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Field
Trial Title
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Before
Respecting or Debiasing Investor Preferences? A Field Experiment on the Effectiveness of Portfolio Recommendations in Enhancing Investor Welfare
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After
Robo‑Advising: How Investors Respond to Preference‑Based vs. Debiasing Recommendations
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Field
Trial Status
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Before
in_development
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After
on_going
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Field
Abstract
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Before
The household finance literature has found that investors often deviate from optimal investment behavior and suffer wealth losses due to factors such as incomplete information, lack of financial knowledge, and behavioral biases. Investment advisors perform multiple functions including information provision, investor education, and asset allocation recommendations, serving as crucial means to assist investors. Currently, there are two popular logics for investment advisor’s asset allocation recommendations: catering to investor preferences and educating investor. Exploring which asset allocation logic and design are more comprehensive, trustworthy, and beneficial to investors’ welfare holds significant implications for the upgrade of advisory services, development of financial markets, and enhancement of social welfare.
The experiment will randomly divide users of a certain bank’s APP into four groups: control group, preference catering group, loss aversion education group, and mental accounting education group. The control group will be recommended a uniform non-personalized asset allocation. The preference catering group will be recommended an asset allocation that is algorithmically calculated to be optimal based on the investor’s behavioral parameters. The loss aversion education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the loss aversion parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. The mental accounting education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the mental accounting parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged.
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After
The household finance literature has found that investors often deviate from optimal investment behavior and suffer wealth losses due to factors such as incomplete information, lack of financial knowledge, and behavioral biases. Investment advisors perform multiple functions including information provision, investor education, and asset allocation recommendations, serving as crucial means to assist investors. Currently, there are two popular logics for investment advisor’s asset allocation recommendations: catering to investor preferences and educating investor. Exploring which asset allocation logic and design are more comprehensive, trustworthy, and beneficial to investors’ welfare holds significant implications for the upgrade of advisory services, development of financial markets, and enhancement of social welfare.
The experiment will randomly divide users of a certain bank’s APP into four groups: control group, preference catering group, loss aversion education group, and mental accounting education group. All users will first report their current asset allocation and complete a series of questionnaires on basic information and behavioral preferences. The control group will not receive any recommendation. The preference catering group will be recommended an asset allocation that is algorithmically calculated to be optimal based on the investor’s behavioral parameters. The loss aversion education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the loss aversion parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. The mental accounting education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the mental accounting parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. Finally, all users will report the asset allocation they consider to be most appropriate.
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Field
Trial Start Date
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Before
December 01, 2023
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After
August 01, 2025
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Field
Trial End Date
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Before
June 01, 2024
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After
August 01, 2026
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Field
Last Published
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Before
December 15, 2023 03:28 PM
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After
February 24, 2026 10:27 PM
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Field
Intervention (Public)
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Before
The experiment will randomly divide users of a certain bank’s APP into four groups: control group, preference catering group, loss aversion education group, and mental accounting education group. The control group will be recommended a uniform non-personalized asset allocation. The preference catering group will be recommended an asset allocation that is algorithmically calculated to be optimal based on the investor’s behavioral parameters. The loss aversion education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the loss aversion parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. The mental accounting education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the mental accounting parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged.
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After
The experiment will randomly divide users of a certain bank’s APP into four groups: control group, preference catering group, loss aversion education group, and mental accounting education group.
All users will first report their current asset allocation and complete a series of questionnaires on basic information and behavioral preferences. The control group will not receive any recommendation. The preference catering group will be recommended an asset allocation that is algorithmically calculated to be optimal based on the investor’s behavioral parameters. The loss aversion education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the loss aversion parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. The mental accounting education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the mental accounting parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. Finally, all users will report the asset allocation they consider to be most appropriate.
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Field
Intervention Start Date
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Before
December 01, 2023
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After
November 01, 2025
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Field
Intervention End Date
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Before
May 01, 2024
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After
May 01, 2026
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Field
Primary Outcomes (End Points)
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Before
Browsing behavior, Asset reallocation behavior
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After
Asset allocation decision
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Field
Experimental Design (Public)
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Before
The experiment will randomly divide users of a certain bank’s APP into four groups: control group, preference catering group, loss aversion education group, and mental accounting education group. The control group will be recommended a uniform non-personalized asset allocation. The preference catering group will be recommended an asset allocation that is algorithmically calculated to be optimal based on the investor’s behavioral parameters. The loss aversion education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the loss aversion parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. The mental accounting education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the mental accounting parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged.
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After
The experiment will randomly divide users of a certain bank’s APP into four groups: control group, preference catering group, loss aversion education group, and mental accounting education group.
All users will first report their current asset allocation and complete a series of questionnaires on basic information and behavioral preferences. The control group will not receive any recommendation. The preference catering group will be recommended an asset allocation that is algorithmically calculated to be optimal based on the investor’s behavioral parameters. The loss aversion education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the loss aversion parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. The mental accounting education group will be recommended an asset allocation that is algorithmically calculated to be optimal but with the modification of the mental accounting parameter to be fully rational while keeping the investor’s other behavioral parameters unchanged. Finally, all users will report the asset allocation they consider to be most appropriate.
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Field
Planned Number of Clusters
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Before
about 2000000 individuals
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After
About 200,000 individuals exposed to the experiment (about 8,000 actually entering the experiment)
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Field
Planned Number of Observations
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Before
about 2000000 individuals
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After
About 200,000 individuals exposed to the experiment (about 8,000 actually entering the experiment)
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Field
Sample size (or number of clusters) by treatment arms
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Before
500000 individuals control group, 500000 individuals preference catering group, 500000 individuals loss aversion education group, 500000 individuals mental accounting education group
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After
50,000 individuals exposed (about 2,000 actually entering the experiment) in control group; 50,000 individuals exposed (about 2,000 actually entering the experiment) in preference catering group; 50,000 individuals exposed (about 2,000 actually entering the experiment) in loss aversion education group; 50,000 individuals exposed (about 2,000 actually entering the experiment) in mental accounting education group
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Field
Secondary Outcomes (End Points)
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Before
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After
Browsing behavior
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