Reference health

Algorithm Aversion in Financial Investing

https://doi.org/10.2139/ssrn.3364850
CiteStamped reference-health badge
40/40 checkable references clean · checked 2026-08-01

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

11 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 40 checked references that resolve
resolves10.1162/003355301556400
Boys will be Boys: Gender, Overconfidence, and Common Stock Investment
resolves10.1037/0022-3514.54.4.569
Outcome bias in decision evaluation.
resolves10.1002/bdm.2155
A systematic review of algorithm aversion in augmented decision making
resolves10.1007/BF00120146
An individual level analysis of the mutual fund investment decision
resolves10.3386/w23798
FinTech Adoption Across Generations: Financial Fitness in the Information Age
resolves10.1177/0022243719851788
Task-Dependent Algorithm Aversion
resolves10.1017/S1930297500001819
Measuring Risk Literacy: The Berlin Numeracy Test
resolves10.1037/0003-066X.34.7.571
The robust beauty of improper linear models in decision making.
resolves10.1002/jcpy.1266
Consumers Object to Algorithms Making Morally Relevant Tradeoffs Because of Algorithms’ Consequentialist Decision Strategies
resolves10.1177/0956797620948841
People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error
resolves10.1037/xge0000033
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
resolves10.1080/014492999118832
User agreement with incorrect expert system advice
resolves10.1080/014492998119526
Persuasiveness of expert systems
resolves10.1093/qje/qjy013
Global Evidence on Economic Preferences*
resolves10.1111/j.1540-6261.2010.01598.x
Luck versus Skill in the Cross‐Section of Mutual Fund Returns
resolves10.1016/j.jbef.2021.100524
Reducing algorithm aversion through experience
resolves10.1007/s10683-006-9159-4
z-Tree: Zurich toolbox for ready-made economic experiments
resolves10.1111/jofi.12514
Retail Financial Advice: Does One Size Fit All?
resolves10.1007/s40881-015-0004-4
Subject pool recruitment procedures: organizing experiments with ORSEE
resolves10.1037/1076-8971.2.2.293
Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical–statistical controversy.
resolves10.3905/jpm.2017.43.4.055
Man vs. Machine: <i>Comparing Discretionary and</i><i>Systematic Hedge Fund Performance</i>
resolves10.1093/rof/rfw011
Fooled by Randomness: Investor Perception of Fund Manager Skill
resolves10.1111/j.1754-9434.2008.00058.x
Stubborn Reliance on Intuition and Subjectivity in Employee Selection
resolves10.1111/1911-3846.12641
The Effect of Humanizing <scp>Robo‐Advisors</scp> on Investor Judgments*
resolves10.1016/j.frl.2022.103046
The potential use of robo-advisors among the young generation: Evidence from Italy
resolves10.1111/jofi.12223
Asymmetric Learning from Financial Information
resolves10.1111/jofi.12995
The Misguided Beliefs of Financial Advisors
resolves10.1016/j.socec.2020.101573
Financial education and digital asset management: What's in the black box?
resolves10.1016/j.obhdp.2018.12.005
Algorithm appreciation: People prefer algorithmic to human judgment
resolves10.1093/jcr/ucz013
Resistance to Medical Artificial Intelligence
resolves10.1371/journal.pone.0239277
Robo-investment aversion
resolves10.1002/bdm.637
The relative influence of advice from human experts and statistical methods on forecast adjustments
resolves10.1002/bdm.542
Do patients trust computers?
resolves10.1016/j.jedc.2019.03.002
Determinants of investor expectations and satisfaction. A study with financial professionals
resolves10.1016/B978-0-12-812282-2.00021-8
Inclusion or Exclusion? Trends in Robo-advisory for Financial Investment Services
resolves10.1177/0272989X12453501
Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System?
resolves10.1016/j.jfi.2019.100833
Fintech and banking: What do we know?
resolves10.1016/j.jfineco.2011.03.006
Financial literacy and stock market participation
resolves10.1509/jmr.11.0368
Misresponse to Reversed and Negated Items in Surveys: A Review
resolves10.1002/bdm.2118
Making sense of recommendations
The 11 references without a DOI — listed, not checked
no DOI — not checkedref7
no DOI — not checkedThe promises and pitfalls of robo-advising
no DOI — not checkedref10
no DOI — not checkedref28
no DOI — not checkedref30
no DOI — not checkedref34
no DOI — not checkedref37
no DOI — not checkedref38
no DOI — not checkedRobo-advice and the future of delegated investment
no DOI — not checkedInvestor characteristics and their impact on the decision to use a robo-advisor
no DOI — not checkedref44
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

checked 2026-08-01 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record

Embed this badge

Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.

<a href="https://citestamp.com/citestamped/10.2139/ssrn.3364850"><img src="https://citestamp.com/citestamped/10.2139/ssrn.3364850/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[![CiteStamped reference-health badge](https://citestamp.com/citestamped/10.2139/ssrn.3364850/badge.svg)](https://citestamp.com/citestamped/10.2139/ssrn.3364850)