Purpose For policymakers and participants of financial markets, predictions of trading
volumes of financial indices are important issues. This study aims to address such a …

Managing the prediction of metrics in high‐frequency financial markets is a challenging task.
An efficient way is by monitoring the dynamics of a limit order book to identify the information …

Predictions of financial index trading volumes represent an essential issue to market
participants and policy makers. We investigate this problem for the high-frequency one …

Stock total market value forecasting is a significant issue for policy makers and investors.
This study explores usefulness of the nonlinear autoregressive neural network for this …

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 …

Presently, managing prediction of metrics in high frequency financial markets is a
challenging task. An efficient way to do it is by monitoring the dynamics of a limit order book …

As a critical consideration in investment decisions, stock liquidity has significance for all
stakeholders in the market. It also has implications for the stock market's growth. Liquidity …

This paper proposes a dynamic model to forecast intraday volume percentages by
decomposing the trade volume into two parts: The average part as the intraday volume …

Adversarial training is a method for enhancing neural networks to improve the robustness
against adversarial examples. Besides the security concerns of potential adversarial …

Changes in intraday trading volume are integral to any algorithmic trading strategy.
Accordingly, forecasting the change in trading volume is paramount to better understanding …