A Dyadic Particle Filter for Price Prediction

Abstract

The most difficult task in financial forecasting is the accurate price prediction based on previous values. Two cases are studied: stock price prediction and flight price prediction. A dyadic particle filter is proposed that is based on sequential importance resampling. This dyadic particle filter captures the dynamic evolution of a pair of latent vectors. In stock price prediction, one latent vector is defined for each stock. This latent vector is paired with a market segment latent vector introduced for each group of companies of the same category. Both latent vectors capture the hidden information of the stock market and reinforce the state estimation procedure. This hidden information influences strongly the performance of the particle filter, yielding more accurate prediction of stock prices than the state-of-the-art techniques. For flight price prediction, the pair of latent vectors corresponds to route and destination, respectively. Given the price range of each flight, promising results are disclosed.