We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or with highly disparate time scales. We compare the ability of vanilla and extended long short...

We proposed a novel development trend prediction approach of digital financial inclusion (DFI) based on exploratory spatial data analysis (ESDA) and graph convolutional neural network (GCN), named ESDAGCN. Firstly, to address the limitation of standard GCN in lacking financial background...

The debate about the efficacy of mergers and acquisitions as a growth strategy in terms of ex-post value creation has been developing for decades. This paper aims to develop an artificial neural network to examine trends in the financials and mark the potential sources of value creation in M&A;...

With the aim of understanding the usefulness of cross-country interactions and long memory dependencies in exchange rate return forecast, we propose, compare, and evaluate various neural network architectures. These architectures include the proposed deep latent factor (DLF) and deep prediction...

Managing inflation is vital for a stable economy, but forecasting remains challenging. ML methods, like neural networks, have shown promise in forecasting inflation and other macroeconomic variables. In this paper, I propose DPCNet, a deep multi-task learning model, to jointly forecast inflation...

Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the Generalized Linear Models (GLMs) currently used in...

With the advent of Big Data and machine learning/AI technologies, actuaries can now develop advanced models in a data-rich environment to achieve better forecasting performance and provide added value in many applications. Traditionally, economic forecasting for actuarial applications is...

Randomness dynamics in financial markets can be captured by the volatility, which directly links to the risk aversion of investors. As a consequence, a successful volatility prediction can be highly profitable from many aspects: For the investment manager, it can be a signal to hedge his...

Motivated by the short-lived arbitrage model, we jointly imply volatilities and virtual interest rates of options to develop minimum variance hedge ratios. We use artificial neural networks to capture the dynamics between the underlying asset price and correlated state variables, which improves...

This paper explores Artificial Neural Network (ANN) as a model-free solution for a calibration algorithm of option pricing models. We construct ANNs to calibrate parameters for two well-known GARCH-type option pricing models: Duan’s GARCH and the classical tempered stable GARCH that...