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Financial Risk Prediction Model in the Context of Big Data - Corporate Financial Risk Control Based on LSTM Deep Neural Networks

   | 09 gru 2023

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Li, S., & Chen, X. (2022). Research on financial risk crisis prediction of listed companies based on iwoabp neural network. Journal of Internet Technology. Search in Google Scholar

Rong, Y. (2017). 15.study on financial risk prediction of listed companies based on logistic regression model. Boletin Tecnico/technical Bulletin, 55(20), 107-114. Search in Google Scholar

Takeda, Akiko, Fujiwara, Shuhei, Kanamori, & Takafumi. (2014). Extended robust support vector machine based on financial risk minimization. Neural Computation. Search in Google Scholar

Huang, B., & Wei, J. (2021). Research on deep learning-based financial risk prediction. Scientific programming(Pt.13), 2021. Search in Google Scholar

Tsai, M. F., & Wang, C. J. (2016). On the risk prediction and analysis of soft information in finance reports. European Journal of Operational Research, S037722171630529X. Search in Google Scholar

Kim, K., & Yoon, K. (2021). News article based industry risk index prediction for industry-specific evaluation. Journal of web engineering(3), 20. Search in Google Scholar

Ribeiro, B., Chen, N., & Kovacec, A. (2017). Shaping graph pattern mining for financial risk. Neurocomputing, 326-327(JAN.31), 123-131. Search in Google Scholar

Hovingh, Kees, G., Boekholdt, Matthijs, S., Kastelein, & John, J. P., et al. (2017). Lipoprotein(a) improves cardiovascular risk prediction based on established risk algorithms. Journal of the American College of Cardiology. Search in Google Scholar

Gao, & Long. (2015). Collaborative forecasting, inventory hedging and contract coordination in dynamic supply risk management. European Journal of Operational Research, 245(1), 133-145. Search in Google Scholar

Jammazi, R., & Nguyen, D. K. (2017). Estimating and forecasting portfolio’s value-at-risk with wavelet-based extreme value theory: evidence from crude oil prices and us exchange rates. Journal of the Operational Research Society, 68(11), 1352-1362. Search in Google Scholar

Persand, G., & Brooks, C. (2010). Volatility forecasting for risk management. Journal of Forecasting, 22(1), 1-22. Search in Google Scholar

Kim, A., Yang, Y., Lessman, S., Ma, T., & Johnson, J. E. V. (2019). Can deep learning predict risky retail investors? a case study in financial risk behavior forecasting. European Journal of Operational Research, 283(1). Search in Google Scholar

Xu, Q., Chen, L., Jiang, C., & Liu, Y.(2023) Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model. Journal of Forecasting. Search in Google Scholar

Qu, M., & Li, Y. (2021). Financial risk early-warning model based on kernel principal component analysis in public hospitals. Mathematical Problems in Engineering, 2021(1), 1-7. Search in Google Scholar

Fox, R. P., & Baker, R. D. (2010). Capital investment appraisal: a new risk premium model. International Transactions in Operational Research, 10(2). Search in Google Scholar

Richter, W., & Kruger, P. S. (2012). Rexs : a financial risk diagnostic expert system. South African Journal of Industrial Engineering, 3(1). Search in Google Scholar

Oikonomou, L., Brooks, C., & Pavelin, S. (2012). The impact of corporate social performance on financial risk and utility: a longitudinal analysis. Financial Management, 41(2), 483-515. Search in Google Scholar

Stéphane, Verguet, Zachary, D, Olson, & Joseph, et al. (2015). Health gains and financial risk protection afforded by public financing of selected interventions in ethiopia: an extended cost-effectiveness analysis. Lancet Global Health. Search in Google Scholar

Fernandez, J. L., & Forder, J. (2010). Equity, efficiency, and financial risk of alternative arrangements for funding long-term care systems in an ageing society. Oxford Review of Economic Policy, volume 26(4), 713-733. Search in Google Scholar

Saksena, P., Adélio Fernandes Antunes, Xu, K., Musango, L., & Carrin, G. (2011). Mutual health insurance in rwanda: evidence on access to care and financial risk protection. Health Policy, 99(3), 203-209. Search in Google Scholar

DUCLOS, ROD, (AUTHOR), WEN, WAN, & ECHO, et al. (2013). Show me the honey! effects of social exclusion on financial risk-taking. Journal of Consumer Research. Search in Google Scholar

Venter, G. V. D., Michayluk, D., & Davey, G. (2012). A longitudinal study of financial risk tolerance. Journal of Economic Psychology, 33(4), 794-800. Search in Google Scholar

Chatterjee, S., & Ionescu, F. (2012). Insuring student loans against the financial risk of failing to complete college. Quantitative Economics, 3(3), 393-420. Search in Google Scholar

Horton, T. A., Hajirasouliha, I., Davison, B., & Ozdemir, Z. (2021). Accurate prediction of cyclic hysteresis behaviour of rbs connections using deep learning neural networks. Engineering structures(247-Nov.15). Search in Google Scholar

Nakamura, K., Soatto, S., & Hong, B. W. (2021). Block-cyclic stochastic coordinate descent for deep neural networks. Neural Networks. Search in Google Scholar

Aghaei, M. H., Baghban, M. H., Hashemi, E. S., & Hashemi, S. A. (2022). Predicting effective parameters in cyclic behavior of reinforced masonry walls with shotcrete using artificial neural networks. Solid State Phenomena, 329, 71-78. Search in Google Scholar

Kefalas, A., Ofner, A. B., Pirker, G., Posch, S., Geiger, B. C., & Wimmer, A. (2021). Detection of knocking combustion using the continuous wavelet transformation and a convolutional neural network. Energies, 14. Search in Google Scholar

Wang, H., Liang, T., & Cheng, Y. (2021). Prediction of perceived utility of consumer online reviews based on lstm neural network. Mobile Information Systems. Search in Google Scholar

Ni, Y., Li, X., Zhao, H., Yang, J., Xia, W., & Gui, G. (2022). An effective hybrid v2v/v2i transmission latency method based on lstm neural network. Physical communication(Apr.), 51. Search in Google Scholar

Zhao, J., Deng, F., Cai, Y., & Chen, J. (2019). Long short-term memory - fully connected (lstm-fc) neural network for pm 2.5 concentration prediction. Chemosphere, 220(APR.), 486-492. Search in Google Scholar

Dec, G. R. (2023). Fpga-based learning acceleration for lstm neural network. Parallel Processing Letters, 33(01n02). Search in Google Scholar

Ventosa, S., Schimmel, M., & Stutzmann, E. (2019). Towards the Processing of Large Data Volumes with Phase Cross-Correlation. Seismological Research Letters. Search in Google Scholar

Shuman, B. R., Schwartz, M. H., & Steele, K. M. (2017). Electromyography data processing impacts muscle synergies during gait for unimpaired children and children with cerebral palsy. Frontiers in Computational Neuroscience, 11, 50-. Search in Google Scholar

Zhang, J., Zhang, Z., Li, H., & Liu, X. (2022). Optimizing double-phase method based on gradient descent algorithm with complex spectrum loss function. Optics Communications, 514, 128136-. Search in Google Scholar

Xu, C., Liu, X., & Zhao, W. (2022). Salient object detection network with center poolingand distance-weighted affinity loss function. Journal of electronic imaging(2), 31. Search in Google Scholar

eISSN:
2444-8656
Język:
Angielski
Częstotliwość wydawania:
Volume Open
Dziedziny czasopisma:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics