# Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction

## TL;DR

This paper introduces ReVol, a novel normalization method that mitigates distribution shifts in stock price data, combining return-volatility normalization with neural networks and geometric Brownian motion to improve prediction accuracy.

## Contribution

ReVol is the first method to explicitly address distributional discrepancies in stock data by normalizing features and integrating long-term and short-term modeling techniques.

## Key findings

- ReVol improves prediction metrics significantly across multiple datasets.
- It outperforms state-of-the-art models with an average IC increase of over 0.03.
- ReVol effectively reduces the impact of market anomalies on predictions.

## Abstract

How can we address distribution shifts in stock price data to improve stock price prediction accuracy? Stock price prediction has attracted attention from both academia and industry, driven by its potential to uncover complex market patterns and enhance decisionmaking. However, existing methods often fail to handle distribution shifts effectively, focusing on scaling or representation adaptation without fully addressing distributional discrepancies and shape misalignments between training and test data. We propose ReVol (Return-Volatility Normalization for Mitigating Distribution Shift in Stock Price Data), a robust method for stock price prediction that explicitly addresses the distribution shift problem. ReVol leverages three key strategies to mitigate these shifts: (1) normalizing price features to remove sample-specific characteristics, including return, volatility, and price scale, (2) employing an attention-based module to estimate these characteristics accurately, thereby reducing the influence of market anomalies, and (3) reintegrating the sample characteristics into the predictive process, restoring the traits lost during normalization. Additionally, ReVol combines geometric Brownian motion for long-term trend modeling with neural networks for short-term pattern recognition, unifying their complementary strengths. Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0.03 in IC and over 0.7 in SR across various settings.