Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias

2017 ◽  
Vol 111 (3) ◽  
pp. 1775-1799 ◽  
Author(s):  
Daniel Fonseca Costa ◽  
Francisval de Melo Carvalho ◽  
Bruno César de Melo Moreira ◽  
José Willer do Prado
Keyword(s):  
Decision Making ◽  
Bibliometric Analysis ◽  
Behavioral Finance ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Anchoring Effect

Confirmation bias in decision making for fingerprints, DNA and eyewitness evidence cannot be explained by cognitive style or thinking dispositions.

2019 ◽  
Author(s):  
Daniel Edgcumbe
Keyword(s):  
Decision Making ◽  
Cognitive Style ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Criminal Case ◽  
Court Cases ◽  
Legal Proceedings ◽  
Thinking Dispositions ◽  
Evaluation Of Evidence ◽  
Facial Composite

Pre-existing beliefs about the background or guilt of a suspect can bias the subsequent evaluation of evidence for forensic examiners and lay people alike. This biasing effect, called the confirmation bias, has influenced legal proceedings in prominent court cases such as that of Brandon Mayfield. Today many forensic providers attempt to train their examiners against these cognitive biases. Nine hundred and forty-two participants read a fictional criminal case and received either neutral, incriminating or exonerating evidence (fingerprint, eyewitness, or DNA) before providing an initial rating of guilt. Participants then viewed ambiguous evidence (alibi, facial composite, handwriting sample or informant statement) before providing a final rating of guilt. Final guilt ratings were higher for all evidence conditions (neutral, incriminating or exonerating) following exposure to the ambiguous evidence. This provides evidence that the confirmation bias influences the evaluation of evidence.

scholarly journals A Bibliometric Analysis of Behavioral Finance and Behavioral Accounting

2021 ◽  
Vol 24 (2) ◽  
pp. 198-230
Author(s):  
Bharati Singh
Keyword(s):  
Bibliometric Analysis ◽  
Behavioral Finance ◽  
Financial Literacy ◽  
Cognitive Biases ◽  
Research Work ◽  
Behavioral Accounting ◽  
Cognitive Constraints ◽  
Vos Viewer ◽  
Emerging Themes ◽  
And Performance

This paper presents a bibliometric analysis of relevant publications in the field of behavioral finance and behavioral accounting. The analysis shows that the emerging themes of research in recent years in behavioral finance is on investors’ sentiment, social media, investors’ attention, and financial literacy. In the field of behavioral accounting, biases such as  overconfidence, framing effects or cognitive constraints on information processing, have been explored in greater detail. Other than cognitive biases, this field includes studies such as behavioral tax, organizational ecology, and performance evaluative style of organization, among others. Interestingly, our analysis suggests that research in behavioral accounting is comparatively underdeveloped than research in behavioral finance. This bibliometric analysis has been extended by network analysis using, “Visualization of similarities, (VOS) viewer” software. Using the themes generated here the direction for future scope of research work has been discussed.

scholarly journals PENGAMBILAN KEPUTUSAN HUTANG PADA USAHA KECIL DAN MENENGAH (UKM) DI PULAU LOMBOK: SUATU PERSPEKTIF BEHAVIORAL FINANCE

2018 ◽  
Vol 7 (4) ◽  
Author(s):  
Siti Aisyah Hidayati ◽  
Embun Suryani ◽  
M Muhdin
Keyword(s):  
Decision Making ◽  
Behavioral Finance ◽  
Loss Aversion ◽  
Sampling Method ◽  
Confirmation Bias ◽  
Sampling Technique ◽  
Factor Group ◽  
Dominant Factor ◽  
The Third ◽  
Lombok Island

The purpose of this study is to find out what factors determine decision making of debt and what are the most dominant factors in  decision making of debt for SMEs on the island of Lombok.  This research is an explanatory research with quantitative approach. The population is all SMEs located in Lombok island. The sample is selected by Non probability sampling technique with a judgment sampling method where the SMEs that selected as samples are SMEs in handicraft industry of pottery and already exporting the products. Of the existing population, there are 25 (twenty five) SMEs that can be sampled. Respondents in this study are managers who also the owner of the SMEs. Data was collected using questionnaire. To achieve the research objectives, the data obtained will be processed according to needs using Factor Analysis.The results of this study indicate there are three groups of factors that determine  decision making of debt, namely the First Factor Group consists of: Variable Excessive Optimism, Variable Overconfidence, Variable Confirmation Bias and Variable Aversion to sure loss. This factor is named Factor Overconfidence. The Second Factor Group consisted of Representativeness Variables, Avaibility Variables and Anchoring and Adjustment Variables. This factor is named the Avaibility Factor. The third factor group consists of Affect Variables and Aversion Loss Variables. This factor is named the Factor of Loss Aversion. The most dominant factor in determining debt decision making for SMEs in Lombok Island is the Overconfidence factor group consisting of Variable Excessive Optimism, Variable Overconfidence, Variable Confirmation Bias and Variable Aversion to sure loss .

Overcoming Confirmation and Blind Spot Biases When Communicating Science

2017 ◽  
Author(s):  
Kate Kenski
Keyword(s):  
Decision Making ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Blind Spot ◽  
Future Research ◽  
Costs And Benefits ◽  
Process Information ◽  
Bias Blind Spot

This chapter focuses on two biases that lead people away from evaluating evidence and scientific studies impartially—confirmation bias and bias blind spot. The chapter first discusses different ways in which people process information and reviews the costs and benefits of utilizing cognitive shortcuts in decision making. Next, two common cognitive biases, confirmation bias and bias blind spot, are explained. Then the literature on “debiasing” is explored. Finally, the implications of confirmation bias and bias blind spot in the context of communicating about science are examined, and an agenda for future research on understanding and mitigating these biases is offered.

scholarly journals The Measurement of Individual Differences in Cognitive Biases: A Review and Improvement

2021 ◽  
Vol 12 ◽  
Author(s):  
Vincent Berthet
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Biases ◽  
Empirical Work ◽  
Confirmation Bias ◽  
Generic Measures

Individual differences have been neglected in decision-making research on heuristics and cognitive biases. Addressing that issue requires having reliable measures. The author first reviewed the research on the measurement of individual differences in cognitive biases. While reliable measures of a dozen biases are currently available, our review revealed that some measures require improvement and measures of other key biases are still lacking (e.g., confirmation bias). We then conducted empirical work showing that adjustments produced a significant improvement of some measures and that confirmation bias can be reliably measured. Overall, our review and findings highlight that the measurement of individual differences in cognitive biases is still in its infancy. In particular, we suggest that contextualized (in addition to generic) measures need to be improved or developed.

scholarly journals PENGAMBILAN KEPUTUSAN HUTANG PADA USAHA KECIL DAN MENENGAH (UKM) DI PULAU LOMBOK: SUATU PERSPEKTIF BEHAVIORAL FINANCE

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Siti Aisyah Hidayati ◽  
Embun Suryani ◽  
M Muhdin
Keyword(s):  
Decision Making ◽  
Behavioral Finance ◽  
Loss Aversion ◽  
Sampling Method ◽  
Confirmation Bias ◽  
Sampling Technique ◽  
Factor Group ◽  
Dominant Factor ◽  
The Third ◽  
Lombok Island

The purpose of this study is to find out what factors determine decision making of debt and what are the most dominant factors in  decision making of debt for SMEs on the island of Lombok.  This research is an explanatory research with quantitative approach. The population is all SMEs located in Lombok island. The sample is selected by Non probability sampling technique with a judgment sampling method where the SMEs that selected as samples are SMEs in handicraft industry of pottery and already exporting the products. Of the existing population, there are 25 (twenty five) SMEs that can be sampled. Respondents in this study are managers who also the owner of the SMEs. Data was collected using questionnaire. To achieve the research objectives, the data obtained will be processed according to needs using Factor Analysis.The results of this study indicate there are three groups of factors that determine  decision making of debt, namely the First Factor Group consists of: Variable Excessive Optimism, Variable Overconfidence, Variable Confirmation Bias and Variable Aversion to sure loss. This factor is named Factor Overconfidence. The Second Factor Group consisted of Representativeness Variables, Avaibility Variables and Anchoring and Adjustment Variables. This factor is named the Avaibility Factor. The third factor group consists of Affect Variables and Aversion Loss Variables. This factor is named the Factor of Loss Aversion. The most dominant factor in determining debt decision making for SMEs in Lombok Island is the Overconfidence factor group consisting of Variable Excessive Optimism, Variable Overconfidence, Variable Confirmation Bias and Variable Aversion to sure loss .Keyword:Behavioral finance, decision making of debt, SMEs

Behavioral Finance

2019 ◽  
Author(s):  
H. Kent Baker ◽  
Greg Filbeck ◽  
John R. Nofsinger
Keyword(s):  
Decision Making ◽  
Behavioral Finance ◽  
Social Factors ◽  
Cognitive Biases ◽  
Risk Tolerance ◽  
Market Anomalies ◽  
The World ◽  
Financial Decision ◽  
The Common ◽  
And Gender

People tend to be penny wise and pound foolish and cry over spilt milk, even though we are taught to do neither. Focusing on the present at the expense of the future and basing decisions on lost value are two mistakes common to decision-making that are particularly costly in the world of finance. Behavioral Finance: What Everyone Needs to KnowR provides an overview of common shortcuts and mistakes people make in managing their finances. It covers the common cognitive biases or errors that occur when people are collecting, processing, and interpreting information. These include emotional biases and the influence of social factors, from culture to the behavior of one’s peers. These effects vary during one’s life, reflecting differences in due to age, experience, and gender. Among the questions to be addressed are: How did the financial crisis of 2007-2008 spur understanding human behavior? What are market anomalies and how do they relate to behavioral biases? What role does overconfidence play in financial decision- making? And how does getting older affect risk tolerance?

scholarly journals Using Design Thinking to improve Strategic Decisions during Collaborative Sensemaking

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Mikko KORIA ◽  
Ekaterina KOTINA ◽  
Sharon PRENDEVILLE
Keyword(s):  
Decision Making ◽  
Design Thinking ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Positive Influence ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Strategic Decisions ◽  
Cognitive Limitations ◽  
Collaborative Sensemaking

Human cognitive limitations affect strategic decision-making. One of such effects is emergence of cognitive biases, deviations from rationality in judgment. These biases can negatively influence an organisation's capability to capture and utilize new ideas, thus inhibiting innovation. Researchers have documented different strategies for mitigating cognitive biases – and many of them overlap with the ones emphasised in design thinking. However, research so far does not offer any specific “recipes” for mitigation of cognitive biases. This paper links together research on challenges of strategic decision-making, cognitive biases and design thinking. The paper investigates the effects of applying design-thinking tool in collaborative sensemaking stage, within a small business team, aiming to mitigate confirmation bias. The study indicated that newly introduced design-thinking tools did not have the expected positive influence on decision-making. The research contributes to the field by developing a new framework on how to identify and mitigate confirmation bias in strategic decision-making.

scholarly journals How Behavioral Economics and Nudges Could Help Diminish Irrationality in Suicide-Related Decisions

2019 ◽  
Vol 15 (1) ◽  
pp. 44-61 ◽  
Author(s):  
Brian W. Bauer ◽  
Daniel W. Capron
Keyword(s):  
Decision Making ◽  
Suicide Attempt ◽  
Behavioral Economics ◽  
Bounded Rationality ◽  
Suicide Prevention ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Computational Power ◽  
Behavioral Economic ◽  
New Evidence

People regularly make decisions that are not aligned with their own self-interests. These irrational decisions often stem from humans having bounded rationality (e.g., limited computational power), which produces reliable cognitive biases that occur outside of people’s awareness and influences the decisions people make. There are many important decisions leading up to a suicide attempt, and it is likely that these same biases exist within suicide-related decisions. This article presents an argument for the likely existence of cognitive biases within suicide-related decision making and how they may influence people to make irrational decisions. In addition, this article provides new evidence for using a behavioral economic intervention—nudges—as a potential way to combat rising suicide rates. We explore how nudges can help increase means safety, disseminate suicide prevention skills/materials, diminish well-known biases (e.g., confirmation bias), and uncover biases that may be occurring when making suicide-related decisions.

De-Biasing Legal Factfinders

2021 ◽  
pp. 395-410
Author(s):  
Frank Zenker
Keyword(s):  
Decision Making ◽  
Special Reference ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Future Research ◽  
Research Needs ◽  
Base Rate Neglect ◽  
Human Decision ◽  
Social Biases ◽  
Evidential Basis

This chapter examines the psychological studies of biases and de-biasing measures in human decision-making with special reference to adjudicative factfinding. Research shows that factfinders are prone to cognitive biases (such as anchoring, framing, base-rate neglect, and confirmation bias) as well as social biases. Driven by this research, multiple studies have examined the extent to which those biases can be mitigated by de-biasing measures like “consider the opposite” and “give reasons.” After a brief overview of the research, the author points to the problematic evidential basis and identifies future research needs, and concludes that empirical research on de-biasing measures has so far delivered less than one would hope for.

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