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74 Seiten, Note: 1,3
2. Literature Review
2.3. Impact on Economic Behavior
2.4. Impact on Company Value and Overall Welfare
2.5. Preliminary Conclusion
3. Measurement Methods
3.1. Methods based on Qualitative Expressions
3.2. Methods based on Subjective Valuation
3.3. Action-based methods
3.4. Preliminary Conclusion
4. Empirical Evidence
4.3. Bias and Gender
4.4. Bias and Industry Affiliation
4.5. Bias and Company Life Cycle
4.6. Bias and Remuneration Risk Profile
4.7. Bias and Individual Success
4.8. Preliminary Conclusion
List of Figures
Figure 1: Organization of Literature Review
Figure 2: Summarized Findings of Literature Review
List of Tables
Table 1: Definition of Variables
Table 2: Variables Characteristics
Table 3: Correlation Matrix
Table 4: Bias according to Gender
Table 5: Significance of Bias Differences amongst Genders
Table 6: Bias according to Industry Affiliation
Table 7: Significance of Bias Differences amongst Industries
Table 8: Bias according to Company Life Cycle
Table 9: Significance of Bias Differences amongst Company Life Cycle
Table 10: Regression Analysis Risk Profile
Table 11: Regression Analysis Success
List of Abbreviations
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Economic theory normally focuses on rational agents optimizing individual utility. Since the second half of the 20th century, this viewpoint has been enriched by findings from the field of psychology. A new trait of research was created called “behavioral economics”. It takes into account subjective characteristics such as asymmetric preference and judgment, or limits of rational processing, willpower, and greed.
This paper aims to give an overview of two related human traits that have attracted particularly wide interest, namely overconfidence and overoptimism. The two are closely related to each other, and often used synonymously. Broadly speaking, overconfidence results in underestimation of future risks, e.g. the riskiness of future cash flows, whilst overoptimism leads to an overestimation of future positive outcomes, e.g. the future returns of a company. Besides, the paper wants to deduct suggestions for further research, by systematically identifying uncovered topics in existing literature.
Usually Alpert and Raiffa (1969) are credited with the first discovery of overconfidence. However, the most influential study is probably Russo and Schoemaker (1992). It was published in the Sloan Management Review and communicated the topic to a broader audience for the first time. In particular, it revealed that assumingly rational managers were prone to overconfidence, too. This challenged traditional management doctrines and generated interest in a better understanding of the topic and further research. To exemplify overconfidence, Russo and Schoemaker (1992) asked managers to give numerical intervals for ten general-knowledge questions, such that nine out of the ten answers would be correct. On average participants included the correct value within their interval only 5 out of 10 times, i.e. they underestimated potential errors in their estimations.
Svenson (1981) is probably the most influential source regarding overoptimism. He made the subject intuitively understandable and established a standard measurement method that could be easily used for subsequent research. To give an example of overoptimism: Svenson (1981) asked students to compare their driving skills to those of their classmates. Roughly 80% believed they belonged to the top 50%, i.e. they overestimated their abilities.
This paper also provides a closer look at the empirical methods normally applied in field studies. Although the phenomena are intuitively understandable, empirical research still presents itself as a mosaic of fragmented testing rather than a coherent framework. One may assume that this is mainly caused by the difficult measurability of overconfidence and overoptimism: On the one hand, the decision maker, convinced of his own rationality, contributes zero overconfidence or overoptimism to his actions. On the other hand, even a neutral observer cannot specify any degree of biasedness a priori, as stochastic outcomes per definition do not allow for perfect prediction. Therefore, scientists frequently rely on proxy variables that at least allow for measuring a group’s average overoptimism or overconfidence.
Furthermore, this paper empirically examines several considerations regarding existing research and measurement methods. It particularly aims to connect biasedness with certain personal and economic characteristics, namely participants’ gender, industry affiliation, company life cycle, success and risk preferences. Additionally, different methods are employed for measuring overoptimism. By comparing the strength of bias indicated by each scaling, one gets interesting insights into the influence that question design has on test results.
The remainder of the paper is organized as follows: In part two the definitions of overoptimism and overconfidence found in earlier studies are described and evaluated. The most appropriate definition then helps to sort and synthesize existing research, and to discover blank fields for future research (0). In part three, methods used to measure biasedness are compared regarding their reliability and significance. Findings allow for recommending certain test designs for future surveys (3). Part four builds on these findings and empirically examines the relationship between biasedness and several personal and economic characteristics. Additionally, the reliability and significance of several scalings capturing overoptimism is tested (4). Part five concludes (5).
The following chapters aim to convey a broad understanding of overoptimism and overconfidence in a bottom-up fashion. This includes definitions and theoretical models as well as empirically-verified correlations with certain circumstances, characteristics and decisions. As overconfidence and overoptimism are closely related to each other and follow similar structures, both biases are mostly described in parallel.
The paper suggests structuring existing research on overoptimism and overconfidence as visualized in figure 1. On the one hand, this hierarchy allows for differentiating between very specific bias patterns and broad economic implications. On the other hand, it helps to identify influencing factors and the impacts of their horizontal relationships. However, as the selection of papers remains limited and subjective, comments and suggestions on structure and contents are welcome.
First of all, the phenomena are defined building on existing research (2.1). Second, characteristics of overoptimism and overconfidence are described. These include inherent structures, dependencies on agent and project characteristics, and influence of agent-project relationship (2.2). Third, the impact of biasedness on economic behavior is looked at in both private as well as professional decisions (2.3). Finally, the influence of decisions on company value and overall welfare is analyzed (2.4).
illustration not visible in this excerpt
Figure 1: Organization of Literature Review
(Source: Own representation)
In existing research, definitions of overoptimism and overconfidence usually differ according to the dimension looked at: One group of papers concentrates on individuals’ justification for biasedness, i.e. individual skills versus external development. The other group refers to the parameter underlying individuals’ expectations, i.e. risk estimations versus return estimations.
Camerer and Lovallo (1999) or Malmendier and Tate (2005) for example belong to the first group. Accordingly, an overconfident person expects his own behavior to be abnormally successful, which is often referred to as the “better-than-average effect”. An overoptimistic person estimates events beyond his control to be outstandingly positive.
Other sources such as Kyle and Wang (1997), Odean (1998), Hackbarth (2004) and Brettel and Kasch (2006) belong to the second group. They define overoptimism and overconfidence looking at the underlying development characteristics of growth and volatility. An overoptimistic person predicts that favorable events are more likely or more positive than they actually are. In investment decisions for example he overestimates a project’s future returns or growth rates. An overconfident person believes they have more precise knowledge about unknown events than they actually have. In investment decisions for example he underestimates the riskiness or volatility of the project’s future cash flows. Hackbarth refers to overoptimism as “growth perception bias” and to overconfidence as “risk perception bias”.
Both definitions described above are frequently used in literature, as each perspective contributes to the understanding of certain behavior nuances:
If one follows group one and differentiates according to bias justification, one can better argue for the extent or amplitude of biasedness. According to Weinstein (1980) for example, an overoptimistic person has even more positive expectations if they personally contributes to the project’s outcome. Similarly Adler (1980) finds that managers believe they are able to modify the risk of projects they are in control of. He calls such overconfident managers “risk makers”. These findings build on general psychological studies by Langer (1975). She finds that people normally believe they can influence outcomes that they demonstrably have no influence over. Langer (1975) calls this tendency an “illusion of control”. According to her studies, this bias is particularly strong in situations where certain skills come into play.
If one applies the definition of group two and differentiates according to underlying parameters, one can more precisely predict the type or effect direction of biasedness. Hackbarth (2004), for example, models that an overoptimistic manager might prefer other sources of financing than his overconfident counterpart. The resulting pecking order of financing might even contradict the traditional one justified by asymmetric information. This contradiction mainly rests in the different impacts that growth and risk expectations have on debt and equity valuations.
To capture the contributions made by both definitions, this paper combines both ideas and applies the following wording. As with group two, the term “overoptimism” describes a growth perception bias, whilst “overconfidence” stands for a risk perception bias. Where it is not possible or suitable to differentiate between these two, the paper talks of “expectation bias”, “bias”, or “biasedness”. Hereby, variable wording does not imply different meanings, but simply tries to avoid unaesthetic repetitions. As with group one, deviations from rational growth or volatility expectations are named “endogenous” if the expectation bias is justified by the agent’s (perceived) ability to influence outcomes, and “exogenous” if the bias relates to developments of the environment beyond the agent’s reach.
The following chapters explain and sort existing research on overoptimism and overconfidence. First of all, general bias patterns are described (2.2.1). Second, the influence of certain agent characteristics on biasedness is looked at (2.2.2). Third, the effect of project characteristics is examined (2.2.3). Fourth, the relationship between agent, project and biasedness is analyzed (2.2.4).
Researchers have found several patterns in biasedness that remain independent of situational factors. The most prominent ones are described in this chapter. Although it cites papers on overoptimism only, it seems reasonable to assume that similar mechanisms are in place amongst overconfident individuals, too. First, the strength of bias is linked to heterogeneous beliefs and self-enhancing perception (188.8.131.52). Second, the impact of psychological traits on bias distribution is examined (184.108.40.206).
Van den Steen (2002) models the connection between individual perception differences, overoptimism and further self-enhancing interaction with other biases. Accordingly, even in a group of rational agents with symmetric information, each member might result as being overoptimistic. Van den Steen (2002) observes that subjects disposing of the same information and cognitive abilities might naturally attribute varying subjective probabilities to the same outcomes. Based on their randomly different “priors”, i.e. based on their individual beliefs, agents choose the project they expect to be most successful. Thus, each agent views their project more optimistically than the other agents do, and expects their future success to be above average as compared to their peer group. In other words, each member of the group is overoptimistic. Furthermore, when in a joint venture setting each agent chooses the work they consider to be most relevant to the project’s outcome, everyone will consider their own contribution to be more important than that of their colleagues. Miller and Ross (1975), for example, empirically verify that generally people expect their behavior to produce success.
Taylor and Brown (1988) explain the wide existence of overoptimism by its positive effect on mental health and psychological well-being. They find overoptimism with healthy participants, but rather accurate perceptions with depressed people. Their assertion contradicts traditional psychological theory which views accurate perception of the self as a precondition for mental balance, such as Maslow (1950) and Jahoda (1953). According to Taylor and Brown (1988), various modern studies find overly optimistic expectations amongst a broad range of social groups. This general pattern can best be explained by the positive effect overoptimism has on a person’s happiness, compassion and creativity.
In their field study, Miller and Ross (1975) find that an overoptimistic person contributes failure to bad luck rather than to his personal bad choice of action or missing skill. That person discerns a closer covariation between behavior and outcomes in the case of increasing success than in the case of constant failure. This argumentation helps people to strengthen their conviction and is therefore often called “self-serving bias”.
According to Miller and Ross (1975), such self-serving bias also leads to insufficient learning as people see no need to alter their behavior. One can deduct that this predicts a relatively stable persistence of bias over age and job experience.
Summing up, biasedness is a natural psychological behavior which evolves from individually different character traits even amongst rational individuals. As it both contributes to well-being as well as dampens learning mechanisms it is probably rather constant over time and different social groups.
Camerer and Malmendier (2004) point out that endogenous overoptimism seems to be unevenly distributed in itself. As described above, Svenson (1981) asks students to rank themselves within their peer group in terms of driving abilities. Interestingly, overoptimism is rather strong around the average categories, but weaker at the top: 24% of students believe they belong to the 71-80% best drivers, whilst only 2% place themselves into the 91-100% category. Camerer and Malmendier (2004) view this as a sign of modesty that keeps people from boasting that they are the best. Accordingly, this suggests lower biasedness in smaller, more socially-integrated groups. If verified, this would have important implications for organizational design.
Puri and Robinson (2007) examine the correlation between biasedness and private decisions. Within the group of overoptimistic people, they find two subgroups which they call “moderate optimists“ and “extreme optimists”. Their behavior patterns differ significantly: Moderate optimists show prudent financial behavior, whilst extreme optimists tend to make what one would normally consider irrational financial decisions.
Overall, these studies report a high amount of mildly biased individuals and a small amount of highly biased individuals within one population. The big, moderate group makes rather constructive decisions, whilst members of the small, extreme group tend towards destructive ones.
When further examining the impact of biasedness on (economic) behavior, researchers should pay special attention to this group of extremely biased individuals. Mild degrees of overoptimism or overconfidence seem to yield only neutral or maybe slightly deviating behavior. Extreme biasedness however significantly alters behavior and induces negative outcomes.
Several studies have looked into the correlation between estimation bias and certain agent characteristics, especially gender, age, and intelligence. This chapter follows this focus and summarizes the findings on first, the influence of biological properties (220.127.116.11), and second, mental abilities (18.104.22.168). Again, where research exists on one bias only, it still seems reasonable to assume that overoptimism and overconfidence have similar impacts.
Kovalchik et al. (2005) empirically study the relationship between aging and economic decision behavior. They observe more endogenous overconfidence amongst younger than amongst older participants. This means that older people are better able to assess the limits of their cognitive abilities. One explanation could be that they build on their more profound life experience, which helps them to countercheck their expectations.
Barber and Odean (2001) empirically test the relationship between biasedness and gender. They find higher estimation bias amongst male participants than amongst female. To measure biasedness, they rely on Odean (1998) who predicts that overconfident investors trade irrationally often and incur below-average returns. Actually, Odean only bases his model on endogenous overconfidence. Yet, higher growth expectations and lower risk discount rates both increase return estimations. Therefore, it seems reasonable that Barber and Odean (2001) view their results as evidence for overoptimism, too.
Overall, biasedness is found to be stronger amongst male individuals, and to diminish with increasing age.
Biais et al. (2005) cannot verify a statistically significant correlation between endogenous overconfidence and participants’ intelligence quotient (IQ) test score. This is in line with other studies, which found overconfidence amongst very different professions and social groups. Murphy and Brown (1984) and Malmendier and Tate (2005) for example find biasedness amongst weather forecasters as well as Fortune 500 CEOs.
Overall, biasedness seems to depend more on psychological characteristics than on cognitive abilities. It is independent of IQ, but – as mentioned in chapter 22.214.171.124 above – positively correlates to mental well-being.
However, no study has explicitly looked at whether biasedness indeed is similar throughout different industries. Therefore, further research should try to empirically validate this proposition.
 See Camerer et al. (2004), p. 3-9, also relevant for the rest of the paragraph above.
 Camerer et al. (2004), p. 4.
 See chapter 2.1 for more details on the definitions of overoptimism and overconfidence used in this paper.
 See Alpert and Raiffa (1969), p. 294-305, as referred to by Cesarini et al. (2006), p. 454.
 See Russo and Schoemaker (1992), p. 7-17, as referred to by Cesarini et al. (2006), p. 454.
 See Russo and Schoemaker (1992), p. 8.
 See Svenson (1981), p. 143-148, as referred to by Cesarini et al. (2006), p. 454.
 See Svenson (1981), p. 144.
 See Camerer and Lovallo (1999), p. 306-318, or Malmendier and Tate (2005), p. 2661-2700.
 See Alicke et al. (1995), p. 804.
 See Camerer and Lovallo (1999), p. 306, or Malmendier and Tate (2005), p. 2662.
 See Kyle and Wang (1997), p. 2073-2090, Odean (1998), p. 1887-1934, Hackbarth (2004), p. 1-36, or Brettel and Kasch (2006), p. 1-32.
 See Kyle and Wang (1997), p. 2073, Odean (1998) p. 1892, Hackbarth (2004), p. 2, or Brettel and Kasch (2006), p. 2.
 Hackbarth (2004), p. 2.
 See Weinstein (1980), p. 806-820.
 See Adler (1980), as referred to by March and Shapira (1987), p. 1410.
 Adler (1980), as referred to by March and Shapira (1987), p. 1410.
 See Langer (1975), p. 311-328, also relevant for the rest of the paragraph above.
 Langer (1975), p. 313.
 See Hackbarth (2004), p. 14-15, also relevant for the rest of the paragraph above.
 See Van den Steen (2002), p. 7-11; also relevant for the rest of the paragraph above.
 Van den Steen (2002), p. 2.
 See Miller and Ross (1975), p. 213-225.
 See Taylor and Brown, (1988), p. 193-210, also relevant for the rest of the paragraph above.
 See Maslow (1950), p. 11-34, and Jahoda (1953), p. 349, as referred to by Taylor and Brown (1988), p. 193-194.
 See Miller and Ross (1975), p. 213-225, also relevant for the rest of the paragraph above.
 For example Larwood and Whittaker (1977), p. 194, or Camerer and Malmendier (2004), p. 30.
 See Miller and Ross (1975), p. 213-225.
 See Camerer and Malmendier (2004), p. 66.
 See Svenson (1981), p. 145, also relevant for the rest of the paragraph above.
 See Camerer and Malmendier (2004), p. 66, also relevant for the rest of the paragraph above.
 See Puri and Robinson (2007), p. 1-49, also relevant for the rest of the paragraph above.
 Puri and Robinson (2007), p. 5.
 See Kovalchik et al. (2005), p. 79-94.
 See. Kovalchik et al. (2005), p. 82-83: Examining both students as well as neurologically healthy elderly people, they found that older participants exhibited overconfidence in only 17% of their total responses, whilst younger subjects were overconfident in 48% of all responses.
 See Barber and Odean (2001), p. 261-292, also relevant for the rest of the paragraph above.
 See Odean (1998), p. 1888.
 See Biais et al. (2005), p. 305-306.
 See Murphy and Brown (1984), p. 369-393, and Malmendier and Tate (2005), p. 2661-2664.
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