上市公司下阶段的发展运营情况对股民进行投资具有一定的参考价值，同时对企业管理者作出风险预判、适时调整经营战略也有重要借鉴作用，但是上市公司财务报表的发布耗时长、时效性不强，某种程度上制约了企业管理者和金融投资者作出理性决策，互联网的飞速发展为解决时效性差、提前预测上市公司发展状况提供了新思路。搜索引擎伴随互联网产生，它的出现和普及改变了用户获取信息的方式，提高了用户收集资源的效率，实现了信息在不同时空用户之间的共享。人们已经习惯在作出消费或投资决策前，利用搜索引擎收集相关的信息，权衡利弊后再有所行动。同样，当用户在某段时间内对某家上市公司的关注度飙升，其相关搜索量会大幅上升，上市公司的部分财务数据也会有所反应。因此，本文借助用户的互联网搜索行为，对上市公司的财务指标进行研究。

本文以A公司为实证研究对象，首先从消费者购买决策理论出发，从定性角度分析了网络搜索关键词和上市公司财务数据之间的相关关系，再利用Spearman相关系数从定量角度给予证明。接着使用时差分析法和K-L信息量法对关键词和财务指标同时进行筛选，确定领先关键词及滞后阶数和最终的待预测指标。最后分别构建多个预测模型：ADL、随机森林、SVM和GBDT模型，对比传统时间序列模型和机器学习模型的拟合效果。通过研究，本文共得出以下结论：

（1）网络搜索数据与财务指标之间存在较强的相关性。

（2）存在能够提前预测A公司财务指标的关键词及能够被提前预测的财务指标，且使用网络搜索数据进行预测效果较好。

（3）机器学习算法在测试集中的预测效果要优于传统时间序列模型，且GBDT的综合预测误差最小，预测结果比官方提前一季度。

The development and operation situation of listed companies in the next stage has certain reference value for investors to invest in. It also has important reference value for enterprise managers to make risk predictions and adjust business strategies in a timely manner. However, the release of financial statements of listed companies takes a long time and lacks timeliness, which to some extent restricts enterprise managers and financial investors from making rational decisions. The rapid development of the Internet has solved the problem of poor timeliness Predicting the development status of listed companies in advance provides new ideas. Search engines emerged with the emergence of the Internet, and their emergence and popularity have changed the way users obtain information, improved the efficiency of resource collection, and achieved the sharing of information among users in different time and space. People have become accustomed to using search engines to collect relevant information before making consumption or investment decisions, weighing the pros and cons before taking action. Similarly, when users' attention to a listed company soars during a certain period of time, their related search volume will significantly increase, and some financial data of the listed company will also be reflected. Therefore, this article uses users' internet search behavior to study the financial indicators of listed companies.

This article takes Company A as the empirical research object. Starting from consumer purchasing decision theory, it qualitatively analyzes the correlation between online search keywords and financial data of listed companies, and then uses Spearman correlation coefficient to prove it quantitatively. Then, the time difference analysis method and K-L information method are used to screen keywords and financial indicators simultaneously, determining the leading keywords and lagging orders, as well as the final predicted indicators. Finally, several prediction models are constructed respectively: ADL, random forest, SVM and GBDT models to compare the fitting effects of traditional time series models and machine learning models. Through research, the following conclusions have been drawn in this article:

(1) There is a strong correlation between online search data and financial indicators.

(2) There are keywords that can predict Company A's financial indicators in advance and financial indicators that can be predicted in advance, and the use of network search data for prediction results is good.

(3) The prediction performance of machine learning algorithms is superior to traditional time series models, and GBDT has the smallest prediction error, with prediction results one quarter ahead of official ones.