How ReVol’s Return‑Volatility Normalization Reduces Distribution Shift in Stock Price Prediction

The paper introduces ReVol, a three‑stage framework that normalizes price features, uses an attention‑based estimator to recover return and volatility, and denormalizes predictions, demonstrating consistent improvements of over 0.03 in IC and 0.7 in Sharpe ratio across multiple time‑series models.

Deep LearningFinancial AIattention estimator

0 likes · 15 min read

How ReVol’s Return‑Volatility Normalization Reduces Distribution Shift in Stock Price Prediction

Bighead's Algorithm Notes

Nov 11, 2025 · Artificial Intelligence

A New Method: Dynamic Higher-Order Relations and Event-Driven Modeling for Stock Price Prediction

The article reviews a novel stock price prediction model that integrates a Hawkes‑process layer to capture sudden co‑movements and a dynamic hypergraph to represent high‑order relationships, detailing its formulation, training objective, extensive experiments on S&P 500 data, and superior performance over transformer, graph, and hypergraph baselines.

Financial AIHawkes processTime Series

0 likes · 12 min read

A New Method: Dynamic Higher-Order Relations and Event-Driven Modeling for Stock Price Prediction

Python Programming Learning Circle

Jul 14, 2022 · Artificial Intelligence

End‑to‑End Time Series Forecasting with LSTM in Python

This tutorial walks through loading Google stock data, preprocessing it with scaling, constructing past‑window features, building and tuning an LSTM model using GridSearchCV, evaluating predictions, and finally forecasting future values, all illustrated with complete Python code.

KerasLSTMPython

0 likes · 14 min read

End‑to‑End Time Series Forecasting with LSTM in Python