Predictive Performance of LSTM Networks on Sectoral Stocks in an Emerging Market: A Case Study of the Pakistan Stock Exchange

ArXiv ID: 2509.14401 “View on arXiv”

Authors: Ahad Yaqoob, Syed M. Abdullah

Abstract

The application of deep learning models for stock price forecasting in emerging markets remains underexplored despite their potential to capture complex temporal dependencies. This study develops and evaluates a Long Short-Term Memory (LSTM) network model for predicting the closing prices of ten major stocks across diverse sectors of the Pakistan Stock Exchange (PSX). Utilizing historical OHLCV data and an extensive set of engineered technical indicators, we trained and validated the model on a multi-year dataset. Our results demonstrate strong predictive performance ($R^2 > 0.87$) for stocks in stable, high-liquidity sectors such as power generation, cement, and fertilizers. Conversely, stocks characterized by high volatility, low liquidity, or sensitivity to external shocks (e.g., global oil prices) presented significant forecasting challenges. The study provides a replicable framework for LSTM-based forecasting in data-scarce emerging markets and discusses implications for investors and future research.

Keywords: LSTM, stock price forecasting, technical indicators, emerging markets, time series analysis, Equities

Complexity vs Empirical Score

• Math Complexity: 5.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper uses standard deep learning architectures (LSTM) with moderate mathematical complexity, but demonstrates high empirical rigor through detailed data preprocessing, feature engineering with explicit code, and backtest-ready trading strategy analysis on real emerging market data.

  flowchart TD
    A["Research Goal<br>Forecast PSX Stock Prices<br>using LSTM Networks"] --> B["Data Collection & Preparation<br>OHLCV + Technical Indicators"]
    B --> C["LSTM Model Architecture<br>Input: Lagged Features<br>Output: Next Day Closing Price"]
    C --> D["Training & Validation<br>Multi-year Historical Data"]
    D --> E["Performance Evaluation<br>R², RMSE, MAE Metrics"]
    E --> F{"Outcome"}
    F --> G["High Accuracy (R² > 0.87)<br>Stable, High-Liquidity Sectors<br>(Power, Cement, Fertilizers)"]
    F --> H["Challenging Forecasts<br>High Volatility, Low Liquidity<br>Oil-Price Sensitive Sectors"]