{"title":"Technical indicator selection and trading signal forecasting: varying input window length and forecast horizon for the Pakistan Stock Exchange","authors":"B. Bashir, Faheem Aslam","doi":"10.21314/jntf.2021.005","DOIUrl":"https://doi.org/10.21314/jntf.2021.005","url":null,"abstract":"","PeriodicalId":41885,"journal":{"name":"Journal of Network Theory in Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67704815","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}
Computation of spectral structure and risk measures from networks of multivariate financial time series data has been at the forefront of the statistical finance literature for a long time. A standard mode of analysis is to consider log returns from the equity price data, which is akin to taking first difference ($d = 1$) of the log of the price data. Sometimes authors have considered simple growth rates as well. Either way, the idea is to get rid of the nonstationarity induced by the {it unit root} of the data generating process. However, it has also been noted in the literature that often the individual time series might have a root which is more or less than unity in magnitude. Thus first differencing leads to under-differencing in many cases and over differencing in others. In this paper, we study how correcting for the order of differencing leads to altered filtering and risk computation on inferred networks. In summary, our results are: (a) the filtering method with extreme information loss like minimum spanning tree as well as filtering with moderate information loss like triangulated maximally filtered graph are very susceptible to such d-corrections, (b) the spectral structure of the correlation matrix is quite stable although the d-corrected market mode almost always dominates the uncorrected (d = 1) market mode indicating under-estimation in the standard analysis, and (c) the PageRank-based risk measure constructed from Granger-causal networks shows an inverted U-shape evolution in the relationship between d-corrected and uncorrected return data over the period of analysis 1972-2018 for historical data of NASDAQ.
{"title":"Fractional differencing: (in)stability of spectral structure and risk measures of financial networks","authors":"A. Chakrabarti, A. Chakrabarti","doi":"10.21314/jntf.2021.002","DOIUrl":"https://doi.org/10.21314/jntf.2021.002","url":null,"abstract":"Computation of spectral structure and risk measures from networks of multivariate financial time series data has been at the forefront of the statistical finance literature for a long time. A standard mode of analysis is to consider log returns from the equity price data, which is akin to taking first difference ($d = 1$) of the log of the price data. Sometimes authors have considered simple growth rates as well. Either way, the idea is to get rid of the nonstationarity induced by the {it unit root} of the data generating process. However, it has also been noted in the literature that often the individual time series might have a root which is more or less than unity in magnitude. Thus first differencing leads to under-differencing in many cases and over differencing in others. In this paper, we study how correcting for the order of differencing leads to altered filtering and risk computation on inferred networks. In summary, our results are: (a) the filtering method with extreme information loss like minimum spanning tree as well as filtering with moderate information loss like triangulated maximally filtered graph are very susceptible to such d-corrections, (b) the spectral structure of the correlation matrix is quite stable although the d-corrected market mode almost always dominates the uncorrected (d = 1) market mode indicating under-estimation in the standard analysis, and (c) the PageRank-based risk measure constructed from Granger-causal networks shows an inverted U-shape evolution in the relationship between d-corrected and uncorrected return data over the period of analysis 1972-2018 for historical data of NASDAQ.","PeriodicalId":41885,"journal":{"name":"Journal of Network Theory in Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41904578","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}
A block-structured model for the reconstruction of directed and weighted financial networks, spanning multiple countries, is developed. In a first step, link-probability matrices are derived via a fitness model that is calibrated to reproduce a desired density and reciprocity for each block (i.e. country and cross-border sub-matrix). The resulting probability matrix allows for fast simulation through bivariate Bernoulli trials. In a second step, weights are allocated to a sampled adjacency matrix via an exponential random graph model (ERGM), which fulfills the row, column, and block weights. This model is analytically tractable, calibrated only on scarce publicly available data, and closely reconstructs known network characteristics of financial markets. In addition, an algorithm for the parameter estimation of the ERGM is presented. Furthermore, calibrating our model to the EU interbank market, we are able to assess the systemic risk within the European banking network by applying various contagion models.
{"title":"A block-structured model for banking networks across multiple countries","authors":"Janina Engel, A. Pagano, M. Scherer","doi":"10.21314/jntf.2021.003","DOIUrl":"https://doi.org/10.21314/jntf.2021.003","url":null,"abstract":"A block-structured model for the reconstruction of directed and weighted financial networks, spanning multiple countries, is developed. In a first step, link-probability matrices are derived via a fitness model that is calibrated to reproduce a desired density and reciprocity for each block (i.e. country and cross-border sub-matrix). The resulting probability matrix allows for fast simulation through bivariate Bernoulli trials. In a second step, weights are allocated to a sampled adjacency matrix via an exponential random graph model (ERGM), which fulfills the row, column, and block weights. This model is analytically tractable, calibrated only on scarce publicly available data, and closely reconstructs known network characteristics of financial markets. In addition, an algorithm for the parameter estimation of the ERGM is presented. Furthermore, calibrating our model to the EU interbank market, we are able to assess the systemic risk within the European banking network by applying various contagion models.","PeriodicalId":41885,"journal":{"name":"Journal of Network Theory in Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67705122","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}
Michael Lebacher, Samantha Cook, N. Klein, G. Kauermann
To capture the systemic complexity of international financial systems, network data is an important prerequisite. However, dyadic data is often not available, raising the need for methods that allow for reconstructing networks based on limited information. In this paper, we are reviewing different methods that are designed for the estimation of matrices from their marginals and potentially exogenous information. This includes a general discussion of the available methodology that provides edge probabilities as well as models that are focussed on the reconstruction of edge values. Besides summarizing the advantages, shortfalls and computational issues of the approaches, we put them into a competitive comparison using the SWIFT (Society for Worldwide Interbank Financial Telecommunication) MT 103 payment messages network (MT 103: Single Customer Credit Transfer). This network is not only economically meaningful but also fully observed which allows for an extensive competitive horse race of methods. The comparison concerning the binary reconstruction is divided into an evaluation of the edge probabilities and the quality of the reconstructed degree structures. Furthermore, the accuracy of the predicted edge values is investigated. To test the methods on different topologies, the application is split into two parts. The first part considers the full MT 103 network, being an illustration for the reconstruction of large, sparse financial networks. The second part is concerned with reconstructing a subset of the full network, representing a dense medium-sized network. Regarding substantial outcomes, it can be found that no method is superior in every respect and that the preferred model choice highly depends on the goal of the analysis, the presumed network structure and the availability of exogenous information.
{"title":"In Search of Lost Edges: A Case Study on Reconstructing FInancial Networks","authors":"Michael Lebacher, Samantha Cook, N. Klein, G. Kauermann","doi":"10.21314/jntf.2019.058","DOIUrl":"https://doi.org/10.21314/jntf.2019.058","url":null,"abstract":"To capture the systemic complexity of international financial systems, network data is an important prerequisite. However, dyadic data is often not available, raising the need for methods that allow for reconstructing networks based on limited information. In this paper, we are reviewing different methods that are designed for the estimation of matrices from their marginals and potentially exogenous information. This includes a general discussion of the available methodology that provides edge probabilities as well as models that are focussed on the reconstruction of edge values. Besides summarizing the advantages, shortfalls and computational issues of the approaches, we put them into a competitive comparison using the SWIFT (Society for Worldwide Interbank Financial Telecommunication) MT 103 payment messages network (MT 103: Single Customer Credit Transfer). This network is not only economically meaningful but also fully observed which allows for an extensive competitive horse race of methods. The comparison concerning the binary reconstruction is divided into an evaluation of the edge probabilities and the quality of the reconstructed degree structures. Furthermore, the accuracy of the predicted edge values is investigated. To test the methods on different topologies, the application is split into two parts. The first part considers the full MT 103 network, being an illustration for the reconstruction of large, sparse financial networks. The second part is concerned with reconstructing a subset of the full network, representing a dense medium-sized network. Regarding substantial outcomes, it can be found that no method is superior in every respect and that the preferred model choice highly depends on the goal of the analysis, the presumed network structure and the availability of exogenous information.","PeriodicalId":41885,"journal":{"name":"Journal of Network Theory in Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72407136","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}
{"title":"Credit rating analysis based on the network of trading information","authors":"Ximei Wang, Boualem Djehiche, Xiaoming Hu","doi":"10.21314/JNTF.2019.050","DOIUrl":"https://doi.org/10.21314/JNTF.2019.050","url":null,"abstract":"","PeriodicalId":41885,"journal":{"name":"Journal of Network Theory in Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73906326","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}
{"title":"Default cascades and systemic risk on different interbank network topologies","authors":"N. Scholtes","doi":"10.21314/JNTF.2019.049","DOIUrl":"https://doi.org/10.21314/JNTF.2019.049","url":null,"abstract":"","PeriodicalId":41885,"journal":{"name":"Journal of Network Theory in Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49134722","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}
Amanah Ramadiah, Domenico Di Gangi, D. L. Sardo, Valentina Macchiati, M. Pham, F. Pinotti, M. Wilinski, P. Barucca, G. Cimini
Amanah , , D. Ruggiero Lo Sardo, Valentina Macchiati, Tuan Pham Minh, Francesco Pinotti, Mateusz Wilinski, Paolo Barucca and Giulio Cimini
{"title":"Network Sensitivity of Systemic Risk","authors":"Amanah Ramadiah, Domenico Di Gangi, D. L. Sardo, Valentina Macchiati, M. Pham, F. Pinotti, M. Wilinski, P. Barucca, G. Cimini","doi":"10.21314/jntf.2019.056","DOIUrl":"https://doi.org/10.21314/jntf.2019.056","url":null,"abstract":"Amanah , , D. Ruggiero Lo Sardo, Valentina Macchiati, Tuan Pham Minh, Francesco Pinotti, Mateusz Wilinski, Paolo Barucca and Giulio Cimini","PeriodicalId":41885,"journal":{"name":"Journal of Network Theory in Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80191757","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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