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AI outperforms analysts in financial forecasting
(MENAFN) The former Global Head of Research at Morgan Stanley and the previous Head of Research, Data, and Analytics at UBS Group highlights that it's expected for AI to outperform stock analysts on average in earnings predictions. Similarly, it's not surprising that strategies based on rules tend to offer better financial advice than a personal banker.
Systematic investing has always been advantageous, even before the recent progress in generative AI. Although these methods might miss out on the small fraction of stocks or market changes that yield significant returns, they consistently prove to be valuable.
Current advancements in AI suggest that we can surpass traditional rule-based recommendations. The core components of investing—macroeconomics, accounting, and statistics—are areas where large language models excel, demonstrating strong performance in advanced evaluations across these fields. These models can synthesize and distill more information and insights than humans, which is particularly beneficial for macroeconomic strategies. So why does it remain challenging for analysts and portfolio managers to adapt to these technological advancements?
Insights from data scientist Cesar Hidalgo’s research into human-machine judgment offer some explanations. People tend to focus on the performance of a machine when using a program, and any prediction errors can erode trust in the tool. Even if an algorithm generally performs better than a human, financial advisors often rely on their intuition and experience.
Hidalgo’s findings suggest that we evaluate human recommendations differently, considering not just performance but also the advisor's intentions. When working with a private banker or entrusting money to a fund manager, we expect that their goals align with ours, especially if there is a performance-based incentive in place. This expectation leads us to be more forgiving of poor returns when we believe the advisor’s intentions are in our best interest.
Systematic investing has always been advantageous, even before the recent progress in generative AI. Although these methods might miss out on the small fraction of stocks or market changes that yield significant returns, they consistently prove to be valuable.
Current advancements in AI suggest that we can surpass traditional rule-based recommendations. The core components of investing—macroeconomics, accounting, and statistics—are areas where large language models excel, demonstrating strong performance in advanced evaluations across these fields. These models can synthesize and distill more information and insights than humans, which is particularly beneficial for macroeconomic strategies. So why does it remain challenging for analysts and portfolio managers to adapt to these technological advancements?
Insights from data scientist Cesar Hidalgo’s research into human-machine judgment offer some explanations. People tend to focus on the performance of a machine when using a program, and any prediction errors can erode trust in the tool. Even if an algorithm generally performs better than a human, financial advisors often rely on their intuition and experience.
Hidalgo’s findings suggest that we evaluate human recommendations differently, considering not just performance but also the advisor's intentions. When working with a private banker or entrusting money to a fund manager, we expect that their goals align with ours, especially if there is a performance-based incentive in place. This expectation leads us to be more forgiving of poor returns when we believe the advisor’s intentions are in our best interest.

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