Using AI to Predict Stock Prices

Can artificial intelligence (AI), in the form of a deep-learning neural network, outperform human analysts in predicting stock prices at an annual horizon? In the May 2024 revision of his paper entitled “Can AI Replace Stock Analysts? Evidence from Deep Learning Financial Statements”, Nathan Dong tests the ability of a 5-layer neural network to predict stock prices 12 months hence using recent financial statements, interest rates and stock prices. He does not impose any valuation formulas (such as discounted cash flow) or valuation ratios on the AI model. Instead, he lets the neural network, running on inexpensive computer equipment, devise its own approach. He splits his dataset dynamically into an in-sample training subset and an out-of-sample prediction subset. For example, to make a 12-month prediction in the first quarter of 2022, the training subset consists of 5,000 iterations based on the four quarterly financial statements from 2020 and interest rates/stock prices from 3, 6, 9 and 12 months ago. He measures AI model and human analyst accuracy based on mean squared error between target prices and actual future prices and the ability of variation in target prices to explain variation in actual future prices (R-squared). He also explores the factors that drive similarities/differences in target prices between human analysts and the AI model. Using quarterly financial statements, analyst 12-month target prices and actual prices for a broad sample of U.S. stocks as available, and contemporaneous yields for  10-year constant maturity U.S. Treasury notes and Moody’s seasoned Baa corporate bonds during 2010 through 2023, he finds that:

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