A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID ()
ABSTRACT
Predicting stock price movement direction is a challenging
problem influenced by different factors and capricious events. The
conventional stock price prediction machine learning
models heavily rely on the internal financial features, especially the stock
price history. However, there are many outside-of-company features that deeply interact with the companies’
stock price performance, especially during
the COVID period. In this study, we selected 9 COVID vaccine companies and collected their relevant features
over the past 20 months. We added handcrafted external information, including
COVID-related statistics and company-specific vaccine progress information. We implemented,
evaluated, and compared several machine learning
models, including Multilayer Perceptron Neural Networks with logistic regression
and decision trees with boosting and bagging algorithms. The results suggest that
the application of feature engineering and data mining techniques can effectively
enhance the performance of models predicting stock price movement during the COVID period. The results show that COVID-related
handcrafted features help to increase the model prediction accuracy by 7.3%
and AUROC by 6.5% on average. Further exploration
showed that with data selection the decision tree model with gradient,
boosting algorithm achieved 70% in AUROC and 66% in the accuracy.
Share and Cite:
He, B. , Han, W. and Hon, S. (2022) A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID.
Journal of Data Analysis and Information Processing,
10, 1-21. doi:
10.4236/jdaip.2022.101001.