Digital financePub Date : 2021-07-10DOI: 10.1007/s42521-021-00046-2
Helmut Wasserbacher, M. Spindler
{"title":"Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls","authors":"Helmut Wasserbacher, M. Spindler","doi":"10.1007/s42521-021-00046-2","DOIUrl":"https://doi.org/10.1007/s42521-021-00046-2","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"4 1","pages":"63 - 88"},"PeriodicalIF":0.0,"publicationDate":"2021-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47294143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital financePub Date : 2021-06-01DOI: 10.1007/s42521-021-00039-1
Vikram Ojha, Jeonghoe Lee
{"title":"Correction to: Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008–2009","authors":"Vikram Ojha, Jeonghoe Lee","doi":"10.1007/s42521-021-00039-1","DOIUrl":"https://doi.org/10.1007/s42521-021-00039-1","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 1","pages":"205"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-021-00039-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47484180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital financePub Date : 2021-01-01Epub Date: 2021-08-07DOI: 10.1007/s42521-021-00038-2
Daniele Ballinari, Simon Behrendt
{"title":"How to gauge investor behavior? A comparison of online investor sentiment measures.","authors":"Daniele Ballinari, Simon Behrendt","doi":"10.1007/s42521-021-00038-2","DOIUrl":"10.1007/s42521-021-00038-2","url":null,"abstract":"<p><p>Given the increasing interest in and the growing number of publicly available methods to estimate investor sentiment from social media platforms, researchers and practitioners alike are facing one crucial question - which is best to gauge investor sentiment? We compare the performance of daily investor sentiment measures estimated from Twitter and StockTwits short messages by publicly available dictionary and machine learning based methods for a large sample of stocks. To determine their relevance for financial applications, these investor sentiment measures are compared by their effects on the cross-section of stocks (i) within a Fama and MacBeth (J Polit Econ 81:607-636, 1973) regression framework applied to a measure of retail investors' order imbalances and (ii) by their ability to forecast abnormal returns in a model-free portfolio sorting exercise. Interestingly, we find that investor sentiment measures based on finance-specific dictionaries do not only have a greater impact on retail investors' order imbalances than measures based on machine learning approaches, but also perform very well compared to the latter in our asset pricing application.</p>","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 2","pages":"169-204"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39847700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital financePub Date : 2021-01-01Epub Date: 2021-08-02DOI: 10.1007/s42521-021-00037-3
Fabian Woebbeking
{"title":"Cryptocurrency volatility markets.","authors":"Fabian Woebbeking","doi":"10.1007/s42521-021-00037-3","DOIUrl":"https://doi.org/10.1007/s42521-021-00037-3","url":null,"abstract":"<p><p>By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market's expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture 'normal' market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.</p>","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 3-4","pages":"273-298"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-021-00037-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39292053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}