Paper: arXiv 2509.14401

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.

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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"]

Cited References (16)

  1. Long Short-Term Memory (1997)
  2. Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions (2018)
  3. Predicting Stock Prices in the Pakistan Market Using Machine Learning and Technical Indicators (2024)
  4. Stock Market Trend Prediction Using Deep Learning (2025)
  5. Study on the Pakistan stock market using a new stock crisis prediction method (2022)
  6. Stock Analysis for New Investors Using Machine Learning (2022) — link
  7. Testing Weak Form Efficiency for Indian Stock Markets (2006) — link
  8. Long-run and Short-run Relationship Between Macroeconomic Variables and Stock Prices in Pakistan: The Case of Lahore Stock Exchange (2009) — link
and 8 more
  1. The Behaviour of Stock Returns in an Emerging Market: A Case Study of Pakistan [with Comments] (1993) — link
  2. Comparative Risk Analysis and Price Prediction of Corporate Shares Using Deep Learning Models Like LSTM and Machine Learning Models (2024) — link
  3. A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model (2023) — link
  4. Predictive Modeling for the Moroccan Financial Market: A Nonlinear Time Series and Deep Learning Approach (2025) — link
  5. Enhancing Financial Time Series Forecasting: A Comparative Study of Discrete Wavelet Transform and LSTM Models for Selected Global Indices (2025) — link
  6. Financial Time Series Models—Comprehensive Review of Deep Learning Approaches and Practical Recommendations (2023) — link
  7. Efficient Capital Markets: A Review of Theory and Empirical Work (1970) — link
  8. Analysis of Financial Time Series (2010)

0 of 16 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.