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This is the first book to provide a systematic description of statistical properties of large-scale financial data. Specifically, the power-law and log-normal distributions observed at a given time and their changes using time-reversal symmetry, quasi-time-reversal symmetry, Gibrat's law, and the non-Gibrat's property observed in a short-term period are derived here. The statistical properties observed over a long-term period, such as power-law and exponential growth, are also derived. These subjects have not been thoroughly discussed in the field of economics in the past, and this book is a compilation of the author's series of studies by reconstructing the data analyses published in 15 academic journals with new data. This book provides readers with a theoretical and empirical understanding of how the statistical properties observed in firms' large-scale data are related along the time axis. It is possible to expand this discussion to understand theoretically and empirically how the statistical properties observed among differing large-scale financial data are related. This possibility provides readers with an approach to microfoundations, an important issue that has been studied in economics for many years.
List of contents
Chapter 1. Introduction.- Chapter 2. Non-Gibrat's Property in the Mid-scale Range.- Chapter 3. Quasi-statistically Varying Power-law and Log-normal Distributions.- Chapter 4. Extension of Non-Gibrat's Property.- Chapter 5. Long-term Firm Growth Derived from Non-Gibrat's Property and Gibrat's Law.- Chapter 6. Firm-age Distribution and the Inactive Rate of Firms.- Chapter 7. Statistical Properties in Inactive Rate of Firms.- Chapter 8. Power Laws with Different Exponents in Firm-Size Variables.- Chapter 9. Why does Production Function Take the Cobb-Douglas Form?.
About the author
Atushi Ishikawa, Kanazawa Gakuin University
The author was originally a theoretical physicist of elementary particles. He now specializes in Econophysics and is primarily engaged in the study of the statistical properties of firms' large-scale financial data. The study covers a wide range of other topics, including analyzing point-of-sale (POS) data, analyzing Twitter, and analyzing land prices.
