Resumen de Change detection of orders in stock markets using a Gaussian mixture model

Bungo Miyazaki, Kiyoshi Izumi, Fujio Toriumi, Ryo Takahashi

• We propose a method for detecting changes in the order balance in stock markets by applying a stochastic model to the feature vectors extracted from the order-book data of stocks. First, the data are divided into training and test periods. Next, a Gaussian mixture model is estimated from the feature vectors extracted from the order-book data in the training period. Finally, the goodness of fit of the feature vectors in the test period over this model is calculated. Using the proposed method, we found that the order balances of stocks for which insider trading was reported were unusual