Results 111 to 120 of about 32,823 (296)

The influence of emotional intelligence and behavioural biases on mutual fund churning frequency: Evidence from India

open access: yesActa Psychologica
Behavioural finance invalidates the rationalistic assumptions of the efficient market hypothesis by proposing a realistic explanation for overreaction and underreaction.
R. Annapurna, Savitha Basri
doaj   +1 more source

Perilaku Overconfidence Di Bursa Efek Indonesia (BEI) (Studi kasus pada Index LQ45 periode 2014-2016)

open access: yesMedia Ekonomi dan Manajemen, 2018
This study purposed to identify overconfidence behavior investor in Indonesia Stock Exchange from 2014 until 2016. Overconfidence is a psychological bias that can cause investors to excessive trading as the effect of the belief that they have specific ...
Indri Hartiyaningsih, Yanuar Rachmansyah
doaj   +1 more source

Optimising Human–AI Decision Performance: A Trust and Capability Framework for Knowledge Management

open access: yesKnowledge and Process Management, EarlyView.
ABSTRACT Organisations struggle to optimise human–AI collaboration in knowledge‐intensive decision‐making. This paper proposes the Trust–Complementarity Model of Collective Intelligence (TCM‐CI), explaining how calibrated trust and complementary capability utilisation drive superior organisational performance.
Eduardo Carlos Dittmar, Martin Sposato
wiley   +1 more source

Overconfident in Hindsight: Memory, Hindsight Bias and Overconfidence

open access: yes, 2020
Overconfidence and Hindsight Bias are two well-knowncognitive biases. Herein, it is argued these biases may berelated to one another and human memory limitations;specifically, that memory limitations result in hindsight bias,causing people to recall being right more often than theyactually were, which leads to overconfidence as people applythis ...
openaire   +1 more source

A machine learning assisted method for rapidly annotating benthic megafauna in large volumes of marine imagery

open access: yesLimnology and Oceanography: Methods, EarlyView.
Abstract Recent technological advancements have rapidly expanded our capacity for collecting image data in the marine environment, but processing images into meaningful ecological metrics remains a manual, time‐consuming, and biased process. This is particularly challenging with electro‐optical cabled imaging systems which generate images at a rate ...
Katharine T. Bigham, Ada Carter
wiley   +1 more source

BEHAVIORAL FINANCE DALAM KEPUTUSAN INVESTASI SAHAM [PDF]

open access: yes, 2014
Penelitian ini meneliti behavioral finance dalam keputusan investasi saham mahasiswa di kota Bandung yang difokuskan pada faktor bias perilaku yang terdiri dari Overconfidence, Representativeness, Herding, Anchoring, Regret Aversion, Cognitive Dissonance,
Ratnadewi, Fury
core  

Calibrating p‐values in ecology: a practical framework for integrating prior plausibility into statistical inference

open access: yesOikos, EarlyView.
Misinterpretation of p‐values, coupled with insufficient consideration of the prior plausibility of ecological hypotheses, leads to overconfident and often unreliable inference in ecological research. To address this issue, we present a methodological framework for p‐value calibration that reinterprets conventional p‐values through minimum Bayes ...
Rafael Dettogni Guariento   +2 more
wiley   +1 more source

Large‐language‐models for pediatric diagnosis: Performance evaluation using real‐world clinical notes from common and rare cases

open access: yesPediatric Investigation, EarlyView.
• Advanced large language models exhibited superior diagnostic accuracy compared to clinicians, particularly for rare diseases, with Claude‐3.5 Sonnet and o1‐preview demonstrating the highest consistency between query iterations. ABSTRACT Importance Rigorous evaluation of large language models (LLMs) in pediatric diagnosis using authentic clinical ...
Cristian Launes   +12 more
wiley   +1 more source

Machine learning‐based predictive models versus traditional risk scores in hemodialysis patients with comorbid urolithiasis

open access: yesPrecision Medical Sciences, EarlyView.
Machine learning‐based predictive models outperform traditional risk scores in hemodialysis patients with comorbid urolithiasis by capturing nonlinear, dialysis‐specific interactions. These approaches enable more accurate prediction of stone recurrence, sepsis, hospitalization, and mortality, supporting personalized risk stratification and precision ...
Dipal Chaulagain   +4 more
wiley   +1 more source

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