Pub Date : 2023-09-27DOI: 10.1007/s42521-023-00096-8
Piotr Wójcik, Ewelina Osowska
Abstract This article examines the impact of Federal Open Market Committee (FOMC) statements on stock and foreign exchange markets with the use of text-mining and predictive models. We take into account a long period since March 2001 until June 2023. Unlike in most previous studies, both linear and non-linear methods were applied. We also take into account additional explanatory variables that control for the current corporate managers’ and retail customers’ assessment of the economic situation. The proposed methodology is based on calculating the FOMC statements’ tone (called sentiment) and incorporate it as a potential predictor in the modeling process. For the purpose of sentiment calculation, we utilized the FinBERT pre-trained NLP model. Fourteen event windows around the event are considered. We proved that the information content of FOMC statements is an important predictor of the financial markets’ reaction directly after the event. In the case of models explaining the reaction of financial markets in the first minute after the announcement of the FOMC statement, the sentiment score was the first or the second most important feature, after the market surprise component. We also showed that applying non-linear models resulted in better prediction of market reaction due to identified non-linearities in the relationship between the two most important predictors (surprise component and sentiment score) and returns just after the event. Last but not least, the predictive accuracy during the COVID pandemic was indeed lower than in the previous year.
{"title":"Predicting the reaction of financial markets to Federal Open Market Committee post-meeting statements","authors":"Piotr Wójcik, Ewelina Osowska","doi":"10.1007/s42521-023-00096-8","DOIUrl":"https://doi.org/10.1007/s42521-023-00096-8","url":null,"abstract":"Abstract This article examines the impact of Federal Open Market Committee (FOMC) statements on stock and foreign exchange markets with the use of text-mining and predictive models. We take into account a long period since March 2001 until June 2023. Unlike in most previous studies, both linear and non-linear methods were applied. We also take into account additional explanatory variables that control for the current corporate managers’ and retail customers’ assessment of the economic situation. The proposed methodology is based on calculating the FOMC statements’ tone (called sentiment) and incorporate it as a potential predictor in the modeling process. For the purpose of sentiment calculation, we utilized the FinBERT pre-trained NLP model. Fourteen event windows around the event are considered. We proved that the information content of FOMC statements is an important predictor of the financial markets’ reaction directly after the event. In the case of models explaining the reaction of financial markets in the first minute after the announcement of the FOMC statement, the sentiment score was the first or the second most important feature, after the market surprise component. We also showed that applying non-linear models resulted in better prediction of market reaction due to identified non-linearities in the relationship between the two most important predictors (surprise component and sentiment score) and returns just after the event. Last but not least, the predictive accuracy during the COVID pandemic was indeed lower than in the previous year.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535510","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}
Abstract We analyze markets for cryptoassets (cryptocurrencies and stablecoins), investigating market impact and efficiency through the lens of the market order flow. We provide evidence that markets where cryptoassets are exchanged between themselves play a central role on price formation and are more efficient than markets where cryptocurrencies are exchanged with the US dollar. For the first set of markets we observe some evidence of the presence of insiders/contrarians, instead in the latter we observe the predominance of herding and trend-followers.
{"title":"Market impact and efficiency in cryptoassets markets","authors":"Emilio Barucci, Giancarlo Giuffra Moncayo, Daniele Marazzina","doi":"10.1007/s42521-023-00095-9","DOIUrl":"https://doi.org/10.1007/s42521-023-00095-9","url":null,"abstract":"Abstract We analyze markets for cryptoassets (cryptocurrencies and stablecoins), investigating market impact and efficiency through the lens of the market order flow. We provide evidence that markets where cryptoassets are exchanged between themselves play a central role on price formation and are more efficient than markets where cryptocurrencies are exchanged with the US dollar. For the first set of markets we observe some evidence of the presence of insiders/contrarians, instead in the latter we observe the predominance of herding and trend-followers.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135306280","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 : 2023-08-31DOI: 10.1007/s42521-023-00094-w
Efim Zhitomirskiy, Stefan Schmid, M. Walther
{"title":"Tokenizing assets with dividend payouts—a legally compliant and flexible design","authors":"Efim Zhitomirskiy, Stefan Schmid, M. Walther","doi":"10.1007/s42521-023-00094-w","DOIUrl":"https://doi.org/10.1007/s42521-023-00094-w","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43743828","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 : 2023-08-28DOI: 10.1007/s42521-023-00093-x
Felix Bekemeier
Abstract Decentralized finance (DeFi), a blockchain-based form of alternative financial markets, has gained significant public attention in recent months. Despite its relatively short history, DeFi offers a range of opportunities for designing and transferring digital assets. This establishes market structures that bear resemblance to traditional financial markets. Notably, the landscape of DeFi projects has expanded to include insurance protocols that offer DeFi-inherent mechanisms for hedging DeFi-specific risks, particularly those associated with smart contracts. These insurance protocols aim to provide similar value propositions as traditional insurance, namely the minimization and transfer of risks in exchange for a premium. However, it is crucial to acknowledge that most of these risk transfer protocols are strongly dependent on subjective expectations and decentralized governance structures. This article aims to develop a taxonomical understanding of DeFi insurance. Moreover, it seeks to assess the insurability of risks related to smart contracts. By doing so, this study contributes to the emerging body of knowledge surrounding DeFi insurance, paving the way for further research and analysis in this evolving field.
{"title":"A primer on the insurability of decentralized finance (DeFi)","authors":"Felix Bekemeier","doi":"10.1007/s42521-023-00093-x","DOIUrl":"https://doi.org/10.1007/s42521-023-00093-x","url":null,"abstract":"Abstract Decentralized finance (DeFi), a blockchain-based form of alternative financial markets, has gained significant public attention in recent months. Despite its relatively short history, DeFi offers a range of opportunities for designing and transferring digital assets. This establishes market structures that bear resemblance to traditional financial markets. Notably, the landscape of DeFi projects has expanded to include insurance protocols that offer DeFi-inherent mechanisms for hedging DeFi-specific risks, particularly those associated with smart contracts. These insurance protocols aim to provide similar value propositions as traditional insurance, namely the minimization and transfer of risks in exchange for a premium. However, it is crucial to acknowledge that most of these risk transfer protocols are strongly dependent on subjective expectations and decentralized governance structures. This article aims to develop a taxonomical understanding of DeFi insurance. Moreover, it seeks to assess the insurability of risks related to smart contracts. By doing so, this study contributes to the emerging body of knowledge surrounding DeFi insurance, paving the way for further research and analysis in this evolving field.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135032952","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 : 2023-08-23DOI: 10.1007/s42521-023-00092-y
I. Coita, M. Iannario, Alfonso Iodice D’Enza, C. Mare
{"title":"Modelling the assessment of taxpayer perception on the fiscal system by a hybrid approach for the analysis of challenging data structures","authors":"I. Coita, M. Iannario, Alfonso Iodice D’Enza, C. Mare","doi":"10.1007/s42521-023-00092-y","DOIUrl":"https://doi.org/10.1007/s42521-023-00092-y","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46030448","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 : 2023-08-03DOI: 10.1007/s42521-023-00090-0
Christoph Wronka
{"title":"Central bank digital currencies (CBDCs) and their potential impact on traditional banking and monetary policy: an initial analysis","authors":"Christoph Wronka","doi":"10.1007/s42521-023-00090-0","DOIUrl":"https://doi.org/10.1007/s42521-023-00090-0","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46388850","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 : 2023-08-01DOI: 10.1007/s42521-023-00089-7
Nicole Königstein
The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the $$alpha _{t}$$ -RIM (recurrent independent mechanism). This architecture makes use of key–value attention to integrate top-down and bottom-up information in a context-dependent and dynamic way. To model the data in such a dynamic manner, the $$alpha _{t}$$ -RIM utilizes an exponentially smoothed recurrent neural network, which can model non-stationary times series data, combined with a modular and independent recurrent structure. We apply our approach to the closing prices of three selected stocks of the S &P 500 universe as well as their news sentiment score. The results suggest that the $$alpha _{t}$$ -RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends. Consequently, this modeling approach markedly improves the generalization performance, that is, the prediction of unseen data, and outperforms state-of-the-art networks, such as long–short-term memory models.
{"title":"Dynamic and context-dependent stock price prediction using attention modules and news sentiment","authors":"Nicole Königstein","doi":"10.1007/s42521-023-00089-7","DOIUrl":"https://doi.org/10.1007/s42521-023-00089-7","url":null,"abstract":"The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the $$alpha _{t}$$ -RIM (recurrent independent mechanism). This architecture makes use of key–value attention to integrate top-down and bottom-up information in a context-dependent and dynamic way. To model the data in such a dynamic manner, the $$alpha _{t}$$ -RIM utilizes an exponentially smoothed recurrent neural network, which can model non-stationary times series data, combined with a modular and independent recurrent structure. We apply our approach to the closing prices of three selected stocks of the S &P 500 universe as well as their news sentiment score. The results suggest that the $$alpha _{t}$$ -RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends. Consequently, this modeling approach markedly improves the generalization performance, that is, the prediction of unseen data, and outperforms state-of-the-art networks, such as long–short-term memory models.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136020837","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 : 2023-08-01DOI: 10.1007/s42521-023-00088-8
Raphael A. Auer, Bernhard Haslhofer, Stefan Kitzler, Pietro Saggese, Friedhelm Victor
{"title":"The technology of decentralized finance (DeFi)","authors":"Raphael A. Auer, Bernhard Haslhofer, Stefan Kitzler, Pietro Saggese, Friedhelm Victor","doi":"10.1007/s42521-023-00088-8","DOIUrl":"https://doi.org/10.1007/s42521-023-00088-8","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43411031","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}
We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new. In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.
{"title":"What drives cryptocurrency returns? A sparse statistical jump model approach","authors":"F. Cortese, Petter N. Kolm, Erik Lindström","doi":"10.2139/ssrn.4330421","DOIUrl":"https://doi.org/10.2139/ssrn.4330421","url":null,"abstract":"We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new. In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"1 1","pages":"1-36"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46543936","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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