Abstract
References
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A Comparative Assessment of Frequentist Forecasting Models: Evidence from the S&P 500 Pharmaceuticals Index
Abstract
This paper compares three forecasting methods, the autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), and neural network autoregression (NNAR) methods, using the S&P 500 Pharmaceuticals Index. The objective is to identify the most accurate model based on the mean average forecasting error (MAFE). The results consistently show the NNAR model to outperform ARIMA and GARCH and to exhibit a significantly lower MAFE. The existing literature presents conflicting findings on forecasting model accuracy for stock indexes. While studies have explored various models, no universally applicable model exists. Therefore, a comparative analysis is crucial. The methodology includes data collection and cleaning, exploratory analysis, and model building. The daily closing prices of pharmaceutical stocks from the S&P 500 serve as the dataset. The exploratory analysis reveals an upward trend and increasing heteroscedasticity in the pharmaceuticals index, with the unit root tests confirming non-stationarity. To address this, the dataset has been transformed into stationary returns using logarithmic and differencing techniques. Model building involves splitting the dataset into training and test sets. The training set determines the best-fit models for each method. The models are then compared using MAFE on the test set, with the model possessing the lowest MAFE being considered the best. The findings provide insights into model accuracy for pharmaceutical industry indexes, aiding investor predictions, with the comparative analysis emphasizing tailored forecasting models for specific indexes and datasets.
Keywords
Forecasting Accuracy Pharmaceutical Industry Indexes S&P 500 ARIMA GARCH NNAR Comparative Analysis
References
- Harris J. (2018). The opportunities and challenges of data analytics in health care. Brookings. https://www.brookings. edu/research/the-opportunities-and-challenges-of-data-analytics-in-health-care/ google scholar
- Islam, M. R., & Nguyen, N. (2020). Comparison of Financial Models for Stock Price Prediction. Journal of Risk and Financial Management, 13(8), 181. https://doi.org/10.3390/JRFM13080181 google scholar
- Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems withApplications, 37(1), 479-489. https://doi.Org/10.1016/J.ESWA.2009.05.044 google scholar
- Lin, Z. (2018). Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. google scholar
- Future Generation Computer Systems, 79, 960-972. https://doi.org/10.1016/J.FUTURE.2017.08.033 google scholar
- Mahajan, V., Thakan, S., & Malik, A. (2022). Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models. Economies, 10(5), 102. https://doi.org/10.3390/ECONOMIES10050102 google scholar
- Merh, N., Prakash Saxena, V., & Raj Pardasani, K. (2010). A comparison between Hybrid Approaches of ANN and ARIMA for Indian Stock Trend Forecasting Computational Aspects of I-Function View project. https://www. researchgate.net/publication/45602108 google scholar
- Mikulic, M. (2022). Market share top pharma companies Rx drugs sales globally 2026 | Statista. Statistica. https:// www.statista.com/statistics/309425/prescription-drugs-market-shares-by-top-companies-globally/ google scholar
- Niaki, S. T. A., & Hoseinzade, S. (2013). Forecasting S&P 500 index using artificial neural networks and design of experiments. Journal of Industrial Engineering International, 9(1). https://doi.org/10.1186/2251-712X-9-1 google scholar
- Niu, H., Xu, K., & Wang, W. (2020). A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network. AppliedIntelligence, 50(12), 4296-4309. https://doi.org/10.1007/S10489-020-01814-0 google scholar
- OECD. (2021). Sustainable and Resilient Finance OECD Business and Finance Outlook 2020 9HSTCQE*diefgj+ Artificial Intelligence, Machine Learning and Big Data in Finance Opportunities, Challenges and Implications for Policy Makers. OECD Journal. google scholar
- Sean Ross. (2021). What Percentage of the Global Economy Is the Financial Services Sector? Investopedia. https:// www.investopedia.com/ask/answers/030515/what-percentage-global-economy-comprised-financial-services-sector.asp google scholar
- Sharaff, A., & Choudhary, M. (2018). Comparative Analysis of Various Stock Prediction Techniques. Proceedings of the 2nd International Conference on Trends in Electronics and Informatics, ICOEI 2018, 735-738. https:// doi.org/10.1109/ICOEI.2018.8553825 google scholar
- Simpkin, V., Namubiru-Mwaura, E., Clarke, L., & Mossialos, E. (2019). Investing in health R&D: where we are, what limits us, and how to make progress in Africa. BMJ Global Health, 4(2). https://doi.org/10.1136/ BMJGH-2018-001047 google scholar
- Sunarya, I. W. (2019). Modelling and Forecasting Stock Market Volatility of Nasdaq Composite Index. Economic and Accounting Journal, 2(3), 181-189. https://doi.org/10.32493/EAJ.V2I3.Y2019.P181-189 google scholar
- Tofallis, C. (2017). A better measure of relative prediction accuracy for model selection and model estimation. Journal of the Operational Research Society, 66(8), 1352-1362. https://doi.org/10.1057/JORS.2014.103 google scholar
- Yadav, S., & Sharma, K. P. (2018). Statistical Analysis and Forecasting Models for Stock Market. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 117-121. https://doi. org/10.1109/ICSCCC.2018.8703324 google scholar
Details
| Primary Language | English |
|---|---|
| Subjects | Data Management and Data Science (Other) |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | August 15, 2023 |
| Published in Issue | Year 2023 Issue: 1 |
