The emergence and growing popularity of Bitcoins have attracted the attention of the financial world. However, few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market. It is of great importance for investors and policymakers to take...

Although Bitcoin has attracted significant attention from investors and policy makers, the empirical works in the Bitcoin forecasting and trading support systems are still at an early stage. This study proposes a novel data decomposition based hybrid bidirectional deep learning model, namely...

In recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to...

There is a research gap in accurately predicting an individual stock's finances from industry environment factors. Therefore, to predict trading strategies for a target stock's closing price, this study constructed a prediction module and an environment module for a hybrid variational mode...

Developing efective strategies to earn excess returns in the stock market is a cuttingedge topic in the feld of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news...

Big data technology has revolutionized the research paradigm of economic forecasting regardless of the data source, forecasting method, or forecasting result. This study evaluates the current literature on economic forecasting using big data and employs bibliometric approaches to offer a...

By considering a financial market of fundamentalists and trend followers in which the price trend of trend followers is formed as a weighted average of historical prices, we establish a continuous-time financial market model with time delay and examine the impact of time delay on market price...

Crude oil price series are nonlinear and highly volatile, making it difficult to obtain satisfactory performance for traditional statistical-based forecasting methods. To improve prediction accuracy, this study proposes a novel learning paradigm by integrating the trajectory similarity method...

This study addresses a significant gap in the existing literature by conducting a comprehensive systematic review of the art market over the past 50 years, utilizing big data analysis and bibliometric methods. Through descriptive statistical analysis, we gained insights into research trends,...