The new age of AI enabled investing

By FINTECH Books Contributor, Hari Pillai
Follow: @hpillai05

A roboadvice platform diagnostic/risk tolerance questionnaire as part of the signup process could ask:

What happens if your investment portfolio lost 10% of value in a month?

How do you describe the frequency of your trading?

Do you tend to save or spend disposable income?

 

Questions are crafted to understand loss aversion, status quo, and self-control biases of a financial market participant (FMP). This helps the platform algorithms to understand FMPs’ cognitive and emotional biases using behavioral portfolio theory concepts.

Investors’ goals are identified as part of the experience (saving for retirement, buying a house etc.) and risk tolerance is associated with each goal.

A portfolio is then constructed taking into account layers (Obligational Needs, Priorities Desires, and Aspirational etc.) depending on the investor’s goals. This is a different approach than the modern portfolio theory where the holistic approach to portfolio construction is taken into account (asset class correlations, optimal asset allocation etc.).

Facebook recently announced a fascinating future project that includes reading brain waves and sending full rich thoughts to others directly through brain/computer interfaces.

In the case of behavioral finance, experimentation is happening to combine neuroscience, psychology and economics to attempt to explain how humans make economic decisions. Combining advancements in both these innovations may present an opportunity to make suggestions to the betterment of wealth management.

In the future, if brain waves and rich text are combined and used to send someone a request to buy a financial product based on a friend recommendation (a bias called) or a Facebook post,

AI bots could better understand the meaning of that post and could take steps to and suggest better outcomes for the FMPs, potentially combining a variety of actively and passively managed solutions and better educating the FMP the utility of the solutions correct the cognitive and emotional biases