Women are more likely than men to consider investment advice from artificial intelligence (AI), partly as it lacks gender biases presented by human advisers, according to a research paper.
The paper, Man v Machine: The Influence of AI Forecasts on Investor Beliefs, by Gertjan Verdickt at the University of Auckland and Francesco Stradi from KU Leuven in Belgium explored whether investors trust analyst forecasts made by AI.
Firms such as Morgan Stanley, BlackRock, JP Morgan and Vanguard have already been vocal in their use on AI across wealth management and asset management. In the case of Morgan Stanley, 90 per cent of its advisers are using AI on a monthly basis.
Speaking at a conference in Sydney, Jeff McMillan, head of analytics, data and innovation, said: “It is no different to hiring a junior member of staff who you have to train up, that can take years. The technology will only operate within the confines of what you let it do and operate with the values and ethics of your business.
“It isn’t about machine versus human; it’s about how do we help people do their jobs more effectively?”
Exploring this, the two researchers examined 1,800 US participants who were presented with documents using Goldman Sachs stock market outlook wording; one was described as written by analysts, one written by analysts incorporating an advanced AI model, and one was described as written by an advanced AI model. All estimates and text remained the same, only the purported source differed.
The overall finding was that investors tended to be less responsive when an analyst incorporated AI, stemming from a lower perceived credibility of AI-generated forecasts.
“We document that AI forecasts are perceived as less credible than those from humans. Perceived credibility plays a crucial role in shaping investors’ beliefs. We conclude that these insights have implications for designing and implementing AI in financial decision-making, highlighting that technical excellence by itself cannot generate trust.”
Interestingly, the report noted women were more responsive than men to AI forecasts and posited one reason for this could be the gender biases they experience when visiting a human financial adviser.
Money Management previously wrote how financial advice practices can better work with female clients, including the mistaken belief they have lower risk tolerances or desire to de-risk a portfolio or being shown lower-risk assets.
The study stated: “On average, women exhibit a greater propensity to positively update their return belief to AI-generated forecasts, particularly when they have larger initial misperceptions. Indeed, while the average investor moves away from the signal from AI sources, female investors seem to update their beliefs towards the signal.
“Prior research has shown that women are often open to seeking and following financial advice (e.g. Bucher-Koenen et al., 2023). Therefore, this could extend to novel sources of advice, such as AI. Additionally, studies have found that women may be more likely to admit uncertainty in financial matters (Barber & Odean, 2001), which could make them more receptive to external inputs, including AI forecasts.
“If women have historically been less confident in their financial decision-making (as some literature suggests), they might view AI as an objective, non-discriminatory source of advice, free from potential gender biases.”
In a second study of 600 participants, when the AI was identified as being ChatGPT, it found participants exhibited a negative reaction to forecasts despite its brand-name recognition, indicating possible awareness by the participants of its decreasing accuracy.
In conclusion, the pair wrote that the content of the forecast is more likely to prompt a reaction than its source.
“The evidence reveals a nuanced picture of investor behaviour to analyst forecasts, highlighting the general responsiveness to new information and a reluctance towards AI-integrated analyst recommendations. This complexity in reactions underscores the interplay between traditional financial advice and the emerging AI technology in influencing financial decision-making and return belief-updating processes.
“Our manipulations of information richness, analyst dispersion, and revisions demonstrate that investors are more likely to respond to the informational content of the forecast rather than its source.”
