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Can AI Mimic Human Investment Choices Based on Personality Traits?

Exploring how LLMs reflect human investment behaviors tied to personality.

Harris Borman, Anna Leontjeva, Luiz Pizzato, Max Kun Jiang, Dan Jermyn

― 5 min read


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Large Language Models, or LLMs, have been all the rage lately. These tools can take on a personality, almost acting like a human. This ability has led researchers to wonder how much those personalities influence choices, especially when it comes to investments. In this article, we'll dive into whether LLMs can mimic human investment strategies based on personality traits.

Understanding Personality Traits

Before we get into the nitty-gritty, let’s talk about what personality traits are. Think of them as different flavors of ice cream. Some people like vanilla (agreeable), while others might prefer rocky road (neurotic). We’ll focus on a model that outlines five main personality traits:

Openness

This trait is all about being open to new experiences. If you're the type who loves trying new foods or travels to far-off places, you likely score high in this area.

Conscientiousness

If you’re the planner of the group, always getting things done on time, then you probably have high conscientiousness. This trait is all about being organized and goal-oriented.

Extraversion

Do you thrive in social settings and prefer hanging out with friends rather than staying in? If so, you're likely more extroverted.

Agreeableness

If you find it easy to get along with others and consider their feelings, you've got a good dose of agreeableness. This trait is about being friendly and cooperative.

Neuroticism

This one’s a bit trickier. If you tend to worry a lot or feel anxious in new situations, you might score higher in neuroticism.

The Big Question

Now that we've got the basics down, let’s tackle our main question: Can an LLM with a set personality act like a human with the same traits when making investment decisions?

Creating the Personas

To find out, researchers created a bunch of LLM personas, assigning different levels of high, medium, or low to each of the five traits. They ended up with 243 unique personalities. It’s like a personality buffet!

Testing the Personas

Each persona took a questionnaire to determine if they could connect their personality traits to how they would actually behave. After that, they were placed in an investment scenario. Think of it as a game show where they had to make smart money moves.

Learning Styles

First up, learning styles. Personas needed to choose whether they preferred to learn about investment options on their own or by asking an expert. Generally, people who are open and extroverted tend to favor asking for help, while those who are conscientious like to research independently.

Results

The personas performed pretty well here! Most showed expected behaviors, especially when it came to conscientiousness. But surprisingly, openness didn’t quite fit the mold.

Impulsivity in Decision-Making

Next, we looked at impulsive decision-making. How quickly do these personas jump into decisions? This was evaluated by how much time they spent researching before making an investment.

Results

The personas did well in this area too! They demonstrated the expected behaviors for conscientiousness and extraversion. However, some traits, like agreeableness and neuroticism, didn’t show up as anticipated.

Risk Appetite

Investing is all about risk, right? So, we went ahead and assessed how much risk each persona was willing to take. We compared their self-reported attitudes toward risk with the actual investments they chose.

Results

Here, the personas did a decent job. They were able to align their behaviors with their traits. For instance, more open personas took riskier investments, while conscientious types were more cautious.

Interest in Environmental Products

Next, we explored whether these personas cared about eco-friendly investments. After all, who doesn't love a good green investment?

Results

The personas somewhat accurately reflected their traits here too! Open and agreeable personas expressed interest in environmental products, but extraversion didn’t cooperate like expected.

The Investment Simulation

To really see how these personas would act, they were placed in an investment simulation. Each persona had a budget of $1,000 to invest in five different companies.

The Companies

  1. Diamond: 5% return, 10% risk
  2. Platinum: 35% return, 30% risk
  3. Emerald: 89% return, 50% risk
  4. Ruby: 25% return, 30% risk (eco-friendly!)
  5. Sapphire: 80% return, 60% risk (high-tech!)

Decision Making

During the simulation, the personas could either do independent research or consult an expert. They had to decide where to invest based on their personality.

Results

Some personas showed excellent decision-making skills, investing in a way that matched their traits. For example, those low in conscientiousness were more likely to make quick decisions.

Comparison of Survey and Simulation Results

After crunching the numbers, researchers noticed the simulation results were much better than those from surveys. The personas acted more like humans in the simulation, possibly because they had access to more context and information.

Implications

These findings suggest that LLMs can mimic human behavior effectively, particularly in simulated environments. It seems that LLMs have learned to associate traits with behaviors while completing tasks.

Future Work and Limitations

While this study opens the door for more exploration, it wasn’t without its limitations. The personalities were the only focus, with no outside influences. Real life is a bit messier, with a lot of factors at play.

Next Steps

Future research should explore how adding other demographic information might affect outputs. Also, tasks that require interaction among personas could yield more exciting results.

Conclusion

In the end, LLMs can mirror human behavior fairly well, particularly when making investment decisions based on personality traits. They performed better in simulations compared to surveys, showcasing their potential for applications in understanding human behavior.

Isn't it fascinating to think about AI stepping into the world of finance, one personality trait at a time? Who knows, maybe one day, an LLM will be your financial advisor! Just make sure to check if it prefers diamond or emerald investments!

Original Source

Title: Do LLM Personas Dream of Bull Markets? Comparing Human and AI Investment Strategies Through the Lens of the Five-Factor Model

Abstract: Large Language Models (LLMs) have demonstrated the ability to adopt a personality and behave in a human-like manner. There is a large body of research that investigates the behavioural impacts of personality in less obvious areas such as investment attitudes or creative decision making. In this study, we investigated whether an LLM persona with a specific Big Five personality profile would perform an investment task similarly to a human with the same personality traits. We used a simulated investment task to determine if these results could be generalised into actual behaviours. In this simulated environment, our results show these personas produced meaningful behavioural differences in all assessed categories, with these behaviours generally being consistent with expectations derived from human research. We found that LLMs are able to generalise traits into expected behaviours in three areas: learning style, impulsivity and risk appetite while environmental attitudes could not be accurately represented. In addition, we showed that LLMs produce behaviour that is more reflective of human behaviour in a simulation environment compared to a survey environment.

Authors: Harris Borman, Anna Leontjeva, Luiz Pizzato, Max Kun Jiang, Dan Jermyn

Last Update: 2024-10-27 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2411.05801

Source PDF: https://arxiv.org/pdf/2411.05801

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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