Oct 2026· Journal of Risk and Financial Management· 0 citations· 78 references
Financial Markets and Investment Strategies
Abstract
Market-capitalization concentration in the Standard & Poor’s 500 (S&P 500) rose sharply between 2016 and 2025, and the explanation usually offered is the economy of bits: negligible marginal cost, network effects, and intangible capital are held to let a few firms scale without scaling their balance sheets. This paper tests that explanation against firm-level measurement of the characteristic that it names. Year-end index membership is reconstructed from a historical constituent file, repaired for ticker renames against filing histories held by the U.S. Securities and Exchange Commission (SEC), and merged with firm fundamentals drawn from the SEC’s Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system for 2008–2025. A firm-level index of bit intensity is constructed from asset lightness, intangible share, research and development intensity, and gross margin. The concentration result survives the repair; the explanation does not. Bit intensity predicts membership of the top five percent of the capitalization distribution and of wider tails, but not of the top one percent. A Gabaix–Ibragimov rank regression finds the tail among bit-intensive firms to be thinner rather than fatter than the tail among their less bit-intensive counterparts, and the sign survives standardization within sector. Gibrat’s law of proportionate growth is not rejected, which closes the mechanical alternative. What did change over the period is the balance sheet of the firms that operate in computing: their capital intensity rose several-fold, a divergence from other large firms that is neither an artifact of the 2019 change in lease accounting nor a general size effect, and which carries them most of the way from the software sector towards the utility sector. That capital did not capture the corresponding value. Of the index share that the compute stack gained, the larger part went to the layer that designs processors and owns no fabrication plants, and the majority of that gain is attributable to a single firm.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The results show that the embedded industry has been able to apply agile methods in its development processes and that the appreciation of the agile methods and their individual practices appears to increase once adopted and applied in practice.
O. Salo, P. Abrahamsson· IET Software· 238 citations· ⚡9
Consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts are found.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· Journal of Systems and Softw...· 236 citations· ⚡13
The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed, which helped develop an agile development approach for mobile application development.
P. Abrahamsson, Antti Hanhineva, H. Hulkko et al.· Conference on Object-Oriente...· 225 citations· ⚡18
Related blog posts
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
Requirements in large systems rarely exist in isolation. Their meaning depends on the wider project context - other requirements, policies, decisions, tests, and implementation details. That becomes especially important when AI is used for review, because spotting a possible conflict or gap is only the beginning. ReqSpace explores how AI, visualisation, and connected project context can help reviewers understand those findings, trace the relationships behind them, and focus on the questions that…
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.