Superiority of Neural Networks for Trading Volume Forecasts of Stocks and Cryptocurrencies

Trading volume is an important variable to successfully capture market risks along with asset price/returns. Recently, there has been a growing interest in deep learning methods to forecast the trading volume of stocks using historical volatility as a feature. Unlike the existing work, a novel datad...

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Bibliographic Details
Published in2023 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 146 - 151
Main Authors Bowala, Sulalitha, Thavaneswaran, Aerambamoorthy, Thulasiram, Ruppa, Hoque, Md Erfanul, Paseka, Alex
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.12.2023
Subjects
Artificial neural networks
Cryptocurrencies
Cryptocurrency
Deep learning
Demand forecasting
Neural Networks
Predictive models
Solid modeling
Trading Volume
Volume measurement
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Summary:Trading volume is an important variable to successfully capture market risks along with asset price/returns. Recently, there has been a growing interest in deep learning methods to forecast the trading volume of stocks using historical volatility as a feature. Unlike the existing work, a novel datadriven log volatility forecast is proposed in this paper as an extra feature to improve trading volume forecasts. Recently, neural networks for volatility and neural nets for electricity demand forecasting, constructed with nnetar function, have shown to be superior. The novelty of this paper is to demonstrate the neural network based on the nnetar function from the forecast package in R for trading volume forecast shows superiority over the other neural network.
ISSN:2472-8322
DOI:10.1109/SSCI52147.2023.10371869