This technology relates to a method and apparatus for predicting stock price fluctuations. Specifically, it is a deep learning technology that predicts stock price movements by combining news, stock price, and investor data.
Existing technologies faced limitations in predicting short-term stock price movements and delivering insufficient marginal returns. To address this, the present technology proposes a configuration that uses a deep learning model to predict stock price fluctuations, incorporating methods that consider both numerical and text data.
Accordingly, this technology can improve the accuracy of stock price prediction by considering various factors and data types. It has practical value in the finance, insurance, and software sectors.
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