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Composite leading search index: a preprocessing method of internet search data for stock trends prediction

机译：综合领先搜索索引：用于股票趋势预测的互联网搜索数据的预处理方法

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摘要

Previous studies have revealed that Internet search data is a new source of data that can be used to predict the stock market. In this new, data-driven research field, choosing a method for preprocessing data is crucial to achieving accurate prediction performance. This paper proposes a preprocessing method of Internet search data: composite leading search index (CLSI), which is composed of three steps: (a) keyword selection, (b) time difference measurement, and (c) leading index composition. We demonstrate the validity of CLSI by comparing this method's results with the results from search volume index (SVI), which is most commonly used in previous literatures. We build a time series model (TS) with error correction and support vector regression (SVR) for stock trend prediction, and combine into four models for comparison: SVI-TS, CLSI-TS, SVI-SVR, and CLSI-SVR. We test these four models in the context of the Chinese stock market, which interests more and more investors nowadays, and analyzed results in nine datasets: stable periods, peak periods and trough periods of Shanghai Composite Index, Shenzhen Composite Index, and Hushen 300 index respectively. The results show that using TS and SVR as forecasting models, CLSI performs better than SVI on majority of the test dataset while has almost the same performance with that of SVI on the remaining test dataset. It is to some extent convincing that CLSI is a more efficient preprocessing method of Internet search data for stock trend prediction.
机译：先前的研究表明，互联网搜索数据是可用于预测股市的新数据源。在这个新的，数据驱动的研究领域，选择一种预处理数据的方法对于实现准确的预测性能至关重要。本文提出了一种互联网搜索数据的预处理方法：复合领先搜索索引（CLSI），它由三个步骤组成：（a）关键词选择；（b）时差测量；（c）领先索引组成。我们通过将这种方法的结果与以前文献中最常用的搜索量指数（SVI）的结果进行比较，证明了CLSI的有效性。我们建立了带有误差校正和支持向量回归（SVR）的时间序列模型（TS），用于股票趋势预测，并合并为四个模型进行比较：SVI-TS，CLSI-TS，SVI-SVR和CLSI-SVR。我们在当今越来越受投资者关注的中国股市中测试了这四个模型，并分析了九个数据集的结果：上证综合指数，深证综合指数和沪深300指数的稳定期，高峰期和低谷期。分别。结果表明，使用TS和SVR作为预测模型，CLSI在大多数测试数据集上的性能优于SVI，而在其余测试数据集上的性能几乎与SVI相同。从某种程度上说服了CLSI是用于股票趋势预测的Internet搜索数据的一种更有效的预处理方法。

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