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Development of fuzzified neural network for enterprise bankruptcy risk estimation

Sinkovskyi, Artem; Shulakov, Volodymyr - In: Technology audit and production reserves 3 (2024) 2/77, pp. 19-22

Persistent link: https://www.econbiz.de/10015076455

Predicting the returns of the US real estate investment trust market : evidence from the group method of data handling neural network

Zhang, Wendi; Li, Bin; Liew, Alan Wee-Chung; Roca, Eduardo - In: Financial innovation : FIN 9 (2023) 1, pp. 1-33

market using GMDH and then compare its accuracy with that derived from the traditional prediction method. Design … GMDH model are controlled to investigate their impact on the accuracy of the machine learning approach. Corresponding … with GARCH, GMDH's accuracy is much higher, indicating that the machine learning approach can provide a highly accurate …

Persistent link: https://www.econbiz.de/10014363989

Efficacies of artificial neural networks ushering improvement in the prediction of extant credit risk models

Aranha, Meera; Bolar, Kartikeya - In: Cogent economics & finance 11 (2023) 1, pp. 1-13

proxy for uncertainty is added to the model as a default driver. We considered the covid pandemic a black swan event that … operating expenditure (primarily decrease) as our proxy for uncertainty as firms were forced to cut down majorly on their … proxy for uncertainty is added to the model. …

Persistent link: https://www.econbiz.de/10014500824

A bibliometric analysis of machine learning econometrics in asset pricing

Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

Persistent link: https://www.econbiz.de/10013475217

A state-of-the-art appraisal of bankruptcy prediction models focussing on the field’s core authors : 2010-2022

Soukal, Ivan; Mačí, Jan; Trnková, Gabriela; … - In: Central European Management Journal 32 (2023) 1, pp. 3-30

Purpose The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors...

Persistent link: https://www.econbiz.de/10014552829

Negative economic sentiment index based on Finnish news titles

Avela, Aleksi; Lehmus, Markku - In: Journal of the Finnish Economic Association : JFEA 4 (2023) 1, pp. 49-63

We construct an index for measuring negative economic sentiment in Finland by using news titles collected from the Finnish broadcasting company Yle's archive. Our approach uses supervised machine learning text classification for detecting news titles featuring negative economic sentiment, and...

Persistent link: https://www.econbiz.de/10014430214

Machine learning portfolio allocation

Pinelis, Michael; Ruppert, David - In: The Journal of finance and data science : JFDS 8 (2022), pp. 35-54

We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is...

Persistent link: https://www.econbiz.de/10014433682

Performance attribution of machine learning methods for stock returns prediction

Daul, Stéphane; Jaisson, Thibault; Nagy, Alexandra - In: The Journal of finance and data science : JFDS 8 (2022), pp. 86-104

We analyze the performance of investable portfolios built using predicted stock returns from machine learning methods and attribute their performance to linear, marginal non-linear and interaction effects. We use a large set of features including price-based, fundamental-based, and...

Persistent link: https://www.econbiz.de/10014433684

A predictive analytics model for designing deep underground foundations using artificial neural networks

Agbemenou, Atsou Komla Herve; Motamed, Ramin; … - In: Decision analytics journal 7 (2023), pp. 1-11

this study is to improve prediction accuracy which is important and can save design and construction costs and time. A … generalization and prediction accuracy with a Root Mean-Squared Error of 2807.08 Kilopounds force (kips), a Mean Absolute Error of …

Persistent link: https://www.econbiz.de/10014436673

Churn rate modeling for telecommunication operators using data science methods

Zatonatska, Tetiana; Fareniuk, Yana; Shpyrko, Viktor - In: Marketing i menedžment innovacij : m&mi 14 (2023) 2, pp. 163-173

Persistent link: https://www.econbiz.de/10014373841