Volume Prediction With Neural Networks

Daniel Libman, Simi Haber, Mary Schaps

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish
Article number21
JournalFrontiers in Artificial Intelligence
Volume2
DOIs
StatePublished - 9 Oct 2019

Bibliographical note

Publisher Copyright:
© Copyright © 2019 Libman, Haber and Schaps.

Funding

This research was based upon work supported by Google Cloud.

FundersFunder number
Google Cloud

    Keywords

    • LSTM
    • change in volume
    • finance
    • machine learning
    • neural networks
    • volume prediction

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