Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. In this work, we provide statistical evidence for the result promulgation influence on the public pharma market value. Whereas most works focus on retrospective impact analysis, the present research aims to predict the numerical values of announcement-induced changes in stock prices. For this purpose, we develop a pipeline that includes a BERT-based model for extracting sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. The challenge of the problem lies in inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to the negative news. Moreover, such phenomenon as the drop in stocks after the positive announcements affirms the counterintuitiveness of the price behavior. Importantly, we discover two crucial factors that should be considered while working within a predictive framework. The first factor is the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of small drug diversification. The second one is the network effect of the events related to the same company or nosology. All findings and insights are gained on the basis of one of the biggest FDA (the Food and Drug Administration) announcement datasets, consisting of 5436 clinical trial announcements from 681 companies over the last five years.
翻译:制药公司在严格监管和高度风险的环境中运作,这种环境中单滑会导致严重的财务影响。因此,临床试验结果的公布往往决定未来事件的方向,因此受到公众的密切监测。在这项工作中,我们为结果的公布对公共药店市场价值的影响提供统计证据。大多数工作的重点是追溯性影响分析,而目前研究的目的是预测宣布导致的股票价格变化的数字值。为此目的,我们开发了一个管道,其中包括基于BERT的提取情绪对立公告模型、预测预期回报的时空变异变变变器、记录事件关系的图解变异网络和预测价格变化的梯度加速。问题的挑战在于对正和负面公告的内在不同反应模式,反映在对负面消息的更强烈和更明显的反应中。此外,在积极宣布后股票下降等现象证实了价格行为的反直觉性。重要的是,我们发现在预测框架内工作时应考虑的两个关键因素。第一个因素是预测事件变异变异的图网络的药物组合规模,即公司对54年的最大变异数据的预测,表明公司的最大变异性,最后一个是公司最大的变异性的网络。