Market turnover levels and liquidity changes across various territories significantly influence currency prices, leading to continuous fluctuations. Consequently, traders and investors constantly seek strategies to mitigate exchange rate risks. This study aimed to measure and assess foreign...

The study's objective is to check whether the predictive power of Machine Learning Techniques is better than Logistic Regression in predicting the bankruptcy of firms and that the same predictive power of ascertaining bankruptcy improves when a proxy for uncertainty is added to the model as a...

The study investigated the effectiveness of technical analysis indicators in trading spot exchange rates of emerging economies' currencies under integration with artificial neural networks (ANN) and the quantitative testing of these indicators' success in this matter with the goal of...

This research article presents a comparative analysis between logistic regression as a traditional method, artificial neural networks (ANNs), and decision tree as machine learning techniques for predicting credit risk. It meticulously examines and evaluates these three methods, elucidating their...

In this study, an attempt has been made to find out why investors still prefer broker-sold fund over direct-sold fund despite the superior performance of the latter. We find the sensitivity of funds flow in selected direct-sold funds and broker-sold funds in India. We do not find any evidence...

The efficient market hypothesis describes an efficient market as one in which investors cannot consistently predict stock returns because prices instantly reflect all the information flowing into the market. However, return predictability has been documented in many markets. This study tests the...

In this research, we test whether common trading oscillators can outperform the buy-and-hold strategy (B&H) using six popular ETFs for the period of the last 20 years. We use the original setups of those oscillators and also other setups or oscillators combinations in order to achieve the best...

We built an early warning model for financial risk using a back propagation neural network. To this end, the financial data of 136 listed Internet financial companies in the People's Republic of China were selected, spanning from 2010-2019, as the sample for the empirical test. We categorized...

The interconnection of stock markets offers valuable insights into the broader dynamics of global financial markets. This study uses the Diebold and Yilmaz index model to analyze and measure volatility spillovers and interconnectedness among APEC stock markets. The objective is to identify major...

This study examines the impact of key macroeconomic variables on mutual funds' financial performance in Ghana. We employ the Pooled Mean Group (PMG) estimation of the Autoregressive Distributed Lag (ARDL) model to analyze the macroeconomic determinants of mutual funds in Ghana for the period...