Tensor Representation in High-Frequency Financial Data for Price Change Prediction
This work addresses the challenge of analyzing rapid price movements for high-frequency traders, but it appears incremental as it applies existing multilinear methods to financial data.
The paper tackled the problem of predicting mid-price changes in high-frequency trading by using tensor representations of financial data, and found that multilinear models outperformed vector-based and other competing methods on a dataset of over 4 million limit orders.
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.