This study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning recurrent model called long short term memory (LSTM). Instead of using count-based traditional sentiment index methods, the study uses its own sum and relevance based sentiment index mechanism. Hourly price data has been used in this research as daily data is too late and minutes data is too early for getting the exclusive effect of sentiments. Normally, hourly data is extremely costly and difficult to manage and analyze. Hourly data has been rarely used in similar kinds of researches. To built sentiment index, text analytic information has been parsed and analyzed, textual information that is relevant to selected stocks has been collected, aggregated, categorized, and refined with NLP and eventually converted scientifically into hourly sentiment index. News analytic sources include mainstream media, print media, social media, news feeds, blogs, investors’ advisory portals, experts’ opinions, brokers updates, web-based information, company’ internal news and public announcements regarding policies and reforms. The results of the study indicate that sentiments significantly influence the direction of stocks, on average after 3–4 h. Top ten companies from High-tech, financial, medical, automobile sectors are selected, and six LSTM models, three for using text-analytic and other without analytic are used. Every model includes 1, 3, and 6 h steps back. For all sectors, a 6-hour steps based model outperforms the other models due to LSTM specialty of keeping long term memory. Collective accuracy of textual analytic models is way higher relative to non-textual analytic models.

本研究试图在自然语言处理 (NLP) 技术和称为长短期记忆 (LSTM) 的深度学习循环模型的帮助下，使用文本分析来解开股市预测难题。该研究没有使用基于计数的传统情绪指数方法，而是使用自己的基于总和和相关性的情绪指数机制。本研究使用每小时价格数据，因为每日数据为时已晚，分钟数据为时过早，无法获得情绪的排他性影响。通常，每小时的数据非常昂贵且难以管理和分析。每小时数据很少用于类似的研究。为了构建情绪指数，文本分析信息已经被解析和分析，与选定股票相关的文本信息已经被收集、聚合、分类、并用 NLP 精炼，最终科学地转化为小时情绪指数。新闻分析来源包括主流媒体、印刷媒体、社交媒体、新闻提要、博客、投资者咨询门户、专家意见、经纪人更新、网络信息、公司内部新闻和有关政策和改革的公告。研究结果表明，情绪显着影响股票的方向，平均在 3-4 小时后。选取了高科技、金融、医疗、汽车行业的前十名公司，使用了六个 LSTM 模型，三个使用文本分析，其他不使用分析。每个模型都包括 1、3 和 6 小时的后退步骤。对于所有领域，由于 LSTM 保持长期记忆的特殊性，基于 6 小时步骤的模型优于其他模型。