A Modified Levy Jump-Diffusion Model Based on Market Sentiment Memory for Online Jump Prediction
Statistical Finance
2017-09-13 v1 Computational Engineering, Finance, and Science
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.
Keywords
Cite
@article{arxiv.1709.03611,
title = {A Modified Levy Jump-Diffusion Model Based on Market Sentiment Memory for Online Jump Prediction},
author = {Zheqing Zhu and Jian-guo Liu and Lei Li},
journal= {arXiv preprint arXiv:1709.03611},
year = {2017}
}