News and narratives in financial systems: Exploiting big data for systemic risk assessment

Nyman, Rickard; Kapadia, Sujit; Tuckett, David

doi:10.1016/j.jedc.2021.104119

Cited by 65 publications

(43 citation statements)

References 43 publications

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“…The spread of the Internet and social media has created a huge amount of new data containing potentially revealing information about the sentiments, opinions, expectations and fears of its users. A better understanding of behavior in financial markets is expected to provide a more solid basis for political and economic decision-making and support risk management strategies [3,6,75]. So far, however, the analysis of social media data has mostly provided short-term indications that are of limited use for fundamental analysis.…”

Section: Economic and Financial Risks Forecastingmentioning

confidence: 99%

“…Nevertheless, social media analysis is an area that is considered to have great potential for future exploration and research [1]. An algorithmic analysis of sentiment trends in large volumes of financial news documents was used, for instance, by Nyman et al to assess how narratives and moods play a role in influencing developments in the financial system [3,76]. According to Nyman et al, in their study, changes in emotional content in market narratives are highly correlated across data sources.…”

Section: Economic and Financial Risks Forecastingmentioning

confidence: 99%

“…pandemics, financial collapses, terrorist attacks), as sometimes the existence of independent observations is wrongly assumed and cascading errors that can occur in complex systems are not considered. Big data, machine learning, and predictive analytics offer new opportunities for understanding and managing risks in complex environments and there is a widespread belief that big data can aid in improving risk forecasts [1][2][3]. Many institutions are already adopting innovative ways to use big data analytical methods to improve their risk assessment processes and to predict risks from economic, social, or environmental data [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Risk Forecasting in the Light of Big Data

Kernchen¹

2021

JRACR

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Life in modern society is increasingly connected by networks that link the world around us and create numerous exciting opportunities, new services and advantages for humanity. Yet concurrently, these underpinning networks have provided routes by which potentially dangerous and harmful incidents can propagate quickly and worldwide. This complexity poses a considerable challenge for risk analysis and forecasting. Conventional methods of risk analysis tend to underestimate the probability and impact of risks (e.g. pandemics, financial collapses, terrorist attacks), as sometimes the existence of independent observations is wrongly assumed and cascading errors that can occur in complex systems are not considered. The purpose of this article is to assess critically the potential of big data to profoundly change the current capability for risk forecasting in diverse areas and the assertion that big data leads to better risk predictions. In particular, the focus is on big data implications for risk forecasting in the areas of economic and financial risks, environmental and sustainable development risks, and public and national security risks. The article concludes that big data and predictive analytics offer substantial opportunities for improving risk forecasting but may not replace the significance of appropriate assumptions, adequate data quality and continuous validation.

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Section: Economic and Financial Risks Forecastingmentioning

confidence: 99%

Section: Economic and Financial Risks Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Risk Forecasting in the Light of Big Data

Kernchen¹

2021

JRACR

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“…There is an equally influential strand of literature that looks at newspaper articles as a source of sentiments. Illustratively, Nyman et al (2016) used the Thomson-Reuters News archive (consisting of over 17 million English news articles) to assess macroeconomic trends in the U.K. Similarly, using macro news sentiment scores provided by a professional database agency, Brandt and Gao (2019) find news related to macro fundamentals have an impact on the oil price in the short run and significantly predict oil returns in the long run.…”

Section: Motivation and Received Literaturementioning

confidence: 99%

Deciphering Indian inflationary expectations through text mining: an exploratory approach

2021

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Inflationary forecasts tend to play a crucial role in macroeconomic and financial decision/policy making. In particular, in an inflation-targeting framework, it is of paramount importance. While traditionally, model-based and survey-based inflation expectations are being used, in recent times, a literature has emerged to forecast various macro-aggregates using text-based sentiment estimates. Taking a cue from this approach, in this paper we attempt to decipher inflationary sentiments using text mining from two leading financial dailies, viz., the Economic Times and Business Line. We consciously avoid using social media news due to severe challenges and high noise-to-signal ratio. In our algorithm we aggregate CPI basket level (viz., food, fuel, cloth & miscellaneous) sentiment into an overall index of inflation, adapting techniques from natural language processing. Our results from this text-based model indicate significant success in tracking actual inflation.

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“…The most common uses for big data are nowcasting and forecasting, followed, among others, by stress-testing and fraud detection (see [20]). Some examples of projects carried out by CBs with new sources of data are: improving GDP forecasting exploiting newspaper articles [58] or electronic payments data (e.g., [3,27]); machine learning algorithms to increase accuracy in predicting the future behavior of corporate loans (e.g., [55]); forecasting private consumption with credit card data (e.g., [18,27]); exploiting Google Trends data to predict unemployment [24], private consumption [34,19], or GDP [42]; web scraping from accommodation platforms to improve tourism statistics [48]; data from online portals of housing sales to improve housing market statistics [49]; sentiment analysis applied to financial market text-based data to study developments in the financial system [54]; and machine learning for outlier detection [31].…”

Section: Introductionmentioning

confidence: 99%

New Data Sources for Central Banks

Ghirelli

Hurtado

Pérez

et al. 2021

Data Science for Economics and Finance

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Central banks use structured data (micro and macro) to monitor and forecast economic activity. Recent technological developments have unveiled the potential of exploiting new sources of data to enhance the economic and statistical analyses of central banks (CBs). These sources are typically more granular and available at a higher frequency than traditional ones and cover structured (e.g., credit card transactions) and unstructured (e.g., newspaper articles, social media posts, or Google Trends) sources. They pose significant challenges from the data management and storage and security and confidentiality points of view. This chapter discusses the advantages and the challenges that CBs face in using new sources of data to carry out their functions. In addition, it describes a few successful case studies in which new data sources have been incorporated by CBs to improve their economic and forecasting analyses.

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News and narratives in financial systems: Exploiting big data for systemic risk assessment

Cited by 65 publications

References 43 publications

Risk Forecasting in the Light of Big Data

Risk Forecasting in the Light of Big Data

Deciphering Indian inflationary expectations through text mining: an exploratory approach

New Data Sources for Central Banks

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