Harnessing Machine Learning Emerging Technology in Financial Investment Industry: Machine Learning Credit Rating Model Implementation

Abstract

Credit risk ratings consist of assessing the creditworthiness of the issuer and gauge the risks associated with buying its debt. Any delay in updating the credit risk ratings could have a severe impact on the financial system such as the financial crisis in 2008. This paper discusses a case that leverages emerging technology and breakthrough cognitive analytics in the financial industry. It specifically describes the design and implementation of a predictive modeling case based on the Machine Learning Approach and its application in credit risk forecasting and portfolio management. Using big data and Machine Learning, it is possible to improve credit risk analysis and forecasting by allowing the algorithms to search for patterns using large sets of data.

Machine Learning in Investment and Credit Risk Modeling

“…The existing study determined to examine the emerging ML technology in the investment industry by analyzing the implementation of ML credit rating design [17]. The study concludes, with ML's importance in CR rating modeling and its advantage in the applications.…”

“…CR analysis is the biggest challenge in the financial sector due to an abundant element that has different influences on the solvency issues of institutions through various channels. The existing study determined to examine the emerging ML technology in the investment industry by analyzing the implementation of ML credit rating design [17]. Especially, the study focused on the benefits of new technology and innovative cognitive analytics in the financial sector.…”

“…• The existing study has a limited focus on future investors and does not discuss more stockholders in commercial products [11]. • The convention study has not discussed various dimension reduction methods and more details regarding the parameters [17].…”

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

“…With the deepening and improvement of the basic theory of forecasting by researchers, forecasting models have become increasingly abundant. It is mainly divided into two categories: traditional statistical methods that rely on the characteristics of time series [12], and artificial intelligence heuristic algorithms relying on large-scale historical data [13]. Zhou et al [14] proposed an improved Bass model for fast-fashion clothing demand forecasting based on the influence of consumer preference and seasonality on demand forecasting.…”

Application of Genetic Optimization Algorithm in Financial Portfolio Problem

“…However, the theory is not perfect. en Wang et al proposed a multifactor model, which we call arbitrage pricing model [8]. Setiawan and Rosadi made an in-depth study on the securities price in the capital market by using statistical methods from both theoretical and empirical aspects in 1965 and put forward the efficient market theory [9].…”