{"title":"The better turbulence index? Forecasting adverse financial markets regimes with persistent homology","authors":"Eduard Baitinger, Samuel Flegel","doi":"10.1007/s11408-020-00377-x","DOIUrl":null,"url":null,"abstract":"<p>Persistent homology is the workhorse of modern topological data analysis, which in recent years becomes increasingly powerful due to methodological and computing power advances. In this paper, after equipping the reader with the relevant background on persistent homology, we show how this tool can be harnessed for investment purposes. Specifically, we propose a persistent homology-based turbulence index for the detection of adverse market regimes. With the help of an out-of-sample study, we demonstrate that investment strategies relying on a persistent homology-based turbulence detection outperform investment strategies based on other popular turbulence indices. Additionally, we conduct a stability analysis of our findings. This analysis confirms the results from the previous out-of-sample study, as the outperformance prevails for most configurations of the respective investment strategy and thereby mitigating possible data mining concerns.</p>","PeriodicalId":44895,"journal":{"name":"Financial Markets and Portfolio Management","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Financial Markets and Portfolio Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11408-020-00377-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
Persistent homology is the workhorse of modern topological data analysis, which in recent years becomes increasingly powerful due to methodological and computing power advances. In this paper, after equipping the reader with the relevant background on persistent homology, we show how this tool can be harnessed for investment purposes. Specifically, we propose a persistent homology-based turbulence index for the detection of adverse market regimes. With the help of an out-of-sample study, we demonstrate that investment strategies relying on a persistent homology-based turbulence detection outperform investment strategies based on other popular turbulence indices. Additionally, we conduct a stability analysis of our findings. This analysis confirms the results from the previous out-of-sample study, as the outperformance prevails for most configurations of the respective investment strategy and thereby mitigating possible data mining concerns.
期刊介绍:
The journal Financial Markets and Portfolio Management invites submissions of original research articles in all areas of finance, especially in – but not limited to – financial markets, portfolio choice and wealth management, asset pricing, risk management, and regulation. Its principal objective is to publish high-quality articles of innovative research and practical application. The readers of Financial Markets and Portfolio Management are academics and professionals in finance and economics, especially in the areas of asset management. FMPM publishes academic and applied research articles, shorter ''Perspectives'' and survey articles on current topics of interest to the financial community, as well as book reviews. All article submissions are subject to a double-blind peer review. http://www.fmpm.org
Officially cited as: Financ Mark Portf Manag