They all do it, will you? Event-related potential evidence of herding behavior in online peer-to-peer lending

Hua, Yu; Dan, MengHan; Ma, Qingguo

doi:10.1016/j.neulet.2018.05.021

Cited by 23 publications

(15 citation statements)

References 23 publications

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“…Chen, Huang, & Ye, 2018), appearance (Jin, Shang, & Ma, 2019), voluntary information (Han, Chen, Liu, Luo, & Fan, 2018;Michels, 2012), friendship (Liu, Brass, Lu, & Chen, 2015), and herding behavior among investors (Herzenstein, Dholakia, & Andrews, 2011;Y. Jiang, Ho, Yan, & Tan, 2018;Kim, 2020;Luo & Lin, 2013;Yu, Dan, Ma, & Jin, 2018). The related literature is summarized in Table 1.…”

Section: Review Of Related Literaturementioning

confidence: 99%

“…(2019) Ma et al (2018), Malekipirbazari and Aksakalli (2015), Serrano-Cinca et al (2015) Present models that predict repayment performance with ex-post determinants Theme 3: Determinants of ex-ante funding decision-making P2P lending Cai et al (2016), X.-h. Chen et al (2016), Feng et al (2015, Kgoroeadira et al (2019) Overall factors including a borrower's demographic characteristics, loan terms, credit and financial information Herzenstein, Sonenshein, et al (2011), Kim et al (2018), Larrimore et al (2011) Linguistic styles Barasinska and Schafer (2014) Gender Burtch et al (2014) Cultural and geographical differences D. Chen et al (2014), Wan et al (2016) Trust in borrowers or intermediaries X. Chen et al (2018) Punctuation used in loan descriptions Jin et al (2019) Appearance Han et al (2018), Michels (2012) Voluntary information Liu et al (2015) Friendship , Kim (2020), Luo and Lin (2013), Yu et al (2018) Herding behavior Platform-generated, Loan-describing, Borrower-attributed II, II-1, II-2, IV, IV-1, IV-2 Repayment Platform-generated, Loan-describing, Borrower-attributed I-1-1, I-2-1, III-1-1, III-2-1 Funding Loan-describing, Borrower-attributed I-1-2, I-2-2, III-1-2, III-2-2 Funding Platform-generated, Borrower-attributed I-1-3, I-2-3, III-1-3, III-2-3 Funding Platform-generated, Loan-describing…”

Section: Categorymentioning

confidence: 99%

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Can investors’ collective decision-making evolve? Evidence from Peer-to-Peer lending markets

Kim

2021

Preprint

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This study tries to identify the accuracy of individual investors' capability to predict a borrower’s creditworthiness in peer-to-peer lending markets and examine whether their ability is likely to evolve over time. The results of this study show that there is no significant difference between the predictive power of investors' ex-ante funding decision model and that of the ex-post repayment model over a borrower’s repayment performance. Furthermore, the predictive power of investors' ex-ante funding decision over a borrower’s repayment performance is shown to improve over time . It is also found that the main reason why investors’ predictive power improve over time is because investors can assess more accurately the information provided by the platform operator and describe the borrower's characteristics. The results of this study are important as they confirm the possibility of optimizing and streamlining the P2P lending market, through the evolution of investors’ decision making.

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Section: Review Of Related Literaturementioning

confidence: 99%

Section: Categorymentioning

confidence: 99%

Can investors’ collective decision-making evolve? Evidence from Peer-to-Peer lending markets

Kim

2021

Preprint

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“…Herding is a commonly observed phenomenon in the financial market. It is a common tendency of human nature to refer, observe, and imitate other's behavior during the irregular condition in financial markets (Yu, Dan, Ma, & Jin, 2018). At the presence of herding, investors do not act rationally in their investment choices.…”

Section: Herding Behaviormentioning

confidence: 99%

An Analysis of Behavioral Biases in Investment Decision-Making

Madaan¹,

Singh²

2019

IJFR

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Individual investor’s behavior is extensively influenced by various biases that highlighted in the growing discipline of behavior finance. Therefore, this study is also one of another effort to assess the impact of behavioral biases in investment decision-making in National Stock Exchange. A questionnaire is designed and through survey responses collected from 243 investors. The present research has applied inferential statistics and descriptive statistics. In the existing study, four behavioral biases have been reviewed namely, overconfidence, anchoring, disposition effect and herding behavior. The results show that overconfidence and herding bias have significant positive impact on investment decision. Overall results conclude that individual investors have limited knowledge and more prone towards making psychological errors. The findings of the study also indicate the existence of these four behavioral biases on individual investment decisions. This study will be helpful to financial intermediaries to advice their clients. Further, study can be elaborated to study other behavioral biases on investment decisions.

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“…Consistent with this idea, event-related potential (ERP) studies have revealed that being misaligned with majority opinions evokes ERP components associated with conflict detection and expectancy violation, including the feedback-related negativity (FRN) ( Kim et al. , 2012 ; Yu et al. , 2018 ) and N400 ( Huang et al.…”

Section: Introductionmentioning

confidence: 92%

The hierarchical sensitivity to social misalignment during decision-making under uncertainty

Lin

Luan

et al. 2021

Social Cognitive and Affective Neuroscience

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Social misalignment occurs when a person’s attitudes and opinions deviate from those of others. We investigated how individuals react to social misalignment in risky (outcome probabilities are known) or ambiguous (outcome probabilities are unknown) decision contexts. During each trial, participants played a forced-choice gamble, and they observed the decisions of four other players after they made a tentative decision, followed by an opportunity to keep or change their initial decision. Behavioral and event-related potential data were collected. Behaviorally, the stronger the participants’ initial preference, the less likely they were to switch their decisions; whereas the more their decisions were misaligned with the majority, the more likely they were to switch. Electrophysiological results showed a hierarchical processing pattern of social misalignment. Misalignment was first detected binarily (i.e., match/mismatch) at an early stage, as indexed by the N1 component. During the second stage, participants became sensitive to low levels of misalignment, which were indexed by the feedback-related negativity. The degree of social misalignment was processed in greater detail, as indexed by the P3 component. Moreover, such hierarchical neural sensitivity is generalizable across different decision contexts (i.e., risky and ambiguous). These findings demonstrate a fine-grained neural sensitivity to social misalignment during decision-making under uncertainty.

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They all do it, will you? Event-related potential evidence of herding behavior in online peer-to-peer lending

Cited by 23 publications

References 23 publications

Can investors’ collective decision-making evolve? Evidence from Peer-to-Peer lending markets

Can investors’ collective decision-making evolve? Evidence from Peer-to-Peer lending markets

An Analysis of Behavioral Biases in Investment Decision-Making

The hierarchical sensitivity to social misalignment during decision-making under uncertainty

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