Big Data: Epistemological Reflections and Impacts in Finance and Capital Market Studies
DOI:
https://doi.org/10.17524/repec.v11i4.1634Keywords:
Big Data, Abductive Method, Epistemology, FinanceAbstract
Objective and method: Access to data series plays a central role in the area of Finance. The increasing availability of large volumes of data, in different formats and at high frequency, combined with the technological advances in data storage and processing tools, have created a new scenario in academic research in general, and in Finance in particular, generating new opportunities and challenges. Among these challenges, methodological issues emerge, which are widely discussed among researchers from different areas, but also epistemological issues that deserve greater space for discussion. Thus, the objective of this theoretical essay is to analyze the conceptual and epistemological aspects of the use of intensive data and its reflections for the area of Finance. Results and contributions: We consider that the hypothetical-deductive method of empirical research, which is the most recurrent, limits the construction of knowledge in the socalled 'Big data era', as this approach starts from an established theory and restricts research to testing the hypothesis(es) proposed. We advocate the use of an abductive approach, as argued in Haig (2005), which converges with the ideas of grounded theory and which seems to be the most appropriate approach to this new context, as it permits greater capacity to collect value information for the data.References
Aksu, M., & Kosedag, A. (2006). Transparency and disclosure scores and their determinants in the Istanbul Stock Exchange. Corporate Governance - an International Review, 14(4), pp. 277-296. DOI: https://doi.org/10.1111/j.1467-8683.2006.00507.x/abstract
Bianchi, E. M. P. G., & Ikeda, A. A. (2008). Usos e aplicações da grounded theory em admi-nistração. Revista Eletrônica de Gestão Organizacional, 6(2), pp.231-248.
Cartea, A., & Karyampas, D. (2011). Volatility and covariation of financial assets: a high-frequency analysis. Journal of Banking and Finance, 35(12), pp.3319-3334. DOI: https://doi.org/10.1016/j.jbankfin.2011.05.012
Chen, C. L. P., & Zhang, C.Y. (2014). Data-intensive applications, challenges, techniques, and technologies: a survey on Big Data. Information Science, 27(5), pp. 314-374. DOI: https://doi.org/10.1016/j.ins.2014.01.015
Chong, A., Lopez-de-Silanes, F. (2007). Investor protection and corporate governance: intra-firm evidence across Latin-America. Palo Alto: Stanford University Press.
Damodaran, A. (2012). Investment valuation: tools and techniques for determining the value of any assets. 3rd edition. New Jersey: Wiley&Sons.
Demchenko Y., Grosso, P., De Laat C., & Membrey, P. (2013). Addressing Big Data issues in scientific data infrastructure. Collaboration Technologies and Systems (CTS), International Conference on 2013.
Einav L. & Levin J. (2014). Economics in the age of big data. Science, 346 (6210), pp. 12430891–12430896. DOI: https://doi.org/10.1126/science.1243089
Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., Suri, V. R., Tsou, A., Weingart, S., & Sugimoto, C. R. (2015). Big Data, bigger dillemas: a critical review. Journal of the Association for Information Science and Technology, 66(8), pp. 1523-1545.
Fan, J., Han, F., & Liu, H. (2014). Challenges of Big Data Analysis. National Science Review, 1(2), pp. 293–314. DOI: https://doi.org/10.1093/nsr/nwt032
Gigerenzer, G., & Marewski, J. N. (2015). Surrogate science: the idol of a universal method for scientific inference. Journal of Management, 41(2), pp.421-440. DOI: https://doi.org/10.1177/0149206314547522
Glaser, B, G., & Strauss, A. L. (1967). The discovery of grounded theory: strategies for quali-tative research. New York: Aldine de Gruyter.
GSAM – Goldman Sachs Asset Management (2016). Perspectives: the role of Big Data in investing. Recuperado em 26 de abril, 2017, de: https://www.gsam.com/.
Haig, B. D. (2005). An abdutive theory of scientific method. Psychological Methods, 10(4), pp. 371-388. DOI: https://doi.org/10.1037/1082-989X.10.4.371
Hey, T., Tansley, S., & Tolle K. (2009). Jim Grey on eScience: A transformed scientific method. In: Hey T, Tansley S and Tolle K (eds) The fourth paradigm: data-intensive sci-entific discovery. Redmond: Microsoft Research, pp. xvii–xxxi.
Big Data, New Epistemologies and Paradigm Shift (PDF Download Available). Recuperado em 15 de setembro, 2017, de: https://www.researchgate.net/publication/271525133_Big_Data_New_Epistemologies_and_Paradigm_Shift.
IBM, 2016. Recuperado em 08 de abril, 2107, de: https://www-01.ibm.com/software/data/bigdata/what-is-bigdata.html.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learn-ing. New York: springer. DOI: https://doi.org/10.1007/978-1-4614-7138-7
Kitchin, R. (2014). Big Data, new epistemologies and paradigms shift. Big Data & Society, April-June, pp. 1-12. DOI: https://doi.org/10.1177/2053951714528481
Lakatos, I., & Musgrave, A. (1979). A crítica e o desenvolvimento do conhecimento. São Pau-lo: Editora da Universidade de São Paulo. DOI: http://dx.doi.org/10.11606/issn.2447-9799.cienciaefilosofi.1980.107354
Lau, R. Y.K., Zhao, J.L., Chen, G., & Guo, X. (2016). Big Data commerce. Information & Management, 53, pp. 929-933. DOI: https://doi.org/10.1016/j.im.2016.07.008
Louzis, D.P., Xanthopoulos-Sisinis, S., & Refenes, A.P., (2013). The role of high-frequency intra-daily data, daily range and implied volatility in multi-period value-at-risk forecast-ing. Journal of Forecasting, 32(6), pp. 561–576.
Martins,O. S. & Paulo E. (2014). Assimetria de informação na negociação de ações, caracte-rísticas econômico financeiras e governança corporativa no mercado acionário brasileiro. Revista Contabilidade & Finanças (Online), 25(64), pp. 33-45. jan./fev./mar./abr. 2014
Miller, H. J. (2010). The data avalanche is here. Shouldn’t we be digging? Journal of Regional Science, 50(1), pp. 181-201. DOI: https://doi.org/10.1111/j.1467-9787.2009.00641.x
Oracle (2012). Financial services data management: Big Data technology in financial services. Oracle Financial Services, An Oracle White paper.
Seth, T., & Chaudhary, V. (2015). “Big Data in Finance”. In: Li, K.; Jiang, H.; Yang, L. T.; Cuzzorea, A. (Eds) “Big Data: algorithms, analytics and applications”. Chapman and Hall/ CRC, pp. 329-356.
Spens, K. M., & Kovács, G. (2006). A content analysis of research approaches in logistics research, International Journal of Physical Distribution & Logistics Management, 36(5), pp. 374-390. DOI: https://doi.org/10.1108/09600030610676259
Ye, G. (2010). High frequency trading models. NJ: John Wiley & Sons Inc.
Zervoudakis, F., Lawrence, D., Gontikas, G., & Al Merey, M. (2017). Perspectives on high-frequency trading. Recuperado em 30 de março, 2017, de: http://www0.cs.ucl.ac.uk/staff/f.zervoudakis/docs/hft.pdf.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 3.0 Unported License, which allows the sharing of the work and recognition of authorship and its initial publication in this journal. This license allows others to distribute, remix, adapt, or create derivative works, even for commercial purposes, provided credit is given for the original creation.
b)There is no financial compensation to the authors in any capacity, for articles published in RePEc.c) The articles published in RePEc are the sole responsibility of the authors.