英文标题：
《Discovering Language of the Stocks》
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作者：
Marko Po\\v{z}enel and Dejan Lavbi\\v{c}
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最新提交年份：
2019
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英文摘要：
Stock prediction has always been attractive area for researchers and investors since the financial gains can be substantial. However, stock prediction can be a challenging task since stocks are influenced by a multitude of factors whose influence vary rapidly through time. This paper proposes a novel approach (Word2Vec) for stock trend prediction combining NLP and Japanese candlesticks. First, we create a simple language of Japanese candlesticks from the source OHLC data. Then, sentences of words are used to train the NLP Word2Vec model where training data classification also takes into account trading commissions. Finally, the model is used to predict trading actions. The proposed approach was compared to three trading models Buy & Hold, MA and MACD according to the yield achieved. We first evaluated Word2Vec on three shares of Apple, Microsoft and Coca-Cola where it outperformed the comparative models. Next we evaluated Word2Vec on stocks from Russell Top 50 Index where our Word2Vec method was also very successful in test phase and only fall behind the Buy & Hold method in validation phase. Word2Vec achieved positive results in all scenarios while the average yields of MA and MACD were still lower compared to Word2Vec.
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中文摘要：
股票预测一直是研究人员和投资者关注的领域，因为它可以带来巨大的经济收益。然而，股票预测可能是一项具有挑战性的任务，因为股票受到许多因素的影响，这些因素的影响随时间迅速变化。本文提出了一种结合NLP和日本烛台的股票趋势预测新方法（Word2Vec）。首先，我们从OHLC源数据创建一种简单的日语烛台语言。然后，使用单词句子来训练NLP Word2Vec模型，其中训练数据分类还考虑了交易佣金。最后，利用该模型对交易行为进行预测。根据获得的收益率，将所提出的方法与三种交易模式Buy&Hold、MA和MACD进行了比较。我们首先评估了Word2Vec在苹果、微软和可口可乐三家公司的股票上的表现，其表现优于比较模型。接下来，我们对罗素50强指数的股票进行了Word2Vec评估，我们的Word2Vec方法在测试阶段也非常成功，只是在验证阶段落后于买入持有法。Word2Vec在所有情况下都取得了积极的成果，而MA和MACD的平均产量仍然低于Word2Vec。
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Computational Engineering, Finance, and Science 计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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