The stock market is very complex and volatile. It is impacted by positive and negative sentiments which are based on media releases. The scope of the stock price analysis relies upon ability to recognise the stock movements. It is based on technical fundamentals and understanding the hidden trends which the market follows. Stock price prediction has consistently been an extremely dynamic field of exploration and research work. However, arriving at the ideal degree of precision is still an enticing challenge. In this paper, we are proposing a combined effort of using efficient machine learning techniques coupled with a deep learning technique—Long Short Term Memory (LSTM)—to use them to predict the stock prices with a high level of accuracy. Sentiments derived by users from news headlines have a tremendous effect on the buying and selling patterns of the traders as they easily get influenced by what they read. Hence, fusing one more dimension of sentiments along with technical analysis should improve the prediction accuracy. LSTM networks have proved to be a very useful tool to learn and predict temporal data having long term dependencies. In our work, the LSTM model uses historical stock data along with sentiments from news items to create a better predictive model.

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使用情绪分析从财经新闻文章中得出的可解释股价预测

股市非常复杂且动荡。它受到基于媒体发布的正面和负面情绪的影响。股票价格分析的范围取决于识别股票走势的能力。它基于技术基础并了解市场所遵循的隐藏趋势。股价预测一直是勘探和研究工作中极为活跃的领域。但是，达到理想的精确度仍然是一个诱人的挑战。在本文中，我们提出了结合使用高效的机器学习技术和深度学习技术（长期短期记忆（LSTM））的共同努力，以使用它们来高精度地预测股票价格。用户从新闻头条中得出的情绪对交易者的购买和销售模式产生巨大影响，因为他们很容易受到阅读内容的影响。因此，将情感的另一维度与技术分析融合在一起可以提高预测的准确性。LSTM网络已被证明是学习和预测具有长期依赖性的时间数据的非常有用的工具。在我们的工作中，LSTM模型使用历史库存数据以及新闻条目的情绪来创建更好的预测模型。