E-TRENDS: Enhanced LSTM Trend Forecasting for Equities
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911518061232128 |
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| author | Buchanan, Harris Benhamou, Eric |
| author_facet | Buchanan, Harris Benhamou, Eric |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14453 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | E-TRENDS: Enhanced LSTM Trend Forecasting for Equities Buchanan, Harris Benhamou, Eric Trading and Market Microstructure 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 |
| title | E-TRENDS: Enhanced LSTM Trend Forecasting for Equities |
| topic | Trading and Market Microstructure |
| url | https://arxiv.org/abs/2603.14453 |