by N. Hagadeesh and S. Titman (1993) looked at the momentum factor and

found that short-term trends (over a period of six months to one year) in the movements in

individual stock prices tend to predict future movements in the same direction. Thus, unlike

long-term trends which tend to reverse themselves, relatively short-term trends tend to

persist. In the wake of such evidence, even Fama admitted that stock returns can be predicted

from past returns, and this represents a departure from the conclusions reached earlier.

2. Challenge to the Semi-strong Form EMH: The evidence against the semi-strong form

efficiency seems to be even greater. Here are some of the important pieces of such evidence.

Post-Earnings Announcement Drift: in general, empirical studies have frund that the

market adjusts gradually, not rapidly, to announcement of unanticipated changes in

quarterly earnings.

Size Effect: some researchers found that small stocks yielded higher returns than the

large stocks (monthly returns in the month of January each year).

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P/E Ratios: In a pioneering study, Sanjay Basu (1977) examined the investment

performance of common stocks in relation to their P/E ratios. He found that low P/E

stocks outperformed high P/E stocks, even after adjustment for risk. Subsequent

studies found similar evidence.

Book Value – Market Value (BV/MV) Ratios: Eugene Fama and Kenneth

French(1993) evaluated the joint effects of market beta size, earnings-price ratio,

leverage and BV/MV ratio on a cross section of average returns. They found that the

BV/MV ratio and size dominated other ratios.

3. Stock prices reaction to Non-information: According to the EMH, stock prices should not

react to non-information. Shiller’s pioneering work sparked a debate relating to the volatility

of stock prices. He has presented evidence that stock prices jump around much more that

what is justified by variations in corporate dividends and cash flow (The Crash of 1987, fell

over 23 percent).

4. Inclusion in an Index: Evidence is that prices react to uninformed shifts in demand when

stocks are included in an index. When a company is included in an index like the S&P 500

Index in the U.S. or the S&P CNX Nifty Index in India, a significant number of its shares are

acquired by index funds and other funds which keep close to the index. Thus such inclusion

stimulates a substantial uninformed demand for the shares of the company, leading to a price

increase.

An Assessment of EMH:

As a normative benchmark of how the world should be, the EMH has been very

useful. In a rational world, the EMH would be true. Without the rational model as a starting

point, it would not have been possible to do research in behavioural finance.

As a descriptive model of asset markets, the report card on the EMH is mixed. The

“no-free-lunch” component of the EMH is mostly true because mutual funds and professional

investors, in general have not been able to outperform, or even match, the performance of the

relevant benchmark indices.

The “price is right” component of the EMH, however is perhaps not true – and for

many important questions, this is the more relevant component. In a very provocative article,

titled “Noise,” which appeared in the July, 1986 issue of Journal of Finance, the eminent

financial economist Fisher Black opined that, “we might define an efficient market as one in

which price is within a factor of 2 of value, i.e., the price is more than half of value and less

that twice value. That factor of 2 is arbitrary, of course.

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Rechared Thaler seriously challenged the “price is right” component. He says: “My

conclusion: the price is often wrong, and sometimes very wrong. Furthermore, when prices

diverge from fundamental value by such wide margins, the misallocation of resources can be

quite big.”

3.3. Market Predictability

Stock Market regressions:

Rt+1−rt = a+b1x1t+b2x2t+...+bkxkt+εt+1, - (1)

Rt+1 is the one-period (day, week, month,..) holding return on a stock index defined by

Rt+1 = (Pt+1 + Dt+1 − Pt)/Pt, - (2)

Pt is the stock price at the end of the period and D t+1 is the dividend paid out over the period t

to t + 1, and xit , i = 1, 2, ..., k are the factors/variables thought to be important in predicting

stock returns. Finally, rt is the return on the government bond with one period to maturity (the

period to maturity of the bond should exactly be the same as the holding period of the stock).

Rt+1 − rt is known as the excess return (return on stocks in excess of the return on the safe

asset). Note also that rt would be known to the investor/trader at the end of period t, before

the price of stocks, Pt+1, is revealed at the end of period t + 1.

Examples of possible stock market predictors are past changes in macroeconomic variables

such as interest rates, inflation, dividend yield (Dt/Pt−1), price earnings ratio, output growth,

and term premium (the difference in yield of a high grade and a low grade bond such as AAA

rated minus BAA rated bonds).

For individual stocks the relevant stock market regression is the capital asset pricing model

(CAPM), augmented with potential predictors:

Ri,t+1 = ai+b1ix1t+b2ix2t+...+bkixkt+βiRt+1+εi,t+1, - (3)

where Ri,t+1 is the holding period return on asset i (shares of firm i), defined similarly as

Rt+1. The asset-specific regressions (3) could also include firm specific predictors, such as

Rit or its higher order lags, book-to-market value or size of firm i. Under market efficiency,

as characterized by CAPM,

ai = 0, b1i = b2i = .... = bki = 0.

Only the “betas”, βi, will be significantly different from zero. Under CAPM, the value of βi

captures the risk of holding the share i with respect to the market.

Market Efficiency and Stock Market Predictability:

It is often argued that if stock markets are efficient then it should not be possible to predict

stock returns, namely that none of the variables in the stock market regression (1) should be

statistically significant. But this line of argument is not satisfactory and does not help in

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furthering our understanding of how markets operate. The concept of market efficiency needs

to be defined separately from predictability. In fact, it is easily seen that stock market returns

will be non-predictable only if market efficiency is combined with risk neutrality.

Risk Neutral Investors: Suppose there exists a risk free asset such as a government bond

with a known payout. In such a case an investor with an initial capital of At, is faced with two

options:

• Option 1: holding the risk-free asset and receiving (1 + rt)At, at the end of the next period,

• Option 2: switching to stocks by purchasing At/Pt shares and holding them for one period

and expecting to receive (At/Pt) (Pt+1 + Dt+1), at the end of period t + 1.

A risk-neutral investor will be indifferent between the certainty of (1 + rt)A t, and the his/her

expectations of the uncertain payout of option 2. Namely, for such a risk neutral investor

(1 + rt)At = E [(At/Pt) (Pt+1 + Dt+1) |It] - 4

Where It is the investor’s information at the nend of period t. This relationship is called the

“Arbitrage Condition”.

Using (2) we now have Pt+1 + Dt+1 = Pt (1 + Rt+1), and the above arbitrage condition can be

Simplifies to

E [(1 + Rt+1) |It] = (1+rt),

or

E (Rt+1 − rt |It) = 0. - 5

This result establishes that if the investor forms his/her expectations of future stock (index)

returns taking account of all market information efficiently, then the excess return, R t+1 − rt,

should not be predictable using any of the market information that are available at the end of

period t. Notice that rt is known at time t and is therefore included in It.

Hence, under the joint hypothesis of market efficiency and risk neutrality we must also have

E (Rt+1 |It) = rt.

The above set up can also be used to derive conditions under which asset prices can be

characterised as a random walk model. Suppose, the risk free rate, rt, in addition to being

known at time t, is also constant over time. Then using (4) we can also write

that equates the level of stock price to the present discounted stream of the dividends

expected to occur to the asset over the infinite future. The transversality condition rules out

rational speculative bubbles and is satisfied if the asset prices are not expected to rise faster

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than the exponential decay rate determined by the discount factor, 0 < 1/(1 + r) < 1. It is now

easily seen that if Dt follows a random walk so will Pt.

Risk averse Investors: Risk neutrality is a behavioural assumption and need not hold even if

all market information is processed efficiently by all the market participants. A more

reasonable way to proceed is to allow some or all the investors to be risk averse.

In this more general case the certain pay out, (1 + rt)At, and the expectations of the uncertain

pay out, E [(At/Pt) (Pt+1 + Dt+1) |It ], will not be the same and differ by a (possibly) time-

varying risk premium which could also vary with the level of the initial capital, At. The

extent to which excess returns can be predicted will depend on the existence of a historically

stable relationship between the risk premium, λt, and the macro and business cycle indicators

such as changes in interest rates, dividends and various business cycle indicators.

Market inefficiencies provide further sources of stock market predictability by introducing a

wedge between a “correct” ex ante measure of E (Rt+1 − rt |It), and its estimate by market

participants. Denoting the latter by ˆE (Rt+1 − rt |It) we have E (Rt+1 − rt |It) = λt + ξt,

Evidence of Stock Market Predictability: Economists have long been fascinated by the

sources of variations in the stock market. By the early 1970’s a consensus had emerged

among financial economists suggesting that stock prices could be well approximated by a

random walk model and that changes in stock returns were basically unpredictable. Fama

(1970) provides an early, definitive statement of this position. Historically, the ‘random walk’

theory of stock prices was preceded by theories relating movements in the financial markets

to the business cycle. A prominent example is the interest shown by Keynes in the variation

in stock returns over the business cycle. According to Skidelsky (1992) “Keynes initiated

what was called an ‘Active Investment Policy’, which combined investing in real assets - at

that time considered revolutionary - with constant switching between short-dated and long-

dated securities, based on predictions of changes in the interest rate”.

Recently, a large number of studies in the finance literature have confirmed that stock returns

can be predicted to some degree by means of interest rates, dividend yields and a variety of

macroeconomic variables exhibiting clear business cycle variations.

3.4. Limits of Arbitrage model:

3.5. Asset Management and behavioral factors:

Several psychological biases have been shown to affect asset prices, and two pronounced

ones are overconfidence and limited attention. Motivated by the overconfidence model of

Daniel, Hirshleifer, and Subrahmanyam (2001), Hirshleifer and Jiang (2010) propose a

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behavioral factor, the underpriced-minus-overpriced (UMO) factor, based on firms’ external

financing activities. The new issues puzzle is well documented. Though there are possible

risk channels, the widely held views are behavioral explanations, such as “market timing”

and incitement of misvaluation. The “market timing” hypothesis suggests that managers

possess inside information about the true value of their firms and undertake equity (or debt)

issuance or repurchase to exploit pre-existing mispricing. Alternatively, managers may

manipulate earnings upward to induce overpricing before issuing shares, or manage earnings

downward to induce underpricing before a repurchase. In those circumstances, issuing firms

would be overpriced and repurchasing firms underpriced. The UMO factor is constructed by

going long on firms with debt or equity repurchases and short on firms with IPOs, SEOs, and

debt issues over the previous 24 months. They show that UMO indeed captures common

mispricing, and loadings on UMO predict the cross-section of stock returns.

We introduce another behavioral factor, the inattention-to-fundamentals (ITF) factor,

motivated by investors’ limited attention to important information about firm fundamentals.

In the fashion of Fama and French (1993), ITF is constructed on a firm characteristic, net

operating assets, which is important balance sheet information but is likely to be neglected by

investors. According to Hirshleifer et al. (2004), net operating assets measure the relative

shortfall between cumulative operating income (the accounting value added) and cumulative

free cash flow (the cash value added). When this shortfall is large, the favorable accounting

performance receives relatively little affirmation from cash performance. If investors with

limited attention focus on accounting profitability but neglect information about cash

profitability, then net operating assets measures “the extent to which reporting outcomes

provoke over-optimism.” Firms with high net operating assets will be overvalued, and firms

with low net operating assets will be undervalued. ITF is constructed by going long on low

net operating assets firms and short on high net operating assets firms, to capture common

mispricing due to investors’ limited attention to firms’ cash flows or fundamentals.

Intuitively, one might ask why there could be a factor associated with net operating assets.

One mechanism is that net operating assets is related to economic factors, and innovations in

net operating assets are systematic. Owing to limited attention, comovement in net operating

assets drives commonality in mispricing and asset returns across firms. Another channel is

systematic attention shocks. At a given time, all firms with high (low) net operating assets are

overvalued (undervalued) due to limited attention. Shifts in aggregate investor attention, or

attention shocks, therefore cause these firms to become more or less misvalued at the same

time, generating co-movement in returns. Jointly, both systematic innovations in net

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operating assets and shifts in aggregate investor attention drive return co-movement due to

common mispricing, in particular among firms with extreme net operating assets. ITF is

formed by a long-short strategy on those firms, and therefore captures common mispricing

due to investors’ limited attention to important information about firm fundamentals.

3.6. Active Portfolio Management; return statistics and Sources of systematic

underperformance

Active management is the use of a human element, such as a single manager, co-managers or

a team of managers, to actively manage a fund's portfolio. Active managers rely on analytical

research, forecasts, and their own judgment and experience in making investment decisions

on what securities to buy, hold and sell. 

Investors who believe in active management do not follow the efficient market hypothesis.

They believe it is possible to profit from the stock market through any number of strategies

that aim to identify mispriced securities. Investment companies and fund sponsors believe it's

possible to outperform the market and employ professional investment managers to manage

one or more of the company's mutual funds.

The Berkeley study points out results from several other studies, indicating that nearly all

mutual funds underperform the S&P 500, net of fees. These largely refer to dual 10-year

studies that concluded in 1999 and again in 2003, each of which found that active managers,

net excess returns, under-performed the market 71% of the time.

Lesser known are two 10-year studies that concluded in 2008 and in 2013 that showed

drastically different results. These studies, produced by Vanguard and Morningstar, showed

that the annualized excess returns of active funds beat the U.S. stock market 63% of the time

(2008) and 45% of the time (2013). These studies suggest that active funds were worse than

the market 71% of the time from 1989 to 1999 but better than the market 63% of the time

from 1998 to 2008.

The Vanguard study pointed out that performance leadership among equity groups shifted

"from growth stocks to value stocks and from larger stocks to small" between 1999 and 2008

– two items that should benefit active portfolio construction. In fact, value stocks beat out

growth stocks by 35% in this period; small-caps beat large-caps by an astounding 43%. These

patterns mostly continued from 2008 to 2013.

Academic research has demonstrated that active fund managers who employ high active

share strategies will outperform in both good and bad markets when adhering to a buy-and-

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hold approach. The underperformance of active managers stems from the structural decisions

at the fund manager level, including asset bloat, closet indexing, and overdiversification,

which translate into portfolio drag. Nearly 80% of funds, however, display ample

management skill to overcome the higher fees of an actively managed strategy.

There have been six periods of prolonged underperformance since 1926 in which pure value

factors suffered over a business cycle. While growth and momentum may outperform in

certain environments, mean reversion remains one of the most powerful tools investors have

at their disposal. It is the potential for underperformance that creates the idiosyncratic risk

that value investors can take advantage of in the first place. Since 1991, there have been four

distinct windows across eight periods in which active outperformed passive strategies. Active

strategies also tend to perform better when value beats growth, international equities

outperform U.S. equities, and small-cap stocks are more attractive than large-cap stocks.

Based on the assumptions of Markowitz portfolio theory Sharpe (1964), have derived the

Capital Asset Pricing Model (CAPM). The importance and relevance to this thesis of the

CAPM model is derived from its terminology, i.e. its use of the Greek letters ɑ and β, which

is widely used in the context of portfolio management today. The CAPM seems to be an

attractive approach to the active portfolio management. It has, however, received extensive

criticism, particularly from Farma and French (2004), arguing against its empirical

foundation implying that most implications of the model are invalid.

The CAPM model plays an important role when selecting portfolios according to mean-

variance optimization. When using the CAPM forecasts of expected return to construct

optimal mean-variance portfolios, those portfolios will consist simply of positions in the

market and the risk free asset. In other words, optimal portfolios constructed under mean-

variance optimization will differ from the market portfolio and cash if the forecasted excess

return differ from the CAPM excess return. Thus, the expected return is

built on five factors: the risk free interest rate, the sector specific risk adjustment, the market

risk premium, a premium for exceptional market return, and an expected residual return. The

residual return is, ai, is a constant.

3.7. Fundamental information and technical analysis – the case for psychological

influence

By considering that an investor’s decision to buy or sell a stock is based on expectations. The

traditional finance view is embodied by the rational expectations model, which assumes that

investor expectations are derived from using tools such as fundamental analysis and modern

portfolio theory. These tools require making certain assumptions about the future. What

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growth rate will the firm achieve over the next three years? What is its expected return,

expected variance, and expected correlation with other assets? Even the most sophisticated

investors do not agree about which methods produce the most accurate assumptions. The

rational expectations model requires that investors resolve these uncertainties in an unbiased

and rational way, yet evidence indicate that people make biased and non rational choices

driven by emotions and cognitive errors.

This is illustrated by an experiment conducted by kuhnen and Knutson. They have subjects

play a game in which they must continuously choose between investing in a risky asset with

known probabilities for each outcome and a risk free asset. They play for money. Before

playing, positive, neutral and negative emotions are induced through seeing a possibly

provocative image and discussing it. They find that being induced with positive emotions

lead to riskier choices and more confidence in those choices. One reason for this confidence

is that they do not fully incorporate information that contradicts their prior choices.

Even those investors who use quantitative methods such as fundamental analysis can be

influenced by their mood. There is more than just unbiased numbers. Analysis includes

educated guesswork about some assumptions. Some fundamental analysis techniques are

more sophisticated than others, but they all involve in assumptions about the future.

Ex: consider the constant growth model taught to finance students around the world,

PV=D1/(K-G). investors must estimate the constant growth rate, G. Given the influence of

mood on risky and uncertain decisions, the expected value of the growth rate may become

biased. In turn, this biases the value computed in the model.

Certain effects may influence decisions of investors:

The effect of sunshine

The effect of negative emotions

The effect of optimism

The effect of sentiment

UNIT - 4

Behavioral Corporate Finance: Behavioral factors and Corporate Decision on Capital

Structure and Dividend Policy – Capital Structure dependence on market timing – systematic

approach to using behavioral factors in Corporate Decision Making. External Factors and

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Investor Behavior in mechanisms of the External Factor influence on Risk Perception and

Attitudes – Connection to Psychophysiology and Emotional Regulation. Active Portfolio

Management – The Sources of the Systematic Underperformance.

Behavioral Corporate Finance:

Behavioral corporate finance replaces the traditional rationality assumptions with behavioral

assumptions that are based on empirical evidence.

There are two broad

Behavioral corporate Finance and Capital Structure:

Modigliani-Miller tradeoff theory and Myers-Majluf pecking-order theory are the two main

approaches to capital structure.

The tradeoff theory considers the tradeoff between the tax shield provided by debt and the

financial distress associated with debt. Since interest on debt is tax-deductible, capital

structure has a bearing on the post-tax cash flows to the firm’s investors. The value of the tax

shield provided by debt is generally estimated as the product of the amount of debt and the

corporate tax rate.

According to the pecking order theory, there is a pecking order of financing which goes as

follows:

Internal finance (retained earnings)

Debt finance

External equity finance

A firm first taps retained earnings, its primary attraction is that it comes out of profits and not

much effort is required to get if. Further, the capital market ordinarily does not view the use

of retained earnings negatively.

Given the pecking order of financing, there are no well-defined target debt-equity ratios, as

there are two kinds of equity, internal and external. While the internal equity (retained

earnings)is at the top of the pecking order, the external equity is at the bottom. This explains

why highly profitable firms generally use little debt. They borrow less as they don’t need

much external finance and not because they have a low target debt-equity ratio. On the other

hand debt finance comes before external equity in the pecking order.

Behavioral considerations:

In practice, the capital structure decisions of companies are based on traditional conserations

as well as behavioral considerations. The principal behavioral factor relates to market timing,

meaning that managers take advantage of perceived market inefficiencies. They issue equity

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when it is perceived to be overvalued: they repurchase equity when it is perceived to be

undervalued. It must be emphasized that perceptions are the key and they may be sometimes

unbiased and sometimes biased.

A series of interviews conducted with chief financial officers, corporate treasurers,

consultants and financiers revealed that practitioners find the theoretical capital structure

models rather static. The interviewers pointed toward the volatility of their firms’ future cash

flows and uncertainties relating to investment opportunities and conditions in capital market.

In view of this, firms want to have flexibility to take advantage of unexpected investment and

acquisition opportunities and market mispricing.

Dividend policy:

Modigliani and Miller provided the standard neoclassical treatment for dividend policy. The

central premise of the MM framework is that the value of a firm depends solely on its

earnings power and is not influenced by the manner in which its earnings are split between

dividends and retained earnings.

The MM theory assumes a perfect capital market, wherein the following conditions are

assumed:

Information is freely available to everyone equally.

There are no taxes

Floatation and transaction costs do not exist

There are no contracting or agency costs

No one exerts enough power in the market to influence the price of security. This

means all participants are price takers.

Investments and financing decisions are independent.

The real world, however, is characterized by imperfections such as taxes on dividend income

as well as capital appreciation; floatation costs and transaction costs; informational

asymmetry; and agency conflicts.

In the wake of these imperfections, there is no single traditional view about what constitutes

appropriate dividend policy.

Why companies pay dividends:

Investor preference for dividend

Information signaling

Clientele effect

Agency costs

How managers think about dividends:

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The classic answer to this question was provided by John Lintner in 1956. Lintner’s survey, a

pioneering behavioral study, identified four facts:

1. Firms set long-run payout ratios. Mature firms with fairly stable earnings have higher

payout rations whereas rapidly growing firms have lower payout ratios.

2. Managers are concerned more about the change in the dividend than the absolute

level of dividend. Thus, paying a dividend of Rs. 10/- per share, is very important if

the previous year’s dividend per share was Rs.5/-, but not a big deal if the previous

years dividend wars Rs. 10/- per share.

3. Dividends tend to follow earnings, but dividends follow a smoother path than

earnings. Transitory changes in earnings are not likely to have an impact on dividend

payment.

4. Dividends are sticky in nature because managers are reluctant to effect dividend

changes that may have to be reversed. They are particularly concerned about having

to pull back an increase in dividend.

Mergers and Acquisitions

The traditional approach to M&A assumes that the market prices of both the acquiring form

and the target form reflect their intrinsic values, assuming that both remain as stand-alone

firms. However, a merger of the two firms is expected to generate potential synergistic gains.

If the acquiring company pays the target company the latter’s current value plus a premium,

the gains for the shareholders of the acquiring company and target company would be as

follows;

Gain to the shareholders of the acquiring company = Synergistic gains - premium paid

Gain to the shareholders of the target company = premium paid

Clearly the acquiring company will go forward with the acquisition only if the synergistic

gains exceed the premium paid. Further, since all assets are priced correctly, the combination

of cash and stock used to finance the acquisition does not matter.

Behavioral Considerations:

If markets are efficient and acquirers pay a premium which is less than the real synergistic

gains, acquisitions should create value for the shareholders of both the acquiring company

and the target company, regardless of the form of compensation. Further, the level of

acquisition activity should not be a function of the level of the stock market.

Empirical evidence, however, suggests the following:

Acquirers usually pay too much. This benefits the shareholders of the target company, but

hurts the shareholders of the acquiring company.

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CEOs fall in love with deals and don’t walk away when they should.

Mergers and acquisitions thrive during periods of stock market buoyancy.

Acquirers who pay stock compensation are more likely to do value-reducing, deals than

acquirers who pay with cash or debt.

What explains this empirical evidence which is at variance with what the theory predicts?

Several behavioral factors seem to explain the discrepancy. The important ones are;

Winners Curse in a competitive bidding situation, the participating companies rationality.

As Warren Buffet said, the thrill of the chase may blind the acquirer to the outcome

thereof. Hence the winner tends to overpay. In a way, the winner is an unfortunate

winner. This is referred to as the “winner’s curse” hypothesis.

Hubris Out of misplaced confidence, the acquirer’s management tends to overestimate the

synergies that it hopes to realize. As Daniel Kahneman et al. put it: “Mergers tend to

come in waves during periods of economic expansion. At such times, executives can

over-attribute their company’s strong performance to their own actions and abilities rather

to the buoyant economy. This can, in turn, lead them to an inflated belief in their own

talents.

Consequently many M&A decisions may be the result of hubric, as the executives

evaluating an acquisition come to believe that with proper planning and superior

management skills, they could make it more valuable. Research on post-merger

performance suggests that on average, they are mistaken.

Agency conflicts and corporate governance:

The public limited company, which is owned by a number of shareholders protected with

limited liability, has been a major organizational innovation it allows for efficient sharing of

risk among many investors and enables professional managers to run the company.

However, the public limited company given rise to possible conflicts between managers and

shareholders due to the separation of ownership and control Adam Smith had recognized,

very perceptively, the agency problem in his classical work “The wealth of Nations”

published in 1776.

Two centuries later, Michael Jensen and William Meckling provided a formal analysis of the

‘agency problem’ in their seminal paper titled “Theory of the Firm: Managerial Behavior,

Agency Costs, and Ownership Structure,” published in the August 1976 issue of The Journal

of Financial Economics.

The essence of agency problem is that self-interested managers may squander corporate

resources over uneconomic, value-destroying projects and activities. This problem is more

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serious in companies that have substantial free cash flows. On the other hand, in high growth

industries where internal accruals are less than what is needed for supporting profitable

investment opportunities, managers are less likely to squander resources over uneconomic

projects.

Agency costs are borne by the principals and agents, perhaps more by the latter if the

principals are smart. Hence, it is in the interest of the principals as well as the agents to find

ways and means of minimizing the agency costs.

Divergence of Interests

As long as the firm is owned and managed by the same person, there is no room for conflict.

As the stake of managers in the ownership of the firm diminished the scope for agency

problem increases. In a joint stock company, where managers have very little stake in

ownership they are likely to act in ways that are incompatible with the interest of

shareholders.

Traditional approach to agency conflicts

To deal with agency conflicts, the traditional approach requires principals to offer contracts to

agents that provides rewards and penalties with three goals in mind.

The first goal is to induce the agent to participate by offering a contract that is at least as

attractive as the best alternative available to the agent. This goal is called the participant

constraint goal.

The second goal is to motivate the agent to act in the interest of the principal, by appropriate

rewards and penalties. This goal is known as the incentive compatibility constraint goal.

The third goal is to ensure that the agent is not overly compensated. This is called the no

overpayment constraint.

Pay for performance in practice

How well is the pay linked to performance in practice? Academic studies and other evidence

on executive compensation suggest the following;

The CEO pay doe3s not vary sufficiently in relation to performance to be congruent with

traditional theory.

CEOs of poorly performing companies do not face a significant threat of dismissal.

Despite their increased popularity, stock options are often not well designed. Although

options can be appropriate in theory, in practice they tend to be capricious, inefficient,

and very expensive.

Behavioral Phenomena

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Why pay is poorly linked to performance in practice? Two psychological factors seem to

explain this phenomenon: overconfidence and prospect theory.

Overconfidence Due to overconfidence, directors are likely to underestimate the extent of

agency conflicts and behavioral baises of executives that lead to such conflicts. Further, over-

confident directors tend to think that they can address agency conflicts better than they

actually do.

Prospect Theory People tend to overweight low probabilities associated with extreme events

and underweight high probabilities attached to moderate events.

According to prospect theory, people are risk-averse, when they face the probability of only

gains and risk-seeking when they face the prospect of only losses. The aversion to a sure loss

can wreak havoc with the incentives commonly used to resolve agency costs. To see why this

happens, let us look at auditing services.

In theory, auditors are appointed by shareholders but in practice the appointment as well as

the compensation of auditors is determined by management. So auditors can be influenced by

unscrupulous managements to issue clear opinions.

Since audit firms are partnerships not corporations, the traditional view is that audit firms

have a strong incentive to protect their reputation for integrity. Hence, it is in the best interest

of auditors to issue honest assessment rather than clear opinions when the clients do not

deserve them. Further, since a firm’s choice of auditor is an important signal, a firm that

wants to convey that its financial statements are really clean might engage an auditor with

high reputation, whose fees is likely to be high.

But there can be circumstances when audit firms may compromise their integrity, as it

happened at Arthur Andersen. Authur Andersen, a hoary accounting firm, had two divisions,

the consulting division and auditing division. During 1980s, the consulting division had

become very profitable whereas the auditing division was struggling. The compensation of

people in the auditing division lagged behind people in Andersen consulting. In response, the

auditing division began a separate consulting of its own, Authur Andersen consulting, to

compete directly with the sister division Andersen consulting. So, Andersen consulting split-

off to become Accenture. In the wake of this departure, Arthur Anderson introduced a policy

called “2X,” which required its partners to get two dollars of non-auditing work for every

dollar of auditing firm. This prompted its partners to persuade clients to engage Arthur

Andersen for both internal and external auditing services. This led to a dilution of the quality

of audits, because it introduced a potential conflict of interest. The list of Arthur Andersen’s

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audit clients included Enron, WorldCom, Wast Management Inc, Boston Market and

Sunbeam. A major scandal ensured at each of these firms.

UNIT – 5

Emotions and Decisions – Making Experimental measurement of risk-related – Measuring

Risk – Emotional mechanisms in modulating risk-taking attitude – Neurophysiology of risk

taking. Personality traits and risk attitudes in different domains.

Substance of Emotions:

Mental states such as happiness, sadness, pride, greed, anger contempt, surprise, and disgust

are commonly understood as emotions.

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Sandra Hockenbury describes an emotion as “ a complex physiological state that involve

three distinct components: a subjective experience, a physiological response, and an

expressive response.”

John Elster argues that an emotion has six observable features:

1. Cognitive antecedents: Generally an emotional response is triggered by a belief.

2. Intentional objects: Emotions relate to something like a person or situation.

3. Physiological arousal: Emotions are accompanied by change in the hormonal and nervous

system. When a person is enraged, his blood pressure tends to increase.

4. Physiological expressions: Emotions are often characterized by observable expressions

associated with how a person functions.

5. Valence: Valence, a psychological term, is used to rate feelings of pleasure and pain.

Emotions are typically rated on a scale with a neutral point in the centre and negative and

positive feelings at the two end points.

6. Action tendencies: emotions tend to produce action. A person who experiences an emotion

often feels the urge, sometimes a compulsion, to act in a certain way.

The above six features define what an emotion is and how it may be differentiated from other

mental states.

Emotions, Feelings, Affects and Moods: emotions may be differentiated from similar

constructs like feelings, moods and affect, in the field of affective neuroscience. Feelings are

subjective representation of emotions. Moods are diffused effective states that last much

longer and are usually less intense than emotions. Affect is a wider term that emcompasses

emotion, feelings and moods, even though it is commonly used interchangeably with

emotion.

Emotions may be negative or positive. Negative emotions are anger, fear, stress, sadness,

disgust, guilt, hatred, shame, contempt, embarrassment, and so on. Positive emotions are

gratitude, hope, joy, tranquility, enthusiasm, interest, inspiration, awe, amusement, love and

so on.

Theories of Emotions:

Philosophers, researchers, and psychologists have proposed different theories to explain the

what, why and how behind human emotions. The major theories of emotions may be grouped

into two main categories: physiological, and cognitive.

Physiological Theories: Physiological theories suggest that responses within the body cause

emotions.

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According to James-Langer theory, an external stimulus leads to a physiological response

which in turn leads to an emotional reaction depending on how the person interprets the

physiological response.

According to Cannon-Bard theory people feel emotions and physiological reactions (such as

trembling and sweating) simultaneously. More specifically this theory says that both the

emotion and physiological reaction occur when the thalamus sends a message to the brain in

response to a stimulus.

According to facial feedback theory, facial expressions are not only the results of our

emotions but are also capable of influencing their emotions.

Cognitive Theories: Cognitive theories argue that thoughts and other mental activities have

an important bearing on the formation of emotions.

The Schechter-Singer theory, also known as the two factor theory of emotion, is an example

of a cognitive theory of emotion. According to this theory, there are two key components of

an emotion: physical arousal and cognitive label. This theory says that a mere physical

arousal is not enough; the person must also identify the arousal in order to feel the emotion.

Richard Lazarus, an important proponent of this view, argued that emotions must have some

cognitive intentionality. According to this theory, emotion is a disturbance that occurs in the

following order:

1. Cognitive appraisal – The individual assesses the event cognitively which motivates the

emotion.

2. Physiological changes – the cognitive reaction induces biological changes such as

increased heart rate of pituitary adrenal response.

3. Action – The individual feels the emotion and decides how to react.

Types and Dimensions of Emotions:

Plutchik’s Wheel of Emotion:

Emotions have been classified into various types or categories. According to Robert Plutchik,

a psychologist who developed a psycho evolutionary theory of emotions, there are eight basic

or primary emotions: joy, trust, fear, surprise, sadness, anticipation, anger, and disgust.

Pluchik’s wheel of Emotions

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Two dimensions of Emotions:

Emotional experiences may be measured along two dimensions. Viz., valence (how negative

or positive the experience feels) and arousal (how energising or enervating the experience

feels).

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Emotions and Effective feelings:

According to Jan Pankeep, a neuroscientist, there are seven primal emotions and effective

feelings associated with them. They are:

Primal Emotions Effective FeelingsSeeking Enthusiastic

Rage AngeredFear AnxiousLust ArousalCare Tender and LovingPanic Lonely and sadPlay Joyous

Emotions and Investing:

Emotions have a bearing on risk tolerance, and risk tolerance influences portfolio selection.

Investors experience a variety of emotions as they consider alternatives, decide how much

risk to take, watch their decisions play out, assess whether the initial strategy needs

modification, and finally learn how far they have succeeded in achieving their financial

objectives.

The emotions experienced by a person with respect to investment may be expressed along an

emotional time line as shown in the below Exhibit:

Hope Anticipation Pride

Decisions ----------------------------------------------------------------- Goals

Fear Anxiety Regret

Investment decisions lie at the left end of the time line and investment goals at the right end.

According to psychologist Lola Lopes, investors experience a variety of emotions, positive

and negative. Positive emotions are shown above the time-line and negative emotions below

the time line. On the positive side, hope becomes anticipation which finally converts into

pride. On the negative side, fear turns into anxiety which finally transforms into regret.

Hope and fear have a bearing on how investors evaluate alternatives. Fear induces investors

to look at the downside of things, whereas hope causes them to look at the upside. The

downside perspective emphasises security; the upside perspective focuses on potential gains.

According to Lopes, these two perspectives reside in everyone, as polar opposites. However,

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they are often not equally matched, as one pole tends to dominate the other. The relative

importance of these conflicting emotions determines the tolerance for risk.

Personality traits and risk attitudes in different domains:

People differ in the way they resolve decisions involving risk and uncertainty, and these

differences are often described as differences in risk attitude. In the expected utility

framework and its variants, including prospect theory, such apparent differences in risk

attitude are modelled by utility functions that differ in shape, with different degrees of

concavity (convexity) to explain risk aversion (seeking). Risk attitude is the parameter that

differentiates between the utility functions of different individuals and is intended as nothing

more than a descriptive label for the concavity or convexity of the utility function. Popular

interpretations of risk attitude, however, often consider it to be a personality trait.

The consideration of risk attitude as a personality trait has undergone a similar development

as that of personality traits in general. While traits were initially defined as stable (i.e.,

situation-invariant) personality characteristics that were assumed to be the result of biological

differences or early childhood experiences, the empirical observation of low correlations

between trait-related behavior in different situations has given rise to more complex

definitions that acknowledge the situational determinants of behavior while preserving

generality in the way personality traits shape the pattern of behavior across situations.

The following two observations have been problematic for the simple expected-utility

definition of risk attitude as a personality trait. First, different methods of measuring people’s

utility functions (and thus risk attitudes) have been shown to result in different classifications

of individuals. More importantly, even when using the same assessment method, individuals

have not shown themselves to be consistently risk seeking (averse) across different domains

and situations, both in laboratory studies and managerial contexts. MacCrimmon and

Wehrung (1986, 1990) showed, for example, that managers have different risk attitudes when

making decisions involving personal versus company money or when evaluating financial

versus recreational risks. These problems limit the predictive validity of expected-utility

based assessments of risk attitude. Given the lability of expected-utility based assessments of

risk attitude, it should not be surprising that measurement scales based upon them have not

had much success in predicting people’s choices or behaviors across a range of situations.

The observed content-specificity of responses suggests that they should not be combined

across content domains. Nevertheless, the Choice Dilemma Questionnaire, a commonly used

scale, asks people for probability equivalents in twelve choice dilemmas from different

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domains of life, which are then combined into a single score that purportedly represents a

person’s risk attitude. Despite its obvious deficiencies the scale is still in use, primarily for

lack of better alternatives.

Domain Specific Risk Taking (DOSPERT):

People differ in the way they resolve decisions involving risk and uncertainty, and these

differences are often described as differences in risk attitude. In the expected utility

framework and its variants, including prospect theory (Kahneman & Tversky, 1979; Tversky

& Kahneman, 1992), such apparent differences in risk attitude are modeled by utility

functions that differ in shape, with different degrees of concavity (convexity) to explain risk

aversion (seeking). Risk attitude is the parameter that differentiates between the utility

functions of different individuals (e.g., Pratt, 1964) and is intended as nothing more than a

descriptive label for the concavity or convexity of the utility function. Popular interpretations

of risk attitude, however, often consider it to be a personality trait (Weber, 1998).

Empirical investigations have shown systematic individual, group, and cultural differences in

perceptions of the riskiness of risky choice options (Bontempo, Bottom, & Weber, 1997;

Slovic, 1998; Weber, 1988). A smaller number of studies have also documented group

differences in the perception of perceived benefits (e.g., Johnson, Wilke, & Weber, 2004).

After accounting for differences in the perception of the risk or returns of choice alternatives,

however, people's perceived-risk attitude - defined as their willingness to trade off units of

perceived risk for units of perceived return - has shown considerable cross-group and cross-

situational consistency (Weber, 1998, 2001). The domain-specificity of risk taking thus

seems to arise primarily from differences in the perception of the risks (and possibly benefits)

of choice alternatives in different content domains, while the trait (or true attitude towards

risk) that shows consistency across situations lies in the evaluation of risk (as it is perceived)

as something that is either desirable (i.e., worth giving up units of return for) or undesirable

(i.e., something that needs to be compensated by units of return) (Weber, 2001).

The Domain-Specific Risk-Taking (DOSPERT) Scale, that allows researchers and

practitioners to assess both conventional risk attitudes (defined as the reported level of risk

taking) and perceived-risk attitudes (defined as the willingness to engage in a risky activity as

a function of its perceived riskiness) in five commonly encountered content domains, i.e.,

ethical, financial (further decomposed into gambling and investment), health/safety, social,

and recreational decisions.

Example:

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Five Factor model of Personality:

The five-factor model of personality is a hierarchical organization of personality traits in

terms of five basic dimensions: Extraversion, Agreeableness, Conscientiousness,

Neuroticism, and Openness to Experience. Personality psychologists use this model to

measure the relation between these factors and their impact on risk taking and individual

decision making in various domains.

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