Mitigating Attention Bias in Index-Fund Investment: AI-Enabled Decision Support for Retail Investors
Paper Type
Complete
Paper Number
PACIS2026-1102
Description
In information-intensive and high-noise stock markets, investors often face information overload and rely on heuristics and selective attention, which can lead to cognitive biases and reduced decision quality. AI can process vast datasets, identify non-linear patterns, and provide real-time decision support, offering opportunities to improve investment decision-making. Through a progressive systematic literature review, this study finds that incorporating cognitive-bias mechanisms as design drivers in AI-enabled investment artifacts has not been studied, and research on AI-enabled decision support in the index-fund context remains limited. The review identifies attention bias as a focal mechanism, particularly salient for retail investors. Based on these findings, this study develops a conceptual AI-enabled decision support artifact for index fund investment to mitigate retail investors’ attention bias and improve decision quality. A system dynamics simulation provides preliminary directional validation, and an evaluation agenda is outlined for future artifact development and evaluation.
Recommended Citation
Ma, Ruiyi; Zhang, Ying; and Sundaram, David, "Mitigating Attention Bias in Index-Fund Investment: AI-Enabled Decision Support for Retail Investors" (2026). PACIS 2026 Proceedings. 1.
https://aisel.aisnet.org/pacis2026/blockfintech/blockfintech/1
Mitigating Attention Bias in Index-Fund Investment: AI-Enabled Decision Support for Retail Investors
In information-intensive and high-noise stock markets, investors often face information overload and rely on heuristics and selective attention, which can lead to cognitive biases and reduced decision quality. AI can process vast datasets, identify non-linear patterns, and provide real-time decision support, offering opportunities to improve investment decision-making. Through a progressive systematic literature review, this study finds that incorporating cognitive-bias mechanisms as design drivers in AI-enabled investment artifacts has not been studied, and research on AI-enabled decision support in the index-fund context remains limited. The review identifies attention bias as a focal mechanism, particularly salient for retail investors. Based on these findings, this study develops a conceptual AI-enabled decision support artifact for index fund investment to mitigate retail investors’ attention bias and improve decision quality. A system dynamics simulation provides preliminary directional validation, and an evaluation agenda is outlined for future artifact development and evaluation.
Comments
06-Fintech