Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique

ArXiv ID: 2507.01964 “View on arXiv”

Authors: Adebola K. Ojo, Ifechukwude Jude Okafor

Abstract

Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. The model was evaluated using performance metrics and compared with other deep learning models like Artificial and Convolutional Neural Networks (CNN). The experimental results showed that the LSTM model can predict future stock market prices and returns with over 90% accuracy when trained with a reliable dataset. The study concludes that LSTM models can be useful in predicting financial time-series-related problems if well-trained. Future studies should explore combining LSTM models with other deep learning techniques like CNN to create hybrid models that mitigate the risks associated with relying on a single model for future equity stock predictions.

Keywords: LSTM, Stock Prediction, Deep Learning, Time Series Analysis, Artificial Neural Networks, Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 3.5/10
• Quadrant: Philosophers
• Why: The paper uses standard LSTM architecture without advanced mathematical derivations, fitting a low complexity score; while it reports metrics like accuracy, the lack of specific backtesting details, cross-validation, or live performance data results in low empirical rigor.

  flowchart TD
    A[""Research Goal:
    Predict Nigerian Equity Stock Returns""] --> B[""Data Source:
    Historical NSE Dataset""]
    B --> C[""Preprocessing:
    Data Cleaning & Normalization""]
    C --> D[""Modeling Technique:
    LSTM (Long Short-Term Memory)""]
    D --> E[""Evaluation & Comparison:
    Metrics vs. CNN & ANN Models""]
    E --> F[""Outcome:
    >90% Prediction Accuracy""]
    F --> G[""Conclusion:
    LSTM is effective for financial time-series forecasting""]