{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-k-generalised-distribution-for-stock","title":"The $κ$-generalised Distribution for Stock Returns","arxiv_id":"2405.09929","date":"2024-05-16","proceeding":null,"authors":["Samuel Forbes"],"abstract":"Empirical evidence shows stock returns are often heavy-tailed rather than normally distributed. The $\\kappa$-generalised distribution, originated in the context of statistical physics by Kaniadakis, is characterised by the $\\kappa$-exponential function that is asymptotically exponential for small values and asymptotically power law for large values. This proves to be a useful property and makes it a good candidate distribution for many types of quantities. In this paper we focus on fitting historic daily stock returns for the FTSE 100 and the top 100 Nasdaq stocks. Using a Monte-Carlo goodness of fit test there is evidence that the $\\kappa$-generalised distribution is a good fit for a significant proportion of the 200 stock returns analysed.","url_abs":"https://arxiv.org/abs/2405.09929v1","url_pdf":"https://arxiv.org/pdf/2405.09929v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-k-generalised-distribution-for-stock","repo_url":"https://github.com/saf92/stock-returns","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}