The Responsibility Of The Partners In Their Own Funds For The Debts And Obligations Of The Limited Liability Company

Emad Kendory, Khalid Obaid Ahmed, Ahmed Saad Jari

Resumen


Investing in stocks is fraught with long risks that makes it tough to manage and predict the choices out there to the investor. A decision making to an in- vestment can open up losses that accumulate them cause bankruptcy. There- fore, the extent of disclosure of stocks in the financial statements; in accord with the International Standard; is so important to investors as well as the different approaches to predict the future prices of stock. Among the foremost vital of those is that the neural network. The neural network depend upon the historical prices of stocks to expect the future prices and rank its importance. The researchers conclude that Facebook Inc. comply with the International Accounting Standard (IAS 1) as well as neural network ranks the relative importance of each item that affect the stock price estimate.. For purposes of this topic, the research divided this study into four sections. The first section included the methodology of research and some of the previous studies, the second section is targeted on the theoretical framework of the research, and the third section shows the application of research, while the fourth section was devoted to the foremost vital conclusions and recommendations reached by the researchers.

Palabras clave


Disclosure; financial statements; prediction; neural network

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Referencias


Kraus, M., & Feuerriegel, S. (2017). Decision support from finan- cial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104, 38-48.

Olson, D., & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453-465.

Cretu, C., Sîrbu, C., Gheonea, V., & Constandache, N. (2011). Presentation of Financial Statements According to IPSAS-a Challenge for Professional Accountants. EIRP Proceedings, 6.

Hlaciuc, E., Grosu, V., Socoliuc, M., & Maciuca, G. (2014). Com- parative study regarding the main differences between US GAAP and IFRS. The USV Annals of Economics and Public Administration, 14(2 (20)), 140-145.

Lepădatu, G. V., & Pîrnău, M. (2009). Transparency in Financial Statements (IAS/IFRS). European Research Studies, 12(1).

Thinggaard, F., Wagenhofer, A., Evans, L., Gebhardt, G., Hoogen- doorn, M., Marton, J., Peasnell, K. (2006). Performance reporting the IASB’s proposed formats of financial statements in the exposure draft of IAS 1. Accounting in Europe, 3(1), 35-63.

https://www.iasplus.com/en-gb/standards/ias/ias1

Al-Massri, R., Al-Astel, Y., Ziadia, H., Mousa, D. K., & Abu-Nas-

er, S. S. (2018). Classification Prediction of SBRCTs.

Cancers Using Artificial Neural Network.

Chen, F. C. (1990). Back-propagation neural networks for nonlin-

ear self-tuning adaptive control. IEEE control systems Magazine, 10(3), 44-48.

Gelenbe, E., Feng, Y., & Krishnan, K. R. R. (1996). Neural net- work methods for volumetric magnetic resonance imaging of the human brain. Proceedings of the IEEE, 84(10), 1488-1496.

Yang, J., Parekh, R., & Honavar, V. (1999). Dist Al: An inter-pat- tern distance-based constructive learning algorithm. Intelligent Data Anal- ysis, 3(1), 55-73.

Aggarwal, S. K., Saini, L. M., & Kumar, A. (2008). Electricity price forecasting in Ontario electricity market using wave let transform in artificial neural network based model. International Journal of Control, Automation, and Systems, 6(5), 639-650.

Fletcher, D., & Goss, E. (1993). Forecasting with neural networks: an application using bankruptcy data. Information & Management, 24(3), 159-167.

Grossi, E., & Buscema, M. (2007). Introduction to artificial neu- ral networks. European journal of gastroenterology & hepatology, 19(12), 1046-1054.

Gu, R., Shen, F., & Huang, Y. (2013). A parallel computing plat- form for training large scale neural networks. Paper presented at the 2013 IEEE international conference on big data.

Jin, B., Hurson, A. R., & Miller, L. L. (1991). Neural net- work-based decision support for incomplete database systems: knowledge acquisition and performance analysis. Paper presented at the Proceedings of the conference on Analysis of neural network applications.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Image net classification with deep convolutional neural networks. Paper presented at the Advances in neural information processing systems.

Zhang, Y., Ding, X., Liu, Y., & Griffin, P. (1996). An artificial neu- ral network approach to transformer fault diagnosis. IEEE Transactions on Power Delivery, 11(4), 1836-1841.

Islam, S. R., Ghafoor, S. K., & Eberle, W. (2018). Mining Illegal Insider Trading of Stocks: A Proactive Approach. Paper presented at the 2018 IEEE International Conference on Big Data (Big Data).

Milosevic, N. (2016). Equity forecast: Predicting long term stock price movement using machine learning. arXiv preprint arXiv:1603.00751.




Universidad del Zulia /Venezuela/ opción/ revistaopcion@gmail.com /ISSN: 1012-1587 / e-ISSN: 2477-9385


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