Essays In Big Data Finance
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Essays In Big Data Finance
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Author : Sebastian Schreiber
language : en
Publisher:
Release Date : 2023
Essays In Big Data Finance written by Sebastian Schreiber and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Three Essays On Big Data In International Finance
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Author : Ziqi Zang
language : en
Publisher:
Release Date : 2019
Three Essays On Big Data In International Finance written by Ziqi Zang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
This dissertation presents an introduction to big data that can potentially be used in nowcasting key macroeconomic variables for advanced economies. It also explores the forecastability of big data in short-term exchange rate forecasting. Finally, it draws on evidence from a sentiment analysis of Article IV Consultations over the period of 2012 to 2018 and examines the development of member countries' perceptions of IMF policy advice. Chapter 1 uses big data from Google search data to form better nowcasts of macroeconomic variables. My empirical strategy contributes to the macroeconomic nowcasting literature on three fronts. First, I take a number of steps to identify the most comprehensive set of relevant search queries that capture people's search behavior in relation to each monetary policy variable, such as the unemployment rate and inflation. Second, I consider regularization and dimension reduction methods to handle the underlying high-dimensional regressor space with highly correlated covariates. Third, I evaluate both average point forecasts and conditional point forecasts against benchmark models with DMW test and CSPA test, respectively. According to the test statistics, I find that Google search data offer significant improvements in nowcasting macroeconomic variables both unconditionally and conditionally. Chapter 2 examines the short-term forecastability of exchange rates using machine learning models in a rich data environment. I investigate the performance of different machine learning models, such as variable selection models, dynamic factor model, and decision regression trees in obtaining accurate forecasts of three currency pairs (U.S./U.K., Japan/U.S. and U.S./Australia). I consider three types of forecasts: point forecasts, unconditional weighted directional forecasts and conditional weighted directional forecasts. According to the DMW test, out-of-sample forecasts of every currency rejects the null hypothesis of equal forecasting errors with the random walk with at least one machine learning model. Furthermore, the conditional weighted directional forecasts allow us to know when exactly our models are more profitable than the random walk with zero profit. And it turns out that our weighted directional forecasts are significantly positive especially on the tails of the conditioning variable distribution. Chapter 3 constructs multi-aspect policy sentiment measurements to interpret authorities' tones in response to specific policy advice in IMF Article IV Consultations. Specifically, we use a topic-based sentiment analysis approach that entails the application of a latent Dirichlet allocation (LDA) model as well as sentiment prediction machine learning models. Therefore, we are able to provide the stylized facts that provide useful input for assessing the impact of Fund advice on macroeconomic development of member countries.
Essay On Big Data And Machine Learning In Finance
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Author : Gunsu Son
language : en
Publisher:
Release Date : 2023
Essay On Big Data And Machine Learning In Finance written by Gunsu Son and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Despite structural differences between the options and stock markets, few studies have discussed the behavior and impact of high-frequency traders (HFTs) in the options market. Options exchanges identify high-frequency/algorithmic traders as Professional Customers (PCs). In this study, we use granular data that identifies trades by customers, PCs, and Market Makers (MMs). We find that PCs mainly trade as a counterparty to customers, similar to MMs. However, the liquidity provision by PCs leads to order flow toxicity: PCs use a "cream skimming" strategy that imposes adverse selection costs on MMs. PCs mainly trade with uninformed customers, most likely leveraging their speed and algorithmic advantage. PCs provide less liquidity when the market and stock volatility are high. Customer call option trades made with PCs have one-tenth of price impact and no return or volatility predictability, while there is significant price impact in addition to return and volatility predictability when executed against MMs during the next 30 minutes. Our finding on HFTs' non-arbitrage channel of order flow toxicity is new and suggests that the role of HFTs should be better understood in the context of the options market structure.
Essays In Financial Economics And Big Data
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Author : Jannic Alexander Cutura
language : en
Publisher:
Release Date : 2020
Essays In Financial Economics And Big Data written by Jannic Alexander Cutura and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Essays In Macroeconomic And Financial Forecasting Using Big Data Econometric Methods
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Author : Aristeidis Raftapostolos
language : en
Publisher:
Release Date : 2022
Essays In Macroeconomic And Financial Forecasting Using Big Data Econometric Methods written by Aristeidis Raftapostolos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Big Data And Artificial Intelligence In Digital Finance
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Author : John Soldatos
language : en
Publisher: Springer Nature
Release Date : 2022
Big Data And Artificial Intelligence In Digital Finance written by John Soldatos and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Artificial intelligence categories.
This open access book presents how cutting-edge digital technologies like Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTechs, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also introduces some of the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance. Introduces the latest advances in Big Data and AI in Digital Finance that enable scalable, effective, and real-time analytics; Explains the merits of Blockchain technology in digital finance, including applications beyond the blockbuster cryptocurrencies; Presents practical applications of cutting edge digital technologies in the digital finance sector; Illustrates the regulatory environment of the financial sector and presents technical solutions that boost compliance to applicable regulations; This book is open access, which means that you have free and unlimited access.
Large Data Sets And Nonlinearity
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Author : Hyeyoen Kim
language : en
Publisher:
Release Date : 2009
Large Data Sets And Nonlinearity written by Hyeyoen Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Big Data In Finance
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Author : Thomas Walker
language : en
Publisher: Springer Nature
Release Date : 2022-10-03
Big Data In Finance written by Thomas Walker and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-03 with Business & Economics categories.
This edited book explores the unique risks, opportunities, challenges, and societal implications associated with big data developments within the field of finance. While the general use of big data has been the subject of frequent discussions, this book will take a more focused look at big data applications in the financial sector. With contributions from researchers, practitioners, and entrepreneurs involved at the forefront of big data in finance, the book discusses technological and business-inspired breakthroughs in the field. The contributions offer technical insights into the different applications presented and highlight how these new developments may impact and contribute to the evolution of the financial sector. Additionally, the book presents several case studies that examine practical applications of big data in finance. In exploring the readiness of financial institutions to adapt to new developments in the big data/artificial intelligence space and assessing different implementation strategies and policy solutions, the book will be of interest to academics, practitioners, and regulators who work in this field.
Thinking Ahead Essays On Big Data Digital Revolution And Participatory Market Society
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Author : Dirk Helbing
language : en
Publisher: Springer
Release Date : 2015-04-10
Thinking Ahead Essays On Big Data Digital Revolution And Participatory Market Society written by Dirk Helbing and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-10 with Computers categories.
The rapidly progressing digital revolution is now touching the foundations of the governance of societal structures. Humans are on the verge of evolving from consumers to prosumers, and old, entrenched theories – in particular sociological and economic ones – are falling prey to these rapid developments. The original assumptions on which they are based are being questioned. Each year we produce as much data as in the entire human history - can we possibly create a global crystal ball to predict our future and to optimally govern our world? Do we need wide-scale surveillance to understand and manage the increasingly complex systems we are constructing, or would bottom-up approaches such as self-regulating systems be a better solution to creating a more innovative, more successful, more resilient, and ultimately happier society? Working at the interface of complexity theory, quantitative sociology and Big Data-driven risk and knowledge management, the author advocates the establishment of new participatory systems in our digital society to enhance coordination, reduce conflict and, above all, reduce the “tragedies of the commons,” resulting from the methods now used in political, economic and management decision-making. The author Physicist Dirk Helbing is Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences and an affiliate of the Computer Science Department at ETH Zurich, as well as co-founder of ETH’s Risk Center. He is internationally known for the scientific coordination of the FuturICT Initiative which focuses on using smart data to understand techno-socio-economic systems. “Prof. Helbing has produced an insightful and important set of essays on the ways in which big data and complexity science are changing our understanding of ourselves and our society, and potentially allowing us to manage our societies much better than we are currently able to do. Of special note are the essays that touch on the promises of big data along with the dangers...this is material that we should all become familiar with!” Alex Pentland, MIT, author of Social Physics: How Good Ideas Spread - The Lessons From a New Science "Dirk Helbing has established his reputation as one of the leading scientific thinkers on the dramatic impacts of the digital revolution on our society and economy. Thinking Ahead is a most stimulating and provocative set of essays which deserves a wide audience.” Paul Ormerod, economist, and author of Butterfly Economics and Why Most Things Fail. "It is becoming increasingly clear that many of our institutions and social structures are in a bad way and urgently need fixing. Financial crises, international conflicts, civil wars and terrorism, inaction on climate change, problems of poverty, widening economic inequality, health epidemics, pollution and threats to digital privacy and identity are just some of the major challenges that we confront in the twenty-first century. These issues demand new and bold thinking, and that is what Dirk Helbing offers in this collection of essays. If even a fraction of these ideas pay off, the consequences for global governance could be significant. So this is a must-read book for anyone concerned about the future." Philip Ball, science writer and author of Critical Mass “This collection of papers, brought together by Dirk Helbing, is both timely and topical. It raises concerns about Big Data, which are truly frightening and disconcerting, that we do need to be aware of; while at the same time offering some hope that the technology, which has created the previously unthought-of dangers to our privacy, safety and democracy can be the means to address these dangers by enabling social, economic and political participation and coordination, not possible in the past. It makes for compelling reading and I hope for timely action.”Eve Mitleton-Kelly, LSE, author of Corporate Governance and Complexity Theory and editor of Co-evolution of Intelligent Socio-technical Systems
Essays On Empirical Analysis Of Large Macroeconomic And Financial Data
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Author : Soroosh Soofi-Siavash
language : en
Publisher:
Release Date : 2017
Essays On Empirical Analysis Of Large Macroeconomic And Financial Data written by Soroosh Soofi-Siavash and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.