Science – Page 49

Predicting the Fed: An AI System for Smarter Rate Forecasts

19.12.2025 by qfx

FedSight leverages a multi-agent artificial intelligence system to forecast the decisions of the Federal Open Market Committee, effectively modeling the complex interplay of factors influencing monetary policy.

Researchers have developed an AI-powered multi-agent system capable of forecasting Federal Funds target rates by analyzing a wide range of economic data.

The Quiet Signal of Market Moves

19.12.2025 by qfx

Order-flow entropy demonstrably predicts the magnitude of five-minute returns-declining from 8.14 to 3.75 basis points across quintiles-thereby forecasting volatility, though it provides no indication of return direction, as evidenced by an analysis of 828,907 second-resolution observations over 36 trading days, significantly differing from the unconditional mean of 5.29 basis points.

New research reveals how the complexity of trading activity can foreshadow the size of price swings, even without indicating which way they’ll go.

Pricing Options with a Speed Boost: Machine Learning Meets Fourier Transforms

19.12.2025 by qfx

$The relationship between the point at which a theoretical model is truncated and the resulting option prices demonstrates a fundamental limit to predictive power - as the model simplifies, its capacity to accurately reflect market value diminishes, mirroring the inevitable loss of information beyond the event horizon of any constructed theory, much like the formula $P = f(K, T, \sigma)$ becomes increasingly imprecise with omitted variables.$

A new framework leverages the power of machine learning and fast Fourier transforms to deliver significantly faster and more accurate option pricing compared to traditional methods.

Decoding the Market with Deep Learning

19.12.2025 by qfx

Return predictability significantly improves with the introduction of consensus learning, peaking at a $λ$ value between 0.3 and 0.4, and consistently outperforms benchmarks even at higher $λ$ values, while the accuracy of consensus variable approximation increases steadily with increasing $λ$.

A new framework combines the power of artificial intelligence with analyst insights to predict stock returns and offer economic transparency.

Beating the Market with Quantum-Boosted Prediction

19.12.2025 by qfx

A new hybrid approach combining classical machine learning with quantum-enhanced features significantly improves the accuracy of S&P 500 directional prediction.

Racing the Void: Why AI Struggles in High-Frequency Trading

19.12.2025 by qfx

Individual agent performance metrics, while superficially positive-such as agent Kimberly’s reported contribution of +$3.56-prove illusory when subjected to scrutiny, as the deduction of round-trip transaction fees (0.08%) reveals an underlying negative economic value, demonstrating how localized gains can mask systemic losses.

New research reveals that even cutting-edge artificial intelligence can be defeated by the inherent limitations of ultra-fast market dynamics.

When Algorithms Blink: Financial Machine Learning Under Attack

19.12.2025 by qfx

A new review reveals how subtle manipulations of data can destabilize financial models, impacting everything from risk assessment to fair lending.

When Social Media Moves Markets: Detecting Manipulation in the Age of Reddit

19.12.2025 by qfx

Market anomaly detection excels at identifying pronounced market disruptions, yet struggles with nuanced manipulation, while a more comprehensive approach-integrating social coordination, bot activity, and traditional market signals-demonstrates improved robustness and broader applicability in discerning manipulative behavior.

A new AI framework fuses social media sentiment with stock data to identify coordinated efforts to artificially inflate or deflate share prices.

The Bug-Hunting AI: Can Machines Reliably Recreate Deep Learning Errors?

19.12.2025 by qfx

RepGen demonstrates a capacity to rival DeepSeek-R1, suggesting a novel approach to code generation achieves comparable performance to established large language models.

A new approach uses artificial intelligence to automatically reproduce notoriously difficult-to-recreate bugs in deep learning models, paving the way for more robust and reliable AI systems.

Predicting Harvests Isn’t Enough: The Limits of Machine Learning in Agriculture

19.12.2025 by qfx

Model performance, assessed through SHAP values, demonstrates that feature importance varies significantly across machine learning models-specifically Random Forest and XGBoost-when predicting agricultural yields, yield gaps, yield gap ratios, and yield anomalies, highlighting the nuanced relationship between model efficacy and predictive accuracy.

New research reveals that high accuracy in crop yield prediction doesn’t always translate to reliable performance in changing conditions, raising concerns about the interpretability of model insights.