Stock Price Forecasting Using a Time-Series Long Short-Term Memory Model
DOI:
https://doi.org/10.37075/FABA.2025.2.13Keywords:
Stock price, LSTM, Prediction accuracy, Python-based implementationAbstract
Purpose: This study aims to introduce and evaluate a novel application of Long Short-Term Memory (LSTM) networks for stock price forecasting by integrating multi-stock comparative analysis across different volatility regimes, addressing a key gap in the literature regarding model robustness and generalizability.
Design/Methodology/Approach: Using a time-series covering 2019–2023, the study implements an LSTM model within a Python-based framework. The model is trained on data from 01/01/2022 to 12/31/2023 and tested on 01/01/2019 to 12/31/2021. Mean squared error is employed as the primary evaluation metric to assess forecasting accuracy across heterogeneous stocks.
Findings: Empirical results show that the LSTM model effectively captures complex temporal dependencies and nonlinear patterns in financial time series, producing reliable stock price forecasts. It outperforms conventional time-series benchmarks and demonstrates strong adaptability across stocks with differing volatility characteristics.
Practical Implications: For investors, LSTM-based forecasts provide deeper insights into risk–return dynamics and support more informed, data-driven investment strategies. For policymakers, the results highlight the increasing importance of machine learning tools in enhancing transparency, stability, and efficiency in financial markets.
Originality/Value: The study offers a unique contribution by demonstrating that a unified LSTM framework can generalize across multiple stocks and volatility regimes, establishing both its theoretical relevance and practical utility in algorithmic trading and financial forecasting.
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