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Benford law states that the occurrence of digits from 0-9 in a large set of data is not uniformly distributed but instead in a decreasing logarithmic distribution with 1 occurring at most. Almost all set of data follows this trend however this law is widely used as a base for various fraud detection and forensic accounting. Benford’s law is an observation that leading digits in data derived from measurements doesn’t follow uniform distribution. Different financial statements such as cash flows, income statement and balance sheet of the 20 tech companies of the Fortune 500 are analyzed in this project. Cash flow is the net amount of cash and cash-equivalents moving into and out of a business. Income statement is a financial statement that measures a company's financial performance over a specific accounting period. Balance sheet is a financial statement that summarizes a company's assets, liabilities and shareholders’ equity at a specific point in time. All of these data of financial statements are extracted from Morning Star database and are analyzed by Python program written by me.I also wrote the Python program to calculate Benford's second digit and third digit probability using the formula. I would like to thank Prof. Erin Wagner and Dr. Courtney Taylor for helping in this research project.
This template is derived from the GigaScience LaTeX template. The original template is now customised to GigaBytes requirement and can be used to submit Updates for GigaBytes. Overleaf and Oxford University Press (OUP) have created the initial template for authors submitting manuscripts to GigaScience.
This template allows authors to prepare and edit their manuscripts in the OUP 'contemporary' layout used by GigaBytes.
To begin writing, simply click the Open as Template button, above. Additional guidelines for preparing your submission are included within the template itself.
River Valley Technologies Ltd and GigaScience
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