Impact of Overoptimism and Overconfidence on Economic Behavior

Diplomarbeit von Andreas Müller

Introduction:

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 are credited with the first discovery of overconfidence. However, the most influential study is probably Russo and Schoemaker. 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 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 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 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).

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Chapter 2.4, Impact on Company Value and Overall Welfare:

To recapitalize the findings described in the chapters above: First of all, (extreme) biasedness mainly induces imprudent behavior. The impact on performance remains unambiguous. Second, biasedness is a persistent trait of company leaders. Third, managers’ individual characteristics have a significant influence on company conduct.

In business life, biased managers should therefore consistently undertake value-destructing investments and install unnecessarily costly debt ratios for example. Overall, this logical chain suggests that companies led by biased managers should suffer from lower profitability than their peers or even negative returns. Then in rational markets, biased companies should disappear, outlived only by the small percentage of companies under unbiased management.

Two questions arise: Why does one not observe such a mass mortality of corporations? If biasedness is value-destructing, then why is it consistently found amongst companies’ top levels? Six possible answers are found in literature, or are suggested by this paper:

First, Bernardo and Welch model the value of additional information that overoptimistic entrepreneur’ actions and failures convey to their social group. They suggest a tradeoff between the value of the additional information gained through an entrepreneur’s failure and the cost of that bankruptcy. Both factors depend on the size of the group, the degree of overconfidence, and the accuracy of individuals’ private information. In the model, Bernardo and Welch show that when groups compete against each other, entrepreneurial activity assures an evolutionary advantage and therewith survival of the group. Therefore, higher rates of market entry and company failure are indeed value-creating.

Second, de Meza and Southey observe surprisingly high failure rates of start-ups. In their model, they can explain this by overoptimism amongst entrepreneurs, which leads to high rates of market entry. Besides, their model also predicts high reliance on bank credits and other special, rather irrational characteristics of small-scale businesses. Overall, their findings can answer the first question: On the one hand, biasedness leads to abnormally high rates of company pullouts. On the other hand, it also induces high entry rates. As both effects neutralize each other, the average number of corporations remains constant.

Third, business life’s complexity leaves room for unlimited variety of business models. Therefore, biasedness may lead to heterogeneity amongst company practices, but may not be value-destructing. It merely leads to the discovery of new approaches.

Fourth, the exit of one market participant potentially strengthens the remaining firms. For example, they can capture more market share and benefit from less intense competition. Subsequently, this leads to lower failure rates than predicted in the argumentation above, even though the remaining participants are biased, too. Instead of a “survival of the fittest” one would have to recognize a “survival of the least biased”. Oyer and Schaefer for example find overvaluation of stock options by employees. Companies could exploit this bias by granting inferior stock-option plans instead of cash remuneration. Then lower returns for employees equal cost savings for the company.

Fifth, no survey so far has empirically verified higher biasedness amongst corporate leaders than amongst shop-floor workers. Perhaps biasedness is frequently found amongst upper management, as there simply is no better, less biased alternative. A mildly biased manager, so to say, is “the best one can get”.

Sixth, the value-enhancing effect of biasedness is not covered by the argumentation. As described above in chapter 2.3.2.2, biasedness may be directly linked to positive attitudes, such as motivation, engagement, leadership and creativity. These characteristics might be influential enough to offset or even exceed negative effects of biasedness. This could explain why overoptimism and overconfidence are so widely found amongst organization leaders. Additionally, biasedness might indirectly encourage positive behavior. For example, higher leverage resulting in higher interest obligations might help to align shareholder and management’s interests.

Overall, the persistent existence of biasedness amongst human beings suggest a prevalence of positive effects. However, most of these positives have not been covered systematically, and have not been verified empirically. Therefore, they should be an interesting field for further research.