Abstract
In this chapter, we examine the research issues related to the real-time and mobile data analytics in the area of FinTechs. The issues examined consist of the non-traditional data analytics approach, news media sentiment analysis and opinion mining, asset pricing modeling, real-world financial multi-case study, and mobile cloud computing creation. We develop the multifactor asset pricing model, the multiword text analytics approach, the supply demand framework of financial service innovation, and the Big Data mobile prototype system. The promising research results include the technology transfer to a start-up firm, the university-industry cooperation, the set of three apps in the Android store, and the multi-case study reports and academic publications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Knowledge Management Winner, http://kmw.chinatimes.com/.
- 3.
Knowledge Management Winner, http://kmw.chinatimes.com/.
- 4.
Knowledge Management Winner, http://kmw.chinatimes.com/.
References
Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259–1294.
Arner, D. W., & Barberis, J. (2015). FinTech in China: From the shadows? The Journal of Financial Perspectives, 3, 78–91.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680.
Brown, G. W., & Cliff, M. T. (2005). Investor sentiment and asset valuation. The Journal of Business, 78(2), 405–440.
Buot, M. (2006). Probability and computing: Randomized algorithms and probabilistic analysis. Journal of the American Statistical Association, 101(473), 395–396.
Choi, D., Chung, K. S., & Shon, J. (2010). An improvement on the weighted least-connection scheduling algorithm for load balancing in web cluster systems. In Grid and distributed computing, control and automation (pp. 127–134).
Chuen, D. L. K., & Teo, E. G. S. (2015). Emergence of fintech and the LASIC principles. The Journal of Financial Perspectives, 3, 24–37.
Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.
Deloitte. (2016). Perspectives: Banking and securities outlook 2017. http://www2.deloitte.com/us/en/pages/financial-services/articles/banking-industry-outlook.html
Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. The Journal of Finance, 47(2), 427–465.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22.
Fang, L., & Peress, J. (2009). Media coverage and the cross-section of stock returns. The Journal of Finance, 64(5), 2023–2052.
Financial Supervisory Commission. (2016). Fintech-development strategy white paper. Taipei: Financial supervisory commission.
Garcia, D. (2013). Sentiment during recessions. The Journal of Finance, 68(3), 1267–1300.
Gulamhuseinwala, I., Bull, T., & Lewis, S. (2015). FinTech is gaining traction and young, high-income users are the early adopters. The Journal of Financial Perspectives, 3, 16–23.
Harchol-Balter, M. (2013). Performance modeling and design of computer systems: Queueing theory in action. Cambridge: Cambridge University Press.
Hu, N., Bose, I., Koh, N. S., & Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674–684.
Ittoo, A., & Bouma, G. (2013). Term extraction from sparse, ungrammatical domain-specific documents. Expert Systems with Applications, 40(7), 2530–2540.
Kearney, C., & Liu, S. (2014). Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, 33, 171–185.
KPMG. (2015a). Mobile banking is a key selling point for a growing number of customers (pp. 1–8). Amstelveen: KPMG International Cooperative.
KPMG. (2015b). Digital offerings in mobile banking—The new normal (pp. 1–10). Amstelveen: KPMG International Cooperative.
KPMG. (2015c). Mobile banking-global trends and their impaction banks (pp. 1–33). Amstelveen: KPMG International Cooperative.
Kumar, A., & Lee, C. (2006). Retail investor sentiment and return comovements. The Journal of Finance, 61(5), 2451–2486.
Kurov, A. (2010). Investor sentiment and the stock market’s reaction to monetary policy. Journal of Banking & Finance, 34(1), 139–149.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.
Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35–65.
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Scoring, term weighting and the vector space model. In Introduction to information retrieval (Vol. 100, pp. 2–4).
Seng, C., Wu, T., & Chang, Y. (2017). Innovative, integrated, mobile financial services (MOST three-year final research project report, MOST 104-2627-E-004-001).
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442.
Teo, Y. M., & Ayani, R. (2001). Comparison of load balancing strategies on cluster-based web servers. Simulation, 77(5–6), 185–195.
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139–1168.
Tetlock, P. C., Saar-Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms’ fundamentals. Journal of Finance, 63(3), 1437–1467.
World~Economic~Forum.~(2015).~Global~agenda~council~on~the~future~of~financing~& capital:~The~future~of~FinTech~a~paradigm~shift~in~small~business~finance.~Retrieved fromhttp://www3.weforum.org/docs/IP/2015/FS/GAC15_The_Future_of_FinTech_Paradigm_Shift _Small_Business_Finance_report_2015.pdf
World Economic Forum. (2017). Beyond Fintech: How the successes and failures of new entrants are reshaping the financial system. Part of the Future of Financial Services series | Prepared in collaboration with Deloitte, August 2017. http://www3.weforum.org/docs/Beyond_Fintech_-_A_Pragmatic_Assessment_of_Disruptive_Potential_in_Financial_Services.pdf
Xu, K., Liao, S. S., Li, J., & Song, Y. (2011). Mining comparative opinions from customer reviews for competitive intelligence. Decision Support Systems, 50(4), 743–754.
Xu, Z., & Huang, R. (2009). Performance study of load balancing algorithms in distributed web server systems (CS213 Parallel and Distributed Processing Project Report, 1).
Yin, R. K. (1994). Case study research: Design and methods (2nd ed.). Newbury Park: Sage Publications.
Zhang, Y., Dang, Y., & Chen, H. (2013). Research note: Examining gender emotional differences in web forum communication. Decision Support Systems, 55(3), 851–860.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Seng, JL., Chiang, YM., Chang, PR., Wu, FS., Yen, YS., Tsai, TC. (2018). Big Data and FinTech. In: Chen, SH. (eds) Big Data in Computational Social Science and Humanities. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-95465-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-95465-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95464-6
Online ISBN: 978-3-319-95465-3
eBook Packages: Computer ScienceComputer Science (R0)