Volume 84, Number 6, December 2008
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||12 January 2009|
Fluctuations of company yearly profits vs. scaled revenue: Fat-tail distribution of Lévy type
Dipartimento di Fisica, Università di Milano-Bicocca - Piazza della Scienza 3, 20126 Milano, Italy, EU
2 Hewlett-Packard - Via Giuseppe Di Vittorio 9, 20063 Cernusco sul Naviglio (MI), Italy, EU
3 Institut für Festkörperphysik, Technische Universität Darmstadt - Hochschulstr. 8, 64289 Darmstadt, Germany, EU
Corresponding author: email@example.com
Accepted: 19 November 2008
We analyze annual revenues and earnings data for the 500 largest-revenue U.S. companies during the period 1954–2007. We find that mean year profits are proportional to mean year revenues, exception made for few anomalous years, from which we postulate a linear relation between company expected mean profit and revenue. Mean annual revenues are used to scale both company profits and revenues. Annual profit fluctuations are obtained as difference between actual annual profit and its expected mean value, scaled by a power of the revenue to get a stationary behavior as a function of revenue. We find that profit fluctuations are broadly distributed having approximate power law tails with a Lévy-type exponent α 1.7, from which we derive the associated break-even probability distribution. The predictions are compared with empirical data.
PACS: 89.65.Gh – Economics; econophysics, financial markets, business and management / 05.45.Tp – Time series analysis / 05.40.-a – Fluctuation phenomena, random processes, noise, and Brownian motion
© EPLA, 2008
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.