E-TRENDS: Enhanced LSTM Trend Forecasting for Equities

2026-03-15

AI Summary

This study addresses the challenge of accurately capturing short-term stock trend dynamics in non-stationary and nonlinear markets, where traditional trend-following strategies often falter. The authors propose an LSTM-based predictive framework that incorporates time series differencing to reduce both bias and variance in forecasts, specifically targeting the next-day trend differential (Δt) for the top 30 constituents of the S&P 500. Evaluated across multiple market cycles from 2005 to 2025, the proposed method is systematically benchmarked against OLS, Ridge, Lasso, and LightGBM models. Empirical results demonstrate that the LSTM-driven approach yields significantly higher economic returns in portfolio simulations, consistently outperforming all baseline models in terms of overall profit-and-loss (PNL) metrics, thereby confirming its effectiveness and robustness in dynamic market environments.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Trend-following strategies underpin many systematic trading approaches yet struggle under nonstationary and nonlinear market regimes. We propose an LSTM-based framework to forecast next-day trend differences ($Δ_t$) for the top 30 S\&P 500 equities, validated across market cycles (2005--2025). Key contributions include: (i) formal proof of bias-variance reduction via differencing, (ii) exhaustive empirical benchmarks against OLS, Ridge, and Lasso, (iii) portfolio simulations confirming economic gains in terms of overall PNL compared to other models like OLS, Ridge, Lasso or LightGBM Regressor

Problem

• trend forecasting
• nonstationary markets
• nonlinear market regimes
• equity prediction
• systematic trading

Innovation

• LSTM
• trend forecasting
• bias-variance reduction
• differencing
• portfolio simulation