论文标题
使用情感分析和印度市场深入学习的股票价格预测
Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets
论文作者
论文摘要
股票市场预测一直是一个很长一段时间的积极研究领域。计算的到来，然后是机器学习提高了研究速度，并开了新的途径。作为这项研究的一部分，我们旨在利用历史价格提供情感数据的有助于人的股票股票发展。使用两个模型作为练习的一部分，LSTM是第一个以历史价格作为自变量的模型。使用强度分析仪捕获的情绪分析被用作第二部分的随机森林模型的主要参数，例如黄金，石油价格，美元汇率和印度政府等宏观参数。证券收益率还添加到模型中，以提高模型的准确性。作为最终产品，价格为4股。使用上述两个模型预测了Reliance，HDFC银行，TCS和SBI。使用RMSE度量评估结果。
Stock market prediction has been an active area of research for a considerable period. Arrival of computing, followed by Machine Learning has upgraded the speed of research as well as opened new avenues. As part of this research study, we aimed to predict the future stock movement of shares using the historical prices aided with availability of sentiment data. Two models were used as part of the exercise, LSTM was the first model with historical prices as the independent variable. Sentiment Analysis captured using Intensity Analyzer was used as the major parameter for Random Forest Model used for the second part, some macro parameters like Gold, Oil prices, USD exchange rate and Indian Govt. Securities yields were also added to the model for improved accuracy of the model. As the end product, prices of 4 stocks viz. Reliance, HDFC Bank, TCS and SBI were predicted using the aforementioned two models. The results were evaluated using RMSE metric.
