The Value of AI Stock Portfolio Weighting

Can Google’s large language model (LLM), Gemini, beat simple benchmarks by picking a small portfolio of stocks? In their September 2024 paper entitled “Can AI Beat a Naive Portfolio? An Experiment with Anonymized Data”, Marcelo Perlin, Cristian Foguesatto, Fernanda Müller and Marcelo Righi test the ability of Gemini 1.5 Flash to weight a portfolio of five U.S. stocks for different investment horizons (1, 6, 12 or 36 months) using either financial data, price data or a combination of both. The tests employ real but anonymized financial data, ensuring that confounding training data about specific firms does not infect stock weighting decisions. Testing involves 18,000 iterations of five steps:

• Select a random date during 2004 through 2023 with equal daily probability.
• Based on this date, randomly select five U.S firms/stocks that have financial data, prices over $1.00 and average daily volumes over $250,000 over the prior five years.
• Create anonymized versions of the financial and price data.
• Ask Gemini to assign allocations from a fixed total amount to the five stocks, with the possibility of also allocating to the contemporaneous 5-year U.S. Treasury note yield.
• Compare performance of the Gemini-weighted portfolio during the given investment horizon to that of the S&P 500 Index and an equal-weighted portfolio of the same stocks/Treasury note yield.

Using inputs as specified for 1,522 distinct firms during 2004 through 2023, they find that: (more…)