Volume Prediction With Neural Networks

Daniel Libman; Simi Haber; Mary Schaps

doi:10.3389/frai.2019.00021

Volume Prediction With Neural Networks

Front Artif Intell. 2019 Oct 9:2:21. doi: 10.3389/frai.2019.00021. eCollection 2019.

Authors

Daniel Libman¹, Simi Haber¹, Mary Schaps¹

Affiliation

¹ Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel.

Abstract

Changes in intraday trading volume are integral to any algorithmic trading strategy. Accordingly, forecasting the change in trading volume is paramount to better understanding the financial markets. This paper introduces a new method to forecast the log change in trading volume, leveraging the power of Long Short Term Memory (LSTM) networks in conjunction with Support Vector Regression (SVR) and Autoregressive (AR) models. We show that LSTM contributes to a more accurate forecast, particularly when constructed as part of a hybrid model with AR. The algorithm is extended to include data about the time of day, helping the model associate the log change in trading volume with the current hour, which yields the best performance of all trials.

Keywords: LSTM; change in volume; finance; machine learning; neural networks; volume prediction.