Trading the foreign exchange market with technical analysis and Bayesian Statistics

Hassanniakalager, Arman; Sermpinis, Georgios

doi:10.1016/j.jempfin.2021.07.006

Cited by 11 publications

(1 citation statement)

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“…In its structure, the class variables are treated as parents of the other attribute variables, and it is assumed that the attribute variables are independent of each other, provided that the class variables are known. For more details on NBs and their modeling design, we refer the interested reader to Hassanniakalager et al [71]. The DT algorithm is a binary tree decision method similar to that used in risk management theory and a conditional branching structure in discrete mathematical flowchart theory, where probability calculations are used to classify the categories (Breiman et al [63]).…”

Section: Stage Ii: Credit Risk Assessment Modelsmentioning

confidence: 99%

A Hybrid XGBoost-MLP Model for Credit Risk Assessment on Digital Supply Chain Finance

Stasinakis

Yeo

2022

Forecasting

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Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased the potential risk of commercial banks, with credit risk being the biggest risk they face. Therefore, credit risk assessment based on the application of digital SCF is of great importance to commercial banks’ financial decisions. This paper uses a hybrid Extreme Gradient Boosting Multi-Layer Perceptron (XGBoost-MLP) model to assess the credit risk of Digital SCF (DSCF). In this paper, 1357 observations from 85 Chinese-listed SMEs over the period 2016–2019 are selected as the empirical sample, and the important features of credit risk assessment in DSCF are automatically selected through the feature selection of the XGBoost model in the first stage, then followed by credit risk assessment through the MLP in the second stage. Based on the empirical results, we find that the XGBoost-MLP model has good performance in credit risk assessment, where XGBoost feature selection is important for the credit risk assessment model. From the perspective of DSCF, the results show that the inclusion of digital features improves the accuracy of credit risk assessment in SCF.

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Section: Stage Ii: Credit Risk Assessment Modelsmentioning

confidence: 99%