Abstract:
Financial markets are known for their complexity and challenging dynamics, making it difficult to predict market movements accurately. While previous research has attempted to forecast financial markets using machine learning techniques, the results have been insufficient. In this study, we propose a novel approach using Natural Language Processing (NLP) and a one-dimensional Convolutional Neural Network (CNN) model to forecast changes in the financial market. Our model utilizes customized one-dimensional convolutional layers to analyze historical financial trading data over time, extracting relevant features without relying on traditional technical indicators. By avoiding biases associated with the selection of indicators and predefined coefficients, our model provides a more comprehensive and accurate prediction. We rigorously evaluate the performance of our CNN model using backtesting on historical trade data for six futures contracts from January 2010 to October 2017. The results demonstrate that our model outperforms previous machine learning techniques, producing more stable and profitable financial performance.
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