Quantifying News Narratives to Predict Movements in Market Risk

Dierckx, Thomas; Davis, Jesse; Schoutens, Wim

doi:10.1007/978-3-030-66891-4_12

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…In addition, several FinTech and InsurTech applications (like personalized healthcare insurance based on medical devices and improved car insurance based on connected car sensors) take advantage of contextual data associated with finance and insurance services to offer better quality of service at a more competitive cost, thanks to the proliferation of IoT devices and applications (like Fitbits, smart phones, and smart home devices) [5]. Also, new opportunities for accurate, automated, and customized services [6] can be found in non-traditional data sources like social media and online news.…”

Section: Literature Reviewmentioning

confidence: 99%

Unified Data Analytics Platform For Financial Sector Using Big Data

Surendranadha Reddy Byrapu Reddy

2023

tjjpt

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Organizations in today's data-driven digital economy are seeking ways to leverage the immense value of massive amounts of information so as to make more informed decisions. They are able to not only uncover new prospects, but also learn more and improve their performance thanks to big data analytics. Although many businesses have poured resources into big data analytics projects, most have failed to reap the benefits. While there has been a lot of research into the topic of big data analytics, relatively little is known about the strategies that organizations use to merge the various aspects of this discipline. The authors of this paper attempt to solve the problem by developing a comprehensive big data analytics maturity model that can be used by executives to evaluate their own proficiency levels and plan for future development. In this research, we combined traditional quantitative techniques with qualitative meta-synthesis. To begin, we conducted an extensive literature search to catalog the skills and procedures associated with big data analytics maturity. Later, using a quantitative survey method, experts' perspectives on the proposed fundamental skills and practices were analyzed and graded. Finally, a focus group was used to allocate the capabilities to maturity levels according to their priority of deployment, taking into account the architecture of the big data analytics maturity model. We propose a model with four primary capabilities, nine key dimensions (KDs), and five stages of development. Its framework is CMMI-based, which stands for "capability maturity model integration." Questionnaires and focus groups were used to illustrate the big data maturity model. As a guide for effective adoption of big data analytics, we provide the capabilities and KDs, together with their suggested deployment order and weight in the maturity model. Using the offered methodology, businesses may assess their current big data analytics capabilities and steer themselves toward the most effective paths for improvement. Because of this, it enables managers to assess their strengths and weaknesses and establish investment objectives. This work uses a meta-synthesis, which hasn't been done in this subject before, to create an extensive maturity model. The proposed method makes substantial contributions to large data research, and it is both descriptive and prescriptive. A framework is presented in this paper for evaluating different big data analytics projects and settling on a coherent plan of action for their future growth and development.

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Section: Literature Reviewmentioning

confidence: 99%

Unified Data Analytics Platform For Financial Sector Using Big Data

Surendranadha Reddy Byrapu Reddy

2023

tjjpt

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“…When using machine learning models to collect alternative data, commonly used machine learning models include logistic regression, support vector machines, adaboost [4], etc. Taking stock market forecasting as an example, recent literature data shows that machine learning models are generally superior to statistical and econometric models.…”

Section: Alternate Data Gathering and Collectingmentioning

confidence: 99%

Study of the Applications of Alternative Data in the Field of Economics and Finance

Huang

2023

AEMPS

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The term "alternative data" sometimes known as alternative data or non-traditional data, is most often used to describe regionally specific, valuable information that is distinct from typical financial data. Alternative data does not require a specialized data network or administration and may be collected and processed in real time. As a result, "alternative data" has quickly evolved in the financial sector in recent years. "Alternative data" is a well-known illustration of "big" data since, to start, it's enormously large in quantity as shown by its scope and transmission. The second element is the real-time or very close-to-real-time nature of the data collection and transmission. Data types and data structures come in a huge diversity, which is the third component. This paper studies the different classifications and acquisition methods of alternative data, focusing on the different applications of alternative data in the economic and financial fields, respectively, the application of alternative data in forecasting, especially in macro-economic forecasting and user income forecasting, and the application of alternative data in capital markets, business analysis and decision-making.

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“…(Shiller, 2017, p.967), there has been an emerging literature that theorises and quantifies the economic impact of narratives. While Shiller's focus was predominantly on narratives that could be considered viral, recent studies have expanded this scope to encompasse various issues, with a particular focus on risk and returns in financial markets (Adämmer & Schüssler, 2020;Dierckx, Davis, & Schoutens, 2021;Ke, Kelly, & Xiu, 2019;Borup, Hansen, Liengaard, & Montes Schütte, 2023;Bybee, Kelly, & Su, 2022). Others have demonstrated the economic impact of narratives (Larsen & Thorsrud, 2018;Bybee, Kelly, Manela, & Xiu, 2023;Bertsch, Hull, & Zhang, 2021;Larsen, Thorsrud, & Zhulanova, 2021;van Binsbergen, Bryzgalova, Mukhopadhyay, & Sharma, 2024;Flynn & Sastry, 2022), their political importance (Jelveh, Kogut, & Naidu, 2022;Eliaz & Spiegler, 2020), and used them to measure geopolitical risk (Caldara & Iacoviello, 2022).…”

Section: Introductionmentioning

confidence: 99%

Going Mainstream: Cryptocurrency Narratives in Newspapers

Walker

2023

SSRN Journal

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Quantifying News Narratives to Predict Movements in Market Risk

Cited by 7 publications

References 19 publications

Unified Data Analytics Platform For Financial Sector Using Big Data

Unified Data Analytics Platform For Financial Sector Using Big Data

Study of the Applications of Alternative Data in the Field of Economics and Finance

Going Mainstream: Cryptocurrency Narratives in Newspapers

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