Investing with artificial intelligence (AI): the ChatGPT experiment

Artificial intelligence (AI) has revolutionized several industries, and the world of finance may be one of them: in a groundbreaking move, nearly 25,000 traders undertook an experiment in copy trading using ChatGPT AI.

The stock picks of the artificial intelligence attracted significant attention, with traders collectively investing $14.7 million, hoping to capitalize on the potential 500% return promised by one of the strategies tested in the academic research.

The copy trading experiment of ChatGPT’s AI

The GPT Portfolio experiment involves ChatGPT analyzing as many as 10,000 news articles and 100 company reports to identify 20 stocks for a $50,000 portfolio. 

These selections are updated weekly, allowing traders to mirror the artificial intelligence‘s investment decisions. 

Among the most significant initial choices are well-known companies such as Berkshire Hathaway, Amazon, D.R. Horton, and DaVita Health.

After just two weeks of implementation, the GPT Portfolio achieved a modest gain of about 2%, closely aligning with the overall performance of the stock market over the same period. 

However, an interesting observation was made: while the top five picks posted gains, the bottom five picks suffered larger losses, with Dollar Tree dropping 17% after losing earnings. 

This prompts speculation about the wisdom of selectively investing in the best ideas generated by GPT-4 in the future.

In addition to trading in the stock market, ChatGPT also assists traders in the cryptocurrency market. 

The ChatGPT Crypto Trader account focuses on Ethereum, with AI recommending long positions. 

Debate around the intelligence of large language models

ChatGPT’s performance in financial markets has sparked a debate among analysts about the capabilities and limitations of large language models. 

Although artificial intelligence has shown promise, the relatively modest gains observed so far raise questions about the effectiveness of these models in making accurate investment decisions. 

Critics argue that there are factors beyond linguistic analysis that influence stock prices and market dynamics that may not be fully captured by AI algorithms.

As AI continues to permeate various job sectors, concerns about job losses and social implications persist. 

Although the use of AI in trading and investment decisions has gained popularity, it is essential to recognize the potential consequences. 

The rapid automation of financial activities can lead to job losses, resulting in economic disparities and social unrest. 

Experts warn that addressing these challenges is critical to avoid a potential revolution triggered by widespread job losses.

The Bitget user survey

A survey of Bitget users revealed that as many as 80% of participants had negative outcomes from relying on AI-generated advice. 

False investment recommendations and other forms of misinformation were cited as common problems. 

This development raises important concerns about the limitations of AI tools when it comes to accurately interpreting market nuances and trends.

Gracy Chen, CEO of Bitget, pointed out that although AI tools are robust and resourceful, they lack the human touch necessary to discern market nuances. 

Trading involves complex factors influenced by economic indicators, geopolitical events, and investor sentiment, among other variables. 

These intricate dynamics are not always easy for artificial intelligence algorithms to understand and analyze accurately.

ChatGPT: the limits of artificial intelligence (AI) in trading

The limits of AI in trading go beyond the potential for misinformation. Market volatility, sudden changes in trends, and unexpected events can strain the predictive capabilities of AI models. 

As Bitget’s experience shows, relying solely on AI-generated advice can lead to undesirable results and financial losses.

However, it is important to note that caution should not translate into a total rejection of AI’s potential. 

Although AI models may struggle to capture the full complexity of the market, they can still offer valuable insights and help traders make informed decisions.

Integrating human expertise with AI-generated suggestions can lead to a more robust and reliable trading strategy.

In addition, the experiences of Bitget and other platforms underscore the importance of responsible implementation and ongoing monitoring of AI systems. 

Rigorous testing, feedback loops, and regular updates are essential to refine AI algorithms and minimize the occurrence of misinformation and bad advice.

In the debate over the use of AI in trading, it is critical to strike a balance between the capabilities of AI and the integration of human judgment. 

The synergy between human intuition and AI-driven analysis can potentially enhance trading strategies, improve decision making, and mitigate the risks associated with relying solely on AI-generated recommendations.

Conclusions

AI integration, exemplified by ChatGPT’s involvement in stock market and cryptocurrency trading, is garnering significant attention from traders and analysts. 

Although early results from the GPT Portfolio experiment show promising gains, questions persist about the intelligence of large language models and their ability to consistently outperform the market. 

As the world navigates the transformative era of AI, it becomes critical to strike a balance between technological advances and societal welfare.

Clearly, attention should be paid to the use of AI for trading. Although the integration of AI into trading has shown promise, recent experiences remind us that caution is in order. 

Bitget, a major crypto derivatives platform, recently abandoned the experiment of using AI on its platform because of the potential for misinformation and negative user experiences.

Bitget’s recent abandonment of AI experimentation due to user dissatisfaction and potential misinformation serves as a reminder of the limitations of AI in trading. 

Although AI tools offer valuable insights, they must be employed with caution, with the understanding that market complexities can escape pure algorithmic analysis. 

Combining AI-generated advice with human experience and continuous monitoring can help unlock the full potential of AI in trading while reducing the risks associated with misinformation and inaccurate forecasts.