Predicting Healthcare Mutual Fund Performance Using Deep Learning and Linear Regression

Anuwat Boonprasope and Korrakot Yaibuathet Tippayawong ()
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Anuwat Boonprasope: Program in Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
Korrakot Yaibuathet Tippayawong: Supply Chain and Engineering Management Research Unit, Chiang Mai University, Chiang Mai 50200, Thailand

IJFS, 2024, vol. 12, issue 1, 1-21

Abstract: Following the COVID-19 pandemic, the healthcare sector has emerged as a resilient and profitable domain amidst market fluctuations. Consequently, investing in healthcare securities, particularly through mutual funds, has gained traction. Existing research on predicting future prices of healthcare securities has been predominantly reliant on historical trading data, limiting predictive accuracy and scope. This study aims to overcome these constraints by integrating a diverse set of twelve external factors spanning economic, industrial, and company-specific domains to enhance predictive models. Employing Long Short-Term Memory (LSTM) and Multiple Linear Regression (MLR) techniques, the study evaluates the effectiveness of this multifaceted approach. Results indicate that incorporating various influencing factors beyond historical data significantly improves price prediction accuracy. Moreover, the utilization of LSTM alongside this comprehensive dataset yields comparable predictive outcomes to those obtained solely from historical data. Thus, this study highlights the potential of leveraging diverse external factors for more robust forecasting of mutual fund prices within the healthcare sector.

Keywords: financial modeling; mutual fund performance; multiple linear regression; deep learning; LSTM (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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