Akademik Veri Yönetim Sistemi
Journal of Data Application, cilt.1, sa.1, ss.83-94, 2023 (Hakemli Dergi)
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