Predictive Models for Loan Default Risk Assessment

Coşer, Alexandru; Maer-Matei, Monica Mihaela; Albu, Crisan

doi:10.24818/18423264/53.2.19.09

Cited by 36 publications

(16 citation statements)

References 2 publications

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“…On the other hand, the technical analysis method mainly focuses on the direction of stock price, trading volume, and investors' psychological expectation, which primarily focuses on analyzing the stock index trajectory of individual stocks or the whole market by using Kline chart and other tools. At present, traditional fundamental analysis and technical analysis are still the most commonly employed methods for many organizations and individual investors [4,5]. e accuracy of the traditional fundamental analysis method is difficult to be convincing.…”

Section: Introductionmentioning

confidence: 99%

A CNN-LSTM-Based Model to Forecast Stock Prices

et al. 2020

Complexity

291

100

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Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.

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Section: Introductionmentioning

confidence: 99%

A CNN-LSTM-Based Model to Forecast Stock Prices

et al. 2020

Complexity

291

100

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“…Forecast transmission of infectious diseases by setting up a logistic regression model. Using blog mention count to forecast peak sales for books [37], we use a series of predictive classifiers such as Light GBM, XGBoost, Logistic Regression, and Random Forest in order to evaluate the probability of a customer entering loan default. Gruh et al [38] utilize time series correlation analysis to forecast the timing advance.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining Algorithm for Demand Forecast Analysis on Flash Sales Platform

Zhang

Yixin

2021

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With the development of the digital economy, the emerging marketing strategy of the e-commerce flash sales has been changing the traditional purchasing habits of customers. This imposes new decision-making challenges for companies involved in flash sales. It is important for companies to build the accurate product demand forecast analysis focusing on the characteristics of the flash sales and customer behaviors. In this paper, VIPS (Weipinhui, a Chinese e-commerce platform) is taken as a case study with the key focus on how sentiment factors in customer reviews affect product demand in flash sale platforms. The paper adopts two sentiment analysis methods based on emotional dictionaries. The method with a higher evaluation index is adopted to integrate the emotional factors into the autoregressive model for product demand and assessment. The experiments prove that the autoregressive model for integrating the sentiment factors demonstrates better forecasting performances than the models without sentiment factors. The experiments further confirm that when product demand for the previous two weeks and customer review sentiment factors in the previous week are taken into consideration, demand forecast effects are most accurate.

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“…However, used in ensembles like RF, which also appeared in four studies, it can become a powerful classifier. In two of the analyzed studies RF outperformed LR, LightGBM and XGBoost when correcting for sample imbalance (Coser, Maer-Matei, & Albu, 2019), respectively SVM, DT and NN (Nabende & Senfuma, 2019). In both of the other two comparisons (Abdemoula, 2015;Çığşar & Ünal, 2019), RF was less performant that LR.…”

Section: Other Algorithmsmentioning

confidence: 99%

“…It is important to note that these studies focused on a wide range of countries, periods and borrower types. Three studies analyzed P2P data (Coser, Maer-Matei, & Albu, 2019;Turiel & Aste, 2020;Turlík, 2018), all being recent publications. This indicates increased interest for P2P platforms in academia.…”

Section: Applicationsmentioning

confidence: 99%

Will they repay their debt? Identification of borrowers likely to be charged off

Căplescu

Panaite

Pele

et al. 2020

Management &Amp; Marketing. Challenges for the Knowledge Society

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Recent increase in peer-to-peer lending prompted for development of models to separate good and bad clients to mitigate risks both for lenders and for the platforms. The rapidly increasing body of literature provides several comparisons between various models. Among the most frequently employed ones are logistic regression, Support Vector Machines, neural networks and decision tree-based models. Among them, logistic regression has proved to be a strong candidate both because its good performance and due to its high explainability. The present paper aims to compare four pairs of models (for imbalanced and under-sampled data) meant to predict charged off clients by optimizing F1 score. We found that, if the data is balanced, Logistic Regression, both simple and with Stochastic Gradient Descent, outperforms LightGBM and K-Nearest Neighbors in optimizing F1 score. We chose this metric as it provides balance between the interests of the lenders and those of the platform. Loan term, debt-to-income ratio and number of accounts were found to be important positively related predictors of risk of charge off. At the other end of the spectrum, by far the strongest impact on charge off probability is that of the FICO score. The final number of features retained by the two models differs very much, because, although both models use Lasso for feature selection, Stochastic Gradient Descent Logistic Regression uses a stronger regularization. The analysis was performed using Python (numpy, pandas, sklearn and imblearn).

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Predictive Models for Loan Default Risk Assessment

Cited by 36 publications

References 2 publications

A CNN-LSTM-Based Model to Forecast Stock Prices

A CNN-LSTM-Based Model to Forecast Stock Prices

Data Mining Algorithm for Demand Forecast Analysis on Flash Sales Platform

Will they repay their debt? Identification of borrowers likely to be charged off

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