 Analysis of liquidity risk management using neural networks: An applied study on tesla company for the period 2016-2023Aymen Hadi Talib (), Laith Ali Zgair (), Rafid K. Nassif Al-Obaidi () and Oday Lateef Mahmood () Edelweiss Applied Science and Technology, 2025, vol. 9, issue 1, 614-619 Abstract: This research aims to study eagerness risk in Tesla Inc. by utilizing an artificial neural network to investigate financial data from 2016 until 2023. The current ratio, quick ratio, annual returns, stock price dispersion, profitability, debt-to-equity ratio, return on assets (ROA) and return on equity (ROE) were among the financial information collected. The financial information was analyzed by multilayer feed-forward neural network and recognized places where liquidity risk prevailed through mathematical computations as well. The results had shown that the model had achieved a prediction accuracy of 87.5%, thus indicating how neural networks can be used effectively when doing analysis on financial data and assessing liquidity risks. Numerical evidence has been provided by this study as regards the ability of Tesla’s financial liquidity changes prediction via the model making it a good tool for the purposes of financial planning including risk management as well. Keywords: Financial analysis; Liquidity risk; Neural network; Predictive models; Return on assets (ROA); Return on equity (ROE); Tesla inc. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:1:p:614-619:id:4186 Access Statistics for this article More articles in Edelweiss Applied Science and Technology from Learning Gate |
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