Using SVM and ARIMA Models in Stock Price Forecasting of Medical Companies

DOI

10.2991/978-94-6463-748-9_90How to use a DOI?

Keywords

Stock price forecasting; Support Vector Machine; ARIMA model; Machine learning; Time series analysis

Abstract

Stock price forecasting is crucial for investors and financial analysts, especially in the medical sector, which faces rapid technological advancements and fluctuating market conditions. However, accurately predicting stock prices remains a challenge, particularly for companies involved in vaccine development, where market volatility is high and often driven by external factors. Existing literature mainly focuses on linear models, while few studies have explored hybrid models combining machine learning and time series forecasting. This study explores the prediction of Novavax’s stock price using Autoregressive Integrated Moving Average models and Support Vector Machine. The research analyzes daily stock price data from Jan 2018 to the present, comparing individual and hybrid model performances. Some results have been acquired, which show a high sensitivity of 89.01% and an accuracy of 78.29%, while ARIMA struggled with a high Mean Absolute Percentage Error (MAPE) of 3853.29%, indicating limited short-term prediction ability. Combining SVM and ARIMA could offer better performance by leveraging the strengths of both models. The findings point to the need for further improving the model and suggest that integrating deep learning methods with real-time data could significantly enhance prediction accuracy, particularly in the financial field.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Fuwenjie Hao
PY  - 2025
DA  - 2025/07/03
TI  - Using SVM and ARIMA Models in Stock Price Forecasting of Medical Companies
BT  - Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025)
PB  - Atlantis Press
SP  - 816
EP  - 828
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-748-9_90
DO  - 10.2991/978-94-6463-748-9_90
ID  - Hao2025
ER  -