Modelling Crowdfunding Ensemble Learning Prediction
محورهای موضوعی : Financial MathematicsMehran Saeidi Aghdam 1 , Akbar Alam Tabriz 2 , Alireza Bahiraie 3 , Ahmad Sadeghi 4
1 - Department of entrepreneurship, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Department of Industrial management, Shahid beheshti University, tehran, Iran
3 - Department of Mathematics, Semnan University, Semnan, Iran
4 - Department of Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
کلید واژه: Mathematical, entrepreneurship, crowdfunding, Prediction,
چکیده مقاله :
Crowdfunding is a new technology-enabled innovative process that is changing the capital market space. Internet-based applications, particularly those related to Web 2.0, have had a significant impact on sectors of society such as education, business, and medicine. The goal of this research is to fill a gap in the literature on mathematical modelling and prediction of ensemble learning in order to evaluate crowdfunding projects. The Mathematical model determines the cost of funding for the entrepreneur and the return investors will receive per period. A correct financial model is essential in order to keep all three stakeholders involved in the long term. The results show the designed model improved performance in predicting the evaluation of success or failure of Crowdfunding projects.
[1] Allison, T.H., Davis, B.C., Webb, J.W., Short, J.C., Persuasion in crowdfunding: An elaboration likelihood model of crowdfunding performance. J. Bus. Ventur. 2017, 32, P. 707–725, Doi:10.1016/j.jbusvent.2017.09.002
[2] Anglin, A.H., Wolfe, M.T., Short, J.C., McKenny, A.F., Pidduck, R.J., Narcissistic rhetoric and crowdfunding performance, A social role theory perspective, J. Bus. Ventur. 2018, 33, P. 780–812.
Doi: 10.1016/j.jbusvent.2018.04.004.
[3] Bauer, E., Kohavi, R., An empirical comparison of voting classification algorithms: Bagging, Boosting, and variants, Machine Learning, 1999, 36, P. 105–139, Doi: 10.1023/A:1007515423169.
[4] Beaulieu, T., Sarker, S., Discursive meaning creation in crowdfunding: A socio-material perspective, Paper presented at the International Conference for Information Systems, Milan, Italy, 2013.
Doi: 10.1098/102515423169.
[5] Belleflamme, P., Lambert, T., Schwienbacher, A.,Crowdfunding: Tapping the Right Crowd, Journal of Business Venturing, 2014, 29(5), P.585–609. Doi: 10.1016/j.jbusvent.2013.07.003.
[6] Belleflamme, P., Omrani, N., Peitz, M., The economics of crowdfunding platforms, Information Economics and Policy, 2015, 33, P. 11–28, Doi: 10.1016/j.infoecopol.2015.08.003.
[7] Belleflamme, P., Lambert, T., Schwienbacher, A., Digital Business Models: Understanding Strategies, 2010, P. 1–30, Doi: 10.2478/fman-2018-0011.
[8] Boons, M., Stam, D., and Barkema, H.G., Feelings of Pride and Respect as Drivers of Ongoing Member Activity on Crowdsourcing Platforms, Journal of Management Studies, 2015, 52(6), P.717–74.
Doi: 10.1111/joms.12140.
[9] Brabham, D.C., Ribisl. K.M., Kirchner. T. R., Bernhardt, J.M., Crowdsourcing Applications for Public Health, American Journal of Preventive Medicine, 2014, 46(2), P. 179-187, Doi: 10.1016/j.amepre.2013.10.016
[10] Breiman, L., Random forests. Machine Learning, 2001, 45(1),P. 5–32, Doi: 10.1023/A:1010933404324.
[11] Breiman, L., Bagging, Predictors Machine Learning,1996, 24(2), P. 123–140, Doi: 10.1023/A:101805431435 0.
[12] Bruton, G.D., Khavul, S., Chavez, H., Microlending in Emerging Economies: Building a New Line of Inquiry from the Ground Up, Journal of International Business Studies, 2011, 42(5), P. 718–739, Doi: 10.1057/jibs.2010.58.
[13] Burtch, G., Ghose, A., Wattal, S., The Hidden Cost of Accommodating Crowdfunder Privacy Preferences:A Randomized Field Experiment, Management Science, 2015, 61(5), P. 949–962, Doi: 10.1287/mnsc.2014. 2069.
[14] Davoodi, A., Kasbi, I., Dadashi, K., Stock price analysis using machine learning method (Non-sensory-parametric backup regression algorithm in linear and nonlinear mode), Advances in Mathematical Finance and Applications, 2019, 4(3), P. 1-13. Doi: 10.22034/AMFA.2019.1869838.1232.
[15] Dietterich, T.G., Ensemble methods in machine learning, in Proc. Int. Workshop Multiple Classifier Syst. Berlin, Germany: Springer, 2020, P. 1–15, Doi: 10.1007/3-540-45014-9_1.
[16] Dibachi, H., Behzadi, M.H., Izadikhah, M., Stochastic Modified MAJ Model for Measuring the Efficiency and Ranking of DMUs, Indian Journal of Science and Technology, 2015, 8(8), P.1-7. Doi: 10.17485/ijst/2015/v8iS8/71505
[17] Farshadfar, Z., Marcel Prokopczuk, M., Improving Stock Return Forecasting by Deep Learning Algorithm, Advances in Mathematical Finance and Applications, 2019, 4(3), P. 1-13. Doi: 10.22034/AMFA.2019.584494.1 173.
[18] Feller, J., Gleasure, R., Treacy, S., From the wisdom to the wealth of crowds: A meta triangulation of crowdfunding research, 2013, 1(2), Doi: 10.1007/978-3-319-13817-6_36.
[19] Gerber, E. M., Hui, J. S., Kuo, P. Y., Crowdfunding: Why people are motivated to post and fund projects on crowdfunding platforms, 2012, Doi: 10.1016/2012.05.033.
[20] Ghasemzadeh, M., Mohammad-Karimi, N., Ansari-Samani, H., Machine learning algorithms for time series in financial markets, Advances in Mathematical Finance and Applications, 2020, 5(3), P.479-490.
Doi: 10.22034/AMFA.2020.674946.
[21] Giudici, G., Guerini, M., Lamastra, C.R., Why Crowdfunding Projects Can Succeed: The Role of Proponents Individual and Territorial Social Capital, 2013, Doi: 10.2139/ssrn.2255944.
[22] Guo, K., Tang, Y., Zhang, P., CSF: Crowdsourcing semantic fusion for heterogeneous media big data in the internet of things, Trends in Food Science & Technology, 2017, 66, P.63-72, Doi: 10.1016/j.inffus.2017.01.008.
[23] Hemer, J., Schneider, U., Dornbusch, F., Frey, S., Crowd funding undand ere Formen670 informeller Mikrofinanzierung in der Projekt-und Innovationsfinanzierung, 2011, ISBN-13: 978-3839603130.
[24] Howe, J., Crowdsourcing: How the power of the crowd is driving the future of business, New York: Random House, 2008. ISBN-13: 978-0307396211.
[25] Howe, J., Crowdsourcing: A definition Crowdsourcing. 2006, Doi: 10.1007/978-3-319-18341-1_3.
[26] Jenik, I., Lyman, T., Nava A., Crowdfunding and Financial Inclusion. CGAP, US: World Bank Group. 2017.
[27] Jones, O., Gatrell, C., Editorial: The Future of Writing and Reviewing for IJMR.” International Journal of Management Reviews, 2014, 16(3), P.249–264, Doi: 10.1111/ijmr.12038.
[28] Kleemann, F., Vob, G.G., Rieder, K., Un(der)paid Innovators: The Commercial Utilization of Consumer Work Through Crowdsourcing, Translated by S.S. Gissendanner. Science, Technology & Innovation Studies, 2008, 4(1), Doi: 10.17877/DE290R-12790.
[29] Krogh, A., Vedelsby, J., Neural network ensembles, cross validation, and active learning. In Tesauro, G., Touretzky, D.S., Leen, T.K., eds.: Advances in Neural Information Processing Systems 7. MIT Press, Cambridge, 1995, P. 231–238.
[30] Kuncheva, L.I., Whitaker, C.J., Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 2003, 51(2), P. 181–207. Doi: 10.1023/A:1022859003006.
[31] Lambert, T., Schwienbacher, A., An Empirical Analysis of Crowdfunding, 2010.
[32] Larralde, B., Schwienbacher, A., Crowdfunding of Small Entrepreneurial Ventures, The Oxford Handbook of Entrepreneurial Finance, edited by D. Cumming, 2012, 369. Doi: 10.2139/ssrn.1699183
[33] Lehner, O.M., Crowdfunding Social Ventures: A Model and Research Agenda, Venture Capital, 2013, 15(4), P. 289–311, Doi: 10.1080/13691066.2013.782624.
[34] Ley, A., Weaven, S., Exploring Agency Dynamics of Crowdfunding in Start-Up Capital Financing, Academy of Entrepreneurship Journal, 2011, 17(1), P. 85–110.
[35] Li, Y., Zhang, Z., Wang, R., Chen, Y., Consumer Purchase Intention Toward Crowdfunding Products/Services: A Cost–Benefit Perspective, Sustainability, 2019, 11, 3579. Doi: 10.3390/su11133579.
[36] Lu, H., Zhou, H., Wang, Junxian, Wang, T., Dong, X., Zhuang, Z., Li, C., Ensemble learning for independent component analysis of normal galaxy spectra, The Astronomical Journal, 2006, 131, P. 790-805. Doi: 10.1086/498711.
[37] Martínez-Climent, C., Costa-Climent, R., Oghazi, P, Sustainable Financing through Crowdfunding. Sustainability, 2019, 11, P. 934-956. Doi: 10.3390/su11030934.
[38] Mollick, E., The Dynamics of Crowdfunding: An Exploratory Study, Journal of Business Venturing, 2014, 29(1), P. 1–16, Doi: 10.1016/j.jbusvent.2013.06.005.
[39] Mollick, E., Robb, A., Democratizing Innovation and Capital Access: The Role of Crowdfunding, California Management Review, 2016, 58(2), P. 72–87. Doi: 10.1525/cmr.2016.58.2.72
[40] Shravya, Ch., Pravalika, K., Subhani, Sh., Prediction of breast cancer using supervised machine learning techniques, Int. J. Innov. Technol. Explor. Eng, 2019. Doi: 10.1109/R10-HTC.2017.8288944
[41] Simons, A., Kaiser, LF., vom Brocke, J., Enterprise crowdfunding: foundations, applications, and research findings, Bus Inf SystEng, 2019, 61, P. 113–121. Doi: 10.1007/s12599-018-0568-7.
[42] Testa, S., Nielsen, KR., Bogers, M., Cincotti, S., The role of crowdfunding in moving towards a sustainable society, Technol Forecast Soc Chang, 2019, 141, P. 66–73. Doi: 10.1016/j.techfore.2018.12.011.
[43] Wang, S.-H., Zhang, Y.-D., Yang, M., Liu, B., Ramirez, J., Gorriz, J.M. Unilateral sensorineural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression. Integr. Comput-Aided Eng, 2019, 26(4), P. 411–426. Doi: 10.3233/ICA-190605.
[44] Yang, X., Zhao, K., Tao, X., Shiu, E, Developing and Validating a Theory-Based Model of Crowdfunding Investment Intention—Perspectives from Social Exchange Theory and Customer Value Perspective, Sustainability, 2019, 11, P. 25-56. Doi: 10.3390/su11092525.
[45] Zhou, L., Zhang, Z., Chen, Y.-C., Zhao, Z.-Y., Yin, X.-D., Jiang, H.-B, A deep learning-based radiomics model for differentiating benign and malignant renal tumors, Transl. Oncol, 2019, 12(2), P. 292–300. Doi: 10.1016/j.tranon.2018.10.012.
[46] Zhou, Z.H., Jiang, Y., Chen, S.F, Extracting symbolic rules from trained neural network ensembles, AI Communications, 2003, 16(1), P. 3–15. Doi: 10.1007/s11227-017-2228.
[47] Zhu, X., Zhang, C., Liang, G., An empirical study of bagging predictors for different learning algorithms, 25th Conference on Artificial Intelligence, 2011, P. 1802–1803. Doi: 10.1007/978-3-642-25853-4_26.
