Design investing/banking client dashboards UI to mitigate myopic loss aversion

A potential way for banking / investing companies to add value to their clients would be to design their online client dashboards to mitigate as much as possible myopic loss aversion (afterwards MLA) from prompting them to sell the investments that have achieved net gains and keep the ones resulting in net losses, regardless of their future prospects.

This cognitive bias is very well known and established, and the very design of both web-based dashboards and app-based dashboards for mobile use can be used to mitigate its effect.

Potential aspects of the dashboards that can be designed to prevent myopic loss aversion could be:

Timeframe used to display net returns

Returns could be displayed by default over the longest possible time-frame and at least tending to display yearly returns but no shorter returns. Of course, it would depend on the trading activity of the client, as for an active client, you do need to display returns from their buy date. But such a design would avoid intermediary returns for example, and would default to the longest possible return.

The default return displayed could even be from the purchase date for each individual asset, even if the dates are different, by opposition to standardizing the returns on fiscal years, etc. The return of the portfolio would default to a long time-frame of at least a year and maybe even figure prominently in the display.

Visual display of loss/gain for more active clients

For more active clients, it should be possible to design the display of the net loss/net gain from the purchase date so as to visually nudge clients to NOT take action. Possible nudges could include a visual display of the volatility of the asset along with a minimal education explaining that the more volatile the asset, the more short-term losses will occur when checking ones account.

Personalization of dashboard: by financial advisor and/or automated

We can even envision to design several versions of the client dashboards to better fit different behavioral types. Each version would aim at counteracting the natural tendency of each type to be subject to myopic loss aversion.

Financial advisors all try to identify the behavioral type of their clients before giving any advice, and identifying how prone a client is subject to MLA should be an integral part of this step. Then, depending on which type the client is assigned to, he/she will have a dashboard that will be designed to mitigate as exactly as possible its tendency to succumb to MLA.

This process can also be automated. We can envision a few valid measures of how prone a customer is to trade based on MLA by analyzing its log-in, view and trade analytics. Such measures will be based on:

  • how frequently the client is checking his/her dashboards
  • what is the gain/loss status of each asset in the account when he logs in
  • what data does she view in addition to the data available on her homepage
  • what trading action are taken within the same session or in the few sessions done shortly after this one

These analytics could be combined in an individual score measuring the tendency of the client to trade based on MLA. This score could then be used to choose which dashboard design to show to the client and to alert the financial advisor if the score is crossing predetermined thresholds.