The complexity of stock price fluctuations stems from its multi-scale characteristics, nonlinear dynamic characteristics, and fractal structure. To better capture the fractal characteristics of stock prices, this paper creatively proposes a predictio...
Synchronization, which has been a common natural phenomenon, occurs frequently in complex financial systems and is an important contagion mechanism for systemic financial risks and even financial crises. In view of this, we construct a coupled stocha...
The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, ...
Regime switching in a time series is an important and challenging issue in complex financial system analysis. Existing regime models have focused on the features of fluctuations at a single point in financial time series, often neglecting time series...
The occurrence of financial distress in enterprises not only leads to operational difficulties but also may trigger chain reactions such as bankruptcy, debt arrears, layoffs, etc., which in turn have a negative effect on investors, creditors, and the...
In financial markets, predicting stock returns is an essential task for investors. This paper is one of the first studies using business efficiency scores calculated from data envelopment analysis to predict stock returns. In the meantime, this is al...
In this paper, we study the impact of momentum, volume and investor sentiment on U.S. tech sector stock returns using Principal Component Analysis-Hidden Markov Model (PCA-HMM) methodology. Price and volume are two well-known aspects in general equil...
With the increasing complexity and prosperity of global financial markets, stock market forecasting plays a critical role in investment decision-making, market regulation, and economic planning. This study proposes a hybrid prediction model that inte...
The rapid development of edge computing and artificial intelligence has brought growing interest in collaborative training. While prior research has addressed technical aspects of resource allocation, less attention has been paid to the underlying ec...
In this study, we present a novel approach to analyzing financial crises of the global stock market by leveraging a modified Autoencoder model based on Recurrent Neural Network (RNN-AE). We analyze time series data from 24 global stock markets betwee...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.