A Review of LSTM-Based Stock Prediction for Long-Short and Market-Neutral Portfolio Construction

Siqian Li

Abstract

In recent years, deep learning algorithm models have gradually been applied to fields such as stock prediction, asset pricing, and portfolio management. Among them, Long Short-Term Memory (LSTM) has been widely used to predict stock prices, returns, and directional movements due to its ability to handle long-term dependencies in time series. Compared to traditional linear financial models, LSTM can better capture non-linear correlations and dynamic fluctuations embedded in financial data, which explains its significant attention in financial forecasting research. However, from a practical investment perspective, a lower prediction error does not necessarily translate into better portfolio performance. This paper reviews the application of LSTM in stock prediction, long-short strategies, and market-neutral portfolio construction. We first introduce the theoretical foundations of LSTM, alpha signals, long-short strategies, and market-neutral portfolios. Subsequently, it delineates the complete research pipeline spanning stock price forecasting, stock alpha ranking and portfolio construction, before analyzing the practical value, existing limitations and prospective research directions of LSTM-based frameworks. Core Viewpoint: The optimal positioning of LSTM is not to predict absolute stock prices, but to serve as an alpha signal generation tool for stock ranking and portfolio optimization. Future research should place more emphasis on point-in-time data, out-of-sample validation, risk neutralization, transaction cost modeling, and comprehensive investment workflow design to improve the usability of LSTM strategies in real-world trading environments.

Source: semanticscholar · PDF

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