New Model Merges Deep Learning and Analyst Consensus to Boost Asset Pricing Accuracy

Global: New Model Merges Deep Learning and Analyst Consensus to Boost Asset Pricing Accuracy

Researchers have unveiled the Consensus‑Bottleneck Asset Pricing Model, or CB‑APM, which combines deep‑learning techniques with aggregated analyst consensus to improve the predictive performance of asset pricing models. In tests covering annual horizons, the model delivered an out‑of‑sample R² that surpasses comparable unconstrained benchmarks and achieved an annualized Sharpe ratio of 1.44.

Model Architecture Integrates Human Beliefs

The CB‑APM embeds the collective forecasts of professional analysts as a structural “bottleneck,” treating these consensus estimates as a sufficient statistic for the market’s high‑dimensional information set. This design imposes a regularizing constraint that aligns machine‑generated signals with established expert judgment.

Interpretability‑Accuracy Amplification

According to the authors, the structural bottleneck produces a pronounced “interpretability‑accuracy amplification effect,” whereby the model’s transparency does not compromise, and may even enhance, forecasting accuracy for yearly returns.

Robust Portfolio Performance

Portfolios constructed on the basis of CB‑APM forecasts exhibit a strong monotonic return gradient across deciles, indicating consistent outperformance. The reported Sharpe ratio of 1.44 remained stable across differing macroeconomic regimes, suggesting resilience to market conditions.

Insights Beyond Traditional Factor Models

Pricing diagnostics revealed that the learned consensus captures priced variation only partially spanned by canonical factor models, highlighting structured risk heterogeneity that standard linear approaches tend to miss.

Implications for Future Research

The findings imply that anchoring machine intelligence to expert belief formation can serve not only as a transparency tool but also as a catalyst for identifying new dimensions of belief‑driven risk premiums. The authors propose further validation across other asset classes and time frames.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.