I spent a year and $5,700 to see if ChatGPT can beat the market (S&P 500)
My goal was to determine if autonomous agents powered by Large Language Models, such as GPT-4, could "beat the market."
"Beating the market" refers to achieving investment returns that exceed the performance of a benchmark index, such as the S&P 500. It implies that an investor's portfolio has generated a higher return compared to the average market return over a specified period. This concept is often used to evaluate the success of investment strategies or the skill of portfolio managers.
I wanted the GPT Investor to compete against the SPDR S&P 500 ETF Trust, also known as $SPY, an exchange-traded fund (ETF) that aims to track the performance of the S&P 500 Index. This index includes 500 of the largest publicly traded companies in the U.S., making $SPY a popular investment vehicle for those seeking broad exposure to the U.S. stock market.
$SPY is a formidable opponent because, over the last 10 years, fewer than 10% of active U.S. stock funds have managed to outperform index funds like $SPY.
If The GPT Investor could beat $SPY, it would mean it outperforms 90% of professional fund managers.(e.g. In the last one year, Warren Buffet couldn't beat $SPY)
Methodology:
I experimented with various methods and technology stacks to generate stock recommendations, meticulously documenting and reporting the results to our subscribers. The common thread among these methods was:
-Using OpenAI's LLMs (GPT-3.5 and GPT-4.0) -Enabling the LLMs to autonomously search the web -Generating stock recommendations for specific durations, such as three months
I utilized platforms like ChatGPT, Godmode, and BabyAGI UI to generate the stock recommendations. Each of these platforms can perform multi-step reasoning, a crucial attribute of autonomous agents. For example, based on a prompt, the agent can create its own to-do list and independently execute the steps to arrive at a result.
I conducted 19 experiments, investing CAD $300 in each, for a total of CAD $5,700 invested. I used a Wealthsimple brokerage account to execute the trades. Since each stock recommendation had a specific duration, I closed the positions at the end of each duration and compiled the returns as part of The GPT Investor portfolio.
For every experiment I ran, I published the entire methodology (tech stack, prompt, LLM, etc.) and results on this platform—The GPT Investor (www.gptinvestor.co)
Results: -Total Invested: CAD 5,700 -Number of Experiments: 19 -Shortest Experiment Duration: 7 days -Longest Experiment Duration: 1 year -Number of Stocks Recommended by The GPT Investor: 31
Total Return:
-The GPT Investor: 11.54% (CAD $658.20) -$SPY: 8.89% (CAD $507.10)
Overall, The GPT Investor Portfolio return was approximately 29.78% better than the $SPY's return.
Number of experiments by LLM
-GPT-4: 11 experiments -GPT-3.5: 8 experiments
The average return for the two LLMs used by the GPT Investor is as follows:
-GPT-4: 15.54% (with a corresponding average $SPY return of 9.74%)
-GPT-3.5: 6.05% (with a corresponding average $SPY return of 7.74%)
*GPT-4's return was approximately 156.86% better than GPT-3.5's return.
The results raise the exciting possibility that as LLMs become more powerful, the returns of The GPT Investor should improve even further.
I publish the real-time status of The GPT Investor here: https://www.gptinvestor.co/the-gpt-investor/