A Modified Levy Jump-Diffusion Model Based on Market Sentiment Memory for Online Jump Prediction

TL;DR

This paper introduces a modified Levy jump-diffusion model incorporating market sentiment memory derived from Twitter data, combined with an online UKF-based algorithm for real-time stock jump prediction, demonstrating effective trend identification.

Contribution

It presents a novel integration of sentiment memory into Levy jump modeling and an online learning algorithm for improved jump prediction accuracy.

Findings

Effective identification of asset return trends

Enhanced jump prediction performance

Successful incorporation of social media sentiment data

Abstract

In this paper, we propose a modified Levy jump diffusion model with market sentiment memory for stock prices, where the market sentiment comes from data mining implementation using Tweets on Twitter. We take the market sentiment process, which has memory, as the signal of Levy jumps in the stock price. An online learning and optimization algorithm with the Unscented Kalman filter (UKF) is then proposed to learn the memory and to predict possible price jumps. Experiments show that the algorithm provides a relatively good performance in identifying asset return trends.