Time Series Analysis of Stock Index Forecasting

Yue Zheng

Diplomová práce

Anotace:

This thesis investigates stock market forecasting by comparing traditional time series models (ARIMA-GARCH), deep learning models (LSTM), and a hybrid ARIMA-LSTM model, focusing on the S&P 500, Euro Stoxx 50, and CSI 300 indices. Accuracy is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), with findings indicating varying model effectiveness based on specific market data characteristics. The ARIMA-LSTM hybrid model excels in markets with strong autocorrelation, such as the S&P 500, demonstrating its ability to capture both linear and non-linear dynamics. In contrast, the single LSTM model is more effective in markets like the Euro Stoxx 50 and CSI 300, where weaker autocorrelation is present.

Abstract:

This thesis investigates stock market forecasting by comparing traditional time series models (ARIMA-GARCH), deep learning models (LSTM), and a hybrid ARIMA-LSTM model, focusing on the S&P 500, Euro Stoxx 50, and CSI 300 indices. Accuracy is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), with findings indicating varying model effectiveness based on specific market data characteristics. The ARIMA-LSTM hybrid model excels in markets with strong autocorrelation, such as the S&P 500, demonstrating its ability to capture both linear and non-linear dynamics. In contrast, the single LSTM model is more effective in markets like the Euro Stoxx 50 and CSI 300, where weaker autocorrelation is present.

Klíčová slova

time series, stock index, ARIMA, LSTM

Keywords

ARIMA, LSTM, stock index, time series

Jazyk práce: angličtina

Datum vytvoření / odevzdání či podání práce: 4. 12. 2023

Obhajoba závěrečné práce

• Obhajoba proběhla 29. 1. 2024
• Vedoucí: Ondřej Šimpach
• Oponent: Karel Helman

Vysoká škola ekonomická v Praze

Magisterský studijní program / obor:
Economic Data Analysis / Data Analysis and Modeling