We study how researchers can apply machine learning (ML) methods in finance. We first
establish that the two major categories of ML (supervised and unsupervised learning) …

Using macroeconomic and financial conditions to forecast credit default swap (CDS)
spreads is a challenging task. In this paper, we propose the Merton-LSTM model, a modified …

Long-term time series forecasting is a challenging problem both in theory and in practice.
Although the idea of information granulation has been shown to be an essential concept and …

Support vector regression (SVR) has long been proven to be a successful tool to predict
financial time series. The core idea of this study is to outline an automated framework for …

We study the variation of sovereign credit default swaps (CDSs) of eurozone countries, their
persistence and co-movements, with particular attention given to the impact of the financial …

Credit default swap which reflects the credit risk of a firm is one of the most frequently traded
credit derivatives. In this paper, we conduct a comprehensive study to verify the predictive …

Market impact cost is the most significant portion of implicit transaction costs that can reduce
the overall transaction cost, although it cannot be measured directly. In this paper, we …

This paper uses sovereign CDS spread changes and their volatilities as a proxy for the
informational efficiency of the sovereign markets and persistency of country risks …

This study provides a rigorous empirical comparison of structural and reduced-form credit
risk frameworks. The literature differentiates between structural models that are based on …

Many machine learning (ML) models can make predictions regarding credit default swaps
(CDS) for the telecommunication (telco) service sector. However, some ML algorithms can …