News and Narratives in Financial Systems: Exploiting Big Data for Systemic Risk Assessment

Nyman, Rickard; Kapadia, Sujit; Tuckett, David; Gregory, David; Ormerod, Paul; Smith, Robert E.

doi:10.2139/ssrn.3135262

Cited by 49 publications

(56 citation statements)

References 75 publications

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“…Unlike survey-based measures of economic sentiment, our index relies on computational text analysis to extract sentiment from economic and financial newspaper articles. Text-based measures of economic activity are becoming more popular among researchers due to their apparent advantages over surveys in terms of cost and scope (see, for example, Fraiberger (2016), Nyman, Gregory, Kapadia, Ormerod, Tuckett, and Smith (2016), Thorsrud (2016a), Thorsrud (2016b), and Calomiris and Mamaysky (2017)). Surveys are inherently expensive to conduct, oftentimes based on relatively small samples of individuals, and therefore may be subject to sampling problems (Ludvigson (2004)).…”

Section: Introductionmentioning

confidence: 99%

Measuring News Sentiment

Shapiro¹,

Sudhof²,

Wilson³

2020

ERWP

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We develop and assess new time series measures of economic sentiment based on computational text analysis of economic and financial newspaper articles from January 1980 to April 2015. The text analysis is based on predictive models estimated using machine learning techniques. We analyze four alternative news sentiment indexes.The news sentiment indexes correlate strongly with contemporaneous business cycle indicators and improve forecasting performance. A positive news sentiment shock appears consistent with an aggregate demand shock, increasing future employment, prices, and the federal funds rate. However, we find muted effects of news sentiment on future consumption. While news sentiment affects overall consumer sentiment, it has no effect on the components of consumer sentiment that drive consumption. * We thank Armen Berjikly and the Kanjoya staff for generously assisting on the project and providing guidance, comments and suggestions. Lily Huang, Kevin Vo and Catherine van der List provided excellent research assistance. The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of San Francisco, or the Board of Governors of the Federal Reserve System. † Federal Reserve Bank of San Francisco, adam

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Section: Introductionmentioning

confidence: 99%

Measuring News Sentiment

Shapiro¹,

Sudhof²,

Wilson³

2020

ERWP

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“…Correct estimates of inflation, spending, employment, etc. are some of the most important aims of economic forecasting (Nyman et al, ). Economic recession of 2008 / 09 was not predicted by the researchers despite data availability Nyman & Ormerod, ).…”

Section: Discussionmentioning

confidence: 99%

“…Nyman, Ormerod, Smith, and Tuckett () describe a new approach to economic forecasting, which is based on the availability of “Big Data”. The main aim is to develop an algorithm to analyze text data which is given in different forms for example news feed, email, office legal documents recording to extract time series of sentiment analysis that are similar in nature, that might forecast aspects for the economy.…”

Section: Big Data Analytics In Smart Citiesmentioning

confidence: 99%

Smart city big data analytics: An advanced review

Soomro

Bhutta

Khan

et al. 2019

WIREs Data Min & Knowl

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With the increasing role of ICT in enabling and supporting smart cities, the demand for big data analytics solutions is increasing. Various artificial intelligence, data mining, machine learning and statistical analysis-based solutions have been successfully applied in thematic domains like climate science, energy management, transport, air quality management and weather pattern analysis. In this paper, we present a systematic review of the literature on smart city big data analytics. We have searched a number of different repositories using specific keywords and followed a structured data mining methodology for selecting material for the review. We have also performed a technological and thematic analysis of the shortlisted literature, identified various data mining/machine learning techniques and presented the results. Based on this analysis we also present a classification model that studies four aspects of research in this domain. These include data models, computing models, security and privacy aspects and major market drivers in the smart cities domain. Moreover, we present a gap analysis and identify future directions for research. For the thematic analysis we identified the themes smart city governance, economy, environment, transport and energy. We present the major challenges in these themes, the major research work done in the field of data analytics to address these challenges and future research directions.

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“…In recent years, big data analysis has been a hot point to discover the hidden information amid huge amounts of data [36,37] and has been widely applied in the financial sector [38], to smart cities [39], in public management [40], and so on. Some researchers also try to mine the information of flooded roads through geospatial big data, such as SNS (social network service) data because geospatial SNS data contain spatial information and semantic information that describe the location and events of users.…”

Section: Extracting Flooded Roads By Big Data With Semantic Informationmentioning

confidence: 99%

Extracting Flooded Roads by Fusing GPS Trajectories and Road Network

She

Zhong

Fang

et al. 2019

IJGI

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Urban roads are the lifeline of urban transportation and satisfy the commuting and travel needs of citizens. Following the acceleration of urbanization and the frequent extreme weather in recent years, urban waterlogging is occurring more than usual in summer and has negative effects on the urban traffic networks. Extracting flooded roads is a critical procedure for improving the resistance ability of roads after urban waterlogging occurs. This paper proposes a flooded road extraction method to extract the flooding degree and the time at which roads become flooded in large urban areas by using global positioning system (GPS) trajectory points with driving status information and the high position accuracy of vector road data with semantic information. This method uses partition statistics to create density grids (grid layer) and uses map matching to construct a time-series of GPS trajectory point density for each road (vector layer). Finally, the fusion of grids and vector layers obtains a more accurate result. The experiment uses a dataset of GPS trajectory points and vector road data in the Wuchang district, which proves that the extraction result has a high similarity with respect to the flooded roads reported in the news. Additionally, extracted flooded roads that were not reported in the news were also found. Compared with the traditional methods for extracting flooded roads and areas, such as rainfall simulation and SAR image-based classification in urban areas, the proposed method discovers hidden flooding information from geospatial big data, uploaded at no cost by urban taxis and remaining stable for a long period of time.

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News and Narratives in Financial Systems: Exploiting Big Data for Systemic Risk Assessment

Cited by 49 publications

References 75 publications

Measuring News Sentiment

Measuring News Sentiment

Smart city big data analytics: An advanced review

Extracting Flooded Roads by Fusing GPS Trajectories and Road Network

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