Prediction of financial strength ratings using machine learning and conventional techniques

Abstract

Financial strength ratings (FSRs) have become more significant particularly since the recent financial crisis of 2007-2009 where rating agencies failed to forecast defaults and the downgrade of some banks. The aim of this paper is to predict Capital Intelligence banks' financial strength ratings (FSRs) group membership using machine learning and conventional techniques. Here the authors use five different statistical techniques, namely CHAID, CART, multilayer-perceptron neural networks, discriminant analysis and logistic regression. They also use three different evaluation criteria namely average correct classification rate, misclassification cost and gains charts. The data are collected from Bankscope database for the Middle Eastern commercial banks by reference to the first decade of the 21st century. The findings show that when predicting bank FSRs during the period

Individual’s financial investment decision-making in reward-based crowdfunding: evidence from China

“…With technological advancements, crowdfunding is becoming an imperative source of financing and is receiving extended attention from the economics and finance research community, as well as from entrepreneurs and practitioners. For instance, scholars have examined the role of trust management (Zheng et al, 2016), geographic distance (Kang et al, 2017), multidimensional social capital (Zheng et al, 2017), and factors for success and failure (Abdou et al, 2016(Abdou et al, , 2017Mollick, 2014) in reward-based crowdfunding. In more relevant literature, Bi et al (2017) investigated the impact of online project information on financial investment decisions of reward-based crowdfunding.…”

Predicting Financial Health of Banks for Investor Guidance Using Machine Learning Algorithms

“…Predicting bankruptcy has been extensively studied in literature (Abdou, Abd Allah, Mulkeen, Ntim, & Wang, 2017; Affes & Kaffel, 2019; Altman, 1968; Ekinci & Erdal, 2017; Halteh, Kumar, & Gepp, 2018; Kolari & Sanz, 2017; Tung et al, 2004; Jones, 2017). While earlier studies focused excessively on bankruptcy prediction, a lack of studies to classify banks based on their financial strength from the perspective of retail depositors, especially to facilitate the retail investor to make informed decisions in choosing a commercial bank for placing their savings as deposits, this is the first research gap this study identified and proposes to address.…”

“…Non-parametric techniques of trait recognition combined with neural network methodology of self-organising maps have also been used (Kolari & Sanz, 2017). Recently, machine learning (Migliore & Chinta, 2017), ensemble learning models (random subspaces) and hybrid ensemble learning models (bagging and multi-boosting) have been used (Ekinci & Erdal, 2017) along with chi-square Automatic Interaction Detector CHAID, CART, multilayer-perceptron neural networks, discriminant analysis and logistic regression (Abdou et al, 2017).…”

Creating a Comprehensive Method for the Evaluation of a Company

“…Consequently, Chen [14] is optimistic that Particle Swarm Optimization in conjunction with the Support Vector Machine method can accurately predict bankruptcy. Even when machine learning techniques are only used to support other models, the results show that they outperform other models [15]. Multilayer Perceptron Neural Networks (MLP) have also been proven to perform very well, and better than other techniques, in predicting the capability of construction companies in the Czech Republic to weather a financial crunch [16].…”