Abstract
Accurate forecasting models are usually large, expensive to update online, and fixed in architecture once trained. We apply ONE-NAS, an online neuroevolutionary architecture search that evolves a population of small recurrent networks as each window of data arrives, to daily cross-sectional stock return prediction, and pilot it on a host and endpoint pipeline: the host runs the search and ships each generation's champion genomes over TCP/IP to a Raspberry Pi 4B, which predicts online. On the Pi a single champion predicts a 50-stock window in 24.6~ms and the ensemble of 40 island champions in 556~ms, far inside the daily decision cycle. On four panels of US mid-cap equities over 2022--2024, reading the population as a rank-mean ensemble of island champions returns $+27.5\%$ net of realised transaction costs, against $+11.3$ to $+14.8\%$ for online LSTM, online GRU and monthly-retrained LSTM baselines and $+4.5\%$ for the single best genome used in prior ONE-NAS work.
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- Green open access
Cite this article
APA 7
Chang, J., & Lyu, Z. (2026). Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction. https://omanscience.com/en/articles/evolve-on-the-host-predict-on-the-edge-deploying-online-neuroevolutionary-architecture-search-for-cross-sectional-stock-return-prediction
MLA 9
Chang, Jonathan, and Zimeng Lyu. "Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction." https://omanscience.com/en/articles/evolve-on-the-host-predict-on-the-edge-deploying-online-neuroevolutionary-architecture-search-for-cross-sectional-stock-return-prediction.
Chicago (author–date)
Chang, Jonathan, and Zimeng Lyu. 2026. "Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction." https://omanscience.com/en/articles/evolve-on-the-host-predict-on-the-edge-deploying-online-neuroevolutionary-architecture-search-for-cross-sectional-stock-return-prediction.
Harvard
Chang, J. and Lyu, Z. (2026) 'Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction', Available at: https://omanscience.com/en/articles/evolve-on-the-host-predict-on-the-edge-deploying-online-neuroevolutionary-architecture-search-for-cross-sectional-stock-return-prediction.
Vancouver
Chang J, Lyu Z. Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction. https://omanscience.com/en/articles/evolve-on-the-host-predict-on-the-edge-deploying-online-neuroevolutionary-architecture-search-for-cross-sectional-stock-return-prediction
IEEE
J. Chang, and Z. Lyu, "Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction," https://omanscience.com/en/articles/evolve-on-the-host-predict-on-the-edge-deploying-online-neuroevolutionary-architecture-search-for-cross-sectional-stock-return-prediction.