Abstract:
The performance of stock markets is influenced by various factors, and
understanding these dynamics is crucial for investors and policymakers. This
research centers on Sri Lanka's capital market, with particular attention to the
Colombo Stock Exchange (CSE), and analyzes the influence of the Standard &
Poor's Sri Lanka 20 (S&P SL 20) index, which represents the top 20 leading
companies listed on the CSE. Specifically, the primary objective of this research
is to compare the effectiveness of traditional time series models with machine
learning and deep learning models in predicting the S&P SL 20 index. These
models, developed using computerized programs, are evaluated based on their
predictive performance within the context of the CSE. The study will use daily
S&P SL 20 stock index data obtained from the CSE data library enclosing the
period 2010 to 2018. This methodology compares Autoregressive Integrated
Moving Average (ARIMA), which is a traditional time series model and Long
Short-Term Memory (LSTM), which is a recurrent neural network model. In this
research, the Python language will be employed for analysis. The ARIMA and
LSTM models are evaluated using three performance metrics: MAE, MAPE, and
RMSE. ARIMA slightly outperforms LSTM in MAE (233.96 vs. 249.37) and
RMSE (269.57 vs. 269.86), which essentially indicates better overall accuracy in
absolute and squared error terms. However, LSTM achieves a marginally lower
MAPE (6.96% vs. 7.04%), showing fewer relative percentage errors. All in all,
both models offer similar performances, with minor differences depending on the
metric. Both ARIMA and LSTM show strengths in predicting the S&P SL 20
Index. ARIMA excels in minimizing absolute errors (MAE), ideal for linear
trends. LSTM’s lower MAPE highlights its ability to capture nonlinear patterns.
With similar RMSE values, both handle overall errors well. ARIMA’s constant
predictions in some periods reveal its limitations with limited or weak trend data.
The choice depends on the forecasting goal: ARIMA for linear trends and LSTM
for complex patterns.
