Ausloos, M, Castellano, R and Cerqueti, R (2016). Regularities and discrepancies of credit default swaps: a data science approach through Benford's law. Chaos, Solitons & Fractals 90, pp. 817.
This work is cited by the following items of the Benford Online Bibliography:
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Ausloos, M, Cerqueti, R and Lupi, C (2017). Longrange properties and data validity for hydrogeological time series: The case of the Paglia river. Physica A: Statistical Mechanics and its Applications 470, pp. 3950. DOI:10.1016/j.physa.2016.11.137.





Ausloos, M, Cerqueti, R and Mir, TA (2017). Data science for assessing possible tax income manipulation: The case of Italy. Chaos, Solitons and Fractals 104, pp. 238–256. DOI:10.1016/j.chaos.2017.08.012.





Bannier, C, EweltKnauer, C, Winker, P and Lips, J (2019). Benford’s law and its application to detecting financial fraud and manipulation. In: Corruption and Fraud in Financial Markets: Malpractice, Misconduct and Manipulation. Ed. by C. Alexander and D. Cumming, John Wiley & Sons, Ltd.





Branets, S (2019). Detecting money laundering with Benford’s law and machine learning . Masters Thesis, University of Tartu.





Riccioni, J and Cerqueti, R (2018). Regular paths in financial markets: Investigating the Benford’s law. Chaos, Solitons and Fractals 107, pp. 186194. DOI:10.1016/j.chaos.2018.01.008.





Wist, H (2019). Assessing The Conformity Of Cryptocurrency Market Data With Benford’s Law. Masters thesis, University of Vaasa, Faculty of Business Studies, Department of Accounting and Finance
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