Mohammed Ali

A week after the Biden administration preliminarily approved a $6.6 billion loan to electric vehicle (EV) maker Rivian Automotive, the White House is again giving billions in financial aid to the EV sector.

The U.K.'s wind energy industry set a new record this year in the amount of power produced that has ultimately gone to waste.

Cryptocurrency is recognized as a leading digital currency by its peer-to-peer transfer capabilities and secure features. Accurately forecasting cryptocurrency price trends holds substantial significance for investors and traders, as they inform critical decisions regarding the acquisition, divestment, or retention of cryptocurrencies, guided by expectations of value, risk assessment, and potential returns. This study also aims to identify a resourceful technique to efficiently forecast prices of cryptocurrencies such as Bitcoin (BTC), Binance (BNB), Ripple (XRP), and Tether (USDT) using optimal data-driven models (LSTM, GRU, and BiLSTM models) using bias correction. The proposed methodology includes collecting cryptocurrency data and precious metal data from Coindesk and BullionVault, respectively, and then finding the optimal model input combination for each cryptocurrency by lag adjustment and correlating feature selection. Hyperparameter tuning was performed by trial-and-error technique, and an early stopping function was applied to minimize time and space complexity. Bias correction (BC) is applied to model-forecasted price trends to reduce errors in evaluation and to enhance accuracy by adjusting model outputs to reduce prediction bias, providing a refined alternative to traditional unadjusted deep learning methods. GRU-BC outperformed other models in forecasting Bitcoin (with MAE 25.291, RMSE 31.266, MAPE 2.999) and USDT (with MAE 0.0006, RMSE 0.0012, MAPE 0.0622) price trends, while BiLSTM-BC was superior in predicting XRP (with MAE 0.0129, RMSE 0.0171, MAPE 2.9013) and BNB (with MAE 2.2759, RMSE 2.8357, MAPE 1.9785) market price flow.

Vancouver's city council is moving forward with plans to include Bitcoin in its financial strategy, guided by Mayor Ken Sim

This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open Access Article by Sibtain Syed

Open Access Article by Ammar Ali Hussein Ammar Ali Hussein Scilit Preprints.org Google Scholar

Bitcoin (BTC) recently jumped to over $106,000 during early Asian trading hours, then dropped back to about $104,500.

The world’s largest single-capacity wind turbine experienced a failure of its typhoon-proof blades during extreme testing off China's coast.

A pair of Japan's largest automakers, Honda and Nissan, are in talks to form a partnership to boost their competitiveness in the electric vehicle (EV) sector.

Purpose: This study aims to demonstrate the impact of Foreign Investment (FI) on the disclosure of digital Financial Inclusion (FIN) through mobile banking (MB) and the performance of banks, as well as the direct impact of disclosing digital FIN indicators through MB on bank performance. Design/methodology/approach: This study utilized actual data from all banks listed on the stock exchange in the Sultanate of Oman between 2015 and 2023. The hypotheses were tested using the partial least squares structural equation model (PLS-SEM). Findings: This study revealed a positive influence of FI on both bank performance and the disclosure of FIN indicators through MB. It also identified a negative effect of disclosing FIN indicators through MB on bank performance. However, the mediation of the disclosure of FIN indicators through MB did not show any impact of FI on bank performance. Practical Implications: This study's results offer valuable insights and recommendations. Firstly, for bank managers, it is crucial to find a balance between expanding MB services and enhancing bank profitability by studying customer trends and preferences in MB usage. This approach will aid in offering banking services that retain existing customers and attract new ones, ultimately boosting bank profitability. Secondly, for policymakers and regulators, this study enhances the understanding of current practices in disclosing FIN indicators through MB, which may prompt a reevaluation of accounting standards related to traditional and digital FIN indicator disclosure. Originality/value: This study is groundbreaking in its examination of the influence of FI on the disclosure of digital FIN indicators through mobile banking. It also represents the first investigation into the indirect impact of FI on banks' financial performance (FP) through the disclosure of digital FIN indicators via mobile banking.