Abstract
With the extensive development of the social economic, different kinds of financial investment products has been emerged. Stock is one of the most active financial products. Successful prediction of behavior of stock market can promise high profits. Moreover, for policymakers, the rationality of policies can be assessed through market trends, so a better development of the stock market can be promoted. However, successfully prediction the trend of stock market is a very difficult problem due to the stock market’s behavior are influenced by various reasons. Financial news offers useful information that can help the investor to make better stock investment decisions. This study incorporates fuzzy set theory into Twin-KSVC (twin support vector classification machine for K-class classification) and develops a new fuzzy hyperplane-based twin support vector machine for K-class classification (FH-Twin-KSVC) to predict the trend of stock market based on financial news. The distinguishing feature of the proposed FH-TWIN-KSVC are that every data sample is assigned a membership degree based on the important level of the corresponding training sample in the training process, and the components for determining the optimal separating hyperplane are fuzzy numbers. The fuzzy hyperplane is useful for capturing the imprecise natures existing in real-world environment by descripting inexact characteristics in the training samples using fuzzy sets. The fuzzy hyperplane of our FH-TWIN-KSVC is able to significantly reduce the influence of noise. The experimental results on the real-world stock prediction application show that the proposed FH-TWIN-KSVC model combines the advantage of Twin-KSVC in improving classification performance in multi-classes classification problem and the advantage of a FH-SVM in increasing robustness and decreasing the influences of noises.
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Hao, PY. (2023). Fuzzy Hyperplane Based Twin-KSVC and Its Applications to Stock Price Movement Direction Prediction Based on News Articles. In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Lecture Notes in Computer Science(), vol 13955. Springer, Cham. https://doi.org/10.1007/978-3-031-37616-0_11
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