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...
This paper aims to measure credit risks of unlisted agricultural enterprises by using the KMV model integrating a CNN-BiLSTM neural network. Initially, the expected default frequencies (EDF) for each listed agricultural enterprise are computed using ...
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 the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their...
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...
Time-series momentum (TSMOM) trading strategies manage positions based on the persistence of return trends. Although long short-term memory (LSTM) deep neural architectures can enhance TSMOM, their performance often deteriorates during abrupt market ...
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 Baltic Dry Index (BDI) is a critical benchmark for assessing freight rates and chartering activity in the global shipping market. This study forecasts the BDI using diverse financial data, including commodities, currencies, stock markets, and vol...
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...
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