2. Definition and manifestations
3. Overconfidence in economic decision making
4. Reasons and Solutions
Overconfidence is believed to be one of the most widespread behavioral biases. Empirical evidence supports this argument in many instances and differentiates between various forms and manifestations. Whether this is in sum economically negative for the individual or society remains unanswered in the literature. I analyze the economic implications of overconfidence based on recent research and connects them to reasons and viable solutions to overcome this bias in certain areas of the economic realm: consumer choices, market entry and decision making of firms, financial markets and bubbles.
The term overconfidence is used to describe one of the most common biases in mankind: many individuals think they are betterthan-average drivers, more sociable than their colleagues and feel quite sure about having answered a question correctly. With the establishment of the field of behavioral economics, overconfidence has received much attention in the last decades and it is believed to be a contributor to many instances where traditional economic theory could not provide a satisfying answer. While economics found it directly linked to excessive entry of firms in contested markets or speculative bubbles, other disciplines hold it accountable for the outbreak of wars, political extremism, excessive litigation or frequent misdiagnosis in medicine.
As one of the first economists referred Adam Smith in his influential book The Wealth of Nations (1776) to overconfidence as an ancient evil which was common throughout all ages. Its reasons are rooted in the psychology of mankind and they are deeply connected to other biases that cause deviations from rational decision making. Despite having negative consequences, Johnson and Fowler (2011) believe it to be evolutionary advantageous in survival and justify therefore its existence. Other evidence connects it to more entrepreneurial and innovative activities. Although much research focused on its negative consequence, this issue has not been resolved conclusively.
This paper provides a brief literature overview of the overconfidence effect with a focus on economic fields. First, the bias is defined, and its several forms are explained. Then, various economic fields are presented where overconfidence was observed. This is followed by reasons and solutions for the bias and it ends with a discussion about the overall effect of overconfidence on economic welfare for the individual and society.
2. Definition and manifestations
Overconfidence is typically defined as confidence in excess. The term captures “the difference between mean confidence and overall accuracy,” as Brenner, Koehler, Liberman and Tversky (1996) have characterized it. Experiments and empirical studies are designed to measure overconfidence and its extent. In the literature, it is refined and divided into three separate biases: overestimation, overplacement and overprecision.
Overestimation is defined as agents having formed an unrealistic selfimage of their capabilities. It appears in the subjective judgment of the own performance, the perceived control of a situation and the chance of success (Moore and Healy, 2008). In predicting future returns of stocks, people tend to overestimate their control of outcomes even if they cannot influence them: the illusion of contro l effect.Kahneman and Tversky (1979) argue that another bias is connected to overestimation:the planning fallacy. It is a tendency to overestimate the own rate of work and underestimate the time needed, the costs and risks involved in completing a task. Overestimation is measured as the difference between expectations and actual performance.
Overplacement was researched by Svenson (1981) who conducted a popular study showing that most people believed to be betterthan-average drives. In contrast to overestimation, overplacement is concerned with interpersonal comparisons: how well performed a person compared to others. It was found among entrepreneurs (Busenitz and Barney, 1997), success in sales (Larwood and Wittaker, 1977) and planning for war (Johnson and Tierney, 2011). The level of overplacement is typically measured by comparing the expected to the actual rank in a task.
Overprecision manifests itself when agents are too sure about the accuracy of their knowledge (Soll and Klayman, 2004). It was found that participants give too narrow confidence intervals when asked how certain they are about their answer. Even though people sometimes hold on average accurate beliefs, they tend to neglect the possible range of outcomes and form too tight probability distributions as Malmendier and Taylor (2015) had found. The effect is also known as miscalibration and appears to be robust especially when difficult items are asked. It is typically assessed by asking questions where a numerical answer must be specified by the confidence interval assigned to it.
Since overconfidence is treated as three distinct biases, the question arises whether and how are they linked. Table 1 depicts the discoveries of Healy and Moore (2007). Task difficulty plays a key role: while overestimation increases with it, the opposite is true for overplacement. Therefore, both effects are negatively correlated. It is referred to as the hardeasy-effect (Moore and Cain, 2007). The effects of overestimation and overprecision cannot be disentangled on a binary question: when someone is extremely sure about giving the right answer, she will always give a too narrow confidence interval.
Table 1: Interaction between the three effects 
Abbildung in dieser Leseprobe nicht enthalten
*the positive correlation was only insignificant
Underconfidence was also observed by researchers and it can be split up in the same three separate forms: underestimation, underplacement and underprecision. Moore and Cain argue that underestimation of the competition in market entry games is common. It also plays a role in various manifestations connected to estimation errors such as the planning fallacy (Burt and Kemp, 1994). Underplacement can be observed in a difficult competition where college students were overly pessimistic about winning and underplaced themselves (Windschitl, Kruger and Simms, 2003). Moore and Healy argue that people believe rare events are “less likely to happen to them than to others” and thereby underplace themselves. The opposite is true for ordinary events. Moore, Tenney and Haran (2015) found instances of underprecision but the evidence is mixed.
3. Overconfidence in economic decision making
The standard economic theory assumes that a rational agent, also dubbed Homo Oeconomicus, maximizes his utility and is perfectly informed. Reality differs from this paradigm greatly: in certain situations, people fall prey to behavioral biases and this results in suboptimal allocations. Although some market participants seem to exploit those instances and profit from it, deviations from rational decision making does not disappear. This paper will focus on consumers, the entry and management decisions of firms and financial markets due to their importance for overall economic welfare.
When overconfident consumers make decisions, they will often fail to maximize their utility. For instance, Sandroni and Squintani (2004) argue that people underestimate their own personal risks and therefore overconfident individuals are more prone to underinsurance. The authors focused on driving and the health insurance market where underinsurance can have severe consequences. It is thought to be a partial explanation for the high U.S. uninsured rate of 9.1 percent in 2015 for health insurance.
In the case when consumers are confronted with uncertainty about demand but must commit to a contract, overconfidence plays a role. Consumers are overprecise by underestimating the variance of their future demand and therefore choose a tariff that is suboptimal compared to an unbiased choice. Grubb (2009) underpinned this by using phone billing records of 2.332 student accounts. Hence, firms such as mobile phone providers or rental car companies exploit this with providing a menu of threepart tariffs. These contracts contain a fixed monthly fee, a certain number of free units and a positive marginal price for additional minutes that exceed their limit. Consumers overpay for their initially given units due to overestimation of their typical usage. Additionally, they tend to underestimate the probability of high usage, where firms charge relatively high prices to extract consumer surplus. Additionally, firms can exploit consumers tendency to overestimate their own abilities and their attention. Grubb (2015) argues that firms create more complicated contracts so that consumers end up not choosing the right options while the contract is valid. They overpay because they forget that actions are required and don’t pay enough attention. When consumers can choose between pervisit payment and a contract, e.g. gym memberships, people overestimate their number of visits per month and irrationally opt for the contract, since they overestimate their selfcontrol. DellaVigna and Malmendier (2006) show that user could have saved on average more than 40 percent when paying per visit instead of having a contract in a health club.
A profitmaximizing contract for firms found by DellaVigna and Malmendier (2004) includes for investment goods pricing below the marginal costs of attendance, automatic contract renewal and transaction costs when canceling the contract. Therefore, the price of investment goods will be perceived as below the marginal costs, i.e. overvaluation, whereas for leisure goods the price will be perceived as higher than the marginal costs. Hence, Grubb concludes that overconfidence will distort an efficient allocation in the intensive and extensive margin because it affects also quantity choices and causes a deadweight loss for consumers. The presence of a fraction of rational consumers does not mitigate this effect as Armstrong (2015) used the term ripoff externality to describe that rational consumers benefit from contracts designed for their overconfident peers. The latter lose.
In sum, the large body of evidence suggests that consumers welfare is reduced by overconfidence. Nevertheless, the bias can give people the courage to pursue activities and aim for higher goals. Thus, the net effect remains unclear.
Firms and their management are also affected by overconfidence. While 61.5 percent of startups fail within five years, as Dunne, Roberts and Samuelson (1988) had found, the reason why there is so much market entry remains controversial. Camerer and Lovallo (1999) studied in an experimental setting participants’ entry decision in a market in relation to overconfidence. By using various control variables, they concluded that overplacement and overestimation of the own success rate results in excess entry. Although most business owners know that the average market participant will lose money and the venture will fail, they still predict that their own business will be profitable. This is exacerbated when people selfselected in a skillbased task. Thus, more people entered the experimental market thereby neglecting the enhanced competition. Although the study relies on business students in renowned institutions, students are likely to resemble a great portion of the future startup scene. Camerer and Lovallo conclude further that overconfidence in business entry decisions should be the most pronounced when criteria of success are vague. Industry profits or total wages could even be negative where the bias is the most prevalent.
Not only in the early stages of a firms’ development was overconfidence observed, but also at the management level. Simon and Houghton (2003) connected CEOs overconfidence to innovation: more pioneering products where introduced than incremental ones. This was confirmed by Hirshleifer, Low and Teoh (2012) with their empirical observations between 1993 to 2003 that overconfident CEOs fostered innovation with higher research and development budgets and obtained more patents. Managers take on overly risky projects because they are too certain about success and underestimate the risks involved. They also tend to overinvest if they have enough internal company funds because they overestimate their achievable returns as Malmendier and Tate (2005) have found. On the contrary, when external funding is required, these CEOs tend to cut investments because they view them as overly costly. Therefore, investment decisions of overconfident CEOs are strongly connected to cash flow and this link is generally stronger in equity dependent firms. This might lead to a suboptimal investment scheme and an inefficient resource allocation. CEOs suffering from overconfidence are more likely to forecast excessive new product sales. Markovitch, Steckel, Michaut, Philip and Tracy (2014) linked the bias also to mistakes in the process of screening out unprofitable projects, higher failure rates for new product ventures and overproduction. The planning fallacy causes that projects are delayed, and costs explode. This is commonly observed for state projects.
When CEOs suffers from managerial hubris (Roll, 1986) and acquire another company, they are more likely to overpay for their target. The results are often a negative net effect for the acquirer with a destruction of value for shareholders. Malmendier und Tate (2006) used a broad data set to study this relationship empirically with the use of elaborate proxy variables for overconfident CEOs. They concluded that those CEOs are 65 percent more likely to initiate an acquisition and the average market reaction compared to their better calibrated counterparts is -70 to -100 basis points at time of the announcement of the merger. Typically, shareholders of the acquiring company lose while the shareholders of the target company profit from the merger. To sum it up, firms profit in some areas from this bias but are also negatively affected by it in others.
As financial markets play a fundamental role in the efficient allocation of resources in an economy, Fama (1970) has shaped the standard paradigm of their efficiency: the efficient market hypothesis. But it failed to explain empirical findings, such as high trading volume, the momentum effect or apparent mispricing in form of return predictability in general. Hence, the field of behavioral finance was established to tackle those issues. Financial markets with their speculative nature are the perfect breeding ground for behavioral biases with its technicality and own jargon. This leads to an enhanced perception of competence of financial professionals. It is still a predominantly male field, who are also more prone to overconfidence (Barber and Odean, 2001) and feedback is indirect and noisy. Not only retail traders (De Bondt, 1998) but also financial experts (Glaser, Langer and Weber, 2005) such as professional traders, investment bankers and financial forecasters, are found to be affected by overconfidence. Gloede and Menkhoff (2014) claimed that it was less pronounced among professionals. When financial forecasters had recent success, more frequent nonherding behavior was observed and their level of overconfidence rose as Hilary and Menzly (2006) had found.
In contrast to the standard economic paradigm, the observed trading volumes seem ridiculously high: in the year 2014 the total turnover of the 500 largest US stocks was $29.5 trillion compared to a U.S. GDP of $17.4 trillion as Daniel and Hirshleifer (2015) have discovered. Trading should only take place if the marginal benefits exceed or equal the costs of a transaction or for other rational reasons. De Bondt and Thaler (1995) argue that the major factor to understand the trading volume puzzle is overconfidence. The literature was focused on explaining the phenomena with overprecision in the trader’s judgment while recent empirical evidence points more towards th e differences in opinion explanation: market participants agree to disagree about the evaluation of a security. When this is combined with overplacement (Shiller, 1999) the effect will amplify and specific opinions about the future performance of an asset will persist even if one knows that others hold different opinions. Barber and Odean found that men’s turnover rate is 1.5 times greater than that of women and successively men underperformed women on average. The most active traders had the worst performance and the difference was in the magnitude of 3.7 percent annually. The reason for the underperformance was higher trading cost. To gauge the possible extent of excessive trading, the model of Kelley and Tetlock (2013) predicted that the market volume would be 100 times smaller without overconfidence. While widespread access to online brokerage reduced trading costs, it could also increase the level of overconfidence of investors. The consequences of this development regarding the welfare effects of trading remain controversial.
 A confidence interval of 90% contained less than 60% of the time the right answer (Soll and Klayman).
 Own adaptation based on Healy and Moore.
 While investment goods, for instance health clubs, yield results later and are costly at the time of consumption, the reverse is true for leisure goods, such as credit card borrowing.
 The experiment incentivized them by an earning opportunity for questions related to sports or trivia.
 Undergraduates and M.B.A. students from Chicago and Wharton.
 Data from 1980 until 1994 for close to 400 U.S. companies.
 Either their delay to exercise stock options beyond a reasonable benchmark (due to overestimation of returns that they can generate) or their portrayal by the business press such as The Wall Street Journal.
 Such as portfolio rebalancing, receiving windfallincome or a change in the risk attitude of the investor.
 It is modeled in finance with too narrow confidence intervals for future performance of a risky asset.
 Greater access to information should reduce the bias but it can also raise the level of perceived skill.
- Quote paper
- Stefan Dietrich (Author), 2017, Overconfidence. Review of its Economic Implications, Munich, GRIN Verlag, https://www.grin.com/document/424186