Pub Date : 2022-01-01Epub Date: 2022-06-15DOI: 10.1007/s42521-022-00052-y
A V Biju, Aparna Merin Mathew, P P Nithi Krishna, M P Akhil
Bitcoin (BTC) prices are fluctuating continuously to the extremes. The Bitcoin market witnessed a crash during the second quarter of 2021 that was purely guided by the investors' sentiments. Are the Bitcoin prices influenced only by market sentiments or do any factors influence them? In this paper, we applied a triangulation approach; mixed-methods research was used in which a qualitative study was complemented by a quantitative method. Both the qualitative and quantitative data of time periods 2016-2021 were examined to find whether the Bitcoin market prices are influenced by market sentiments. For analysing market sentiments, the posts and sentiments from 2016 to 2021 of an internet forum "Bitcointalk" were used. For strengthening the findings of qualitative analysis, we used quantitative data of the BTC market. We also used search data from Google Trends for providing further insights. Our research shows a crossmatch between quantitative trends on Bitcoin market prices and qualitative matrix of sentiments. We have also observed an artificial investment intention in the form of digital nudges playing the field of the Bitcoin market to boost investment.
{"title":"Is the future of bitcoin safe? A triangulation approach in the reality of BTC market through a sentiments analysis.","authors":"A V Biju, Aparna Merin Mathew, P P Nithi Krishna, M P Akhil","doi":"10.1007/s42521-022-00052-y","DOIUrl":"https://doi.org/10.1007/s42521-022-00052-y","url":null,"abstract":"<p><p>Bitcoin (BTC) prices are fluctuating continuously to the extremes. The Bitcoin market witnessed a crash during the second quarter of 2021 that was purely guided by the investors' sentiments. Are the Bitcoin prices influenced only by market sentiments or do any factors influence them? In this paper, we applied a triangulation approach; mixed-methods research was used in which a qualitative study was complemented by a quantitative method. Both the qualitative and quantitative data of time periods 2016-2021 were examined to find whether the Bitcoin market prices are influenced by market sentiments. For analysing market sentiments, the posts and sentiments from 2016 to 2021 of an internet forum \"Bitcointalk\" were used. For strengthening the findings of qualitative analysis, we used quantitative data of the BTC market. We also used search data from Google Trends for providing further insights. Our research shows a crossmatch between quantitative trends on Bitcoin market prices and qualitative matrix of sentiments. We have also observed an artificial investment intention in the form of digital nudges playing the field of the Bitcoin market to boost investment.</p>","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":" ","pages":"275-290"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9199470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40164104","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}
Pub Date : 2022-01-01Epub Date: 2021-11-29DOI: 10.1007/s42521-021-00045-3
Yuting Chen, Don Bredin, Valerio Potì, Roman Matkovskyy
In this paper, we study the role of narratives in stock markets with a particular focus on the relationship with the ongoing COVID-19 pandemic. The pandemic represents a natural setting for the development of viral financial market narratives. We thus treat the pandemic as a natural experiment on the relation between prevailing narratives and financial markets. We adopt natural language processing (NLP) on financial news to characterize the evolution of important narratives. Doing so, we reduce the high-dimensional narrative information to few interpretable and important features while avoiding over-fitting. In addition to the common features, we consider virality as a novel feature of narratives, inspired by Shiller (Am Econ Rev 107:967-1004, 2017). Our aim is to establish whether the prevailing narratives drive or are driven by stock market conditions. Focusing on the coronavirus narratives, we document some stylized facts about its evolution around a severe event-driven stock market decline. We find the pandemic-relevant narratives are influenced by stock market conditions and act as a cellar for brewing a perennial economic narrative. We successfully identified a perennial risk narrative, whose shock is followed by a severe market drop and a long-term increase of market volatility. In the out-of-sample test, this narrative went viral since the start of the global COVID-19 pandemic, when the pandemic-relevant narratives dominate news media, show negative sentiment and were more linked to "crisis" context. Our findings encourage the use of narratives to evaluate long-term market conditions and to early warn event-driven severe market declines.
{"title":"COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic.","authors":"Yuting Chen, Don Bredin, Valerio Potì, Roman Matkovskyy","doi":"10.1007/s42521-021-00045-3","DOIUrl":"10.1007/s42521-021-00045-3","url":null,"abstract":"<p><p>In this paper, we study the role of narratives in stock markets with a particular focus on the relationship with the ongoing COVID-19 pandemic. The pandemic represents a natural setting for the development of viral financial market narratives. We thus treat the pandemic as a natural experiment on the relation between prevailing narratives and financial markets. We adopt natural language processing (NLP) on financial news to characterize the evolution of important narratives. Doing so, we reduce the high-dimensional narrative information to few interpretable and important features while avoiding over-fitting. In addition to the common features, we consider virality as a novel feature of narratives, inspired by Shiller (Am Econ Rev 107:967-1004, 2017). Our aim is to establish whether the prevailing narratives drive or are driven by stock market conditions. Focusing on the coronavirus narratives, we document some stylized facts about its evolution around a severe event-driven stock market decline. We find the pandemic-relevant narratives are influenced by stock market conditions and act as a cellar for brewing a perennial economic narrative. We successfully identified a perennial risk narrative, whose shock is followed by a severe market drop and a long-term increase of market volatility. In the out-of-sample test, this narrative went viral since the start of the global COVID-19 pandemic, when the pandemic-relevant narratives dominate news media, show negative sentiment and were more linked to \"crisis\" context. Our findings encourage the use of narratives to evaluate long-term market conditions and to early warn event-driven severe market declines.</p>","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"4 1","pages":"17-61"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39697277","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}
Pub Date : 2021-11-16DOI: 10.1007/s42521-021-00044-4
Thomas K. Dasaklis, Veni Arakelian
{"title":"Special issue on Financial Forensics and Fraud Investigation in the Era of Industry 4.0","authors":"Thomas K. Dasaklis, Veni Arakelian","doi":"10.1007/s42521-021-00044-4","DOIUrl":"https://doi.org/10.1007/s42521-021-00044-4","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 1","pages":"299 - 300"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47215391","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}
Pub Date : 2021-10-19DOI: 10.1007/s42521-021-00042-6
Apostolos Chalkis, E. Christoforou, I. Emiris, Theodore Dalamagas
{"title":"Correction to: Modeling asset allocations and a new portfolio performance score","authors":"Apostolos Chalkis, E. Christoforou, I. Emiris, Theodore Dalamagas","doi":"10.1007/s42521-021-00042-6","DOIUrl":"https://doi.org/10.1007/s42521-021-00042-6","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 1","pages":"373"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42051525","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}
Pub Date : 2021-08-19DOI: 10.1007/s42521-023-00079-9
I. Chalkiadakis, G. Peters, M. Ames
{"title":"Hybrid ARDL-MIDAS-Transformer time-series regressions for multi-topic crypto market sentiment driven by price and technology factors","authors":"I. Chalkiadakis, G. Peters, M. Ames","doi":"10.1007/s42521-023-00079-9","DOIUrl":"https://doi.org/10.1007/s42521-023-00079-9","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"5 1","pages":"295 - 365"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47738013","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}
Pub Date : 2021-07-22DOI: 10.1007/s42521-021-00036-4
Vikram Ojha, Jeonghoe Lee
{"title":"Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008–2009","authors":"Vikram Ojha, Jeonghoe Lee","doi":"10.1007/s42521-021-00036-4","DOIUrl":"https://doi.org/10.1007/s42521-021-00036-4","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 1","pages":"249 - 271"},"PeriodicalIF":0.0,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-021-00036-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"52726750","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}
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