Framing has been identified as another key behavioural incident. Tversky and Kahneman (1981) established framing bias and argued that people respond differently when the same event is framed differently. Thus, “the frame that a decision maker adopts is controlled partly by the formulation of the problem and partly by the norms, habits and personal characteristics of the decision maker” (Tversky & Kahneman, 1981, p. 453). They showed that the psychological principles that govern the perception of decision situation and the assessment of chances and outcome do change depending on how the problem is presented. In other words, the way a problem is described or
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posed, affects the choices that consumers make. For instance, investors may be favourably disposed to a particular investment when they are told that there is 95 percent chance of success than when they are told that there is 5 percent chance of failure, even though the outcome is the same or identical. Hence, investors’ decisions could be influenced depending on positive or negative frame. Unfortunately, people are usually provided with options within the context of only one of the two frames
2.4.3.2 Mental Accounting
Mental accounting is another topic in the field of BF. This model of investors’ behaviour was propounded by Thaler (1985) and it tries to illustrate the method employed by individuals and households to code, organise and evaluate events or keep track of their financial activities (Thaler, 1990). The proponents of this behavioural bias state that individuals group their assets into a number of different mental accounts (Shefrin & Thaler, 1988). Shefrin and Thaler (1988) further showed that people have different categories of income and marginal propensity to spend differs among the categories. Thaler opines that individual’s process a mixture of outcomes as opposed to individual events and that this leads to irrational financial behaviour.
Mental accounting was explained by Tversky and Kahneman (1981) using the following analogy:
(a) Imagine that you have decided to see a play and have paid the admission price of ₤10 per ticket. As you enter the theatre you discover you have lost the ticket. The seat was not marked, and the ticket cannot be recovered. Would you pay ₤10 for another ticket? OR (b) Imagine you have decided to see a play where the admission is ₤10 per ticket. As you enter the theatre, you discover that you have lost a ₤10 note. Would you still pay ₤10 for a ticket for the play?
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According to Tversky and Kahneman (1981), less than 50 percent of the interviewees answered yes to (a), while almost 90 percent of them answered yes to (b). Economically speaking, one would expect identical responses. However, many would feel that it is too much to pay twice for the play but treat the money lost in isolation to the play (Davies, 2003). Thaler (1999) reviewed the studies on mental accounting and submitted that the bias controls choice. To overcome the irrationality embedded in this bias, a rational, economic being would view money as perfectly fungible when they are being allocated for different purposes and value a Rand the same whether it is received as a gift or earned. Therefore, a dollar dividend income should not be viewed as different from a dollar capital gain income or the former should not be viewed as disposable when the latter is not.
2.4.3.3 Endowment Bias
An investor would want to be paid a higher price for the shares owned by them than they would be ready to pay to acquire the same share. This is known as the endowment or divestiture aversion, which Kahneman, Knetsch and Thaler (1991) describe as ownership effect in the field of social psychology (Beggan, 1992). The bias holds that individuals attach more value to items owned by them (Morewedge & Giblin, 2015). The bias involves two paradigms. First, the bias has to do with a valuation paradigm in which individuals will be predisposed to pay more to have continuous possession of something owned by them than to buy something they do not own even where there is no reason for the attachment or where the item in question is newly acquired. Second, the bias has to do with an exchange paradigm, in which individuals, when given an item, are cautious to swap it for another item of equal worth. For instance, Knetsch (1989) gives an illustration of participants who, when first given chocolate, were hesitant to swap it for a mug of coffee. On the other hand, those who were first given the mug of coffee were equally cautious to exchange it for the former. In the stock market context, many portfolio managers have had dealings with clients who are indisposed to sell stocks willed to them because they perceived selling the asset as a sign of disloyalty (Pompian, 2006). This behavioural bias contravenes the reference-independence
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supposition of rational choice theories and where crowds are influenced by this bias, they cannot be said to be rational.
2.4.3.4 Overconfidence
Overconfidence is a behavioural phenomenon in which investors have unfounded trust in their own instinct, opinion, calculation and cognitive abilities and skills as opposed to rational processing of information. This bias has its source from various experiments and research in cognitive psychology where people overrate their prediction abilities. Fuller (1998) explains that people who claimed to be 90 percent sure of the truism of a statement or occurrence of an event, are more often than not, only 70 percent right. Studies have traced the root of wars and strikes, litigations and market bubbles to overconfidence bias (Moore & Healy, 2008). By experiments, Camerer and Lovallo (1999) showed that excessive business entry is caused by overconfidence and optimism while Barber and Odean (2001) discovered that men trade 45 percent more than women do because of overconfidence.
There are two types of overconfidence bias, namely the miscalibration and better-than- average effects (Hilton, 2001). Miscalibration means excessive trust in one’s accuracy, while better-than-average effect implies overestimating one’s performance relative to others (Moore & Healy, 2008). The miscalibration results from underestimating or overestimating events. A fund manager for instance, asked to make a prediction on the Dollar/Euro exchange rate in six months may be 90 percent sure that the rate will be within 0.64 and 0.74 dollars. Stephan (1998), applying this method, found that 71 percent of foreign exchange dealers failed exchange rate projection. Further, analysts who are 80 percent confident that a particular security will rise are only right about 40 percent of the time. Better-than-average bias may occur, for instance, when the majority of people deem themselves better than the average driver when questioned about their driving ability. People unrealistically overestimate their ability (Merkle & Weber, 2011; Harris & Hahn, 2011). When considering the effect of overconfidence on the investors’ behaviour, it is a self-deception bias, which has resulted in significant increases in trading volume the world over (Shefrin, 2000). Consequently, the trading volume in the
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world stock exchanges is much higher than what EMH would imply. It also increases market depth and decreases average returns of overconfident investors.
2.4.3.5 Herding
It is widely believed that herding occurs among investors in the stock market (Devenow & Welch, 1996). Herding is the tendency for market participants to flock together in their trading decisions, in the same manner as a herd (Sarpong, 2017). Hwang and Salmon (2004, p. 585) state, that “[h]erding arises when investors decide to imitate the observed decisions of others or movements in the market rather than follow their own beliefs and information.” In other words, it is the penchant to copy other investors, which makes a collection of investors to take similar actions (Lemieux, 2004). Herding may be spurious or intentional (Bikhchandani & Sharma, 2001). Spurious herding occurs when investors confronting identical problem or information sets, make similar decision. This type of heading may not violate market efficiency. Intentional herding, on the other hand, arises out of the intent of traders to mimic one another’s action even when they are faced with different problems, which may lead to market inefficiency. However, Bikhchandani and Sharma (2001) note that differentiating the two types of herding might be difficult in reality since there are so many factors that determine investment decisions.
The tendency of investors to mimic each other’s actions has attracted the attention of researchers. A study of this bias by Nofsinger and Sias (1999) revealed that herding by institutional investors affects prices more than herding by individual investors does. Similarly, Sarpong and Sibanda (2014) established that herd habit is common among professional mutual fund managers in South Africa. For example, Lakonishok et al. (1991) are of the opinion that professional investors involve themselves in herding purposely to “window dress”11 their portfolio. On the other hand, Cont and Bouchaud
11strategy used by portfolio managers close to the year end to enhance fund’s performance outlook by selling loosing and buying gaining stocks which are then reported as part of the investments’ holdings.
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(2000) held that uninformed investors are susceptible to the herding habit, which affects stock prices while the behaviour is rare amongst professional investors.
This behavioural bias is normally used to explain correlations in trades arising out of relationships among market traders (Chiang & Zheng, 2010, p. 1911). It is also one of the commonly mentioned reasons for stock return volatility as Christie and Huang (1995) and Teh and De Bondt (1997) posit that stock return volatility can be affected significantly by herding. It means that the effect of investor herding practices can move prices farther than their fundamental values (Tan et al., 2008) and this poses questions on the general efficiency of the market (Lux, 1995). Thus, it is a common argument that financial crises are an outcome of extensive herding amid market traders (Chari & Kehole, 2004, p.128).
2.4.3.6 Affect Heuristic
Affect is a psychological notion which means emotional response (Cherry, 2018). The concept was first introduced in a 1978 paper by Fischhoff, Slovic and Lichtenstein (1978), who introduced affect bias, which can be seen as a fast good or bad emotional reaction to a stimulus; shorter and different from a mood. It involves a mental shortcut that individuals apply when making automatic decisions, which depend majorly on current emotional conditions as opposed to taking the time to think about the future implications of the decision (Cherry, 2018). Current emotions of people, for example fear, surprise and pleasure influence mental shortcuts. This bias can be negative or positive and this influences your awareness of the benefits and risks of a stimulus. Positive affect educes a high benefit, low risk view and vice versa (Fischhoff et al., 1978). It means that the higher the perceived benefits, the lower the perceived risk. Affect-based judgments are quick, involuntary and usually depend on experiences (Slovic, Finucane, Peters & McGregor, 2007). Interestingly, stimuli do not generally spur identical emotion, as someone who had a dog bite as a child and another who owns a dog may have different views of a dog. The immediate emotional response to a stimulus will drastically change how we interpret later events and choose to act. Finucane,
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Alhakami, Slovic and Johnson (2000) opine that observed positive correlation between perceived benefit and risk could be traced to affect heuristic. The affect bias expects an inverse relationship between risk and return for unfamiliar stocks and a direct correlation between risk and return for familiar stocks (Sarpong, 2017). For instance, best stocks may perhaps be ignored by an investor, notwithstanding its return because of the indirect relation of affect heuristic and judgment (Hassan et al., 2013), while Su, Chang and Chuang (2010) showed that negative financial information affects a firm’s corporate image or investors’ stock buying intention. This emotional reaction more often leads to wrong judgment. People are prone to this heuristic when they have no opportunity for reflective assessment or are under pressure, hence, cannot base decisions on assessment of risk return tradeoff between available alternatives.
2.4.3.7 Anchoring and Adjustment Bias
In numerical prediction, when a relative value (an anchor) is given, individuals make estimates by starting from an initial value (the anchor) that is adjusted to yield the final answer (Tversky & Kahneman, 1974). The bias influences investors when they are unnecessarily preoccupied with a given set of information to which inadequate subsequent modifications are made regardless of the availability of new information (Neumann, Roberts & Cauvin, 2011; Bokhari & Geltner, 2011). Supposing one is estimating values of unknown magnitude, people tend to anchor on information that comes to mind and amend until they arrive at a reasonable estimate. In the original formulation, the starting information, or anchor, has a tendency to exert drag on the ensuing adjustment process, ending up with estimates not significantly different to the initial anchor. Wansink, Kent and Hoch (1998) showed that people are likely to buy more items from a shop when each price refers to numerous goods, for instance $2.00 for 4 items, instead of one good, such as $0.50 for each item. The final decision is biased by the anchor, which is the quantity of goods described in the price, such as four cans or one can. A fan who is required to estimate the number of goals scored by Ronaldo (a footballer) a year may have his judgment biased by any numbers they had recently observed. An investor may also be over-influenced by the earliest information
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received when making a buying decision and driven to a conclusion towards the anchor. This bias prevents investors from making rational investment decisions by basing decisions on irrelevant anchors instead of considering the pros and cons of each option.
2.4.3.8 Availability Bias
The availability bias is a principle, whereby an individual evaluates the probability of an event by the extent to which it is readily recollected (Tversky & Kahneman, 1973); it is a cognitive heuristic in which people consider information that is readily available rather than examine further alternatives (Sewel, 2007). It occurs when one, who is asked to judge the rate of recurrence or the probability of an event, tends to do so by the ease with which instances or occurrences can be brought to mind (Tversky & Kahneman, 1973). This heuristic is a common mental shortcut that makes people rely on immediate information or examples that occur to them first when gauging a particular decision. It is based on the assumption that what can be remembered must be very important relative to options that cannot be easily remembered. This could generate a bias concerning the hottest news, events, experiences or memories (Bebbington, 2010). For instance, “Most investors, if asked to identify the “best” mutual fund company, are likely to select a firm that engages in heavy advertising” (Pompian, 2006, p. 96). According to Goetzmann, Kim and Shiller (2016), while historical statistics indicate relatively low probability of occurrence of extreme stock market crashes in a single day, surveys of market participants in the past 26 years in the United States (US) revealed that they judged the likelihood to be far higher, simply because of the ease with which the term is brought to mind.
2.4.3.9 Representativeness Bias
In making judgments under uncertainty, Tversky and Kahneman (1974) state that individuals judge the probability that an object A belongs to group B, by the extent to which A is representative of or looks like B. Tversky and Kahneman define representativeness as "the degree to which [an event] (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the
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process by which it is generated". This heuristic could lead to wrong judgment since the fact that something is more representative does not really make it more likely. There are two main categories of representativeness bias relevant to investment decision making, namely base-rate neglect and sample-size neglect (Pompian, 2006). Base-rate neglect bias occurs when investors try to ascertain probable success of, say, a security of firm A by putting the company in an easily understood classification scheme. For instance, firm A could be classified as size stock and the reward and risk will be evaluated within such classification. Doing so, other variables or diligent information analyses are ignored in the investment evaluation. Sample-size neglect occurs when individuals incorrectly treat small sample size as a representative of large pool of data. This heuristic is employed for the reason that it is an easy computation, but there is the danger of overestimating its accuracy.
2.4.3.10 Regret Aversion Bias
Instead of weighing all alternatives vis-a-vis their probable outcomes, people tend to ponder on the worst possible outcome and how they would feel (regret), hence, they end up picking options that reduce regret even if it is not optimal. Investors succumbed to this bias when they fail to make any decisive decision because they fear that the action will be sub-optimal and in order to avoid the hurt of regret, which accompanies a poor decision (Prince, 2017). In his retirement decision, Harry Markowitz, a Nobel laureate in economics, was a victim to regret aversion stating, “I visualized my grief if the stock market went way up and I wasn’t in it—or if it went way down and I was completely in it. My intention was to minimize my future regret, so I split my retirement plan contributions 50/50 between bonds and equities” (Pompian, 2006, p. 227). The bias could make investors hold onto losing positions for a long time or make investors afraid of considering markets that have experienced loss in recent times or behave like herds by thinking that joining large wagons will reduce possible future regrets (Pompian, 2006).
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