BEHAVIOURAL FINANCE

Introduction:

Behavioural finance is relatively new but quickly expanding field that seeks to provide explanations for

people’s economic decisions by combining behavioural and cognitive psychological theory with

conventional economics and finance. Fuelling the growth of behavioural finance research has been the

inability of the traditional expected utility maximization of rational investors within the efficient markets

framework to explain many empirical patterns. Behavioural finance attempts to resolve these

inconsistencies through explanations based on human behavior, both individually and in group. For

example: behavioural finance helps to explain why and how markets might be inefficient. After initial

resistance from traditionalists, behavioural finance is increasingly becoming part of mainstream finance.

Meaning and Definition of Behavioural Finance:

Behavioural finance is a field of finance that proposes psychology-based theories to explain stock market

anomalies. Within behavioural finance, it is assumed that the information structure and the

characteristics of market participants systematically influence individuals’ investment decisions as well

as market outcomes. There have been many studies that have documented long-term historical

phenomena in securities markets that contradict the efficient market hypothesis and cannot be

captured plausibly in models based on perfect investor rationality. Behavioural finance attempts to fill

the void.

Behavioural finance studies the psychological factors that influence financial behavior both at the level

of the market. So far these results have been used mainly to explain the existence of patterns in asset

prices and to develop investment strategies that exploit them. Behavioural finance is a new approach to

financial markets that has emerged, at least in part, in response to the difficulties faced by the

traditional paradigm. In broad terms, it argues that some financial phenomena can be better understood

using models in which some agents are not fully rational. More specifically, it analyzes what happens

when we relax one, or both of the two tenets that underlie individual rationality. Behavioural finance

allows advisors so better understand client behavior and improve communication strategies in order to

avoid poor decision-making and deepen client relationships.

According to Shefrin, “Behavioural finance is defined by as a rapidly growing area that deals with the

influence of psychology on the behavior of financial practitioners.”

According to Sewell, “Behavioural finance is the study of the influence of psychology on the behavior of

financial practitioners and the subsequent effect on markets”.

Behavioural finance is often viewed as a rejection of investment models and applications promoted by

classical economies. An alternative view is that behavioural finance picks up where traditional financial

models leave off. While the goal is not to solve the puzzles of finance, it is useful to consider both

approaches in formulating investments plans and dealing with market stress.

Factors affecting behavioural finance:

There are three generally accepted factors that come into the research and identification of behavioural

variables as they are related to the study of behavioural finance:

1) Heuristics: This term means a rule of thumb strategy or good guide to pursue so as to reduce

the time required to make a decision. For example, in planning for retirement, a rule of thumb

that has been suggested for having sufficient funds to retire is to invest 10% of annual pre-tax

income. As for what to invest in to reach that retirement goal (i.e. the allocation among asset

classes), a rule of thumb that has been suggested is that the percentage that an investor should

allocate to bonds should be determined by subtracting from 100 the investor’s age. So, e.g. a

45-year old individual should invest 55% of his or her retirement funds in bonds.

2) Framing: This factor is taken into consideration when studying behavioural finance. This refers

to the way that a financial problem or opportunity is presented to the investor. According to the

various behavioural finance theories, the verbiage and presentation of the situation to the

investor will greatly influence the decision that is made. The idea is that if the same facts were

presented with a different approach, the decision reached by the investor would likely be

different. For example, consider a coin toss with a pay-off of Rs. 50 for tails. A gift of Rs. 50 that

is bundled with a bet that imposes a loss of Rs. 50 if the coin comes up heads. In both the cases,

you end up with zero for heads, and Rs. 50 for tails. But the former description frames the coin

toss in terms of risky losses. The difference in framing can lead to different attitudes towards

the bet.

3) Market Inefficiencies: Another basic factor of behavioural finance is referred to as market

inefficiencies. Perhaps the most logical of the three basic factors, market inefficiencies still tend

to be outside the scope of universally accepted explanations for market performance.

Essentially, this factor of behavioural finance looks at the outcome of an event in the

marketplace and identifies contributing elements that experts may or may not have

acknowledge as playing a role in the outcome. Examples of market inefficiencies include such

events as taking market anomalies and making them into market indicators, and isolated events

where goods or services are priced incorrectly.

Behavioural finance is an ongoing process with the effectiveness of the process being hotly debated in

some quarters. Still, the discipline does attract a great deal of attention and there is no doubt that

research using behavioural finance as the basis will continue.

Psychological Traits Affecting Investment Decisions:

Decision:

Modern portfolio theory assumes that people are rational investors and invest only in efficient and

optimal portfolios that provide the maximum return and minimum risk. The truth (as posited by

behavioural economists) is that they are far from rational and are subject to a myriad of psychological

influences and behaviours that prevent us from not only making optimal investment or business

decisions, but can in some cases turn us into morons. When a person buy and hold too long or buy and

sell too quickly; he refuses to accept losses assuming that he will recover money or he will be losing

investments way too soon; people are overconfident about our own abilities or place too much trust in

“experts”; they maintain the status quo and do nothing or change things too frequently.

The dichotomies of living behavior are numerous and fascinating and have lead to creation of field of

study referred to as Behavioural Economics. Some of these psychological traits are explained below:

1. Representative Bias: Investors tend to make judgments based on stereotypes, which often leads

them to be too optimistic about winners are too pessimistic about losers. The use of stereotypes

is more pervasive than an individual thing, as it helps us to make decisions in the face of

information overload. This, however, can lead to bad business decisions. It is simply not true

that all purple people are excellent accountants.

2. Overconfidence: Overconfidence, i.e. thinking that you are smarter than everybody else (even

though you are) can be detrimental to your portfolio and business. Hubris and arrogance have

led to the downfall of many successful business owners and investors (although Donald Trump

apparently continues to thrive). Not all of us can be experts in everything.

3. Over-Optimism/Irrational Exuberance: Thinking that the stock market is going to continue to

rise or that and individual business is going to thrive in the absence of profits and positive cash

flows can be foolhardy. The lessons of dot com bubble still loom large with many investors as

they watched their investment portfolios, and their businesses, melt away.

4. Anchoring and Adjustment: This refers to the behavior where people tend to place too much

stuck in their first impressions and do not react appropriately to new information. This has

bitten many investors and business owners.

5. Aversion to Ambiguity: Investors like to invest in, and do business, with business that they

know. One of the most common and most egregious mistakes is to invest too heavily in

employer’s business, when the stock was delisted. Poor stock performance is often an indication

of trouble within the organization. Worst case scenario is that one losses his retirement savings

and his job at the same time. Business people, similarly tend to have a hard time letting go of

suppliers, employees, etc., that are performing poorly as it can be easier to deal with the “devil

you know”.

6. Loss Aversion: As a person tends to experience losses more intensely than gains, he engages in

behaviours to avoid feeling this pain. Individual might not sell an investment; despite

deterioration in fundamentals in the same way that he might not phase out a losing product line

in the hopes that there will be a turnaround. However, sometimes it is time to cut his losses.

7. Self-Control: People impose limits on themselves that make no sense. For example, a person

might have a stop loss order on equities that fall more than 5%. Similarly, an individual might

not hire someone because they failed to answer one question to his satisfaction. In both cases,

the right decision requires a slightly more reasoned and holistic evaluation.

8. Regret Minimization: The pain of loss combined with the pain of being responsible for the

decision that caused the loss leads to regret and often results in irrational behavior. An

individual may allocate his investments on some arbitrary measure that takes the decision-

making out of it, or he may decide not to start his own business because he does not want to be

responsible for failure. Either way, the decision is an emotional one.

9. Self Attribution Bias: This refers to taking credit for good decisions and blaming others for bad

ones and in addition to being somewhat delusional, can result in terrible investing, business and

life decisions. An individual’s portfolio went up because he is so great, but it went down because

his portfolio manager sucks. Blaming your employees for your business failures, while taking

credit for its success, is another example of this.

Behavioural finance is a fascinating area of study as by identifying the non-rational factors that drive

individuals, it can help to contribute to better decision-making process.

Explaining Biases in Behavioural Finance

If one is looking at higher returns accruing by investing in commodity stocks, the conventional wisdom

of buying a good company may not hold true, especially in the short-run. Commodities have cycles and

most investors make money by getting early in to cycle when the commodity prices start hardening.

However, returns from commodity stocks are governed by the perceptions of a large number of

investors. Their behavior and their perceptions of the commodity stock are dependent on the bottom

line off the company in question.

It will be important to understand the various behavioural biases before we go on to analyse the

commodity stock valuation parameters. These are explained bellow:

1. Heuristics: Heuristics or mental short-cuts play an important role in framing investor decisions.

According to Daniel Kahneman, “Heuristics are simple, efficient rules of thumb which have

been proposed to explain how people make decisions, come to judgments and solve problems,

typically when facing complex problems or circumstances, but in certain cases lead to systematic

cognitive biases”.

2. Representativeness: When commodity prices start moving up, the trend is reflected in the

working results of the leaders in the commodity stocks. The leaders are well-run companies

having a healthy balance sheet and a reasonable market share. Due to their market position,

they command a good respect and following in the markets and are tracked by a majority of the

analysts. They could also be a part of the index. When the leaders start doing well, the laggards

also attract attention from investors, as they are also perceived to be able to deliver good

results in view of the hardening stock prices. Due to the effect of the representativeness

heuristics, we have all the stocks of that company moving upwards and attracting investor

attention. At this point of time, investors do not see if the company is good or bad. They buy it

because it is representative of a commodity whose price is rising.

3. Round Trip Fallacy: Most of the cheap stocks generally have low or moderate PE ratios and only

in exceptional cases have a high PE ratio. By believing that all low PE stocks would necessarily be

cheap stocks one is succumbing to the “round trip fallacy”.

Coming to the commodity stocks, one would believe that a well managed commodity company

producing at a low cost would generate higher returns for the company. In view of its higher

earnings per share it would be able to command a higher PE ratio and thus a higher price. Being

a low-cost producer is an integral attribute for the company. However, investors look at other

attributes, which may not be integral, but can have a good effect on the bottom line.

4. Rising tide and Low-Base Effect: A rising tide lifts everybody. When the tide raises everyone

standing in the water goes up with the tide. A six foot father with his three foot son in the water

would both go up with the tide; however, the son’s rise will be far more significant as his

starting point is half that off his father. This is known as low base effect.

A commodity cycle turnaround lifts all the players in the industry. This is where the low-base

effect becomes very important when we look at returns generated by investors. It is relatively

easier to improve PBDT margins from 5% to 10% than from 20% to 40%. This is where the

laggards score over the leaders. Investors are thus more excited with the laggards as they seem

to be performing better than the leaders. This makes stock go up much faster resulting in good

returns for the investors.

5. Herd Mentality: Investors are always chasing fads and fancies in the markets. A turnaround

story is good news for the investor. Once a commodity turnaround is identified and the trend

shows an upward movement we have the ‘herd’ of investors chasing it. This leads to a spurt in

stock prices, which in turn leads to more investors joining the herd. When such crowd behavior

is at work, there is no rationality. The only goal is to buy faster than the neighbor. As certain

stocks go up and become expensive, the attention turns to other laggards in the same industry,

as they seem cheaper. This again leads to massive demand for such stocks.

Types of Biases in Behavioural Finance:

Overtime it has been noted that investors have a number of biases that negatively affect their

investment performance. Advocates of behavioural finance have been able to explain a number of these

biases based on psychological characteristics. These biases are:

1. Prospect Theory: One major bias is the propensity of investors to hold on to “losers” too long

and sell “winners” too soon. Apparently, investors fear losses much more than they value gains.

This is explained by prospect theory, which contends that utility depends on deviations from

moving reference points rather that absolute wealth.

2. Overconfidence (Confirmation Bias): Another bias is for growth companies is overconfidence in

forecasts, which causes analysts to over-estimate growth rate for growth companies and over-

emphasize good news and ignore negative news for these firms. Analysts and many investors

generally believe that the stocks of growth companies will be “good” stocks. This bias is also

referred to as confirmation bias, whereby investors look for information that supports their

prior opinions and decisions. As a result, they will mis-value the stocks of these generally

popular companies.

3. Noise Traders: A study examined the effect of noise traders (non-professionals with no special

information) on the volatility of closed-end mutual funds. When there is a shift in sentiments,

these traders move together; which increases the prices and the volatility of these securities

during trading hours.

4. Newsletter Writers: The noise traders tend to follow newsletter writers, who in turn tend to

“follow the herd”. These writers and “the herd” are almost always wrong, which contributes to

excess volatility.

5. Escalation Bias: The escalation bias, which causes investors to put more money into a failure

that they feel responsible for rather than into a success. This leads to the relatively popular

investor practice of “averaging down” on an investment that has declined in value since the

initial purchase rather than consider selling the stock if it was a mistake.

6. Familiarity Bias: Familiarity bias may cause some investors to be too concentrated on

opportunities in their own countries. They are more familiar with and confident about local

investment opportunities, so despite the fact that it’s much easier than in the past to diversify

investments across geographies, they go with what they know and can easily understand.

7. Anchoring: Anchoring or becoming fixated on past information and using that information to

make inappropriate investment decisions.

When investors are influenced by this bias, they may not be able to get their mind off a

particular sell-price target, even if new information is available or the investing landscape has

shifted significantly. They become stuck and may even ride markets to the bottom if they cannot

let go off what they think the price “should be”.

8. Availability Bias: According to the availability bias, people tend to heavily weight their decisions

toward more recent information, making any new opinion biased toward that latest news.

This happens in real life all the time. For example, suppose a person see a car accident along a

stretch of road that where he regularly drive to work. Chances are that persons begin driving

extra cautiously for the next week or so. Although the road might be no more dangerous than it

has ever been seeing the accident causes to overreact, but that persons be back to his old

driving habits by the following week.

Fusion Investing:

Fusion investing is the integration of tow elements of investment valuation – fundamental value and

investor sentiment.

Fusion investment is relatively new approach that’s attempt to integrate traditional and behavioural

paradigms to create more robust investment model. There are many paths to get success in investment.

There are many tools to assist with investor’s investing journey. Fusion investing is all about choosing

which path and tools are best for the investor. Many investment advisors and experts claim their path is

the only path to investment success or long term wealth accumulation. Their path may be right for

them, but it is folly to believe it is the only path. There is a best path for each person for investment and

fusion investing helps the investor in deciding which path is correct for them.

Arguably one defect of dividend discount model is that they ignore psychological factors in the

determination of share prices. Shiller proposed a model in which a dividend discount model is combined

with a term that indicates investor sentiment. The combination of dividend discount models and

investor sentiment in the valuation of shares has been referred to as ‘fusion investing’.

In Shiller’s model the market price of a share is the discounted value of expected dividends plus a

sentiment term. The sentiment term indicates the demand from noise traders who reflect investor

sentiment. Noise traders are unsophisticated investors with no particular expertise. When noise traders

are bullish prices will exceed their fair (fundamental) values. Conversely when noise traders are largely

inactive and prices reflect the fair values indicated by dividend discount models. At other times noise

trader sentiment can have a major impact on prices.

Brown and Cliff also used survey data to measure investor sentiment, and found that investor sentiment

affected stock prices. They found that investor optimism let to overvaluation, and subsequent low

returns as prices moved back towards their fundamental values. The relationship appeared to be

strongest amongst large capitalization shares with high price-to-book ratios (i.e., large-cap growth

stocks). Sentiment was more important in causing overvaluation than undervaluation. This asymmetry

could result from the relative difficulty of short selling, so that sales of shares in response to perceived

overpricing is difficult with the result that overpricing could be substantial. Underpricing would be more

readily corrected since there are no restraints on buying stock.

Baker and Wurgler measured investor sentiment by means of proxies such as closed-end fund

(investment trust) discounts and share turnover. They also found that sentiment affected stock prices

such that positive sentiment caused overpricing and subsequent low returns, and negative sentiment

was associated with underpricing and subsequent strong returns. They concluded that sentiment was

most important in the case of stocks that were not easily valued by means of dividend discount models.

They suggested that the companies whose share prices were most affected by sentiment were those

which did not pay dividends or were characterized by being new, small, high, growth, unprofitable or,

distressed. The valuation of such stocks was seen as being very subjective and hence susceptible to

sentiment. On the other hand shares yielding stable dividends were seen as amenable to objective

evaluation and their prices were less affected by investor sentiment. Baker and Wurgler concluded that

the valuation of shares should incorporate investor sentiment.

In a survey of analysis, Glaum and Friedrich, found that discounted cash-flow was the most favoured

technique for estimating the fair prices of shares. They also found that analysts paid considerable

attention to market sentiment when estimating fair prices. These findings suggest that stock analysts

use a form of fusion investing.

Bubble:

The term ‘bubble’, in reference to financial crises, originated in the 1711-1720 form British South Sea

Bubble, and originally referred to the companies themselves, and their inflated stock, rather than to the

crisis itself. This was one of the earliest modern financial cries.

The word ‘bubble’ is used to connect a situation where people have lost touch with reality. In an

economic sense, it is assumed that participants strive towards their economic well-being and act as

rational beings. If history is any guide, then mass hysteria, running contrary to economic belief, is real

and not a myth. The IT bubble from the end of the last century and the more recent real estate and

infrastructure bubbles provide interesting examples for our purpose.

An economic bubble (sometimes referred to as a speculative bubble, a market bubble, a price bubble, a

financial bubble, a speculative mania or a balloon) is “trade in high volumes at prices that are

considerably at variance with intrinsic values”. It could also be described as a trade in products or assets

with inflated values.

While some economists deny that bubbles occur, the cause of bubbles remains a challenge to those who

are convinced that asset prices often deviate strongly from intrinsic values.

Excessive optimism and overconfidence are the main causes of bubbles. In such times, it is important

that investors avoid getting into the bubble trap. There could be a bubble-like situation in the whole

market, or there could be one in a sector as we witnessed in the internet bubble during 1999-2000.

There could be a bubble in a particular stock. This could be due to the sector doing well as we saw in the

case of the power and real estate sectors boom, where we saw stocks of Reliance Energy and India Bulls

Real Estate hitting the roof. A bubble in a particular stock could also be due to some operator rigging, as

we saw in the stock of Mazda leasing during the Harshad Mehta boom in 1992-1993.

Investors need to avoid this bubble trap, and to do so, they need to understand what it takes to make a

bubble.

Formation of Bubble:

Bubbles can only be formed when there are many greedy investors who are willing to allow someone to

exploit their greed. This crowd goes on increasing in size, as other greedy investors join the bandwagon

out of envy. This has a snowballing effect, leading to creation of an illusion in form of bubbles. The

important roles in the formation of bubbles are of:

1. Sentiment plays an important role in the formation of a bubble. Along with sentiment,

perception also changes. This new scenario is then extrapolated too much into the future. A

change in the economic growth of a country, a change in the outlook of a sector, or a change in

the fortunes of a company can be extrapolated to such an extent that irrational projections are

expected to be real and excesses are committed based on these expectations.

2. Media also play a very important role in the formation of a bubble. Through the media,

information, rumors and stories make their way to the people. The media’s job makes all the

current hot information available to the investors.

Bubbles and Behavioural Economics:

It is usually presumed that the investors act in a rational way. The theory of rational choice is the basis

of neo-classical information economics. However there are exceptions systematic cognitive errors can

lead to a different form of decision making. The acceptance or ignoring of risk can sometimes be

explained by behavioural economics and can cause excessive optimism and overconfidence.

Following are the cause of risk:

1. Excessive Optimism: In good times, people become excessively optimistic about the future.

They believe that good times are permanent and refuse to see the darker side. They become

more adventurous and tend to take more risks. Little success is assumed to be the stepping-

stone to bigger success. All caution is thrown to the winds and people become reckless.

Investors become so optimistic that they tend to invest in any stocks in the hope that they will

make money. Success in a couple of such stocks would make them optimistic about other

stocks.

In times of excessive optimism, companies become very aggressive and come out with huge

ambitious pans requiring huge capital outlays. They borrow excessively and approach the capital

markets for funding. Excessively optimistic investors are always ready to take part in the action.

The initial public offering off “Reliance Power Limited (RPL)” is a classic example of what

excessively optimistic promoters and investors can do. It is important to understand that

nothing is permanent in life. Equanimity is the key to success.

2. Overconfidence: Excessive optimism leads to the overconfidence. There is overconfidence in the

knowledge and ability of oneself. When one is fed with so much of information through the

media, one tends to become overconfident of one’s knowledge. However, investors tend to

relate one with the other. We have seen in the markets that stock prices do not move in tandem

with the fundamentals off a company. Overconfidence in one’s ability arises from success in

investing. A string of a few successes in stocks, due to the general improvement in the stock

market sentiment and a bit of luck, makes people overconfident of their ability. From 2003

onwards we have seen such a bull run that everyone who entered the stock markets has made

money. However, this success is not related to one’s ability alone. When the sentiment changes

and the tide turns, stock prices can just collapse even if the fundamentals of the company have

not changed.

3. Herd Behaviour: Herd behavior is usually not a very profitable investment strategy; Investors

that employ a herd-mentality investment strategy constantly buy and sell their investment

assets in pursuit of the newest and hottest investment trends. For example, if a herd investor

hears that internet stocks are the best investments right now, he will free his investment capital

and then dump it on internet stocks. If biotech stocks are all the rage six months later, the

investor probably move his money again, perhaps before he has even experienced significant

appreciation in his internet investment.

This frequent buying and selling incurs a substantial amount of transaction costs, which can eat

away at available profits. Many herd-following investors will probably be entering into the game

too late and are likely to lose money as those at the front of the pack move on to other

strategies.

Behavioural Finance and EMH:

Eugene Fama has set the benchmark for comparing the Efficient Market Hypothesis (EMH) the

behavioural finance theories. He indicates that many alternatives to the EMH are vague and unspecified.

Quite rightly, he argues that alternatives to the efficient market hypothesis need to be well specified

and testable. In particular, he suggests that much of the evidence against markets being informationally

efficient is, in fact, consistent with market efficiency – this is especially so if anomalies are split evenly

between under-reaction and over-reaction. We show, using daily data from the Australian Stock

Exchange (ASX), that the under-reaction and over-reaction anomalies do not split evenly and that,

contrary to EMH-type arguments, information traders do not necessarily correct pricing errors

introduced into the market by noise traders. Consistent with the market being behaviourally efficient,

however, this does not translate into supernormal profit opportunities.

Under certain assumptions, the EMH maintains that asset prices reflect all the relevant information

about the asset, thus it is impossible for investors to get abnormal return and beat the market. The EMH

implies that there is no unexploited profitable opportunity in the financial market. Although the EMH

provides a useful insight through which we look at the financial market, the EMH fails to explain the

more and more anomalies in the financial market. The EMH provides little useful explanation about the

recent financial crisis. The validity of the EMH is questioned and the confidence in the EMH declines.

Moreover, the EMH has even been criticized as the culprit of this financial crisis. Given the criticism the

EMH suffers, scholars have developed varieties of theories so as to explain the anomalies in the financial

market. Among these most influential one is behavior of human beings affects asset prices and the

financial market. Based on the assumption that investors are sometimes irrational and the market is

inefficient and that there are limits to arbitrage, the behavioural finance overall gives better

explanations concerning the anomalies in the financial market than the EMH. The behavioural finance is

a rapidly developing field in the financial economics.

Mathematical Explanation of Behavioural Finance:

Mathematical probability has been used as a tool to address the need to model formally the fact that

actors must often act in the face of uncertainty – uncertainty about what the facts are and uncertainty

about whether an action will bring about an intended outcome (the G parameter). The traditional

approach has been to treat uncertainty as an attribute of the knowledge, and thus we have language

such as “probability that x is true”, and so forth.

Using homo communalities, we can develop different approach, which we term the ‘behavioural

approach’. Knowing and acting are distinct concepts, individuals are always acting; they can,

tautologically, only act on what they take to be the case. This is the relationship between knowledge and

action articulated in the SPCF. For an actor, and therefore for those modeling actors, the central

question about any particular belief, regardless of the certainty of that belief, is the degree to which

they are prepared to act on it. This is the focus of economics and finance – prediction of what actors will

do.

Thus there are two equally important distinctions:

1. What the actor takes to be the case; and

2. The degree to which the actor is prepared to act on what they take to be the case.

Preparedness to act on what they take to be the case is a fact about the actor, not about the knowledge.

Traditionally, all uncertainty has been considered a matter of knowledge uncertainty, with various

mathematical model – probability, Kahneman and Tversky’s W(p), fuzzy set theory and fuzzy logic and so

on. The behavioural approach is to keep the two distinctions separate, formally model preparedness to

act, and incorporate knowledge uncertainty insofar as it is necessary or useful in calculating

preparedness to act, what the actor takes to be the case is modeled formally by the parameter; we

define the degree to which the actor is prepared to act on x as the behavioural certainty of x (to

distinguish it from the most common term “certainty”, which has a long history of interpretation as

‘with probability 1’), denoted β(x), with 0 < β ≤ 1. (The item x may be “doing P will bring about G”). In

some cases, it may be noted that Bayesian probability or a modification of it such as w(p) accurately

models β, or is an input to its calculation, a question to be empirically determined. This allows us to

generalize prospect theory without using probability (unless it is appropriate in specific cases) – a

situation x with value v(x) and behavioural certainty β(x) has a ‘net’ value β(x). v(x). When β(x) = w(p),

this reduces to w(p).v(x), the formulation is prospect theory.

The behavioural approach and behavioural certainty have further implications for practice. It can be

expected that experimental protocols based on the concept of behavioural certainty will yield

significantly different data from those based on mathematical probability and phrased in that language.

Technical analysis and Behavioural Finance:

Technical analysis attempts to exploit recurring and predictable patterns in stock prices to generate

superior investment performance. Technicians do not deny the value of fundamental information, but

believe that prices only gradually close in on intrinsic value. As fundamentals shift, astute traders can

exploit the adjustment to anew equilibrium.

For example, one of the best documented behavioural tendencies is the disposition effect, which refers

to the tendency of investors to hold on to losing investments. Behavioural investors seem reluctant to

realize losses. This disposition effect can lead to momentum in stock prices, even if fundamental values

follow a random walk. The fact that the demand of “disposition investors” for a company’s shares

depends on the price history of those shares means that prices close in on fundamental values only over

time, consistent with the central motivation of technical analysis.

Behavioural biases may also be consistent with technical analysts us of volume data. An important

behavioural trait is overconfidence, a systematic tendency to overestimate one’s abilities. As traders

become overconfident, they may trade more, inducing and association between trading volume data as

well as price history to direct trading strategy.

Finally, technicians believe that market fundamentals can be perturbed by irrational or behavioural

factors, sometimes labeled sentiment variable. More or less random price fluctuations will accompany

any underlying price trend, creating opportunities to exploit corrections as these fluctuations dissipate.

Technical analysis also uses volume data and sentiment indicators. These are broadly consistent with

several behavioural models of investor activity which are as follows:

1. Volume Data: It includes the various analysis techniques.

a. Dow Theory: The father of technical analysis is Dow Theory, named after its creator

Charles Dow (who established the Wall Street Journal). Many of today’s more

technically sophisticated method are essentially variants of Dow’s approach.

The Dow Theory posits three forces simultaneously affecting stock prices:

i. Primary trend is the long-term movement of prices, lasing from several

months to several years

ii. Secondary or intermediate trend are caused by short-term deviations of prices

from the underlying trend line. These deviations are eliminated via corrections

when prices revert back to trend values.

iii. Minor or Tertiary trends are daily fluctuations of little importance

b. Moving Average: The moving of stock index is the average level of the index over a

given interval of time. For example, a 52-week moving average tracks the average index

value over the most recent 52 weeks. Each week, the moving average is recomputed by

dropping the oldest observation and adding the latest.

A period in which prices have generally been falling, the moving average will be above

the current price (because the moving average “averages in” in the older and higher

prices). When prices have been rising, the moving average will be below the current

price.

c. Breadth: The breadth of the market is a measure of the extent to which movement in a

market index is reflected widely in the price movements of all the stocks in the market.

The most common measure of breadth is the spread between the number of stocks that

advance and decline in price. If advances outnumber declines by a wide margin, then

the market is viewed as being stronger because the rally is widespread. These breadth

numbers are reported in the Wall Street Journal.

2. Sentiment Indicators: It includes the following indicators:

a. Trin Statistic: Market volume is sometimes used to measure the strength of a market

rise or fall. Increased investor participation in a market advance or retreat is viewed as a

measure of the significance of the movement. Technicians consider market advances to

be a more favourable omen of continued price increases when they are associated with

increased trading volume. Similarly, market reversals are considered more bearish when

associated with higher volume. The trin statistic is defined as:

Volume declining/Number declining

Trin = ------------------------------------------------

Volume advancing/Number advancing

Therefore, trin is the ratio of average volume in declining issues to average volume in

advancing issues. Ratios above 1.0 are considered bearish because the failing stocks

would then have higher average volume than the advancing stock, indicating net selling

pressure. The Wall Street Journal reports trin everyday in the market diary.

b. Confidence Index: Barron computes a confidence index using data from the bond

market. The presumption is that actions of bond traders reveal trends that will emerge

soon in the stock market.

The confidence index is the ratio of the average yield on 10 top rated corporate bonds

divided by the average yield on 10 intermediate grade corporate bonds. The ratio will

always be below 100% because higher rated bonds will offer lower promised yields to

maturity. When bond traders are optimistic about the economy, however, they might

require smaller default premiums on lower rated debt. Hence, the yield spread will

narrow, and the confidence index will approach 100%. Therefore, higher values of the

confidence index are bullish signals.

c. Put/Call Ratio: Call options give investors the right to buy a stock at a fixed “exercise”

price and therefore are a way of betting on stock price increases. Put options give the

right to sell a stock at a fixed price and therefore are way of betting on stock price

decreases. The ratio of outstanding put options to outstanding call options is called the

put/call ratio. Typically, the put/call ratio hovers around 65%. Because put options do

well in falling markets while call options do well in rising markets, deviations of the ratio

from historical norms are considered to be a signal off market sentiment and therefore

predictive of market movements.

Interestingly, however, a change in the ratio can be given a bullish or a bearish

interpretation. Many technicians see an increase in the ratio as bearish, as it indicates

growing interest in put options as a hedge against market declines. Thus, a rising ratio is

taken as a sign of broad investors’ pessimism and a coming market decline. Contrarian

investors however believe that a good time to buy is when the rest of the market is

bearish because stock prices are then unduly depressed. Therefore, they would take an

increase in the put/call ratio as a signal of buy opportunity.

Portfolio Performance Evaluation: