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Zheng, Y, Glass, R and Olinsky, A (2017)

An Application of Benford's Law to Detect Data Misrepresentation in Mutual Fund Reporting

Academy of Business Research Journal 1, pp. 65-73.

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Abstract: In this study, we used Benford’s Law to detect whether there is potential data misrepresentation in mutual fund reporting among a group of 10,314 mutual funds originating in 39 countries. Data analyzed included Fund Size data reported by mutual fund companies and Market Capitalization data generated from the Stock Exchange. We found that the managerial reported data, which is susceptible to misrepresentation, does not conform to Benford’s Law, while Market Capitalization which cannot be manipulated does conform to Benford’s Law. Based on the research results, management reported data of mutual funds may be subject to misrepresentation. A second analysis was completed comparing mutual funds where the market capitalization data was reported to funds where the data was missing. Results suggest that data misrepresentation is less likely when market data is available. The implication is that robust market reporting may serve to inhibit misrepresentation.

@article{, author = { Yahui Zheng and Richard Glass and Alan Olinsky}, journal = {Academy of Business Research Journal}, pages = {65--73}, volume = {1}, year = {2017}, title = {An Application of Benford's Law to Detect Data Misrepresentation in Mutual Fund Reporting}, }

Reference Type: Journal Article

Subject Area(s): Accounting