{"title":"Dynamical analysis of financial stocks network: improving forecasting using network properties","authors":"Ixandra Achitouv","doi":"arxiv-2408.11759","DOIUrl":null,"url":null,"abstract":"Applying a network analysis to stock return correlations, we study the\ndynamical properties of the network and how they correlate with the market\nreturn, finding meaningful variables that partially capture the complex\ndynamical processes of stock interactions and the market structure. We then use\nthe individual properties of stocks within the network along with the global\nones, to find correlations with the future returns of individual S&P 500\nstocks. Applying these properties as input variables for forecasting, we find a\n50% improvement on the R2score in the prediction of stock returns on long time\nscales (per year), and 3% on short time scales (2 days), relative to baseline\nmodels without network variables.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"51 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.11759","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Applying a network analysis to stock return correlations, we study the
dynamical properties of the network and how they correlate with the market
return, finding meaningful variables that partially capture the complex
dynamical processes of stock interactions and the market structure. We then use
the individual properties of stocks within the network along with the global
ones, to find correlations with the future returns of individual S&P 500
stocks. Applying these properties as input variables for forecasting, we find a
50% improvement on the R2score in the prediction of stock returns on long time
scales (per year), and 3% on short time scales (2 days), relative to baseline
models without network variables.