Application of Financial Prediction for Share Price Improvement in the Business Sector by means of Artificial Neural Network

Application of Financial Prediction for Share Price Improvement in the Business Sector by means of Artificial Neural Network
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-3
Year of Publication : 2023
Author : Rafael Roosell Paez Advincula, Celso Gonzales Chavesta, Lilian Ocares Cunvarahi
DOI : 10.14445/22315381/IJETT-V71I3P211

How to Cite?

Rafael Roosell Paez Advincula, Celso Gonzales Chavesta, Lilian Ocares Cunvarahi, "Application of Financial Prediction for Share Price Improvement in the Business Sector by means of Artificial Neural Network," International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 91-104, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I3P211

Abstract
The use of artificial neural networks has an important role; nowadays, they represent an advantage for solving complex problems with different constraints in comparison with traditional methods. The research presents the theory and model; addresses the analysis of corporate financial statements, using the research tool to apply financial forecasting to improve the corporate stock price. The objective is to determine the results of the application of financial prediction for the improvement of stock prices in the corporate sector by means of artificial neural networks. Also, to build different models to evaluate the behavior of networks in different numbers of input variables or neurons in the hidden layer and the probabilities of success by means of the prediction results in the input variables. The predictive capacity in the methods used is based on perceptron-type layers and a strategy that allows alternative system modelling in the predictive control of financial statements.

Keywords
Artificial Neural Network, Business, Prediction, Financial, Neurons, Model.

References
[1] Marco Bade, “Conversion and Contagion in Entrepreneurship: A Cross-Country Analysis,” Strategic Entrepreneurship Journal, vol. 16, no. 1, pp. 207-239, 2022. Google Scholar | CrossRef | Publisher Link
[2] Edith Baron, Cristian Garcia, and Silvia Karol Sanchez, “Business Intelligence and Data Analytics in Business Processes,” Scientific Journal of Systems and Informatics, vol. 1, no. 2, pp. 38-53, 2021. Google Scholar | CrossRef | Publisher Link
[3] Claes Belfrage, and Markus Kallifatides, “Financing and the New Swedish Model,” Cambridge Journal of Economics, vol. 42, no. 4, pp. 875-900, 2018. Google Scholar | CrossRef | Publisher Link
[4] Gideon Carvalho de Benedicto et al., “Analysis of Credit Risk Faced by Public Companies in Brazil: An Approach Based on Discriminant Analysis, Logistic Regression and Artificial Neural Networks,” Managerial Studies, vol. 35, no. 153, pp. 347-360, 2019. Google Scholar | CrossRef | Publisher Link
[5] Daniele Bianchi, Matthias Büchner, and Andrea Tamoni, “Bond Risk Premiums with Machine Learning,” The Review of Financial Studies, vol. 34, no. 2, pp. 1046-1089, 2021. Google Scholar | CrossRef | Publisher Link
[6] Daniele Bianchi et al., “Corrigendum: Bond Risk Premiums with Machine Learning,” The Review of Financial Studies, vol. 34, no. 2, pp. 1090-1103, 2021. Google Scholar | CrossRef | Publisher Link
[7] Devdeepta Bose et al., “Decision Weights for Experimental Asset Prices based on Visual Salience,” The Review of Financial Studies, vol. 35, no. 11, pp. 5094-5126, 2022. Google Scholar | CrossRef | Publisher Link
[8] Rohit B R et al., "Stock Market Prediction using Machine Learning," SSRG International Journal of Communication and Media Science, vol. 7, no. 2, pp. 6-9, 2020. Google Scholar | CrossRef | Publisher Link
[9] Markus Brunnermeier et al., “Perspectives on the Future of Asset Pricing,” The Review of Financial Studies, vol. 34, no. 4, pp. 2126-2160, 2021. Google Scholar | CrossRef | Publisher Link
[10] Andrea Buraschi, Ilaria Piatti, and Paul Whelan, “Subjective Bond Returns and Aggregation,” The Review of Financial Studies, vol. 35, no. 8, pp. 3710-3741, 2021. Google Scholar | CrossRef | Publisher Link
[11] Alex Chinco, Samuel M. Hartzmark, and Abigail B. Sussman, “A New Test of Risk Factor Relevance,” Journal of Finance, vol. 77, no. 4, pp. 2183-2238, 2022. Google Scholar | CrossRef | Publisher Link
[12] Juan Gustavo Corvalán, “Artificial Intelligence Challenges, Challenges and Opportunities Prometea: Latin America's First Artificial Intelligence at the Service of Justice,” Journal of Constitutional Investigations, vol. 5, no. 1, pp. 295-316, 2018. Google Scholar | CrossRef | Publisher Link
[13] Douglas Cumming, and Lars Hornuf, “Marketplace Lending of Small and Medium-Sized Enterprises,” Strategic Entrepreneurship Journal, vol. 16, no. 1, pp. 32-66, 2021. Google Scholar | CrossRef | Publisher Link
[14] Rieke Pernamasari et al., "Good Corporate Governance and Prediction of Financial Distress to Stock Prices: Atman Z Score Approach," SSRG International Journal of Economics and Management Studies, vol. 6, no. 11, pp. 56-62, 2019. Google Scholar | CrossRef | Publisher Link
[15] Duan Jing, “Modeling Financial Systems Using Deep Neural Networks (DNN) for Effective Risk Assessment and Prediction,” Franklin Institute Journal, vol. 356, no. 8, pp. 4716-4731, 2019. Google Scholar | CrossRef | Publisher Link
[16] Jillian Grennan, “Social Change through Financial Innovation: Evidence from Donor Advised Funds,” Review of Corporate Finance Studies, vol. 11, no. 3, pp. 694-735, 2022. Google Scholar | CrossRef | Publisher Link
[17] Shihao Gu, Bryan Kelly, and Dacheng Xiu, “Empirical Asset Pricing via Machine Learning,” The Review of Financial Studies, vol. 33, no. 5, pp. 2223-2273, 2020. Google Scholar | CrossRef | Publisher Link
[18] Tami Gurley-Calvez, and Josephine Lugovskyy, “The Role of Entrepreneurial Risk in Financial Portfolio Allocation,” Small Business Economics, vol. 53, no. 4, pp. 839-858, 2019. Google Scholar | CrossRef | Publisher Link
[19] İlker Güven, and Fuat Şimşir, “Demand Forecasting with Color Parameter in Retail Apparel Industry using Artificial Neural Networks (ANN) and Support Vector Machines (SMV) Methods,” Computer and Industrial Engineering, vol. 147, no. 1, 2020. Google Scholar | CrossRef | Publisher Link
[20] Matthew Hollow, “Historicizing Entrepreneurial Networks,” Strategic Entrepreneurship Journal, vol. 14, no. 1, pp. 66-88, 2020. Google Scholar | CrossRef | Publisher Link
[21] Ibuomo R. Tebepah, "Digital Signal Processing for Predicting Stock Prices Using IBM Cloud Watson Studio," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 1, pp. 7-11, 2020. Google Scholar | CrossRef | Publisher Link
[22] Tadaaki Hosaka, “Bankruptcy Prediction using Financial Ratios in Images and Convolutional Neural Networks,” Expert Systems with Applications, vol. 117, no. 1, pp. 287-299, 2019. Google Scholar | CrossRef | Publisher Link
[23] Anthony Howell, “Ethnic Entrepreneurship, Start-Up Finance, and Entrepreneurial Performance in China,” Small Business Economics, vol. 52, no. 3, pp. 697-712, 2019. Google Scholar | CrossRef | Publisher Link
[24] Li Li et al., “The Certification Effect of Government R&D Subsidies on Innovative Interpleural Firm' Access to Bank Finance: Evidence from China,” Small Business Economics, vol. 52, no. 1, pp. 241-259, 2019. Google Scholar | CrossRef | Publisher Link
[25] Yang Liu, “Novel Volatility Forecasting Using Deep Learning Long Short-Term Memory Recurrent Neural Networks,” Expert Systems with Applications, vol. 132, no. 1, pp. 99-109, 2019. Google Scholar | CrossRef | Publisher Link
[26] Adil Moghar, and Mhamed Hamiche, “Stock Market Prediction using LSTM Recurrent Neural Network,” Procedia Computer Science, vol. 170, no. 1, pp. 1168-1173, 2020. Google Scholar | CrossRef | Publisher Link
[27] Muhammad Tayyab Yaqoob, Mohd. Khairil Rahmat, and Siti Marwangi Mohamad Maharum, “Modified Teaching Learning Based Optimization for Selective Harmonic Elimination in Multilevel Inverters,” Ain Shams Engineering Journal, vol. 13, no. 5, 2020. Google Scholar | CrossRef | Publisher Link
[28] Helmer Muñoz-Hernández, Roberto Carlos Osorio-Mass, and Luis Manuel Zúñiga-Pérez, “Business Intelligence: Key to Success in the Information Age,” Clio Magazine América, vol. 10, no. 20, pp. 194-211, 2016. Google Scholar | CrossRef | Publisher Link
[29] Mrinalini Smita, "Logistic Regression Model for Predicting Performance of S&P BSE30 Company using IBM SPSS," International Journal of Mathematics Trends and Technology, vol. 67, no. 7, pp. 118-134, 2021. Google Scholar | CrossRef | Publisher Link
[30] David Obstfeld, Marc J. Ventresca, and Greg Fisher, “An Assembly Perspective on Entrepreneurial Projects: Social Networks in Action,” Strategic Entrepreneurship Journal, vol. 14, no. 2, pp. 149-177, 2020. Google Scholar | CrossRef | Publisher Link
[31] Gabi Dei Ottati, “Marshallian Industrial Districts in Italy: The End of a Model or Adaptation to the Global Economy,” Cambridge Journal of Economics, vol. 42, no. 2, pp. 259-284, 2017. Google Scholar | CrossRef | Publisher Link
[32] Robert J. Richmond, “Trade Network Centrality and Currency Risk Premia,” The Journal of Finance, vol. 74, no. 3, pp. 1315-1361, 2019. Google Scholar | CrossRef | Publisher Link
[33] Omer Berat Sezer, and Ahmet Murat Ozbayoglu, “Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach,” Applied Soft Computing, vol. 70, no. 1, pp. 525-538, 2018. Google Scholar | CrossRef | Publisher Link
[34] Xiaojun Sun, and Yalin Lei, “Research on Financial Early Warning of Mining Listed Companies Based on BP Neural Network Model,” Resources Policy, vol. 73, no. 4, 2021. Google Scholar | CrossRef | Publisher Link
[35] Tami Gurley-Calvez, and Josephine Lugovskyy, “The Role of Entrepreneurial Risk in the Allocation of the Financial Portfolio,” Small Business Economics, vol. 53, no. 4, pp. 839-858, 2019. Google Scholar | CrossRef | Publisher Link
[36] A.Maruthi Varaprasad, Derese Getachew, and Tigist Abebe, "Determinants of Financial Performance and Default Prediction: An Empirical Study on Select Commercial Banks in Ethiopia," SSRG International Journal of Economics and Management Studies, vol. 6, no. 5, pp. 59-64, 2019. Google Scholar | CrossRef | Publisher Link
[37] R Venegas, “Artificial Intelligence Applications for Automated Classification of Communicative Intent: In Engineering Reports,” Sign’s Journal, vol. 54, no. 107, pp. 942-970, 2021.  CrossRef
[38] Federico Walas, and Andrés Redchuk, “IIoT/IoT and Artificial Intelligence/ Machine Learning as a Process Optimization Driver under Industry 4.0 Model,” Journal of Computer Science & Technology, vol. 21, no. 2, pp. 170-176, 2021. Google Scholar | CrossRef | Publisher Link
[39] Dan J. Wang, “When do Returned Emigrants Become Entrepreneurs? The Role of Global Social Networks and Institutional Distance,” Strategic Entrepreneurship Journal, vol. 14, no. 2, pp. 125-148, 2020. Google Scholar | CrossRef | Publisher Link
[40] Jinghong Xu, and Daguang Yang, “Financial Risk Control Model Base Don Deep Neural Networks,” Scientific Programming, 2022. Google Scholar | CrossRef | Publisher Link
[41] Xue Yan, Wang Weihan, and Miao Chang, “Research on Financial Assets Transaction Prediction Model based on LSTM Neural Network,” Neural Computing and Applications, vol. 33, no. 1, pp. 257-270, 2021. Google Scholar | CrossRef | Publisher Link
[42] Pengfei Yu, and Xuesong Yan, “Stock Price Prediction Base on Deep Neural Networks,” Neural Computing and Applications, vol. 32, no. 1, pp. 1609-1628, 2020. Google Scholar | CrossRef | Publisher Link
[43] Jingfeng Zhao, and Bo Li, “Credit Risk Assessment of Small and Medium Sized Enterprises in Supply Chain Finance Base Don SVM and BP Neural Network,” Neural Computing and Applications, vol. 34, no. 1, pp. 12467-12478, 2022. Google Scholar | CrossRef | Publisher Link
[44] Youssef Zizi et al., “An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression,” Risks, vol. 9, no. 1, p. 200, 2021. Google Scholar | CrossRef | Publisher Link