TL;DR
This paper finds that the hybrid model yields higher prediction accuracy and smaller errors than the single model by comparing the results of both models.
Stock market forecasting is difficult due to its nonlinear nature, volatility, time dependencies, and fast-changing sentiments of investors. Conventional models of statistical nature offer an important benchmark but might be insufficient in capturing the complexity of market dynamics. The objective of this research is...
Over the years, individuals have been investing in the stock market as a source of passive income. However, such investments are associated with high risk due to the unpredictable behaviour of stock prices. To address this issue, we implemented predictive models to provide investors with stock price predictions to faci...
This study investigates the impact of market liquidity and macroeconomic variables on the forecasting performance of deep learning models in financial markets. The primary objective is to forecast price movements for ten stocks listed on the BIST 30 index using a single-layer Long Short-Term Memory (LSTM) model and ide...
Objective: The main objective of this paper is to compare ARIMA, ARCH, GARCH, RNN, and LSTM forecasting models to determine the best approach for forecasting the adjusted prices. Methodology: Daily stock price data covering the period from April 2015 to March 2025 were obtained from the National Stock Exchange to cond...
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