標題: Titlebook: Data Science for Economics and Finance; Methodologies and Ap Sergio Consoli,Diego Reforgiato Recupero,Michaela Book‘‘‘‘‘‘‘‘ 2021 The Edito [打印本頁] 作者: genial 時間: 2025-3-21 20:07
書目名稱Data Science for Economics and Finance影響因子(影響力)
書目名稱Data Science for Economics and Finance影響因子(影響力)學科排名
書目名稱Data Science for Economics and Finance網(wǎng)絡公開度
書目名稱Data Science for Economics and Finance網(wǎng)絡公開度學科排名
書目名稱Data Science for Economics and Finance被引頻次
書目名稱Data Science for Economics and Finance被引頻次學科排名
書目名稱Data Science for Economics and Finance年度引用
書目名稱Data Science for Economics and Finance年度引用學科排名
書目名稱Data Science for Economics and Finance讀者反饋
書目名稱Data Science for Economics and Finance讀者反饋學科排名
作者: 偽造者 時間: 2025-3-21 23:45
Data Science Technologies in Economics and Finance: A Gentle Walk-In,e of the common issues in economic modeling with data science technologies and surveys the relevant big data management and analytics solutions, motivating the use of data science methods in economics and finance.作者: Enzyme 時間: 2025-3-22 01:56 作者: 尋找 時間: 2025-3-22 06:13 作者: Fallibility 時間: 2025-3-22 09:31 作者: craven 時間: 2025-3-22 16:13 作者: craven 時間: 2025-3-22 17:14
https://doi.org/10.1007/978-1-61779-465-0e of the common issues in economic modeling with data science technologies and surveys the relevant big data management and analytics solutions, motivating the use of data science methods in economics and finance.作者: MEET 時間: 2025-3-22 22:44
Methods for Functional Connectivity Analysissemble method cannot be easily identified in the empirical literature. Third, despite the possibility that machine learning techniques could fail to outperform linear classification methods when standard accuracy measures are considered, in the end they lead to significant cost savings compared to t作者: 男生戴手銬 時間: 2025-3-23 01:30
E. Pasternak,H. B. Mühlhaus,A. V. Dyskinter knowledge graphs. This information can augment source documents and guide exploration processes. Systems for document exploration are tailored to specific tasks, such as investigative work in audits or legal discovery, monitoring compliance, or providing information in a retrieval system to supp作者: output 時間: 2025-3-23 09:26 作者: MAIZE 時間: 2025-3-23 12:12
Andrea Donnellan,Peter Mora,Xiang-chu Yin that a limited number of shareholders control many firms, revealing a significant concentration of power. Finally, we show how these measures computed at different levels of granularity (i.e., sector of activity) can provide useful policy insights.作者: RADE 時間: 2025-3-23 17:39 作者: 小丑 時間: 2025-3-23 21:23
series forecasting for applications in economics and financ.This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applicati作者: 眉毛 時間: 2025-3-23 23:10
Computational Earthquake Science Part Ietric approaches, the empirical literature has shown evidence of some gain in nowcasting ability. In this chapter, we propose to review recent advances of the literature on the topic, and we put forward innovative alternative indicators to monitor the Chinese and US economies.作者: semble 時間: 2025-3-24 04:24 作者: 托運 時間: 2025-3-24 10:33 作者: PRO 時間: 2025-3-24 14:23 作者: 圓錐 時間: 2025-3-24 18:54
Sentiment Analysis of Financial News: Mechanics and Statistics, are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks’ prices, and financial markets in general.作者: 半球 時間: 2025-3-24 20:54 作者: 單純 時間: 2025-3-24 23:33
AMMOS Software: Method and Applicationext, we describe how SL tools can be used to analyze company growth and performance. Finally, we review SL applications to better forecast financial distress and company failure. In the concluding section, we extend the discussion of SL methods in the light of targeted policies, result interpretability, and causality.作者: chisel 時間: 2025-3-25 06:43
The Analysis of Event-Related Potentialss and retain all of the information available. Third, these methods are purely data driven. All of these characteristics contribute to their often better predictive performance. However, as “black box” models, they are still much underutilized in financial stability, a field where interpretability and accountability are crucial.作者: Kaleidoscope 時間: 2025-3-25 08:10 作者: BLINK 時間: 2025-3-25 15:13 作者: CLEFT 時間: 2025-3-25 18:13
Computational Drug Discovery and Designl forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.作者: Obedient 時間: 2025-3-25 22:46 作者: 者變 時間: 2025-3-26 01:00
Computational Earthquake Science Part Iidentiality 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.作者: breadth 時間: 2025-3-26 06:35
Andrea Donnellan,Peter Mora,Xiang-chu YinG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening.作者: COST 時間: 2025-3-26 09:32
Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application tol forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.作者: 寡頭政治 時間: 2025-3-26 14:08 作者: EXTOL 時間: 2025-3-26 19:34 作者: 不可救藥 時間: 2025-3-26 23:12 作者: 辮子帶來幫助 時間: 2025-3-27 01:44 作者: 提升 時間: 2025-3-27 07:27 作者: Commonwealth 時間: 2025-3-27 12:37
http://image.papertrans.cn/d/image/263115.jpg作者: 易受騙 時間: 2025-3-27 14:50
https://doi.org/10.1007/978-1-61779-465-0d information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as .. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can 作者: 楓樹 時間: 2025-3-27 21:11
AMMOS Software: Method and Applicationied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms’ performance. In this chapter, we will illustrate a series of SL approaches to be used fo作者: meretricious 時間: 2025-3-27 23:08
Computational Drug Discovery and Design models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggr作者: 開花期女 時間: 2025-3-28 02:54
The Analysis of Event-Related Potentialsthe complex, nonlinear, time-varying, and multidimensional nature of the data. A strand of literature has shown that machine learning approaches can make more accurate data-driven predictions than standard empirical models, thus providing more and more timely information about the building up of fin作者: 刻苦讀書 時間: 2025-3-28 08:46 作者: 主講人 時間: 2025-3-28 12:46
Garett D. Johnson,Dean J. Krusienskitails on derivatives but their use poses numerous challenges. To overcome one major challenge, this chapter draws from eight different data sources and develops a greedy algorithm to obtain a new counterparty sector classification. We classify counterparties’ sector for 96% of the notional value of 作者: endocardium 時間: 2025-3-28 15:00 作者: 希望 時間: 2025-3-28 18:59 作者: 重疊 時間: 2025-3-29 01:53 作者: Calculus 時間: 2025-3-29 05:14 作者: 推遲 時間: 2025-3-29 07:45
E. Pasternak,H. B. Mühlhaus,A. V. Dyskinr business and financial news. The valuable knowledge in such collections is not directly accessible by computers as they mostly consist of unstructured text. This chapter provides an overview of corpora commonly used in research and highlights related work and state-of-the-art approaches to extract作者: enormous 時間: 2025-3-29 12:12 作者: 玉米 時間: 2025-3-29 18:00 作者: ovation 時間: 2025-3-29 20:30
Andrea Donnellan,Peter Mora,Xiang-chu Yincations using network analysis in economics and finance. Second, we introduce the main network metrics that are useful to describe the overall network structure and characterize the position of a specific node in the network. Third, we model information on firm ownership as a network: firms are the 作者: EVEN 時間: 2025-3-30 03:58
https://doi.org/10.1007/978-3-030-66891-4Open Access; Data Mining; Big Data; Data Analytics; Decision Support Systems; Machine Learning; Semantics 作者: 猜忌 時間: 2025-3-30 04:56
978-3-030-66893-8The Editor(s) (if applicable) and The Author(s) 2021作者: 極微小 時間: 2025-3-30 11:25
Data Science Technologies in Economics and Finance: A Gentle Walk-In,d information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as .. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can 作者: Omniscient 時間: 2025-3-30 13:56
Supervised Learning for the Prediction of Firm Dynamics,ied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms’ performance. In this chapter, we will illustrate a series of SL approaches to be used fo作者: 鴕鳥 時間: 2025-3-30 19:07
Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggr作者: catagen 時間: 2025-3-30 23:05
Machine Learning for Financial Stability,the complex, nonlinear, time-varying, and multidimensional nature of the data. A strand of literature has shown that machine learning approaches can make more accurate data-driven predictions than standard empirical models, thus providing more and more timely information about the building up of fin作者: Insensate 時間: 2025-3-31 03:11 作者: 不滿分子 時間: 2025-3-31 08:39
Classifying Counterparty Sector in EMIR Data,tails on derivatives but their use poses numerous challenges. To overcome one major challenge, this chapter draws from eight different data sources and develops a greedy algorithm to obtain a new counterparty sector classification. We classify counterparties’ sector for 96% of the notional value of 作者: abnegate 時間: 2025-3-31 10:15 作者: hemorrhage 時間: 2025-3-31 14:15
New Data Sources for Central Banks,ential 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., newspape作者: scrutiny 時間: 2025-3-31 17:46
Sentiment Analysis of Financial News: Mechanics and Statistics,tion, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests作者: CERE 時間: 2025-3-31 23:44
