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Predicting Healthcare Mutual Fund Performance Using Deep Learning and Linear Regression

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  • Anuwat Boonprasope

    (Program in Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
    Supply Chain and Engineering Management Research Unit, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Korrakot Yaibuathet Tippayawong

    (Supply Chain and Engineering Management Research Unit, Chiang Mai University, Chiang Mai 50200, Thailand
    Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

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.

Suggested Citation

  • Anuwat Boonprasope & Korrakot Yaibuathet Tippayawong, 2024. "Predicting Healthcare Mutual Fund Performance Using Deep Learning and Linear Regression," IJFS, MDPI, vol. 12(1), pages 1-21, February.
    • Handle: RePEc:gam:jijfss:v:12:y:2024:i:1:p:23-:d:1348442
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    References listed on IDEAS

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    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. Marcus Dillender & Andrew I. Friedson & Cong T. Gian & Kosali I. Simon, 2021. "Is Healthcare Employment Resilient and “Recession Proof”?," NBER Working Papers 29287, National Bureau of Economic Research, Inc.
    3. Eric C. Lin, 2018. "The Effect Of Dow Jones Industrial Average Index Component Changes On Stock Returns And Trading Volumes," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 12(1), pages 81-92.
    4. repec:ibf:ijbfre:v:11:y:2017:i:2:p:81-92 is not listed on IDEAS
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