E-TRENDS: Enhanced LSTM Trend Forecasting for Equities

TL;DR

This paper introduces E-TRENDS, an LSTM-based framework for forecasting stock market trends, demonstrating its effectiveness through theoretical analysis and extensive empirical validation across two decades.

Contribution

It presents a novel LSTM-based approach with bias-variance reduction proof and comprehensive benchmarks, improving trend prediction in equities.

Findings

LSTM model outperforms traditional regression methods.

Differencing reduces bias-variance in trend forecasting.

Portfolio simulations show increased PNL with the proposed method.

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 ($\Delta_t$) 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