Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations, and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.
翻译:为公司建立预测模型往往依赖使用同一工业部门公司的历史数据的推论,然而,公司在相关预测问题中应加以利用的不同层面是相似的。对于大型、复杂的组织来说尤其如此,因为单一行业可能没有很好的定义,而且没有明确的同行。为了能够利用公司信息进行不同层面的预测,我们创建了公司股票的嵌入,Stock2Vec, 这很容易被添加到适用于具有相关股票价格的公司的任何预测模型中。我们描述了从股票价格波动中产生这种丰富的矢量代表的过程,并描述其含义。然后我们进行全面实验,评估应用机器学习问题在不同商业环境中的嵌入情况。我们的实验结果表明,Stock2Vec嵌入的四项特征可以很容易地增加现有的跨公司模型,并加强跨公司的预测。