Literature shows that both market data and financial media impact stock prices; however, using only one kind of data may lead to information bias. Therefore, this study uses market data and news to investigate their joint impact on stock price trends. However, combining these two types of information is difficult because of their completely different characteristics. This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine (SVM). It works by simply inputting heterogeneous multi-view data simultaneously, which may reduce information loss. Compared with the ARIMA and classic SVM models based on single- and multi-view data, our hybrid model shows statistically significant advantages. In the robustness test, our model outperforms the others by at least 10% accuracy when the sliding windows of news and market data are set to 1–5 days, which confirms our model’s effectiveness. Finally, trading strategies based on single stock and investment portfolios are constructed separately, and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.

中文翻译：

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基于多视图异构数据的股票价格预测混合模型

文献表明，市场数据和财经媒体都会影响股价；然而，仅使用一种数据可能会导致信息偏差。因此，本研究利用市场数据和新闻来调查它们对股价趋势的共同影响。然而，由于这两种类型的信息完全不同的特性，将它们结合起来是很困难的。本研究通过将多视图学习与支持向量机 (SVM) 相结合，开发了一种称为 MVL-SVM 的混合模型，用于股票价格趋势预测。它的工作原理是简单地同时输入异构多视图数据，这可以减少信息丢失。与基于单视图和多视图数据的 ARIMA 和经典 SVM 模型相比，我们的混合模型显示出统计上显着的优势。在稳健性测试中，当新闻和市场数据的滑动窗口设置为1-5天时，我们的模型比其他模型的准确率至少高出10%，这证实了我们模型的有效性。最后，分别构建了基于个股和投资组合的交易策略，模拟表明MVL-SVM比基准具有更好的盈利能力和风险控制性能。