Volume Prediction With Neural Networks

Daniel Libman, Simi Haber, Mary Schaps

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء

ملخص

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.

اللغة الأصليةالإنجليزيّة
رقم المقال21
دوريةFrontiers in Artificial Intelligence
مستوى الصوت2
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 9 أكتوبر 2019

All Science Journal Classification (ASJC) codes

  • !!Artificial Intelligence

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