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A novel model structured on predictive churn methods in a banking organization

Silveira, Leonardo José; Pinheiro, Plácido Rogério; … - In: Journal of risk and financial management : JRFM 14 (2021) 10, pp. 1-24

dropouts by new customers costs, on average, 40% more. Aiming to mitigate the churn (customer evasion) phenomenon, this study … of the data and the developments of predictive models of churn were performed using the Orange data mining software. The …

Persistent link: https://www.econbiz.de/10012745384

A gated recurrent unit approach to bitcoin price prediction

Dutta, Aniruddha; Kumar, Saket; Basu, Meheli - In: Journal of risk and financial management : JRFM 13 (2020) 2/23, pp. 1-16

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent...

Persistent link: https://www.econbiz.de/10012173959

The predictability of the exchange rate when combining machine learning and fundamental models

Zhang, Yuchen; Hamori, Shigeyuki - In: Journal of risk and financial management : JRFM 13 (2020) 3/48, pp. 1-16

In 1983, Meese and Rogoff showed that traditional economic models developed since the 1970s do not perform better than the random walk in predicting out-of-sample exchange rates when using data obtained after the beginning of the floating rate system. Subsequently, whether traditional economical...

Persistent link: https://www.econbiz.de/10012174126

Churn management in telecommunications : hybrid approach using cluster analysis and decision trees

Pejić Bach, Mirjana; Pivar, Jasmina; Jaković, Božidar - In: Journal of risk and financial management : JRFM 14 (2021) 11, pp. 1-25

The goal of the paper is to present the framework for combining clustering and classification for churn management in … telecommunications. Considering the value of market segmentation, we propose a three-stage approach to explain and predict the churn in … churn dataset is prepared for the analysis, consisting of demographics, usage of telecom services, contracts and billing …

Persistent link: https://www.econbiz.de/10012795878

Machine learning and financial literacy : an exploration of factors influencing financial knowledge in Italy

Levantesi, Susanna; Zacchia, Giulia - In: Journal of risk and financial management : JRFM 14 (2021) 3/120, pp. 1-21

In recent years, machine learning techniques have assumed an increasingly central role in many areas of research, from computer science to medicine, including finance. In the current study, we applied it to financial literacy to test its accuracy, compared to a standard parametric model, in the...

Persistent link: https://www.econbiz.de/10012485333

Identification of core suppliers based on e-invoice data using supervised machine learning

Hong, Jung-sik; Yeo, Hyeongyu; Cho, Nam Wook; Ahn, Taeuk - In: Journal of risk and financial management : JRFM 11 (2018) 4, pp. 1-13

Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or...

Persistent link: https://www.econbiz.de/10011961285

Ensemble learning or deep learning? : application to default risk analysis

Hamori, Shigeyuki; Kawai, Minami; Kume, Takahiro; … - In: Journal of risk and financial management : JRFM 11 (2018) 1, pp. 1-14

Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data...

Persistent link: https://www.econbiz.de/10011855150

A random forests approach to predicting clean energy stock prices

Sadorsky, Perry A. - In: Journal of risk and financial management : JRFM 14 (2021) 2/48, pp. 1-20

Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices...

Persistent link: https://www.econbiz.de/10012483492

Machine learning the carbon footprint of Bitcoin mining

Calvo Pardo, Héctor F.; Mancini, Tullio; Olmo, Jose - In: Journal of risk and financial management : JRFM 15 (2022) 2, pp. 1-30

Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO2e for 2017, 2018 and 2019 based on a novel bottom-up approach,...

Persistent link: https://www.econbiz.de/10012821293

Neural network models for empirical finance

Calvo Pardo, Héctor F.; Mancini, Tullio; Olmo, Jose - In: Journal of risk and financial management : JRFM 13 (2020) 11/265, pp. 1-22

This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to...

Persistent link: https://www.econbiz.de/10012389830

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