{"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/modeling-stock-markets-through-the","title":"Modeling stock markets through the reconstruction of market processes","arxiv_id":"1803.06653","date":"2018-03-18","proceeding":null,"authors":[],"abstract":"There are two possible ways of interpreting the seemingly stochastic nature\nof financial markets: the Efficient Market Hypothesis (EMH) and a set of\nstylized facts that drive the behavior of the markets. We show evidence for\nsome of the stylized facts such as memory-like phenomena in price volatility in\nthe short term, a power-law behavior and non-linear dependencies on the\nreturns.\n  Given this, we construct a model of the market using Markov chains. Then, we\ndevelop an algorithm that can be generalized for any N-symbol alphabet and\nK-length Markov chain. Using this tool, we are able to show that it's, at\nleast, always better than a completely random model such as a Random Walk. The\ncode is written in MATLAB and maintained in GitHub.","url_abs":"http://arxiv.org/abs/1803.06653v1","url_pdf":"http://arxiv.org/pdf/1803.06653v1.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":"modeling-stock-markets-through-the","repo_url":"https://github.com/joaocarmo/market-reconstruction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}