Modern financial markets are characterized by a rapid flow of information, a vast number of
participants having diversified investment horizons, and multiple feedback mechanisms …

High-entropy alloys (HEAs) are a novel class of alloys that have many desirable properties.
The serrated flow that occurs in high-entropy alloys during mechanical deformation is an …

Learning from graph-structured data is an important task in machine learning and artificial
intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated …

We implement deep learning techniques to forecast the price of the three most widely traded
digital currencies ie, Bitcoin, Digital Cash and Ripple. To the best of our knowledge, this is …

Machine learning and AI‐assisted trading have attracted growing interest for the past few
years. Here, we use this approach to test the hypothesis that the inefficiency of the …

We study the dynamics of volatility spillover across four major cryptocurrency returns namely
Bitcoin, Ethereum, Ripple and Litecoin from 15− 08− 2015 to 18− 01− 2018. In the first step …

Motivated by the risen linkage between events and Bitcoin return, this paper first defines
Bitcoin-related events (BREs) based on the change points analysis and then divides these …

This paper explores Bitcoin intraday technical trading based on artificial neural networks for
the return prediction. In particular, our deep learning method successfully discovers trading …

The article aims to explore the heterogeneous feature in the determination of Bitcoin
volatility using a Markov regime-switching model and test its forecasting ability. The …

This study investigates the scale-dependent structure of asymmetric volatility effect in six
representative cryptocurrencies: Bitcoin, Ethereum, Ripple, Litecoin, Monero, and Dash. By …