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Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news

机译:对包含市场数据和文本新闻的自动盘中股票推荐系统的实证评估

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In this study we evaluate the effectiveness of augmenting numerical market data with textual-news data, using data mining methods, for forecasting stock returns in intraday trading. Integrating these two sources of data not only enriches the information available for the forecasting model, but it can potentially capture joint patterns that may not otherwise be identified when each data source is employed separately. We start with market data and then gradually add various textual data representations, going from simple representations, such as word counts, to more advanced representations involving sentiment analysis. To find the incremental value of each data representation, we build an end-to-end recommendation process including data preprocessing, modeling, validation, trade recommendations and economic evaluation. Each component of the modeling process is optimized to remove human bias and to allow us to impartially compare the results of the various models. Additionally, we experiment with several forecasting algorithms to find the one that yields the "best" results according to a variety of performance criteria. We employ data representation procedures and modeling improvements beyond those used in previous related studies. The economic evaluation of the results is conducted using a simulation procedure that inherently accounts for transaction costs and eliminates biases that have potentially affected previous related data-mining studies. This research is one of the largest-scale data-mining studies for evaluating the effectiveness of integrating market data with textual news data for the purpose of stock investment recommendations. The results of our study are promising in that they show that augmenting market data with advanced textual data representation significantly improves stock purchase decisions. Best results are achieved when the approach is implemented with a nonlinear neural network forecasting algorithm.
机译:在这项研究中,我们评估了使用数据挖掘方法用文本新闻数据扩展数字市场数据的有效性,以预测当日交易中的股票收益。集成这两个数据源不仅可以丰富可用于预测模型的信息,而且可以潜在地捕获在单独使用每个数据源时可能无法识别的联合模式。我们从市场数据开始,然后逐渐添加各种文本数据表示形式,从简单的表示形式(例如字数统计)到涉及情感分析的更高级的表示形式。为了找到每个数据表示的增量值,我们建立了一个端到端的建议流程,包括数据预处理,建模,验证,贸易建议和经济评估。优化了建模过程的每个组件,以消除人为偏见,并使我们能够公正地比较各种模型的结果。此外,我们尝试了几种预测算法,以根据各种性能标准找到能产生“最佳”结果的算法。除了以前的相关研究中使用的方法外,我们还采用数据表示程序和模型改进。使用模拟程序对结果进行经济评估,该模拟程序固有地考虑了交易成本并消除了可能影响先前相关数据挖掘研究的偏见。这项研究是规模最大的数据挖掘研究之一,用于评估将市场数据与文本新闻数据集成以进行股票投资建议的有效性。我们的研究结果令人鼓舞,因为它们表明使用高级文本数据表示来扩展市场数据可以显着改善股票购买决策。当使用非线性神经网络预测算法实现该方法时,将获得最佳结果。

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