Financial Performance Forecasting Using prophet model: An application in BIST manufacturing sector
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| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| Kongre Adı | Pressacademia | ||
| Kongre Tarihi | / | ||
| Basıldığı Ülke | Basıldığı Şehir | ||
| Bildiri Linki | https://www.academia.edu/download/86922108/33.pdf | ||
| UAK Araştırma Alanları |
Uluslararası Muhasebe
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| Özet |
| Purpose-Through processing big data, companies have started to prefer artificial intelligence methods that can reach results faster than classical statistical methods. For this reason, financial performance forecasting studies with artificial intelligence methods have been increasing in recent years. The purpose of this study is to contribute to the gap in the field of financial performance forecasting with the Facebook Prophet model which has not been applied before although there are other time series models in the literature. Methodology-The study employs Facebook Prophet artificial learning model by choosing the net profit/loss as target variable for financial performance forecasting on Python program with the data of 173 companies in the BIST Manufacturing Sector between 2009 and 2020, obtained from the Public Disclosure Platform.Findings-After training the 46 periods of the data set, performance matrices of MSE, RMSE and MAPE were measured for the last 2 periods as MSE values between 0.0185 and 25.0147, RMSE values between 0.1361 and 5.0015 and MAPE values between 0.1002 and 4.6634. In addition to values close to zero, there are also values that move away from zero. The analysis reveals that besides successful predictions, there are also unsuccessful predictions.Conclusion-It may be concluded that the Facebook Prophet method will save companies time without requiring much effort. To further improve accuracy and performance, creating a mixed artificial intelligence model by leveraging the strengths of multiple models is recommended for further studies. |
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