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In this paper, we propose a modified Levy jump diffusion model with marketsentiment memory for stock prices, where the market sentiment comes from datamining implementation using Tweets on Twitter. We take the market sentimentprocess, which has memory, as the signal of Levy jumps in the stock price. Anonline 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 inidentifying asset return trends.

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