Abstract
Newcomb-Benford’s Law (NBL) is a well known regularity in the distribution of first significant digits (FSD) and therefore research in this field is manifold. As of 2012 research in the domain of financial markets is quite scarce, especially in the field of algorithmic trading. We pose the question whether order submission volumes of algorithmic traders and human traders follow NBL. Results in this context might help regulators to detect suspicious market activity and market participants to quantify the amount of algorithmic trading. Our findings indicate that the submitted order volumes of both groups follow NBL more than the uniform distribution. Comparing these two groups, we give a proof that algorithmic traders match NBL better than human traders, as human traders tend to overuse the FSD five.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Newcomb, S.: Note of frequency of use of the different digits in natural numbers. American Journal of Mathematics 4(1), 39–40 (1881)
Benford, F.: The law of anomalous numbers. Proceedings of the American Philosophical Society 78(4), 551–572 (1938)
Hürlimann, W.: Benford’s law from (1881-2006) Working Paper, http://arxiv.org/abs/math/0607168
Beebe, N.H.F.: A Bibliography of Publications about Benford’s Law, Heaps’ Law, and Zipf’s Law. Working Paper (2012), ftp://ftp.math.utah.edu/pub/tex/bib/benfords-law.ps.gz
Buck, B., Merchant, A.C., Perez, S.M.: An illustration of Benford’s first digit law using alpha decay half lives. European Journal of Physics 14(2), 59–63 (1993)
Bhattacharya, S., Xu, D., Kumar, K.: An ANN-based auditor decision support system using Benford’s law. Decision Support Systems 50(3), 576–584 (2011)
Bolton, R.J., Hand, D.J.: Statistical Fraud Detection: A Review. Statistical Science 17(3), 235–249 (2002)
Durtschi, C., Hillison, W., Pacini, C.: The effective use of Benford’s law to assist in detecting fraud in accounting data. Journal of Forensic Accounting 5(1), 17–34 (2004)
Ley, E.: Checking Financial markets via Benford’s law. Working Paper (1996)
Corazza, M., Ellero, A., Zorzi, A.: On the Peculiar Distribution of the U.S. Stock Indexes’ Digits. In: Proceedings of the International Conference MAF 2008 – Mathematical and Statistical Methods for Acturial Sciences and Finance, Shanghai (2008)
Zdravko, K., Zgela, M.: Evaluation of Benford’s Low Application in Stock Prices and Stock Turnover. Informatologia 42(3), 158–165 (2009)
Zhipeng, L., Cong, L., Wang, H.: Discussion on Benford’s Law and its Application. Working Paper, http://arxiv.org/abs/math/0408057
Abrantes-Metz, R.M., Villas-Boas, S.B., Judge, G.: Tracking the Libor rate. Applied Economics Letters 18(10), 893–899 (2011)
Giles, D.E.: Benford’s law and naturally occurring prices in certain ebaY auctions. Applied Economics Letters 14(3), 157–161 (2007)
Burns, B.: Sensitivity to statistical regularities: People (largely) follow Benford’s law. In: Proceedings of the Annual Meeting of the Cognitive Science Society, Amsterdam (2009)
Scott, S.K., Barnard, P.J., May, J.: Specifying executive representations and processes in number generation tasks. The Quarterly Journal of Experimental Psychology 54(3), 641–664 (2001)
Harris, L.: Stock price clustering and discreteness. Review of Financial Studies 4(3), 389–415 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Haferkorn, M. (2013). Humans vs. Algorithms – Who Follows Newcomb-Benford’s Law Better with Their Order Volume?. In: Rabhi, F.A., Gomber, P. (eds) Enterprise Applications and Services in the Finance Industry. FinanceCom 2012. Lecture Notes in Business Information Processing, vol 135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36219-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-36219-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36218-7
Online ISBN: 978-3-642-36219-4
eBook Packages: Computer ScienceComputer Science (R0)